esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Securing ET Detectors Against Adversarial Attacks  
Few papers in the literature have investigated securing ML-based ET detectors against adversarial attacks. They consider either binary detectors [31; 32; 33; 34; 35] or anomaly detectors [24; 36].  
[PERSON] et al. [31] proposed the SearchFromFree evasion attack algorithm and studied its impact on binary detectors. The authors assessed the vulnerability of three different types of neural networks (NNs) and found that malicious consumers could successfully evade detection, even in black-box threat scenarios, while reporting extremely low energy consumption measurements. Moreover, they proposed using a distillation approach to secure ET detectors against evasion attacks. [PERSON] et al. [32] utilized GAN to propose a new evasion attack. This attack generates fabricated low consumption readings that can evade a global ET detector with a 97% attack success rate. In contrast to [32], which targets binary-based global detectors trained on a diverse range of consumption patterns, this work focuses on a different type of evasion attack that employs gradient-based and optimization-based methods specifically within anomaly-based detectors. The attack presented in [32] is inapplicable in our case because we do not use a global detector in this paper. Moreover, in a previous work by [PERSON] et al. [33; 34; 35], they evaluated the robustness of a cluster-based ET binary detector against gradient- and optimization-based evasion attacks. They further proposed using parallel ensemble learning to create a robust cluster-based ET detector under different threat models.  
In contrast to [31; 32; 33; 34; 35], [PERSON] et al. [24] utilized an electricity reading and its neighboring readings to generate evasion samples.The authors observed significant degradation in the performance of benchmark detectors in black-, gray-, and white-box threat scenarios, with decreases of up to 22.2%, 26.9%, and 35.8%, respectively. To counter evasion attacks, they used solely benign data to train a sequential ensemble-based anomaly detector that improved the ET detection rate under evasion attacks. Moreover, in [36], [PERSON] et al. investigated the impact of poisoning attacks on both general and consumer-based ET detectors. They showed that both types of detectors suffered from a significant detection rate deterioration of up to 17% due to these attacks. They proposed a sequential ensemble detector based on a DAE with attention, gated recurrent units, and feed-forward NNs, to enhance the robustness of the detectors, achieving stable detection performance with a maximum deterioration of only 3% even under strong poisoning attacks.
## 3 Results
### Water Quality Controls  
#### 3.3.1 Comparison of Groundwater, River and Lake Water Quality  
Comparison of Phalombe District river, groundwater and Lake Chilwa major-ion water quality indicates that lowest TDS and concentrations occur in the rivers followed by groundwater, with highest TDS and major-ion concentrations (except for nitrate) found in Lake Chilwa (Figure 8). Concentration differences between water types approach an order of magnitude with median (and interquartile ranges) in TDS for rivers (n = 29) of 40 mg/L (24-95 mg/L) and groundwater (n = 276) of 564 mg/L (294-958 mg/L) and Lake Chilwa (n = 3) of median 3120 mg/L. The 95 th percentile TDS for groundwater of 2384 mg/L approaches, and its maximum of 3200 mg/L exceeds, the Lake Chilwa TDS values observed near the lakeshore of approximately 3%o salinity at the lower hyposaline lake threshold. These values compare to the literature lake perimeter salinity of _c._ 2.5%o in the dry season and diluted wet-season salinities of _c._ 1.5%o and a recessed central lake salinity reaching 10-12%o [22].  
Major ion proportions compare across groundwater, river and lake waters, and variations are ascribed to the closed basin system and their flow relationships (Figure 8). Anions decline from predominant bicarbonate, with chloride an order of magnitude lower in all waters, followed by sulphate, with nitrate negligible. Cation concentrations are greatest for calcium and sodium with the former slightly higher on average in groundwater and lake samples. Magnesium is somewhat lower and potassium minor across all waters. Groundwater calcium, sodium and magnesium display similarly wide concentration ranges with comparable (10-100 mg/L) interquartile ranges. Concentration ranges in groundwater exceed river samples that are ascribed to hydrochemical process diversity that is influential over much longer residence times together with mixing of fresh recharge water and older groundwater of contrasting hydrochemistry.  
