esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 2 Theoretical Model of Specular Returns from Inland Waters
### Model Examples for Larger Water Bodies  
Figure 4 illustrates the results from the model calculated for the \"square lake\" case. The left panel is the Earth map, \(M\left(\overrightarrow{x}\right)\) with an isolated 200 m \(\times\) 200 m lake in the center (blue square). The central panel shows that the model output is not a hyperbola. There is a 200 m stretch over the water surface where the range is constant. The hyperbola-like range migration exists only when the nadir point is either approaching or receding the lake shoreline. In essence the hyperbola is flattened at the center.  
The top central panel is echo power with respect to the along track nadir. The complex echoes \(\mathcal{Z}\) are coherently summed over range. It can be seen that the echo power is near constant for the over-water duration and drops precipitously when the radar nadir is just beyond the shorelines. More importantly, the Doppler rests at zero for the over-water duration.  
Figure 5 shows the model results for a \"peanut-shaped\" lake, which has similar size to the square lake, but with an irregular shape. In addition, the lake center has been displaced from the nadir track. The point of this example is to illustrate the important characteristics that are peculiar to realistic water surfaces, i.e., the flat topped amplitude and the zero Doppler velocity during an interval around the CPA, as it is obtained also in this case.  
Figure 4: Model for the square lake example (dimensions are approximately 200 m \(\times\) 200 m).  
Figure 5: Model for the peanut-shaped lake example. Power at CPA is about 70 dB.
## 2 Related Work
### Self-Supervised and Unsupervised Monocular Depth Estimation  
Self-supervised methods leverage temporal information in monocular video sequences to infer depth without requiring ground truth depth labels. The seminal work by [12] introduced an unsupervised framework that jointly learns depth and camera motion from monocular videos by minimizing the photometric consistency loss between consecutive frames. This approach has since been widely adopted and improved in subsequent works. The authors advanced this line of research by proposing the Monodepth2 model, which improved depth estimation by introducing a minimum reprojection loss to handle occlusions [13]. This method led to more accurate depth predictions in static scenes, but like other self-supervised approaches, it struggled in dynamic scenes, where moving objects break the assumptions of static scene geometry. Despite these advances, one of the primary limitations of self-supervised MDE remains its inability to accurately estimate depth in dynamic regions where objects are in motion [14]. These moving objects violate the assumption that the scene remains static between consecutive frames, leading to poor depth predictions. This challenge has led to various methods attempting to model motion explicitly to improve depth prediction in dynamic regions.
## 4 Experimental Case Analysis and Discussion
### Imbalance Dataset
#### 4.4.1 The Strategy of Data Imbalance Resolution  
In Table 8, when the training data are sufficient and balanced, we can obtain the 99% results of any time-frequency analysis, and the balanced dataset generated by DCGAN can obtain a 75% result after the wavelet transformation. When the training data are unbalanced, it can be seen in Table 9. Under signal processing, the smaller the unbalance ratio, the lower the accuracy rate obtained by the test set. It indicates that the data-driven fault diagnosis method is susceptible to an unbalanced training dataset. In the processing of down-sampling, we can improve the accuracy of the diagnostic model with a balanced dataset even if the data amount is insufficient; in the processing of over-sampling-balance (Pro-Balanced), the accuracy rate is increased to more than 90%, which is a significant improvement, and can even be close to the level of the actual balanced dataset; in the processing of over-sampling-expanding (Pro-Expand), the improved accuracy is very similar to that of over-sampling-balanced (Pro-Balanced). It can be seen that the oversampling method has advantages in the solution strategy, which can be close to the accuracy of the actual balanced dataset with high efficiency of over-sampling-balanced (Pro-Balanced). It shows the validity and superiority of DCGAN in generating a new sample.  
Figure 10: Comparison of actual and generated data of bearing outer ring damage.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 3 Materials and Methods
### Network Architecture  
Our network is depicted in Figure 2, and it consists of four steps: feature extraction, cost aggregation, disparity generation, and disparity refinement. As with our benchmark model [21], the feature extraction and disparity refinement tasks are dealt with using hourglass networks. During the disparity generation step, the aggregated costs (\(\mathbf{C_{A\to B}}\) and \(\mathbf{C_{B\to A}}\)) are successively converted into parallax attention maps (\(\mathbf{M_{A\to B}}\) and \(\mathbf{M_{B\to A}}\)) and valid masks (\(\mathbf{V_{A}}\) and \(\mathbf{V_{B}}\)). Then, the initial disparity \(\mathbf{D}_{init}\) is the sum of all disparity candidates weighted by the parallax attention map. For an invalid pixel, its initial parallax is derived from a partial convolution.  
