esa-sceva/satcom-chunk-collection huggingface dataset:
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
###### Abstract  
Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method's detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error.  
around view monitoring; parking space detection; vacancy analysis Article  
Around View Monitoring-Based Vacant Parking Space Detection and Analysis
# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 2 Full-Field Displacement Monitoring of a Structure Based on Natural Texture Feature Monitoring  
### Full-Field Displacement Vector (FFDV) Point Source Generation Method  
In practical applications, an appropriate descriptor that represents homonymous image points on a structural surface must be identified. The natural texture features of a bridge surface are consistent before and after deformation and can be used to form natural homonymous points. Under photogrammetry, these natural texture features can be detected as extreme points in the scale space of an image. Currently, the most typically used method to detect extreme points in an image scale space is scale-invariant feature transform (SIFT) [34; 35; 36; 37; 38; 39]. The SIFT algorithm can detect image feature points in a multiscale space. The extracted feature points exhibit invariable scales, positions, and directions, which can be matched by the generated feature vector descriptor.  
To validate that the image feature points on a bridge can represent homonymous points, we used a chessboard pattern to perform a validation test. Figure 1 shows the feature-point extraction results for two chessboard images at different positions. In the image shown in Figure 1, the position of the chessboard was shifted, whereas the position of the camera was fixed.  
The feature points extracted (as shown in Figure 1) were matched, as shown in Figure 2. The number of feature points extracted from Figure 1 was 2117. To intuitively show the matching results for the feature points, only the matching results for the first 40 feature points are presented in Figure 2. As shown in the figure, the feature points extracted from the chessboard at different positions matched accurately, which verified that the feature points could represent the image's homonymous points.  
Figure 1: Expression of feature points in two images.  
Figure 2: Registration of feature points.
## 4 Implementation and Results  
To verify the agricultural environment monitoring server system proposed in this paper, a prototype was manufactured, installed in the real outdoors, and the system was tested. Even though the initial prototype version 1 could monitor all the outdoors environmental data such as soil information, environmental information, image information, location information acquiring from sensors and other  
Figure 5: Operation Process of the Condition Notification Service.  
physical devices through the GUI, it could not be used normally because the power consumed by the server was significantly larger than the power generated by the solar cells so there was a problem that making the effective use time short. In order to solve such a problem, the prototype version 2 includes a low-power system with embedded board modules, and incorporates a compact web server and database using built using Window CE-based mobile programming, so the power supply problem could be solved, and the outdoors environment could be monitored effectively.
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Multi-Model-Based Pain Recognition  
A recent attempt [16] to recognize pain based on a multi-model approach involves combining face and physiological signals (ECC and SC). The personalization of pain estimation is their main goal and is achieved based on clustering subjects into different profiles and not for each individual, as in previous works. Then, they used the multitask NN (MT-NN) approach, where each task corresponds to one profile. For their experiment, they used the available data set BioVid Heat Pain database. The results exhibited a better performance for high clusters (C = 4), which shows the need for further investigation in future using a larger number of clusters. In addition, [17] performed fusion between physiological signals (EMG, ECG and SC) and face videos. Their approach involves implementing the idea of adaptively using the system to test unknown individuals based on unlabeled data. Therefore, they used multi-stage ensemble-based classification, and were based on the BioVid Heat Pain database. NNs were used at the confidence-estimation stage, which was trained using three different inputs, namely the prediction of one-vs-one classifiers, the continuous pain-level estimation of the regressor and the variance of a bagged ensemble of random forests. Then, the use of NNs will enable us to determine the confidence level of samples by employing a random regressor as an input. After the evaluation of different combinations of inputs to the NN, the highest values of the correlation coefficient and RMSE were 0.183 and 0.347, respectively. They found that the adaptation process is not an easy task because of individual differences in response to pain stimuli, and they therefore require more investigation. More recently, [PERSON] et al. [18] explored several CNNs architectures based on the available BioVid Heat Pain database (Part A). They used the three modalities of signals: EDA, ECG and EMG. They tried two kinds of input to network which are 1D and 2D. In addition, different deep fusion architectures were presented and tested. Their results reached 84.57% and 84.40% for binary classification. Next, we present in Table 1, a summary of all pain-recognition studies based on deep learning. Furthermore, Figure 2 below described the flow chart of the main phases that required in the deepest-based pain recognition methods.