Groundwater quality and surface water quality are generally not indicative of anthropogenic influences, perhaps with some local anomaly exceptions. Very low nitrate and low chloride and sulphate and TDS are found, with increased concentrations aligning with expected natural system trends (shown later). This is consistent with the predominant rural nature of the catchment with non-intensive agriculture predominant having limited chemical fertilizer application historically alongside little if any industry and basic pit latrine sanitation sources that are emergent. This concurs with the findings of Missi and Atekwana [18] in the area although they do observe increased nitrate, in groundwater ascribed to pit latrines and in surface water from agricultural sources. The documented hydrochemistry thus largely serves as a baseline against which future anthropogenic impacts may be measured.
# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
## 4 Experiments
### VGG Fault Diagnosis Model with SDP Feature Information Fusion
When the three sensors were fused simultaneously, the accuracy reached 99.98%, which is 3.68%, 6.40%, and 7.18% better than the accuracy of single sensors 1, 2, and 3, respectively. Apparently, after applying the DS fusion method, the accuracy became higher than that of the single sensors before fusion. In contrast, when using two sensors fused with each other, the accuracy after fusion was not as high as that of three sensors fused at the same time. In this regard, it is necessary, as far as possible, to collect more comprehensive data signals for gear teeth, in order to greatly improve the accuracy of gear tooth fault diagnosis.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 3 Methodology
### Quantum Gated Recurrent Unit (QGRU)  
Quantum Gated Recurrent Unit (QGRU) represents an evolution of traditional GRU networks, integrating with VQCs. The structure of a single QGRU unit is shown in Figure 5.  
A QGRU cell operates based on the following equations:  
\[r_{t} =\sigma(\mathrm{VQC1}(v_{t})), \tag{34}\] \[z_{t} =\sigma(\mathrm{VQC2}(v_{t})),\] (35) \[o_{t} =\mathrm{cat}(x_{t},r_{t}*H_{t-1}),\] (36) \[\widehat{H}_{t} =\tanh(\mathrm{VQC3}(o_{t})),\] (37) \[H_{t} =z_{t}*H_{t-1}+(1-z_{t})*\widehat{H}_{t},\] (38) \[y_{t} =\mathrm{NN}(H_{t}). \tag{39}\]  
In Equations (34)-(39), \(r_{t}\) and \(z_{t}\) represent the reset and update gates of the QGRU at time \(t\), respectively. \(H_{t}\) denotes the hidden state, and \(\widehat{H}_{t}\) is the candidate hidden state. The input to the QGRU cell, \(v_{t}\), is the concatenation of the previous hidden state \(H_{t-1}\) and the current input vector \(x_{t}\). The VQCs are used to process the quantum aspects of the data.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 2 System Model and Problem Formulation
### Bayesian Compressive Sensing
By using the same method, the estimation of \(\sigma^{2}\) can be obtained as follows:  
\[=\prod_{i=1}^{N}(2\pi)^{-\frac{1}{2}}\alpha_{i}^{\frac{1}{2}}e^{- \frac{\alpha_{i}^{2}\sigma_{i}^{2}}{2}},\]1  
The derivations of Equations (13) and (14) are similar to [35]. It can be seen that the hyperparameters \(\boldsymbol{\alpha}\) and \(\sigma^{2}\) are functions of the mean \(\boldsymbol{\mu}\) and covariance matrix \(\boldsymbol{\Sigma}\). The expectation maximization (EM) algorithm shown as Algorithm 1 can be used to update them iteratively. The iteration is terminated when there is a convergency value. Finally, the mean \(\boldsymbol{\mu}\) is regarded as the estimation of the sparse vector \(\mathbf{s}\). The original signal \(\mathbf{x}\) can be reconstructed by using \(\boldsymbol{\Psi}\boldsymbol{\mu}\). ```
1:Initialize \(\alpha_{i}[0]\), \(\forall i=1,2,...\), \(N\) and \(\sigma^{2}[0]\), set tolerance as \(\varepsilon\) and maximum number of iterations as \(K\). 2:for\(k=1:K\)do
3: Calculate the mean \(\mathbf{\mu}[k]\) and covariance matrix \(\mathbf{\Sigma}[k]\). 4: Update the hyperparameters \(\alpha_{i}[k]\), \(\forall i=1,2,...\), \(N\) and \(\sigma^{2}[k]\) according to the results of step 3. 5:if\(|\alpha_{i}[k]-\alpha_{i}[k-1]|<\varepsilon\)then
6: Break. 7:endif
8:endfor
9:Output the final mean \(\mathbf{\mu}[k]\) and covariance matrix \(\mathbf{\Sigma}[k]\) and reconstruct the signal as \(\mathbf{\Psi}\mu\). ```  
