esa-sceva/satcom-chunk-collection huggingface dataset:
Therefore, it is particularly important to effectively process and analyze these signals. [PERSON] et al. [29] proposed an adaptive one-dimensional convolutional neural network (one-dimensional) algorithm applied to GPR data, which successfully classified different types of damaged road surfaces. [PERSON] et al. [30] used YOLO v3 to automatically recognize the signals reflected by rebars in GPR images and accurately estimated the thickness of the protective layer and the diameter of the rebar by using one-dimensional. This indicates that one-dimensional can effectively capture local features in radar A-scan and automatically learn the important features in the signal, thereby effectively classifying different types of damage. In practical applications, using CNN to analyze A-scan data can improve the detection accuracy of potential structural defects. In addition, the adaptive nature of the method enables it to adapt to various signal patterns and environmental conditions, providing strong support for more intelligent and automated structural health monitoring. Based on the characteristics of GPR data and the theoretical basis of one-dimensional and two-dimensional CNN, this paper explores the training process and classification results of different types of defects in small-scale concrete structures using one-dimensional A-scan radar signals and corresponding two-dimensional spectrums as input data, and evaluate the datasets using CNN models. In Section 2, we introduced the theoretical basis of one-dimensional CNN and classical two-dimensional CNN architecture AlexNet. In Section 3, gprMax was used to simulate three-dimensional complex reinforced concrete beams with built-in defects. At the same time, three sets of concrete beam parameters and experimental processes used in laboratory experiments were introduced.
## 5 Results
### Sensor Orientation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Model (RMSE\({}_{\text{2D}}\) (m))** & **No. GCPs** & **ICPs** & **RMSE\({}_{\text{2D}}\) (m) (Mean Value of the Four Deimos-2 Images)** \\ \hline
**RPC0** & 6 & 96 & 1.417 \\ (1.453 \({}^{\text{a}}\)) & 12 & 90 & 1.477 \\  & 18 & 84 & 1.467 \\ \hline
**RPC1** & 6 & 96 & 1.317 \\ (1.268 \({}^{\text{b}}\)) & 12 & 90 & 1.255 \\  & 18 & 84 & 1.231 \\ \hline
**RPC2** & 6 & 96 & 1.301 \\ (1.234 \({}^{\text{b}}\)) & 12 & 90 & 1.213 \\  & 18 & 84 & 1.189 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of mean values of RMSE\({}_{\text{2D}}\) computed at ICPs from Deimos-2 PAN images depending on the number of GCPs (no. GCPs). For each sensor model tested, mean values of RMSE\({}_{\text{2D}}\) in brackets followed by different superscript letters indicate significant differences at a significance level \(p<0.05\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Code** & **Sensor Model** & \multicolumn{3}{c}{**RMSE (m)**} \\ \cline{3-5}  & **X** & **Y** & **2D** \\ \hline  & RPC0 & 0.967 & 0.812 & 1.262 \\
1\_1 & RPC1 & 0.770 & 0.650 & 1.008 \\  & RPC2 & 0.754 & 0.645 & 0.993 \\ \hline  & RPC0 & 1.106 & 1.037 & 1.516 \\
1\_2 & RPC1 & 0.869 & 0.842 & 1.210 \\  & RPC2 & 0.854 & 0.814 & 1.180 \\ \hline  & RPC0 & 0.857 & 1.062 & 1.365 \\
2\_1 & RPC1 & 0.803 & 0.669 & 1.045 \\  & RPC2 & 0.745 & 0.623 & 0.971 \\ \hline  & RPC0 & 0.902 & 1.138 & 1.452 \\
2\_2 & RPC1 & 0.802 & 0.730 & 1.085 \\  & RPC2 & 0.756 & 0.678 & 1.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensor orientation accuracy (RMSE) using rational polynomial coefficient RPC0, RPC1 and RPC2 sensor models supported by 102 GCPs. result in being statistically significant at a significance level \(p<0.05\). Nevertheless, and by analysing the results shown in Table 4, six GCPs were not enough to obtain robust results from the RPC1 and RPC2 sensor models. Therefore, we recommend using RPC1 with 12 GCPs for carrying out Deimos-2 L1B images' sensor orientation. For this case, a RMSE\({}_{\text{2D}}\) value of 1.255 m (for the four repetitions corresponding to the four single images) was found, also recording a low SD value of 0.10 m that aims to obtain reasonably stable results.