## 4 Discussion
### WMF External Load  
WMFs in the present study covered a shorter distance than CMFs and at a slower rate (distance/min). In the 1-4-3-3 formation in youth athletes, it has been reported that WMFs covered the least TD [25], while in the same formation among professional athletes, it was noted that WMFs covered a shorter distance than WDFs but greater than FWDs [24]. In the study by [PERSON] et al. (2020) [16], it was observed that in the 1-4-4-2 formation, WMFs covered the second greatest TD, something not observed in the 1-4-3-3 and 1-3-5-2 formations. In a recent study, it is reported that in the 1-4-4-2 formation, WMFs covered the greatest TD, while in the 1-4-2-3-1 and 1-4-1-4-1 formations, they covered the second greatest TD [11]. In formations with three defenders (1-3-4-3, 1-3-5-2), they covered distances close to the team's average TD [11]. These findings suggest that the number of players in the defense and midfield can influence WMF's running performance.  
In the present study, WMFs did not differ significantly from other positions in high-speed distances. In sprints, they covered more distance than CDFs, while in accelerations, they only differed from CMFs. According to the literature, Wide Midfielders (WMFs) are among the playing positions that cover a significant distance at high speed [11; 16; 24; 25]. In the formation used in the present study (1-4-2-3-1), the WMFs, along with the SDFs, operate in the wide areas of the pitch and contribute to both the attacking and defensive actions of the team. This active tactical role, along with the space they have to move in (as there is no congestion of players like in the central axis), is the reason why players in this position cover a considerable distance at high speed. In a recent study on youth players regarding accelerations and decelerations, no positional differences were observed in the 1-4-3-3 formation [25]. However, other studies have reported that wide players performed more accelerations and decelerations than central players [33; 34]. Most studies suggest that WMFs cover above-average distances, and players in these positions are distinguished by the intense actions they perform during the match. When comparing accelerations and decelerations across studies, it is important to consider the scale each study uses. WMFs operate in areas where they can reach high speeds and, therefore, can perform high-intensity accelerations and decelerations. CMFs, on the other hand, play in areas where more duels take place, leading to more frequent changes of direction and, thus, a higher number of accelerations and decelerations. However, these may be of lower intensity compared to those performed by players operating in the wide areas of the pitch.
## 4 Empirical Study
### Experimental Settings  
In the experiments, the adopted CNNs included VGG16, VGG19, ResNet50 (RN50), ResNet101 (RN101), ResNet152 (RN152), DenseNet121 (DN121), DenseNet169 (DN169), DenseNet201 (DN201), Inception-V3 (Inv3), and EfficientNetB0 (ENB0). All adopted CNNs were executed using the Keras API and the whole evaluation was conducted on a personal computer with 16 GB RAM, an Intel 8-Core i7-10700 processor, and an Nvidia GeForce RTX 3070 GPU with 8 GB RAM. The recognition quality was measured through seven metrics: sensitivity, specificity, precision, accuracy, F1-score, AUC, and kappa based on a confusion matrix (shown in Table 4). In this matrix, there are four outcomes: true positive (TP), false positive (FP), false negative (FN), and true negative (TN), where positive (P) and negative (N) indicate the numbers of actual positive and negative cases, respectively, in the data. Further, TP and TN indicate the number of hits and number of correct rejections, respectively, while FP and FN indicate the number of false alarms and number of misses, respectively. The definitions for the sensitivity, specificity, precision, accuracy, F1-score, AUC, and kappa metrics are as follows:  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{1}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{2}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{P}+\text{N}} \tag{4}\]  
\[\text{F1}-\text{score}=\frac{2\text{TP}}{2*\text{TP}+\text{FP}+\text{FN}}. \tag{5}\]  
\[\text{Kappa}=\frac{2*(\text{TP}*\text{TN}-\text{FP}*\text{FN})}{(\text{TP}+ \text{FP})*(\text{FP}+\text{TN})*(\text{TP}+\text{FN})*(\text{FN}+\text{TN})} \tag{6}\]  
AUC denotes the area under the receiver operating characteristic curve. In the proceeding experiments, the results are shown in terms of accuracy charts and AUC curves, where the charts and AUC curves were generated by the Microsoft Excel and Python (Matplotlib Pyplot) software, respectively. The major intent of using accuracy as the basic measure was to observe the overall correction rates, including true positives and true negatives simultaneously, which has been widely used in the field of machine learning [42; 43]. Based on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Prediction Result**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline Ground & Positive & True Positive & False Negative \\ Truth & Negative & False Positive & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix.  