**Algorithm 1** Expectation maximization algorithm for Bayesian estimation [35].
## 2 Data and Methods
### Implementation of the Regression NN
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Input Feature**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**StDev**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} \\ \hline \multirow{4}{*}{**Prediction**} & T\({}_{B}\) [K] & 166.8 & 21.3 & 100.0 & 209.7 \\  & SIC [\%] & 80.4 & 20.1 & 18.8 & 100.0 \\  & T\({}_{S}\) [K] & 264.1 & 5.1 & 236.4 & 281.4 \\  & SIT\({}_{SMOS}\) [m] & 0.239 & 0.165 & 0.020 & 0.600 \\ \hline \multirow{4}{*}{**Prediction**} & T\({}_{B}\) [K] & 173.8 & 24.7 & 100.4 & 207.8 \\  & SIC [\%] & 95.3 & 7.5 & 19.9 & 100.0 \\ \cline{1-1}  & T\({}_{S}\) [K] & 260.2 & 4.6 & 239.6 & 272.5 \\ \cline{1-1}  & Fb [m] & 0.108 & 0.070 & 0.000 & 0.399 \\ \cline{1-1}  & SIT\({}_{CS2 SMOS}\) [m] & 1.248 & 0.639 & 0.045 & 2.853 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of the NN training set used to predict thin ice (top) and full-range thickness (bottom). The description includes the Mean, Standard Deviation (StDev), Minimum, and Maximum values of the observations.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 3 Methodology
### Neural Network Model Training  
The development of the learning model requires a history of the training data and provide observation of the data with input. The network captures the meaning of these observations in the output. The neural network learns a mapping function to find an optimal set of model parameters. We tested different network parameters and after their empirical analysis, the following parameter values are used as shown in Table 1.
## 4 Experimental Results and Analysis
### Experimental Results Compared to the State-of-the-Art Models and Traditional Methods  
In this section, we compare the model proposed in this research with other methods, including state-of-the-art models [27, 28]. Other commonly used machine learning models, including k-nearest neighbors algorithm (KNN), SVM and Random Forest [48], are also included. The above machine learning methods are commonly used in numerical data classification problems. This study incorporates these models to explore their effectiveness for time series classification problems. The source codes of [27, 28] are provided in the original papers, other approaches are implemented by this research. In the experimental setting of [27], we selected 20 electronic stocks on the Taiwan Stock Exchange as the related stocks of the target TPE:2330. The positive rate of return is estimated to price up, and vice versa. In the experimental setting of [28], we adopted the CNN module with price volume chart for training. The experimental results are shown in Table 3.  
It can be seen from Table 3 that the architecture of GRU_ATT with technical indicators proposed by this research, has a significant improvement in overall performance compared with other algorithms. The BIAS normalization approach has an improvement of 7.62% and 11.38% in the accuracy measure compared to [27, 28], and the RSI approach has an improvement of 4.02% and 7.78%, respectively. Among other machine learning methods, KNN performs better and has a prediction accuracy close to 70%, while Random Forest does not reach 60%. In the algorithms compared in this study, most models have low precision and recall for price increases and declines, which indicates that these models are less sensitive to time series data.