Figure 2: The overview of our network. The green box areas represents the proposed correction compensation module and the red box area represents the proposed adaptive cost aggregation module. In the training stage, the network’s photometric loss \(L_{\mathrm{P}}\) and disparity smoothness loss \(L_{\mathrm{S}}\) take the compensated images as input.  
In particular, aiming at alleviating the situation in which the corresponding pixels are imperfectly aligned caused by imperfect correction, our network comprises a correction compensation module. In addition, we redesigned a cost aggregation network, i.e., adaptive cost aggregation module. It consists of 12 adaptive parallax attention blocks (APABs), which capture stereo correspondence using group convolution and the proposed interleaved group convolution, and reconstruct the cost volume according to its elements' value differences. These APABs are evenly divided into three groups for different input sizes. APABs in the same group pass information losslessly through a dense connected structure.
## 2 Literature Review  
In this section, the linearization methodologies that have been implied for the RoF systems are discussed. The linearization methodologies consist of Electrical, Optical and Machine Learning Methods. Electrical methods are subdivided in to analog and digital methods. Digital methods are subdivided into predistortion and postdistortion methods. Optical methods consist of largely dual wavelength, singular polarization, mixed polarization, etc., while a newer avenue of linearization has been coined that belongs to machine learning. The higher schematic of these methods is summarized in the Figure 4.  
A detailed literature review is enlisted for the mitigation of the impairments of RoF system that are categorized in Table 1. The table summarizes the method employed, type of linearization, category, parameters evaluated, advantages and disadvantages of respective methods. Figure 4: Different types of linearization. Figure 3: Organization of our work in this paper (Sections 1–8).
# An Overview of Fog Data Analytics for IoT Applications
## 5 Research Challenges
### Authentication and Access  
As we have mentioned earlier, IoT devices are increasing exponentially. These devices access and share resources/services such as storage, PaaS, and SaaS with the omnipotent cloud [86]. Thus, the authentication of these devices becomes the responsibility of the cloud in order to identify legitimate users and keep any other person or bot with malicious intent at bay. Sometimes, these illicit users attack the servers and keep their servers busy by bombarding spam requests. IoT devices are quite vulnerable to cyber-attacks, especially Dos and DDoS attacks [87]. Thus, a robust authentication technique with a quick response time must be used. Some people claim that using a cloud-based technique prevents the devices from malicious attacks [88]. Although authentication via the cloud is secure and robust, it has a latency problem and is also not scalable[89]. Hence, we have to accept the help of fog nodes by performing some authentication processes at the edge in fog nodes [30].  
Authentication and access for the end devices is a challenge in the fog architecture due to many reasons. Firstly, the fog architecture already comes with a resource constraint as it has to serve many devices with limited resources. Thus, ensuring the continuous availability of fog nodes is also a challenge in the authentication scheme. Secondly, if fog nodes have to carry out authentication at the edge, they either have to perform the authentication or communicate with the authenticating agent through API calls. This task increases the workload of the data transfer channels of the fog node, and the available bandwidth also reduces security-related threats because of the transfer of sensitive data, such as credentials near the edge. Therefore, many authentication schemes have been proposed to date. One of the techniques used is the single sign-on (SSO) authentication scheme. In SSO, a single login is required, and it provides access to all of the services within the system, where no further authentication is necessary, but this scheme has a backdoor for man-in-middle attacks [90; 91; 92]. Moreover, if the SSO provider gets compromised, then the security of all other devices under that are compromised as well [86]. Therefore, multi-tier and multi-factor authentication schemes are used to ensure that the security of users is not compromised.  
From the above discussion, it is clear that authentication and access is a double-edged sword. If more focus is placed on making the authentication system more robust by introducing a multi-tier authentication architecture, user experience and convenience is at risk. On the other hand, if user convenience is catered for, the security might get compromised. Dealing with both simultaneously by maintaining the right amount of balance between the two is one of the greatest challenges in authentication in fog-enabled architecture. Table 3 summarizes the various authentication techniques implemented to date, along with their pros and cons.
