esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
#### 3.4.4 VGG-19 CNN Architecture  
VGG-19, an enhanced version of VGG-16, is a model that brings significant benefits with its three additional convolutional layers. This model, with its fully connected structure, max pooling and dropout layers, and 19 deep trainable layers for convolution, is designed to capture intricate features within images with greater precision [50]. It is an optimal choice for applications that require advanced feature extraction [45]. Similarly, VGG-19 follows a uniform 3 \(\times\) 3 convolutional kernel pattern--just like VGG-16--simplifying its architecture and the training process. VGG-19 is a frequently implemented choice when the highest level of precision is necessary, and when there are sufficient computational resources to handle its more complex architecture [45]. Table 3 provides a visual representation of the VGG-19 model's architecture.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Layer Type** & **Output Shape** & **Number of Filters** & **Kernel Size** & **Stride** \\ \hline Input & 224 \(\times\) 224 \(\times\) 3 & - & - & - \\ Convolutional & 112 \(\times\) 112 \(\times\) 64 & 64 & 7 \(\times\) 7 & 2 \\ Residual Block & 112 \(\times\) 112 \(\times\) 64 & 64 & 3 \(\times\) 3 & 1 \\ Downsampling & 56 \(\times\) 56 \(\times\) 128 & 128 & 3 \(\times\) 3 & 2 \\ Residual Block & 56 \(\times\) 56 \(\times\) 128 & 128 & 3 \(\times\) 3 & 1 \\ Downsampling & 28 \(\times\) 28 \(\times\) 256 & 256 & 3 \(\times\) 3 & 2 \\ Residual Block & 28 \(\times\) 28 \(\times\) 256 & 256 & 3 \(\times\) 3 & 1 \\ Residual Block & 28 \(\times\) 28 \(\times\) 256 & 256 & 3 \(\times\) 3 & 1 \\ Downsampling & 14 \(\times\) 14 \(\times\) 512 & 512 & 3 \(\times\) 3 & 2 \\ Residual Block & 14 \(\times\) 14 \(\times\) 512 & 512 & 3 \(\times\) 3 & 1 \\ Residual Block & 14 \(\times\) 14 \(\times\) 512 & 512 & 3 \(\times\) 3 & 1 \\ Global Average Pooling & \(1\times 1\times\) 512 & - & - & - \\ Fully Connected & \(1\times 1\times\) 1000 & - & - & - \\ \hline \end{tabular}
\end{table}
Table 2: ResNet-50’s CNN architecture [49].  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Layer Type** & **Output Shape** & **Number of Filters** & **Kernel Size** & **Stride** \\ \hline Input & 224 \(\times\) 224 \(\times\) 3 & - & - & - \\ Convolutional & 224 \(\times\) 224 \(\times\) 64 & 64 & 3 \(\times\) 3 & 1 \\ Convolutional & 112 \(\times\) 112 \(\times\) 64 & - & 2 \(\times\) 2 & 2 \\ Convolutional & 112 \(\times\) 112 \(\times\) 128 & 128 & 3 \(\times\) 3 & 1 \\ Convolutional & 112 \(\times\) 112 \(\times\) 128 & 128 & 3 \(\times\) 3 & 1 \\ [PERSON] & 56 \(\times\) 56 \(\times\) 128 & - & 2 \(\times\) 2 & 2 \\ Convolutional & 56 \(
## 2 Observations and Method for Data Analysis
### TEC Data and Methodology  
The vertical TEC maps generated with the NASA JPL (Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA) IGS Ionosphere Center are used in this study and they are provided by: [[https://sideshow.jpl.nasa.gov/pub/iono_daily/IONEX_rapid](https://sideshow.jpl.nasa.gov/pub/iono_daily/IONEX_rapid)]([https://sideshow.jpl.nasa.gov/pub/iono_daily/IONEX_rapid](https://sideshow.jpl.nasa.gov/pub/iono_daily/IONEX_rapid)) (accessed on 10 April 2024). However, the TEC data analyzed here were downloaded from: [[https://www.izmiran.ru/ionosphere/weather/grif/Maps/TEC/](https://www.izmiran.ru/ionosphere/weather/grif/Maps/TEC/)]([https://www.izmiran.ru/ionosphere/weather/grif/Maps/TEC/](https://www.izmiran.ru/ionosphere/weather/grif/Maps/TEC/)) (accessed on 10 April 2024). The global TEC data have a time resolution of 1 h and a grid spacing of \(5^{\circ}\times 2.5^{\circ}\) in longitude and latitude, respectively, with an error of \(\sim\)2 TEC Units (TECU, 1 TECU = \(10^{16}\) el/m\({}^{2}\)) [73].  