# Median Filter Aided CNN Based Image Denoising:
## 3 Proposed Work
### Attention-Guided CNN (ADNet)  
There are 17 layers in total in ADNet, which are divided into four blocks, as shown in [4]. The first block is the Sparse Block (SB), the second block is the Feature Enhancement Block (FEB), the third is the Attention Block (AB), and the fourth block is the Reconstruction Block (RB). The SB contains 12 layers, it has two types of convolution--the dilated convolution and the standard convolution. The layers in the SB are of two types, the Dilated Convolution and the Normal convolution, both types being supported by Batch Normalization (BN) followed by the Relu activation function. The Dilated Conv (Dilated convolution) helps in increasing the size of receptive field without increasing the computation cost of the model. A dilated filter with dilation factor of \(a\) can be interpreted as a sparse filter of size \((2a+1)\times(2a+1)\).  
Figure 1: Our proposed ensemble image denoising model. In the figure (**a**) denotes the improved Denoising CNNs (DrCNN) model, (**b**) represents the improved ADNet model, and (**c**) denotes the improved IRCNN model.  
The dilation rate of the dilated convolution used in SB is 8 and, also with each layer in the SB, we add a median filter layer, which is a traditional non-linear filter that replaces the pixel centred in a window with the median of the window, and helps in the efficient removal of impulse noise as mentioned in ([PERSON] et al., 2018) by [PERSON] et al. It is applied to each element of a feature channel in a moving window fashion. In case of each feature channel, we extract a set of patches of size (\(3\times 3\) or \(5\times 5\)) centred at each pixel. Subsequently, we find the median of the sequence formed by all elements in that patch and replace the elements with the median value.  
The FEB consists of four layers and it aims to capture global and local features of the model to enhance the ability to express in image denoising. The global and local features that are used by the FEB are the input noisy image and the output of SB, respectively. The output from the FEB is passed on to the AB. The AB uses the current stage to guide the previous stage for learning the noise information, which is useful in terms of blind denoising and for extremely noisy images. The output of the AB is passed on as input to the RB, which uses a residual learning technique to reconstruct the clean image.
## 2 Methodology
### RSM Model
#### 2.3.2 Numerical Analysis of RSM Model
Conversely, the radius of the curved tunnel does not demonstrate any statistically significant implications for displacement response. However, the angle of the small-radius curved tunnel exhibits a notable impact on the displacement response. Table 7 presents the ANOVA results obtained from the linear model analysis of the acceleration response at point B. The computed F-value of 14.46 and \(p\)-value of 0.0002 indicate that there is a 0.02% probability that the F-value of the model is due to noise. Therefore, the model is deemed significant. The prediction coefficient R\({}^{2}\), which measures the proportion of the variance in the response variable explained by the independent variables, was found to be 0.51898, which corresponds well with the adjustment coefficient R\({}^{2}\) of 0.7162. The observed difference between the prediction and adjustment coefficients was within 0.2 and is consistent with the outcomes documented in extensive statistical literature and related studies. Moreover, the signal-to-noise ratio of 13.1706 was calculated, which is greater than the threshold value of 4 recommended in the relevant statistical literature. This finding indicates that the model has very low noise and can be employed for parametric studies. Furthermore, the ANOVA reveals that the buried depth of the small-radius curved tunnel is the most significant factor affecting acceleration, with an F-value of 30.57, which is consistent with the ANOVA results for the acceleration response at point A. The angle of the small-radius curved tunnel has an F-value of 12.29 and a \(p\)-value of 0.0039, with a slightly significant implication for the acceleration response, while the radius of small-radius curved tunnel does not offer significant implications for the acceleration response.