# A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
## 4 Learning Session Classification  
We use cluster analysis with the K-means clustering method [35] and an Euclidean distance metric to distinguish differences between sessions and identify defining characteristics. The number of times each of the seven high-level learning dynamics appears is used as input for each session. Euclidean distance was selected in order to leverage existing tools and avoid implementation errors. Although studies have shown that distance metrics such as Manhattan [36] and Canberra [37] produce better results, determining which metric is superior in a dataset of this type is beyond the scope of this work.  
To determine the number of clusters, we employ two k-means techniques: the elbow method and the silhouette method [38]. Following that, we calculate the z-scores of each of the clusters in every dimension to visualize their differences.  
The metrics that were left out of the clustering method, such as the duration of each session and the quintile in which they are placed, as well as the z-score values obtained, are used to characterize the traits that set the discovered clusters apart. It is significant to note that because this study focuses on MOOC sessions rather than students, factors that describe the user who completes a session are not included in the clustering method. Nevertheless, unusual session patterns are excluded from the process. We specifically aim to rule out patterns that represent a user's unique behavior that is not reflected in any other instances.
## 3 Satellite IP Route Lookup Using Cuckoo Bloom Hybrid Filter
### Theoretical Performance Analysis on CBHF
The lookup time is a key indicator in satellite IP route lookup, which will be further evaluated in Section 6.
## 2 Methodology
### \(Co_{2}\) Emissions  
CO\({}_{2}\) is one of the significant vehicle emissions and is continuously emitted from the reaction of oxygen with the burning of the carbon content in the fuel. The atomic mass of CO\({}_{2}\) from oxygen and carbon [23] is  
\[\left[\text{CO}_{2}\right]_{\mu}=12_{\mu}+(2\times 16_{\mu})=44_{\mu}, \tag{1}\]  
Figure 4: Vehicle’s data acquisition methodology [20].  
where 16 and 12 are the atomic masses of oxygen and carbon, respectively, and \(\mu\) denotes atomic masses. In a combustion process, CO\({}_{2}\) produced from 1 kg of C in the fuel is [23]  
\[\frac{(12+16+16)}{12}\approx 3.67\ \mathrm{kg\ CO_{2}}, \tag{2}\]  
The carbon content in gasoline is 87% [23]. Thus, the total CO\({}_{2}\) emission from burning gasoline is  
\[E_{CO2}=3.67\times 0.87\times\mathrm{gasoline\ weight}, \tag{3}\]  
where \(E\) denotes the emission. From Equation (3), the mass of carbon dioxide in 1 kg of gasoline is  
\[E_{CO2}=3.19\ [\mathrm{kg}/(1\ \mathrm{kg\ fuel})]. \tag{4}\]  
The density of gasoline fuel is 0.73 \(\left[\frac{\mathrm{kg}}{\mathrm{C}}\right]\)[23]; therefore, the CO\({}_{2}\) emissions from 1 L of gasoline fuel combustion are  
\[E_{CO2}=3.19\times 0.73=2.3\ [\mathrm{kg}/(1\ \mathrm{L\ fuel})]. \tag{5}\]  
From the consumption of 1 L of gasoline, 2.3 kg of carbon dioxide is formed. To obtain CO\({}_{2}\) emissions in a kilometer, Equation (5) is divided by the KPL:  
\[E_{CO2}=[(2.3\ [\mathrm{kg}/1])/(\mathrm{KPL\ [km/L]})]. \tag{6}\]  
The KPL is the number of kilometers per liter of fuel and is given as  
\[\mathrm{KPL}=(\mathrm{Speed\ of\ Vehicle})/\mathrm{LPH}, \tag{7}\]  
where LPH is the liters of fuel per hour and is given as  
\[\mathrm{LPH}=\mathrm{GPH}\times 3.75, \tag{8}\]  
where GPH is the gallons of fuel per hour and is given as  
\[\mathrm{GPH}=\mathrm{MAF}\times 0.805. \tag{9}\]  
From Equations (7)-(9) and (6), for CO\({}_{2}\) emissions per km are obtained as  
\[E_{CO2}=(2.3\times\mathrm{MAF}\times 0.805\times 3.75)/(\mathrm{Speed\ of\ Vehicle}) \tag{10}\]  
In this paper, vehicle speed and MAF were calculated from the vehicle's ECU obtained with the OBD-II device. Table 1 shows the units and the symbols used to estimate carbon dioxide gas emissions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Units** & **Description** \\ \hline \([\mathrm{CO_{2}}]_{\mathrm{u}}\) & Amu & Atomic mass of carbon dioxide \\ \hline E\({}_{\mathrm{CO2}}\) & kg CO\({}_{2}\)/km & Carbon dioxide gas emissions \\ \hline KPL & km/L & Number of kilometers traveled per liter of fuel \\ \hline LPH & L/h & Liters of fuel consumed per hour \\ \hline GPG & ga/h & Gallons of fuel consumed per hour \\ \hline MAF & g/s & The rate of air entering in the vehicle’s engine \\ \hline V & km/h & The speed of the vehicle \\ \hline \(\rho\) & veh/length & The density of traffic \\ \hline RPM & r/min & The revolutions of the engine per minute \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of all the units used in CO\({}_{2}\) emissions estimation.