The original global TEC data were arrayed in terms of the coordinate system of geographical latitude (from \(-87.5^{\circ}\) to \(87.5^{\circ}\) at each \(2.5^{\circ}\)) and longitude (from \(-180^{\circ}\) to \(180^{\circ}\) at each \(5^{\circ}\)). However, the neutral wind and electric field effects on the ionosphere are dependent on the geomagnetic field configuration, as the electrons are constrained to the magnetic field lines. That is why the distribution of the ionospheric parameters, including TEC, is usually presented in geomagnetic latitude instead of geographic one. The modified dip (modip) latitude, introduced by [PERSON], 1963 [74], is used here because it is adapted to the real magnetic field, e.g., to the magnetic inclination (dip). It is particularly preferable for describing the plasma behavior at mid and low latitudes, which is the densest part of the ionosphere, and it will be especially investigated in the present paper. The modip equator is the locus of points where the inclination is 0. It is worth mentioning that in the equatorial zone, the lines of constant modip are practically identical to those of the magnetic inclination, but as latitude increases, they deviate and come nearer to those of constant geographical latitude. The poles are identical to the geographic ones [75]. Then, for the purpose of this study, the global TEC data were re-arrayed in terms of the coordinate system of modip latitude from \(-87.5^{\circ}\) to \(87.5^{\circ}\) at each \(2.5^{\circ}\), and geographic longitude from \(-180^{\circ}\) to \(180^{\circ}\) at each \(15^{\circ}\).  
The TEC dataset is used for considering the climatological features of the ionospheric TEC response to waves coming from the lower thermosphere. To be able to compare the tidal forcing from below with the respective ionospheric response, both datasets have to be analyzed by the same way. Therefore, the ionospheric TEC tidal and SPWs characteristics are separated exactly in the same data analysis method as that utilized in the SABER temperatures described in Section 2.1.1.
## 5 Experiments
### Schedule Optimization  
#### 5.3.1 Baselines  
We compared our framework with the following baselines:  
* RANDOM is a baseline that chooses a random interval from \([1,\text{halflife}]\) to schedule the next review.
* ANKI [7] is a variant of the SM-2 algorithm, which is used in a popular learning application.
* MEMORIZE [20] is a spaced repetition algorithm based on optimal control that is trained to determine the parameter to minimize the expectation of review cost.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **MAE** & **MAPE** \\ \hline Pimsleur & 0.3169 & 165.69\% \\ Leitner & 0.4535 & 133.92\% \\ HLR & 0.1070 & 76.65\% \\ DHP & 0.0779 & 46.35\% \\ GRU-HLR & 0.0307 & 18.88\% \\ \hline THLR (ours) & **0.0274** & **16.70\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for memory prediction. The bold item indicates the best performance.  
* HLR [8] is a baseline to schedule the next review interval equal to the half-life. To test the effectiveness of spaced repetition algorithms in different environments, we conduct experiments on the following memory models:
* EFC [3] is one of the most recognized memory models that illustrates the relationship between the forgetting curve and the time decay.
* DHP [9] is a memory model with the Markov property for explainability and simplicity and handcrafted state-transition equations. It takes half-life, recall probability, recall result, and item difficulty as state variables.
* THLR is the simulation environment proposed in this work.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 1 Introduction
* This research gives a comparative study of three commonly used approaches for three-dimensional path planning by an AGV. The discussion focuses on the challenges that arise while collecting data in wireless sensor networks. * We propose the Nearest Neighbour (NN)-based approach as a method that can be utilized in the real world to solve the three-dimensional path planning problem. This method considers the computing limitations that emerge during the process. * A modified version of the Nearest Neighbour algorithm, the Obstacle-Avoided Nearest Neighbour (OANN)-based approach, is proposed as a solution for avoiding obstacles to the three-dimensional path planning problem. The travelling constraints occurring between particular sensor pairs are considered whenever this strategy is utilized. The rest of the paper will follow this format. Section 2 briefly describes related studies in the literature. In Section 3, the problem is defined and its system model is given. In Section 4, various methods for solving the 3D path planning problem are presented. In Section 5, we present a novel approach by considering the problem with some limitations between some of the sensor pairs. We evaluate the proposed approaches' performance in Section 6. Section 7 concludes the paper and gives the future work.