accuracy, the other six metrics were further chosen by referring to the references [23; 24; 25; 26; 27; 28; 29; 30]. Moreover, the CNN models were constructed using Keras and tensorflow 2.3. The reasons for selecting the software are that they are popular, cheap, and easy to obtain.
## 2 State of Art
### Comparative Analysis of CO2 Emission Prediction Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Dataset Description** & **Model Architecture and Scaling Approach** & **Number of Data** & **Model Accuracy** & **Complexity and Spatio-Temporal Capacity** \\ \hline
[3] & Data from Kaggle: vehicle class, engine size, etc. & LSTM and BiLSTM Macro/ & 12 columns, 7385 rows over 7 years & LSTM: R\({}^{2}\) = 77.81\% BiLSTM: R\({}^{2}\) = 93.78\% & Medium/No \\ \hline
[23] & Data from World Bank, Turkish Statistical Institute, energy consumption data, GDP, etc. & MLP, XGBoost, SVM/ & 47 years of data (1970–2016) & MLP: R\({}^{2}\) = 0.9689 (1970–2016) & Medium to High/No \\ \hline
[16; 17; 18] & Data from OpenDataSoft API, Google API, AirParif: geolocation, traffic flow, speed, etc. & CNN-LSTM Micro/Bottom-up & 48 GB of data, 8 million rows over 4 months & CNN-LSTM: R\({}^{2}\) = 0.91 (1.4 months) & High/Yes \\ \hline
[26] & Data from Data on urban governance elements in China: population, road network density, etc. & Random Forest/Macro/Top-down & Data from 1903 Chinese cities in 2010, 2012, 2015 & \multirow{2}{*}{\
\begin{tabular}{c} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} \end{tabular} } \\ \cline{1-1} \cline{5-5}
[15] & PEMS and GPS data for LDDTs in China & LSTM/Micro/Bottom-up & 2 vehicles on different routes & R\({}^{2}\): 0.986–0.990 (1.165–0.167) & Medium to High/Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of Existing Artificial Intelligence Models for Predicting CO\({}_{2}\) Emissions: Approaches and Methodologies.
## 3 Thermal Study of the Heater
### Inverse Problem  
#### 3.3.1 Measurement Techniques  
As explained in Section 2, the temperature of the heated plate will be measured on its external face (dry side, \(T_{dry}\)) using an infra-red camera and the temperature of the wet side of the plate (\(T_{wet}\)) will be deduced from the dry temperature by solving the heat diffusion equation within the plate.  
The purpose here is to determine if the measurement of the dry temperature will make it possible to deduce the wet temperature with an acceptable uncertainty.  