## 4 Conclusions  
The application of a DBD reactor supplied with nanosecond high-voltage pulses and containing catalysts for tar removal from biomass producer gas was studied. The experiment was focused on the conversion efficiency of three heavy hydrocarbons always present in real gas produced in biomass gasification: naphthalene, toluene, and benzene. Ni- and Rh-based catalysts were used separately as impregnation in the ceramic barrier of the reactor. This way, non-thermal plasma induces surface catalytic reactions resulting in tar conversion, which reached 100% when the Rh-based catalyst was used. This catalyst also allowed char formation to be avoided, which was observed when the Ni catalyst was used, as well as in the reactor without any catalytic material.  
Analysis of the gaseous components shows that in the case of the Rh-based catalyst, all heavy hydrocarbons were transformed into simple gases such as those being the main components of biomass producer gas, i.e., CO, CO\({}_{2}\), CH\({}_{4}\), and H\({}_{2}\). The highest resulting turnover frequency (TOF) was \(1.57\times 10^{-4}\) s\({}^{-1}\). Application of the Ni catalyst led to a lower tar conversion, and consequently to a lower increase in hydrogen concentration. Thus, the highest TOF for this catalyst was \(1.55\times 10^{-4}\) s\({}^{-1}\). As for the main gaseous components, it can be concluded that hydrogen and methane introduced into the reactor are not transformed in the nanosecond DBD plasma. On the other hand, the CO\({}_{2}\) concentration decreases almost linearly with the discharge energy, causing an adequate increase in CO concentration, which is typical behavior for non-thermal plasma reactors.  
Although the energy efficiency of the studied nanosecond DBD combined with a catalyst impregnated in the ceramic inner barrier is not superior compared to non-thermal plasma systems developed by other researchers, the results presented in this paper show that such a one-stage configuration is a proper direction of research and the key factor is the catalytic material.  
Figure 10: Conversion of naphthalene in pyrolytic gas simulator treated by nanosecond DBD plasma.  
**Author Contributions:** Conceptualization, M.D.; methodology, M.D. and D.K.; validation, M.D. and D.K.; formal analysis, M.D.; investigation, M.D. and D.K.; writing--original draft preparation, M.D.; writing--review and editing, M.D.; visualization, D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Performance Evaluation  
In this section, the performance of FASBA is evaluated by quantitative stochastic simulation. FASBA's performance is compared with that of MP-EDCA. For system performance evaluation, we consider three important network performance metrics: throughput, packet delay, and packet retransmission attempt.
# Ras Isoforms from Lab Benches to Lives--What Are We Missing and How Far Are We?
## 5 Missing Pieces in the Ras Puzzle
### One Ras and Many Locations
#### 5.6.3 Ras in the Endosome  
RTKs and GPCRs activate Ras and both these receptors are endocytosed. During the process of endocytosis, the tail of the receptor is exposed to the cytosol and it can still signal. [PERSON] et al. showed that on stimulation of EGFR in the liver parenchyma, there was an internalization of the receptor followed by recruitment of Grb2-SOS within the endosomes [145]. This together with the finding that in rat liver early-sorting endosomes contain activated Raf1-MEK, a possible model was proposed. According to this model, upon EGFR stimulation, the dissociation of the Ras-Raf complex could take place from caveolae in the plasma membrane to the endocytic compartment [146]. Using the bimolecular fluorescence complementation technique [PERSON] et al. visualized Ras activation in the endosomes. They observed the recruitment of Ras along with PI3K-RBD into the endosomes, which results in the accumulation of phosphatidylinositol 3,4,5-triphosphate (PIP3) [147]. [PERSON] et al. also showed that on EGFR stimulation, K-Ras accumulates on EEA1/Rab5 bearing early endosomes, Rab7-marked endosomes, LAMP1/2- marked lysosomes, and multivesicular bodies [148]. Another study shows that on activation of TLR3 and TLR4 signaling in macrophages, RasGEF1b, a GEF is expressed in early endosomes. RasGEF1b binds to the GEF binding site of Ras that is required for the activation of the Ras-Raf-MAPK cascade [149]. Thus, the discovery of Ras GEFs and activation of its effectors in endosomal compartments further stratified signaling platforms for Ras.