esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Effects upon Irradiation  
To apprehend more precisely the thermal effects associated with the so-called \"room temperature\" polymerization induced by radiation, thermocouples were placed in resin samples containing 0.5 wt-% of DAIS-B [29]. The 3 cm thick bulk sample with its 280-300 g of epoxy resin was conveyed under the electron beam to absorb 25 kGy per pass. With an initial energy of 10 MeV, the electron flux goes through the sample with very limited attenuation, as illustrated by the plots of Figure 1. Energy deposition can be considered to be essentially uniform.  
The recorded temperature profile (Figure 4) shows that after a short period of latency, during which the sample is warmed up by conversion of the incoming beam into heat while the first acidic species to be generated in situ are quenched by basic contaminants and moisture, the temperature in the irradiated sample rises up to 180 \({}^{\circ}\)C within about 10 s, the peak being observed as the sample has already moved away from the irradiation zone.  
During the second and third pass, the temperature rise is limited, being only due to the absorption and conversion of radiation. Polymerization proceeds in almost adiabatic conditions. Polymerization seems to have been driven to completion. The kinetics of energy dissipation can be appraised from the very smooth slope of temperature decrease 150 s after the onset of irradiation.  
More complex temperature profiles have been recorded during other experiments conducted with various contents in onium salt. The occurrence of vitrification between two consecutive passes may occur and block further reaction, resumption of the exposure to radiation being almost inefficient in a glassy matrix even if large amounts of unreacted epoxy functions are available for polymerization. Conversion measurements at various stages of the curing process are expected to bring additional information.  
Figure 3: Dependence of epoxy conversion on the nature and content of onium salt in DGEBA after an overall treatment at 100 kGy (2 times 50 kGy, 1 mm thick plates), as measured by NIR transmission spectroscopy.
# Landslide Displacement Prediction Method Based on GA-Elman Model
## 3 Application Research and Method Comparison
### Model Performance Evaluation  
In order to evaluate the predictive performance of the GA-Elman model, the performance indicators in Table 1 are used for evaluation. It can be seen from Table 2 that the RMSE, MAE, MAPE and R\({}^{2}\) of the GA-Elman model for 10 consecutive days are 0.314 mm, 0.222 mm, 0.243% and 0.9979, respectively, which are better than those in other situations. The results show that under the condition of rainfall in flood season, the prediction effect of Jianshanying landslide considering rainfall factors is better than that without considering rainfall factors, which proves that rainfall factors have a certain influence on landslide displacement and shows a certain lag. We can conclude that the accuracy of landslide displacement prediction is relatively high. Considering the landslide displacement prediction is a complex nonlinear problem, the recursive neural network is optimized by GA, and the existing monitoring data are used to train the network. Finally, the nonlinear relationship in the problem of displacement change can be well simulated.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Item** & **Formula** & **Notes** \\ \hline RMSE & \(RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}\) & A common measure of the difference between model predictions \(\hat{y_{i}}\) and actual observations \(y_{i}\), which can reflect the accuracy of the prediction model. \\ \hline MAE & \(MAE=\frac{1}{n}\sum_{i=1}^{n}|\hat{y_{i}}-y_{i}|\) & The mean value of the absolute error between the observed value and the true value, describing the error between the predicted value and the true value. \\ \hline MAPE & \(MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y_{i}}-y_{i}}{\hat{y_{i}}}\right|\) & A measure of the accuracy of a forecasting method, usually expressed as a percentage. \\ \hline R\({}^{2}\) & \(R^{2}=1-\frac{\sum_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}{\sum_{i=1}^{n}\hat{y_{i}} -y_{i}}\) & A measure mainly used to evaluate the degree of agreement between the predicted value and the actual value, its value range is [0,1]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation criteria for prediction performance of machine learning models.  
Figure 7: Prediction and comparison of measured displacement and predicted displacement by GA-Elman model.
## 4 Overview of Trusted AODV  
### Trusted Assumptions  
Here, we made some assumptions for specific roles of entities, further we argue that we are mainly focuses on security solution to the routing behavior of network layer.  
* Server: The server in central cloud layer is capable of having high storage capacity regarding brief history and profile also with vehicle Id and digital certificate that is distributed soon after entering the coverage area via RSU. Further, it is proposed that central server is under strong physical protection and not affected by adversaries.
* Remote Site Unit RSU: Here, we proposed that RSUs act as local trust manager for vehicles on road, but have limited storage capabilities as compared with server in CCL. Here RSU is used to manage the vehicle information e.g., IP address, public or private key etc for short time and update server after one complete trip via V-2-R communication.
* Vehicle: A vehicle can access all its neighbor vehicles and broadcast initial information about itself using V-2-V communication. A user vehicle after interacting provide their feedback about other vehicle. When a new vehicle join the group, the uncertainty towards it is normally high, so its trust value is evaluated after observing its behavior also taking advise from neighbor vehicles. At start, the new incoming vehicle also prove its credibility via exchange of digital certificate, which helps other vehicles to reduce uncertain opinion about it. Once the trust relationship establish vehicles can use our secure routing protocol to reduce extra communication overhead.