# Low-Cost Underwater Communication System: A Pilot Study
## 5 Discussion  
The paper presents the results of a pilot study in which the theoretical and practical aspects of phase--particularly QPSK--modulation and demodulation were developed. Modern devices consisting of an ultrasound transducer, amplifier and power supply circuit and the popular Cortex M4 microcontroller were designed and tested. The feasibility studies have shown the possibility of creating an inexpensive modem for applications of underwater communication. Such modems can be very attractive in the field of underwater robotics. In the proof-of-concept experiments, 4000 bps speed was reached. This link can be utilized by the robots to send simple commands, status, position, etc. Solutions known from the literature describe lower transmission speeds compared to the rate reached in this project [1; 4; 5]. Moreover, each transmitted frame can be treated as an ultrasound probing pulse, so it can deliver information about obstacles around the robot. This is a classic echolocation approach, but at this stage of the project, it is only a potential possibility of the system. The second interesting property of this design is the low level of complexity of the device and the fact that the processor still has free computing power that can be used. For example, it can be used to control the actuators or for the calculation of the attitude and positions of the robot. Other designs require the use of complex technologies, such as FPGA [17; 18; 19], which disability them in popular robotic projects.  
The conditions of the experiment were relatively good. It means the modems together with ultrasound transducers were submerged at a depth of about 1.5 m. There were no obstacles around (just two yachts, moored far away from the platforms), and the water was almost stagnant. The author is aware of many problems related to the propagation of ultrasonic waves in shallow water. Due to the preliminary nature of the project, this issue was not addressed in this paper. However, future works are planned to test the immunity of QPSK modulation to multipath propagation. Another unsolved issue is the influence of the speed of flowing water (alternatively of a moving object with an ultrasonic modem installed on it) on the capability of demodulation of the received transmission. It is well known that the Doppler effect would change the wave carrier frequency [10], and therefore the question remains of whether a simplified implementation of a PLL-free demodulator will be sufficient.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 4 Experimental Studies
### Case 1: Seven-Level PUC MLI  
It has already been mentioned that, to obtain seven levels in the PUC MLI, the voltage of the auxiliary capacitor must be one third of the input voltage of the PUC. Experimental verification is carried out using two different loads (RL\({}_{1}\) and RL\({}_{2}\)). While the input voltage of the PUC MLI is 45 V, the voltage of the auxiliary capacitor is 15 V with the closed loop system. Figure 27a,b represent the output voltage, output current, input voltage, and auxiliary capacitor voltage of the PUC7 MLI for RL\({}_{1}\). When the RL\({}_{1}\) load is used, the voltage and current THD of the PUC7 MLI are 14.24% and 4.62%, respectively.  
Figure 28a,b represent the output voltage, output current, input voltage, and auxiliary capacitor voltage of the PUC7 MLI for RL\({}_{2}\). When the RL\({}_{2}\) load is used, the voltage and current THD of the PUC7 MLI are 11.65% and 4.10%, respectively.  
In the comparison of the RL\({}_{1}\) load and RL\({}_{2}\) load, the output current is lower for RL\({}_{2}\) since the L\({}_{2}\) value is larger than the L\({}_{1}\) value. Furthermore, in terms of harmonic values, it is seen that the THD values of the RL\({}_{2}\) load are lower than the THD values of the RL\({}_{1}\) load.
# Reliability Assessment of Cyber-Physical Distribution Systems Considering Cyber Disturbances
## 3 Distribution Network CPS Reliability Modeling
### Advantages of Combining Fault Trees and Petri Nets
#### 3.3.4 Fast Response to System Topology Changes  
With only the fault tree analysis method, when the system topology is changed, the entire system needs to be reanalyzed and the fault tree model reconstructed; however, when using a combination of the two methods for modeling, only the correlation matrix of the Petri net needs to be partially modified, so that reliability indices can be calculated for the system after the topology change.  
The output and input matrices of the Petri net in Figure 8 are shown in (14) and (15), respectively.  
\[\mathbf{a}_{ij}^{+}=\begin{bmatrix}1&0&0&0&0\\ 1&0&0&0&0\\ 1&0&0&0&0\\ 1&0&0&0&0\end{bmatrix}, \tag{14}\]  
\[\mathbf{a}_{ij}^{-}=\begin{bmatrix}0&1&0&0&0\\ 0&0&1&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{bmatrix}, \tag{15}\]  
Using (14) and (15), the correlation matrix corresponding to this Petri net can be derived, as shown in (16).  