Since boundary conditions are imposed on a single face of the plate (dry side), the heat conduction within the plate is called an inverse problem; such problems are ill-posed according to [PERSON] [11]. This implies that the solution to this problem is very sensitive to potential uncertainties in the boundary conditions. The following section is then dedicated to the study of this key point.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 3 An Interpretable Time-Shift Invariant Harmonic Phase Model
### Demystifying the Phasegram
This explains the horizontal phase evolution for a given harmonic whose local maximum in the magnitude spectra has index \(k_{\ell}\). The fact that phase changes abruptly at harmonic frequencies between spectral index \(k_{\ell}-1\) and \(k_{\ell}\) is explained, for example, with the help of Equation (8). In fact, such phase change is \(\pi(1-1/N)\) which, in practical terms (i.e., for large \(N\)), consists of a phase inversion. Such a phase inversion does not occur for other  
Figure 26: Spectrogram (**a**), phasegram (**b**), and synthetic phasegram (**c**) characterizing the first 4 harmonics of a sawtooth waveform. spectral indices because, in those cases, the polarity of the real-valued Dirichlet kernel function also inverts, thereby canceling out the phase inversion. The remaining spectral lines are 'passive' as far as phase is concerned, which means that their phases are the result of a simple vertical interpolation between the phases of the 'active' spectral lines (i.e., those corresponding to local maxima in the magnitude spectrum). In order to illustrate this, we show in Figure 26c a simple replica of the phasegram of Figure 26b, which was built by defining first the horizontal phase evolution of the harmonics, by performing phase rotation by \(\pi(1-1/N)\) for \(k_{\ell}-1\) spectral indices, and by performing piecewise vertical phase interpolation for the remaining spectral lines. This helps to reinforce the point--and conclusion--that in the case of stationary periodic signals, the phasegram is a highly redundant graphical representation whose unique information is just the starting phase of the fundamental frequency, and the NRD feature vector.
# RPKI Defense Capability Simulation Method Based on Container Virtualization
###### Abstract  
As the main inter-domain routing protocol in today's internet, the Border Gateway Protocol (BGP) faces serious security risks during actual usage. Research on BGP malicious attack methods requires a realistic network environment, and evaluation methods based on physical networks often suffer from high costs and insufficient flexibility. Thus, we propose an efficient BGP simulated network deployment system based on a virtualization technology called the SOD-BGP. This system, combining cloud computing and virtualization technologies, creates a scalable, highly flexible basic network environment that allows for the automated simulation and evaluation of actual BGP prefix hijacking attack scenarios. A Resource Public Key Infrastructure (RPKI) simulation suite is introduced into the system, emulating a certificate issuance system, certificate storage, and a certificate synchronization verification mechanism, thus aligning the simulation environment with real-world usage scenarios. Finally, we propose a data collection and performance evaluation technique to evaluate BGP networks deploying RPKI under different attack scenarios and to explore the effectiveness of RPKI defense mechanisms at various deployment rates. A comparative analysis with other simulation techniques demonstrates that our approach achieves a balanced performance in terms of deployment speed, complexity, and RPKI integrity, providing a solid simulation technology foundation for large-scale BGP security defense strategies.  