## 2 Methods
### Signal-in-Space Range Error  
The SISRE was developed in detail to assess the accuracy of the broadcast ephemeris, and the solution method is shown in Figure 1.  
Figure 1: The flow chart for assessment of Broadcast orbit and clock accuracy and calculation of signal-in-space range error (SISRE).  
According to [PERSON] and [PERSON] [5; 26], the orbit-only SISRE can be expressed as:  
\[\text{SISRE}_{\text{ORB}}=\sqrt{\text{w}_{\text{R}}^{2}\cdot\text{R}^{2}+\text{w} _{\text{A,C}}^{2}\cdot\left(\text{A}^{2}+\text{C}^{2}\right)} \tag{6}\]  
where \(\text{SISRE}_{\text{ORB}}\) is the orbit-only SISRE; \(\text{W}_{\text{R}}\) and \(\text{W}_{\text{A,C}}^{2}\) are the weight factor of orbit errors for radial as well as along-track and cross-track. \(\text{W}_{\text{R}}\) and \(\text{W}_{\text{A,C}}^{2}\) are 0.98 and 1/54 for BDS MEO satellites, 0.99 and 1/126 for BDS GEO and IGSO satellites [5].  
Simultaneously, considering the contribution of the accuracy of clock offsets to SISRE, the equation can be expressed as:  
\[\text{SISRE}=\sqrt{\left(\text{w}_{\text{R}}\cdot\text{R}-\text{c}\cdot\
abla \text{A}\text{t}\right)^{2}+\text{w}_{\text{A,C}}^{2}\cdot\left(\text{A}^{2} +\text{C}^{2}\right)} \tag{7}\]  
The only difference from Equation (6) is that the accuracy of clock offsets \(\
abla\text{A}\)t is considered.
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## 3 Results and Discussion
### Identification of Transformation Products  
#### 3.3.1 Identification of Benzotriazole's TPs  
Several supporting electrolytes were used for EO, using the appropriate concentrations to achieve the same conductivity (3 mS/cm): NaCl, Na\({}_{2}\)SO\({}_{4}\), and H\({}_{2}\)SO\({}_{4}\). Electrolytes Na\({}_{2}\)SO\({}_{4}\) and H\({}_{2}\)SO\({}_{4}\) displayed identical TPs, although the degradation of BTA proceeded somewhat faster in H\({}_{2}\)SO\({}_{4}\) (e.g., after 150 min, 97.9% BTA was removed in H\({}_{2}\)SO\({}_{4}\), compared to 91.5% in Na\({}_{2}\)SO\({}_{4}\)) [19]. Table 3 shows the identified TPs in the sulphate media.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(m/z\) & **Proposed Structure** & **Monoisotopic Mass [Da]** \\ \hline \hline \(134.00\) (ESI\(-\))/\(136.05\) (ESI+) & \(N\) & \(135.0433\) \\ \(148.01\) (ESI\(-\)) & \(N\) & \(149.0225\) \\ \(119.0225\) & \(N\) & \(151.0382\) \\ \(N\) & \(N\) & \(N\) \\ \(N\) & \(N\) & \(151.0382\) \\ \(N\) & \(N\) & \(N\) \\ \(N\) & \(N\) & \(N\) \\ \(N\) & \(N\) & \(151.0382\) \\ \(N\) & \(N\)Several small peaks at higher \(m/z\) were present in the chromatograms (ESI\(-\): \(m/z\) 209.02, 212.06, 220.02, 238.02, and 254.04; ESI+: \(m/z\) 191.05, 211.11, and 222.06), but could not be identified with the low-resolution MS used in the experiments. However, the identified hydroxylated products and done are in good agreement with the TPs determined by [PERSON] et al. [51] and [PERSON] et al. [52] after the ozonation and sulphate-radical oxidation of BTA, respectively. The EO degradation of BTA with the identification of TPs is rarely encountered, but what is available [7] corresponds well with our findings. Figure 5 shows a mass spectrum of the peak for \(1H\)-BTA-4,7-dione.  