## 3 Results
### Model Explanation and Biomarker Identification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Modalities**} & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**F1 (Mean \(\pm\) std)**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**AUC (Mean \(\pm\) std)**} & \multirow{2}{*}{**\(p\)-Value**} \\  & & **(Mean \(\pm\) std)** & & & & \\ \hline SVM & Emoid fMRI & 0.6801 \(\pm\) 0.0824 & \(3.58\times 10^{-8}\) & 0.6912 \(\pm\) 0.0542 & \(2.58\times 10^{-2}\) & 0.7091 \(\pm\) 0.0643 & \(1.92\times 10^{-8}\) \\ SVM & Nback fMRI & 0.6704 \(\pm\) 0.0818 & \(2.98\times 10^{-9}\) & 0.6781 \(\pm\) 0.0823 & \(3.01\times 10^{-5}\) & 0.6847 \(\pm\) 0.0567 & \(2.32\times 10^{-9}\) \\ \hline GIN & Emoid fMRI & 0.7621 \(\pm\) 0.0501 & 0.0219 & 0.7721 \(\pm\) 0.0452 & 0.0154 & 0.7861 \(\pm\) 0.0834 & 0.0322 \\ GIN & Nback fMRI & 0.7814 \(\pm\) 0.0722 & 0.1380 & 0.7801 \(\pm\) 0.0568 & 0.1125 & 0.7995 \(\pm\) 0.0453 & 0.0929 \\ \hline MLP & Emoid fMRI \& & & & & \\ \multicolumn{2}{c}{Nback fMRI} & 0.8097 \(\pm\) 0.0944 & 0.1131 & 0.8085 \(\pm\) 0.0756 & 0.1026 & 0.8109 \(\pm\) 0.0985 & 0.0932 \\ \hline BrainGNN & Emoid fMRI \& & & & & & \\ \multicolumn{2}{c}{Nback fMRI} & 0.6860 \(\pm\) 0.0829 & 0.1121 & 0.6899 \(\pm\) 0.0756 & 0.1729 & 0.7022 \(\pm\) 0.0563 & 0.2382 \\ \hline MVGCN & Emoid fMRI \& & & & & & \\ \multicolumn{2}{c}{Nback fMRI} & 0.8006 \(\pm\) 0.0504 & 0.4730 & 0.8093 \(\pm\) 0.0583 & 0.4372 & 0.8135 \(\pm\) 0.0731 & 0.4572 \\ \hline M-GCN & Emoid fMRI \& & & & & & \\ \multicolumn{2}{c}{Nback fMRI} & 0.7501 \(\pm\) 0.0504 & 0.1131 & 0.7525 \(\pm\) 0.0431 & 0.1092 & 0.7721 \(\pm\) 0.0621 & 0.1356 \\ \hline MGIN* & Emoid fMRI \& & & & & & \\ \multicolumn{2}{c}{Nback fMRI} & 0.8167 \(\pm\) 0.0749 & - & 0.8192 \(\pm\) 0.0683 & - & 0.8269 \(\pm\) 0.0754 & - \\ \hline \hline \end{tabular}  
* The \(p\)-values were computed using a \(t\)-test to compare the regression performance of our MGIN model, in repeated experiments, with that of other competing models. The standard deviation is represented by “std”.  
\end{table} 
Table 5: The comparison of the performance of different models on the PNC dataset. Figure 5: The top 5% of selected ROIs for female and male subjects based on the power264 template. The ROIs with the same color indicate that they are in the same functional network. The figure contains the different views of the brain (axial, coronal, and sagittal). Figure 6: Important intra- and inter-network connections during the different stages of adolescence. In pre-adolescence, females have additional connections between the default mode and visual system. In early adolescence, males have intra-network connections in the salience network. In post-adolescence, females have additional connections between the default mode and visual system, subcortical and sensorimotor system, and fronto-parietal task control.
## 4 Results and Discussions
### LoRaWAN(r) Performance under Different Drone Speeds  
To quantify the Doppler effect on the LoRaWAN(r)-based aerial communication, the performance of the system was examined under different drone speeds. To increase the safety and maneuverability of the drone, measurements were performed in an open and uninhabited area, as shown in Figure 15. The multi-channel GW was stationary, located on the ground, while the EN was mounted on the drone to perform a real-time data update. The data included several measurements, particularly on the percentage of the packet received for this experiment.  