\[\mathbf{A}=\begin{bmatrix}1&1&1&1\\ -1&0&0&0\\ 0&-1&0&0\\ 0&0&-1&0\\ 0&0&0&-1\end{bmatrix}, \tag{16}\]  
Taking the distribution system in Figure 5 as an example, due to the disconnection of isolation switch DS2 for reasons such as the failure of L3, the main line L3 and the load carried by it are supplied by Feeder 2. To evaluate the reliability of the system at this time, if only fault tree modeling is used, the system structure needs to be analyzed again and the fault tree model needs to be reconstructed. However, if the combination of fault tree and Petri net is adopted for modeling, only the output and input matrix need to be changed as shown in (17) and (18), and then the minimum cut set can be solved. This method significantly reduces the modeling time and thus improves the modeling efficiency. Especially for the distribution network CPS, the addition of a cyber system leads to a more complex system structure and more frequent interactions among the parts.  
\[\mathbf{a}_{ij}^{+}=\begin{bmatrix}1&0&0&0\\ 1&0&0&0\\ 1&0&0&0\end{bmatrix}, \tag{17}\]  
\[\mathbf{a}_{ij}^{-}=\begin{bmatrix}0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}, \tag{18}\]  
The advantages of combining the two methods in modeling are shown above and will be further demonstrated in Section 5.
## 3 Experiments  
### Datasets  
Experiments were conducted using three retinal fundus image datasets: EyePaCS, DDR, and Messidor-2. Models were trained using EyePaCS dataset, and the models were evaluated on each of the three datasets. The datasets have five levels of labels from '0' to '4', denoting the severity of DR disease. Re-labeling was performed to make it suitable for the binary classification task in the study. According to the International Clinical Diabetic Retinopathy Scale [46], the severity of levels '0' and '1' is regarded as non-referable DR (No-RDR), while another class is referable DR (RDR) consisting of the levels of '2', '3', and '4'. From this point on, No-RDR will be represented as '0' and RDR will be represented as '1'. EyePaCS provides more than 80,000 fundus photographs of the retina, of which 35,126 images come with labels and the rest are unlabeled data. The total number of labeled data after pre-processing was 35,039. Model training for supervised and unsupervised learning was performed using labeled and unlabeled data, respectively. After re-labeling, the percentages of data samples falling into the categories '0' and '1' were 80.42% and 19.58%, respectively. The re-labeled EyePaCS dataset for the experiments was divided into three subsets (i.e., training, validation, and test sets) with a ratio of 70:15:15. The distribution of data between classes in the training set was continuously imbalanced. This was thus called the imbalanced training set. By undersampling, we created a balanced training set between classes. That is, there were 4802 fundus images for each of the two classes. The split validation and test sets had 4227 and 1029 samples for the majority and minority class, respectively. However, we re-sampled data in an undersampling manner to acquire balanced validation and test sets, i.e., the number of examples in the majority class was reduced to '1029'. The datasets for these descriptions are reported in Table 1. The other test set from the DDR dataset contained '3380' examples and '1690' per category. Messidor-2 had a total of '1748' data samples, of which '1291' belonged to category '0' and '457' to category '1'. The test datasets from DDR and Messidor-2 are described in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Imbalanced Training Set** & **Balanced Training Set** & **Validation Set** & **Test Set** \\ \cline{2-6}  & **Number** & **Percentage** & **Number** & **Percentage** & **Number** & **Number** \\ \hline
0 & 19,725 & 80.42 & 4802 & 50.0 & 1029 & 1029 \\ \hline
1 & 4802 & 19.58 & 4802 & 50.0 & 1029 & 1029 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Imbalanced training, balanced training, validation, and test sets were constructed from EyePaCS data.
## 3 Approaches for Screening the ORR and HER Performances
### Evolution of HER Performance  
To evaluate the HER catalytic performance of NCs, parameters including (i) overpotential, (ii) electrochemically active surface area (ECSA), (iii) Tafel slope, (iv) exchange current density,  
Figure 2: The linear sweep voltammetry curve depicting the onset potential (Eoc) and half-wave potential (E\({}_{1/2}\)).  
(v) Faradic efficiency, (vi) Turnover efficiency, and (vii) stability are taken into consideration. Following are the details of these approaches:  
1. **Overpotential:** The overpotential is the difference between the applied (i.e., experimentally observed potential) and thermodynamically determined potential for any electrochemical reaction. The overpotential is the most important parameter for evaluating the HER performance, and a desired HER catalyst should be capable of lowering the overpotential.
2. **Electrochemically active surface area (ECSA)**: ECSA is the active area (i.e., number of active sites) present on the NC surface accessible to the electrolyte for charge transfer. The ECSA can be determined by using electrochemical techniques such as cyclic voltammetry (CV) and CO-stripping analysis.