In the case of NaCl as a supporting electrolyte, different products were observed (Figure 6). In ESI\(-\), peaks at \(m/z\) 152.16 and 154.12 were observed, corresponding with the isotopic pattern for monochrominated ions. The TP was identified as monochrominated benzotriazole, which aligns well with the scarce literature reports [7]. Other low-intensity peaks at higher \(m/z\) could not be identified.  
Figure 5: Mass spectrum (ESI\(-\)) of \(1H\)-BTA-4,7-dione (\(m\)/z 45–170) in the sample of electro-treated BTA solution. Supporting electrolyte: H\({}_{2}\)SO\({}_{4}\).  
Figure 6: Mass spectrum (ESI\(-\)) of monochrominated benzotriazole (\(m\)/z 45–190) in the sample of electro-treated BTA solution. Supporting electrolyte: NaCl.
## 4 Result and Discussion
Therefore, considering operating voltage conditions is the next important aspect in the design, based on the traction power distribution single-end feeding or double-end feeding and the following contact line systems: OCS, 3R, and ROCS. Simulation results of U\({}_{\text{mu}}\) in the design, presented in Table 9, are analyzed below. Involving a single-end feed, under normal operating conditions, and the minimum mean useful voltage at the pantograph when the train appears at the end of each segment (x = L), the voltage results in the contact line of each type, OCS, ROCS, and 3R, are 553.135 V, 610.908 V, and 618.180 V, respectively, as shown in Table 9. In the table, the smallest potential at the pantograph in the end section is OCS, 553.135 V; however, this voltage is still greater than the minimum allowable limit standard, being 500 V DC. Involving a single-end feed, in the case of a breakdown, the maximum segment length of a station that needs to be supplied is 2 L = L\({}_{\text{D}}\). When a problem occurs, one group rectifier (2000 MW/RS) in the TPS cannot meet the full load, so two group rectifiers with a total power of 4000 MW must be operated in parallel. This time, the feeding voltage at Z (Ud) is 609.201 V and the voltages in contact line networks for OCS, ROCS, and 3R types are 497.691 V, 574.993 V, and 584.688 V, respectively, Table 9 and Figure 7. In these, the useful voltage at the pantograph on OCS is 497.691 V, and this potential is smaller than the minimum limit allowed, being 500 V DC, as shown in Figure 7. In this case, the voltage in the contact line on the ROCS network still ensures an operating voltage of 574.993 V, greater than the Un-min value. Involving a double-end feed, with the distance between the two TPSs (i) and (i + 1) being 2 L = L\({}_{\text{D}}\) under normal operating conditions, the minimum mean useful voltage at the pantograph is x = L\({}_{\text{D}}\)/2. The simulated result of the feeding voltage at Z (Ud) is 748.311 V and the voltage in contact line networks for OCS is 720.496 V; on the ROCS contact network system, it is 739.753 V; and on the 3R system, it is 742.177 V, as shown in  
Figure 7: Minimum U\({}_{\text{mu}}\) at pantograph in single-end feed in case of fault. on the ROCS contact network system, it is 605.224 V; and on the 3R system, it is 627.037 V, as shown in Figure 4:0 In this case, the useful voltage at the pantograph on OCS is the smallest at 431.902 V; this potential is smaller than the minimum limit allowed, being 500 V DC, but the voltage in the contact line using the ROCS network still ensures an operating voltage of 605.902 V, greater than the U\({}_{\text{n-min}}\) value (500 V). In summary, the result is calculated from the power supply design for the MCS PVD line for the contact network using the new ROCS conductive rails, and whether the power supply is from one direction or two directions, the minimum values for mean useful voltage at the pantograph always meet the requirements' forecast for PVD line operation and satisfy the above design requirements.
## 2 Proposed Radio Link Model for Underground Mines
### Propagation Zone Model  
The dividing point calculations are performed for variable cross-sections of the mine tunnels, with some as small as 2 \(\times\) 2 mm\({}^{2}\). The dividing point theory is illustrated in Figure 2.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 4 Proposed Hybrid Deep Learning-Based IDS (DL-IDS)
### Hybridization of DAE and DANN Models (DAE-DANN)
#### 4.2.2 Deep Artificial Neural Network (DANN)  
A multilayer perceptron (MLP) model is adopted in the proposed architecture, which represents a deep neural network. The architecture consists of a typical deep neural network, which has an input layer, multiple hidden layers, and an output layer, as presented in Figure 4.  