RPKI; network simulation; BGP; virtualization; route hijacking Article
## 2 Materials and Methods
### Removal of Cu(II), Pb(II), Zn(II), and Cd(II) Using GO/P (AA-co-AM)  
The treatment effects of GO/P(AA-co-AM) on the removal of Cu(II), Pb(II), Zn(II), and Cd(II) ions from aqueous solutions were systematically investigated here. In this paper, the removal of Cu(II), Pb(II), Zn(II), and Cd(II) ions from aqueous solutions using GO/P (AA-co-AM) was systematically investigated. The procedure was performed using the following conditions: (1) pH = 2-7; Cu(II), Pb(II), Zn(II), and Cd(II) ion concentrations of 1000 mg\(\cdot\)L\({}^{-1}\); temperature of 25 \({}^{\circ}\)C; (2) the range of the temperature was from 25 to 45 \({}^{\circ}\)C, Cu(II), Pb(II), Zn(II), and Cd(II) ion concentrations of 1000 mg\(\cdot\)L\({}^{-1}\); (3) Cu(II), Pb(II), Zn(II), and Cd(II) ion concentrations ranging from 5 to 2000 mg\(\cdot\)L\({}^{-1}\) at 25 \({}^{\circ}\)C, with a contact time ranging from 5 to 60 min; and (4) the competitive adsorption principle for Cu(II), Pb(II), Zn(II), and Cd(II), where the concentration of each metal ion in the solution was 1000 mg\(\cdot\)L\({}^{-1}\) at 25 \({}^{\circ}\)C, and the principles of competitive adsorption were Cu(II), Pb(II), Zn(II) and Cd(II); Cu(II), Pb(II) and Zn(II); Cu(II) and Pb(II); Cu(II) and Zn(II); Pb(II) and Zn(II); Pb(II) and Cd(II); Zn(II) and Cd(II). The used GO/P (AA-co-AM) particles were collected, dried to a constant weight, dissolved and desorbed with 0.1 M HCl solution, and then read. The mixture of graphene oxide hydrogel and heavy metals was stirred in a constant temperature shaking incubator at 200 rmp\(\cdot\)min\({}^{-1}\) for 60 min. The mixture was left to stand, 1 mL of the supernatant was taken for solid-liquid separation, and the concentration of metal ions in the adsorbed solution was determined using atomic absorption. The experiment was repeated three times and the average of the three replicates was taken with an experimental error of 5%.  
The adsorption of heavy metal ions by GO/P(AA-co-AM) was calculated using Equation (1):  
\[Q_{e}=\frac{(C_{0}-C_{e})V}{m} \tag{1}\]  
where \(Q_{e}\) is the metal absorption concentration at equilibrium (mg\(\cdot\)g\({}^{-1}\)), \(C_{0}\) is the initial concentration of the metal ion (mg\(\cdot\)L\({}^{-1}\)), \(C_{e}\) is the equilibrium concentration of the metal ion (mg\(\cdot\)L\({}^{-1}\)), \(V\) is the volume of the solution (L), and \(m\) is the mass of GO/P(AA-co-AM) in the solution (g).
# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 2 Materials and Methods
### Data Collection  
Time series data (1 column) and kinematic data (18 columns) were synchronized from the 8 IMUs and transmitted to a laptop by Wi-Fi dongles. The IMUs output included both the quaternion and Euler data. Kinematic data were recorded by IMU number and position (2 columns), quaternion (4 columns), Euler angle (\({}^{\circ}\), 3 columns), raw acceleration (m/s\({}^{2}\), 3 columns), linear acceleration (m/s\({}^{2}\), 3 columns), and angular velocity (rad/s, 3 columns). The current orientation of IMUs was determined by using Euler angle following the Z-Y-Z sequence: the first rotation occurs around the \(Z\)-axis, followed by a rotation around the \(Y\)-axis and a rotation around the rotated new \(Z\)-axis. Quaternions transformed to Euler angles using Equation (1) [37].  
\[\textbf{Quat2 Eul}(\textbf{q})\textbf{=}\left[\textbf{arctan}\frac{2(\textbf{ q}_{0}\textbf{q}_{1}\textbf{+}\textbf{q}_{2}\textbf{q}_{3})}{\textbf{1}\textbf{-}2( \textbf{q}_{1}^{2}\textbf{+}\textbf{q}_{2}^{2})}\textbf{.arcsin}(2(\textbf{ q}_{0}\textbf{q}_{2}\textbf{-}\textbf{q}_{3}\textbf{q}_{1}))\textbf{.arctan}\frac{2( \textbf{q}_{0}\textbf{q}_{3}\textbf{+}\textbf{q}_{1}\textbf{q}_{2})}{\textbf{1 }\textbf{-}2(\textbf{q}_{2}^{2}\textbf{+}\textbf{q}_{3}^{2})}\right]^{\textbf{ T}} \tag{1}\]  
where, q\({}_{0}\) denotes the scalar part and q\({}_{1}\), q\({}_{2}\) and q\({}_{3}\) denote the vector part of the quaternion.