3. **Tafel slope**: Tafel slope, which is actually the slope of the linear region of a Tafel plot (i.e., the plot between overpotential vs. log |current density|) is used to achieve insight into the reaction pathways (mechanisms) of HER on a NC surface. The HER pathways on NC surfaces can be determined by comparing the Tafel slope value with that of theoretical ones. The theoretical Tafel slope values for the Volmer (i.e., the discharge step), the Heyrovsky (i.e., electrochemical desorption step), and the Tafel (i.e., the chemical desorption/combination) steps are 120 mVdec\({}^{-1}\), 40 mVdec\({}^{-1}\), and 30 mVdec\({}^{-1}\), respectively. For instance, previous studies reported that the Tafel slope is 30 mVdec\({}^{-1}\) for a commercial Pt catalyst in an acidic medium (i.e., 0.5 M H\({}_{2}\)SO\({}_{4}\)), which indicates that HER proceeds through the Volmer-Tafel process on the Pt surface.
4. **Exchange current density**: The exchange current density is the current density at the equilibrium potential (i.e., both the cathodic and anodic currents are equal) and reflects the inherent nature of the NC. The exchange current density can be determined via the intersection of the extrapolated linear part of the Tafel plots to the \(X\)-axis.
5. **Faradic efficiency**: Faradic efficiency is the ratio of the amount of experimentally produced H\({}_{2}\) to the theoretically determined H\({}_{2}\) amount.
6. **Turnover efficiency**: Turnover efficiency refers to the number of desired product molecules that a NC can produce per catalytic site per unit of time.
7. **Stability**: Long term durability/stability is another important aspect for HER catalysts, which describes the ability of a catalyst to maintain its activity when operated for a particular time. The stability can be easily determined by comparing the overpotential and Tafel slopes of the as-prepared condition and after degradation test cycles.
## 6 Discussion
### Interference Effect  
The detection of the beams sent by another LiDAR instrument of the same type causes a measurement error that is called an interference effect. The interference effect causes incorrect range measurements. The widespread use of mobile LiDAR causes intersensory confusion in recording reflected beams (Figure 9). This error is common, especially in LiDAR devices used for unmanned vehicle and robot navigation. The interference effect can be eliminated using the phase lock feature and advanced software. The phase lock feature is used to synchronize the relative rotational position of multiple sensors. Phase locking works by offsetting the firing position based on the rising edge of the signal.
# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 3 Methods
### Improved Dung Beetle Optimizer  
Controlling the quality of the initial population in DBO is crucial for enhancing the algorithm performance. The quality is primarily determined by two factors: the search range and the initial positions. If the search range is too narrow, it hampers the algorithm's ability to explore. Conversely, if the initial positions are close to the global optimal solution, the population can effectively uncover valuable information within a superior solution space. However, the standard DBO adopts a random initialization method to generate the population, which makes it difficult to meet the above two requirements.  
To address this issue, this paper introduces the centroid opposition-based learning strategy (COBL) [33] to generate the initial population. The basic idea is to calculate the reverse solution based on the centroid of the population. This approach allows for searching high-quality solutions in a wider range, effectively improving the quality of the initial population. The initialization of the population using the centroid opposition-based learning strategy is as follows:  
Let (\(X_{1}\),..., \(X_{n}\)) be n points in a D-dimensional search space with unit weight. The centroid of the entire set is defined as follows:  
\[\mathrm{M}=\frac{X_{1}+\cdots+X_{n}}{n} \tag{10}\]  
then we have:  
\[\mathrm{M}_{d}=\frac{1}{n}\sum_{i=1}^{n}x_{i}^{d},i=1,2,\cdots,n,\ d=1,2, \cdots,D \tag{11}\]  
If the centroid of a discrete and uniformly distributed set is denoted \(\mathrm{M}_{d}\), then the opposite-point \(\tilde{\mathrm{X}}_{i}\) of a point is \(\mathrm{X}_{i}\) is defined as follows:  
\[\tilde{\mathrm{X}}_{i}=2\times\mathrm{M}_{d}-\mathrm{X}_{i},i=1,2,\cdots,n \tag{12}\]  
when the reverse point exceeds the search space, recompute the reverse point according to Equation (4):  
\[\tilde{\mathrm{X}}_{i}=\begin{cases}x_{min}^{d}+r\times\left(\mathrm{M}_{d}-x_ {min}^{d}\right),\tilde{\mathrm{X}}_{i}<x_{min}^{d}\\ \mathrm{M}_{d}+r\times\left(x_{max}^{d}-\mathrm{M}_{d}\right),\tilde{\mathrm{ X}}_{i}>x_{max}^{d}\end{cases} \tag{13}\]  
In the equation, \(x_{min}^{d}\) and \(x_{max}^{d}\) represent the upper and lower limit values in the D-dimensional space, respectively. \(r\) is a uniformly distributed random number on \([0,1]\).