The neurons in each layer are fully connected with other neighbor layer neurons, where the data are transformed from layer to layer in a forward direction. Each neuron in the hidden layer is activated by calculating an output value based on the input data from the previous layer, along with the weight (**w**) values and bias (**b**). Let the \(l\ \in\ \{1,\ldots,\ L\}\) index be the hidden layers of the L number. From Figure 4, the output of activating \(a_{i}^{l}\) is shown in Equation (10).  
\[a_{i}^{l}=f(\sum_{i=0}^{n}a_{i}^{l-1}w_{i}^{l}+b_{i}^{l}) \tag{10}\]  
where \(i\) is any hidden neuron, \(n\) is the number of neurons at the hidden layer, \(a_{i}^{l-1}\) is the \(i\) neuron at the previous layer, \(w_{i}^{l}\) is the weight connection between the \(a_{i}^{l}\) and \(a_{i}^{l-1}\) neurons, and \(b_{i}^{l}\) is the bias of layer \(l\). \(f\) is the activation function, and \(ReLU\) is used for the hidden layers.  
Since the network outputs are in multiple classes (labeled attacks), the softmax function is used, which calculates the probability distribution across the classes. The input parameter is converted to be in the form of a one-hot encoded matrix. Equation (9) calculates the probability of class \(z_{i}\) for traffic input \(\mathbf{x}_{i}\), where \(k\) is the class number of **C** classes, and the  
Figure 4: DANN Architecture.  
input value of \(z_{i}\) and \(z_{k}\) are calculated using Equation (11). Each class's output is in the range of [0, 1] and adds up to 1 for all the classes.  
\[P\;(Z=z_{i}|\mathbf{x}_{i})=\frac{e^{z_{i}}}{\sum_{k=1}^{C}e^{z_{k}}} \tag{11}\]  
To calculate the amount of the difference between the predicted class value and the original true class value, the categorical cross-entropy loss function is used. This function is designed for the multi-class classification tasks, where an instance input can belong to one possible class. Equation (12) shows the categorical cross-entropy loss function (CL).  
\[CL=PCL\;(z_{i},\;\widehat{z_{i}})=-\sum_{i=1}^{C}z_{i}\;\log\widehat{z_{i}} \tag{12}\]  
where \(C\) denotes the number of classes, \(z_{i}\) is the true class value, and \(\widehat{z_{i}}\) is the predicted class value that is calculated using the softmax function (Equation (9)). To minimize the loss value during the training process, the Adam optimization algorithm backpropagates to calculate the gradient (change).
# Inactivation of the Plant Pathogen _Pythium ultimum_ by Plasma-Processed Air (PPA)
###### Abstract
Plasma in general is considered as the fourth state of matter and contains charged particles, reactive oxygen and nitrogen species (RONS), excited molecules and UV photons [21; 22]. Furthermore, electrical fields and temperature are important components especially in the direct treatment modes and influence the impact of plasma on microbial inactivation [23; 24]. The mode of plasma exposure to the target can be direct or indirect via the generation of plasma-treated gas (e.g., plasma-processed air, (PPA)) or liquids (e.g., plasma-processed water) [25; 26; 27; 28; 22]. Indirect treatment using PPA based on microwave torches has proven to be efficient in microbial inactivation on the laboratory scale, as well as at the industrial scale for inorganic (e.g., glass), organic (e.g., plastic) and biological surfaces (e.g., fruit, vegetables, and meat) [29; 30; 31; 32; 33; 34]. Moreover, the exposure of seeds from various plant species inoculated with the non-pathogenic spores of _Bacillus atrophaeus_ has shown the bactericidal efficiency of the PPA treatment [35]. In this study, the objective was to perform a proof-of-concept on the efficiency of PPA for the inactivation of the vegetative mycelia stage of the plant pathogen _Pythium ultimum_, to elucidate the general potential of PPA as a seed treatment against the vegetative form of fungal pathogens. In addition to the untreated controls, further control samples were exposed to gas flow only to ensure that inactivation was not the result of drying out by using a gas flow within the PPA treatment system.