Data from the EN were transmitted every 5 s, while data were received by the GW every 9 and 7 s for the case of SF12 and SF7, respectively. The difference in time required to receive data in SF12 and SF7 is due to the on-air time, as SF12 on-air time is longer than SF7. In addition, the drone flew at different speeds with a \(\pm\)5 km/h variation due to unpredictable wind direction during measurement and a fixed altitude of 100 m above the ground.  
Figure 16 shows the percentages of packet delivery at different speeds for SF12 and SF7, which are 100% and 77.27% (on average), respectively. Results show the robustness of SF12 towards the Doppler effect, while SF7, on the other hand, is proven to be sensitive to the drone speed, especially at a speed of 35 km/h and above. This measurement offered a new insight into the performance of LoRaWAN(r)-based aerial communication because the previous study in [46] was limited to 50 km/h.
## 4 Numerical Results and Discussion  
In this section, the static analysis of symmetric ([0\({}^{\circ}\)/90\({}^{\circ}\)/0\({}^{\circ}\)] and [0\({}^{\circ}\)/90\({}^{\circ}\)/90\({}^{\circ}\)/0\({}^{\circ}\)]) and antisymmetric ([0\({}^{\circ}\)/90\({}^{\circ}\)]) laminated cross-ply spherical (R\({}_{1}=\) R\({}_{2}=\) R), cylindrical (either R\({}_{1}=\infty\) or R\({}_{2}=\infty\)), and flat panels (R\({}_{1}=\) R\({}_{2}=\infty\)) subjected to simply supported boundary conditions prescribed on two opposite edges and roller skate-type boundary conditions on the remaining two edges is investigated. A computer program for that purpose was written in the MATLAB program in order to implement both proposed solution methodologies, the BDM and GDQ methods. In this part, several examples are carried out parametrically by changing the effect of curvature, thickness-to-length ratio, lamination, and their interactions. In all of the examples except for the validation study, the following material properties were used. This material was chosen to investigate the impact of material strength on the statical behavior of a composite panel structure. The mechanical properties of each layer of laminate are taken as [49]:  
\[E_{1}=175.78\ \text{GPa},\ E_{1}/E_{2}=25,\ G_{12}=G_{13}=0.5E_{2},\ G_{23}=0.2E_{2}, \text{and}\ \
u_{12}=0.25\]  
in which \(E_{1}\) and \(E_{2}\) are the in-plane Young's moduli in the \(x\) and \(y\) coordinate directions, respectively, while \(G_{12}\) denotes in-plane shear modulus. \(G_{13}\) and \(G_{23}\) are transverse shear moduli in the \(x\)-\(z\) and \(y\)-\(z\) planes, respectively, while \(\
u_{12}\) is a major Poisson's ratio on the \(x\)-\(y\) plane.  
The non-dimensional displacement, \(w^{*}\), and stress, \(\sigma_{i}{}^{*}\), response of cross-ply laminated shells are:  
\[w^{*}=-\frac{10^{3}E_{2}h^{3}}{q_{0}a^{4}}u_{3},\ \ \ \ \sigma_{i}{}^{*}=-\frac{100 \times h}{q_{0}a^{2}}\sigma_{i}\ (\text{i}=x,\ y) \tag{52}\]  
\(w^{*}\) and \(\sigma_{i}{}^{*}\) are computed at the center of the panel. The load term expressions for arbitrary transverse applied distributed load can be defined using a Fourier double series as \[q(x,y)=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}q_{mn}\sin(\alpha x)\sin(\beta y) \tag{53}\]  
where  
\[q_{mn}=\frac{16q_{0}}{\pi^{2}mn}\ (m\ =\ \text{odd}) \tag{54}\]  
As it was said before, the FSDT theory (also known as the Mindlin-Reissner theory) needs a shear correction factor (\(k\)s) in order to eliminate the error in the calculation of constant out-of-plane shear stresses through the thickness direction. This factor connects with several parameters such as the geometrical shape of the structure, the lamina sequence pattern, etc. For the calculations in the proposed solution, correction factor is chosen as \(k\)s = 5/6 for a solid rectangular type of cross-section.
# Dist-YOLO: Fast Object Detection with Distance Estimation
## 4 Discussion, Open Issues, and Future Work  
Training with distance estimation limits used data augmentation techniques. Spatial transformations such as mosaicing [5] or strong resize can distort the visual size of objects and confuse the network. On the other hand, reducing augmentation can increase overfitting. Therefore, the suitable way to train Dist-YOLO is to use curriculum learning [53]. Firstly, the model was trained with data augmentation and we froze (set to zero) the part of the loss responsible for distance estimation to train bounding box detection only. Then, the complete loss was used, and only the not-distort-object-size augmentations were used.  
From Figure 4, it is obvious that the percentage relative error of the distance was for most cases below 10%. The outlier was the distance of one meter, where the relative error was 45%. That behavior is caused by the current form of the loss function, where the squared distance is computed. We experimented with the relative distance loss function, which would force the network to predict a distance with similar relative error for all distances, including one meter. Such a loss function for the class-agnostic version is:  
Figure 5: The figure shows bounding box prediction and distance estimation on the test set using Dist-YOLO. The distance (in meters) is in the format \(\hat{d}/d\), where \(\hat{d}\) is the prediction itself, and \(d\) represents the groundtruth label.  
\[\ell_{5}(i,j)=\omega\frac{|d_{i,j}-\hat{d}_{i,j}|+\sigma}{d_{i,j}+\sigma},\]  
where \(\sigma\in[0,1]\) is a positive smoothing factor to avoid instability for the cases when \(d_{i,j}\approx 0\). However, we were unable to train the model with this loss function due to high loss, even for bounding boxes.  
Because the new versions of YOLO, namely v4 [5] and v5, use the same principles as YOLOv3, the proposed scheme of monocular distance estimation can be integrated into these new variants. This realization remains a future work.
## 3 Results and Discussions
### Influence of Crumb Rubber on Pervious Concrete Properties  
#### 3.1.1 Permeability  
The variations in the permeability coefficient of pervious concrete versus rubber content are shown in Figure 5. It can be concluded that the addition of rubber decreases the permeability coefficient of RMPC. This is because the incorporation of rubber leads to a reduction of porosity. Porosity is the most crucial factor for the permeability of pervious concrete. Compared to CPC, with a permeability coefficient of 3.91 mm/s, the permeability coefficients of CCR- and FCR-modified mixtures are 3.23-3.89 mm/s and 3.36-3.92 mm/s, respectively. When the incorporation levels of the rubber are the same, the permeability coefficients of CCR- and FCR-modified mixtures have almost no difference, which means the rubber particle size has little effect on the permeability coefficient. This is consistent with the conclusion reported by [PERSON] et al. [31].
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 1 Introduction
The algorithm is trained with the data rate, distance, and power and compared with the K-nearest neighbor (KNN) classifier, making a classification process. The trained classifier detects the PUE with high performance; simulation results show better accuracy than conventional PUEA classification techniques, including the KNN [6]. In our previous work, we used entropy detection for spectrum sensing [7], and in [8], we explored the CSS for spectrum sensing but not for detecting the _PUE attack_. In [9] and [10], we use the SVM and KNN to detect PUE attacks with a PUE detector implemented in a single SU. In this paper, we propose a novel DNN algorithm that takes the entropy of the energy detector in a cooperative scenario with CSS, which gives the cognitive system a better sense of the entire network environment and results in lower SNR values. After a learning process, the DNN can detect the PUE in a MCRN, which includes the PUs, three SU, and a PUE attacker. The algorithms are implemented in an SDR device, and the attack is generated in an MCRN network with a frequency hopping protocol in GNU Radio. The DNN algorithm has not been used in the literature to detect PUE attacks in a MCRN environment with a CSS and with SDR implementation; we evaluated the results with other detection techniques in a realistic environment. The paper is organized into six sections. Section 2 analyzes the previous work and contributions. In Section 3, the DNN and CSS detection schemes are illustrated. Section 4 focuses on the model and scenarios. Section 5 shows the experiment results and discussion, and Section 6 shows the conclusions.
## 4 Results
### Publication Trend  
The main focus of the modern RS is to develop the RS applications that will meet the dynamic users' current needs [4; 13; 23; 34; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96]. Although there are several of the secondary studies on DRS, there is a lack of SLR and classification of those studies. In the earliest attempt, [4] provided a review on dynamic features of RS, where the authors classified the DRSs based on different parameters over which the classification is made. The review gives an insight on the dynamic parameters to produce a real-time recommendation. DRS is also examined with respect to temporal models in [13]. In contrast, this paper presents an SLR that identifies the various temporal drifts in the modern DRS and the relevant application domains.  
Figure 4 shows the distribution of research papers by year of publication between 2010 and 2019. As can be noted in Figure 3, it is apparent that publications related to DRS steadily increased between 2010 and 2014, and rapidly increased between 2017 and 2019. This is a clear proof that the field is active and gaining more interest among various researchers and practitioners.