## 5 Features and Data Preparation  
### Data Pre-Processing  
The collected data was then analyzed and processed for it to be later used in training and testing datasets for machine-learning algorithms. The three zones to be classified were first labeled by using GNSS coordinates associated to each data sample. Then, the GNSS information was removed before training the classification algorithm so that it relied on the vehicle's dynamics and energy consumption information as the only available features. This information can be pre-processed to enhance the ML algorithm performance. This  
\begin{table}
\begin{tabular}{c c} \hline Motor & \\ \hline Power & 15 HP \\ Nominal Voltage & 76 V \\ Nominal current & 115 A \\ Peak current & 132 A \\ Max speed & 3000 RPM \\ \hline Battery & \\ \hline Total voltage & 96 V \\ Number of cells & 32 \\ Total Energy Capacity & 28,800 Wh \\ Discharge rating 2C & 0.5 \\ Discharge current & 150 A \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the test EV’s motor and battery.  
\begin{table}
\begin{tabular}{c c} \hline Vehicle brute mass & 1370 kg \\ Wheels’ dynamic radius & 0.25019 m \\ Rolling resistance coefficient & 0.014984 \\ Aerodynamic drag coefficient & 0.436 \\ Vehicle frontal Area & 2.15 m\({}^{2}\) \\ Gearbox efficiency & 0.95 \\ Axle differential efficiency & 0.95 \\ Top Speed & 100 km/h \\ \hline \end{tabular}
\end{table}
Table 3: Test vehicle physical and performance characteristics.  
Figure 7: Test EV powertrain architecture.  
pre-processing includes the selection of the best input features and the combination of them, followed by statistical values calculation.
## 4 Validation of SRSVD
### Case Study II
The denoising results of these three methods are shown in Figure 10a-f. It can be seen that there was no obvious fault feature in their envelope spectra. Only in the VMD method denoising signal envelope spectra, fault frequency(109 Hz) and its third, fifth harmonic (327 Hz, 545 Hz) could be found obviously, but the noise reduction effect was still not as obvious as that of SRSVD. In addition, kurtosis is used to evaluate the signal after noise reduction and a larger kurtosis indicates more fault information. Kurtosis is an important index to evaluate the number of fault information in signal and is widely used in fault diagnosis. Its calculation formula is:  
\[K=\frac{\frac{\frac{1}{n}\sum\limits_{i=1}^{n}{{{({x_{i}}-\mu)}^{4}}}}{{{{ \left({\frac{1}{n}\sum\limits_{i=1}^{n}{{{({x_{i}}-\mu)}^{2}}}}\right)}^{2}}}}} \tag{9}\]  
where \(n\) is the number of data points, and \(\mu\) is the mean value. After calculation, the kurtosis of the original signal was 4.673, and the kurtosis of the signal by SRSVD, L1-kurtosis, RSVD, and VMD denoised were 5.486, 4.759, 5.146, and 5.464, respectively. Therefore, after SRSVD noise reduction, the fault features in the signal were the most obvious, and the fault information was the most abundant. Figure 10: Denoising results of four different methods and their envelope spectra. From top to bottom: L1-Kurtosis (**a**,**b**), RSVD (**c**,**d**), VMD (**e**,**f**), and SRSVD (**g**,**h**). In addition, the same analysis and comparison experiments were performed for the signals at the 50 th minute and 100 th minute. These two signals can be regarded as the stronger noisy case and the less noise case, respectively. Firstly, the 100 th minute signal was processed. The envelope spectra of the signals after denoising by the four methods are shown in Figure 11. It can be seen that when in the middle fault stage, all the four methods can find the fault frequency and its high harmonic frequency, among which VMD and SRSVD were particularly excellent. Their corresponding kurtosis values were \(K_{\text{L1-Kurtosis}}\) = 5.663, \(K_{\text{RSVD}}\) = 5.176, \(K_{\text{VMD}}\) = 5.862, and \(K_{\text{SRSVD}}\) = 5.937, respectively. Secondly, the signal at the 50 th minute was tested, and the obtained envelope spectra comparison results are shown in Figure 12. It can be seen that at the 50 th minute, the performance of the four methods was not ideal; only SRSVD could find the third harmonic frequency of the fault. At this time, their corresponding kurtosis values were \(K_{\text{L1-Kurtosis}}\) = 3.765, \(K_{\text{RSVD}}\) = 3.567, \(K_{\text{VMD}}\) = 3.649, and \(K_{\text{SRSVD}}\) = 3.956, respectively.
# RoseCliff Algorithm: Making Passwords Dynamic
## 2 Encryption  
Encryption comes in two main forms: symmetric and asymmetric. Symmetric encryption offers speed, efficiency, and ease of implementation, thus making it well-suited for encrypting large data volumes and commonly used for password encryption. However, it presents challenges in key distribution, scalability as participants increase, and lacks inherent authentication. In contrast, asymmetric encryption (public key encryption) addresses the key distribution problem, provides authentication, and supports nonrepudiation. Yet, it demands more computational resources, longer key lengths for equivalent security, and involves complex key management [16; 17]. Presently, hybrid encryption combines the advantages of both symmetric and asymmetric encryption, thus ensuring secure data protection. It starts with asymmetric encryption for secure key exchange, thereby subsequently transitioning to symmetric encryption for efficient data encryption and decryption. Hybrid encryption is widely applied in secure communication systems, thus striking a balance between security and performance. Effective data encryption has proven resilient against most attack forms, as the resulting ciphertext is typically lengthy and randomized, thereby thwarting various attacks, including those leveraging machine learning [18; 19].
## 1 Introduction
[21] obtained the digital elevation model of a slope by using UAV aerial photography and evaluated the stability of the slope model using FLAC3D 6.0 (Itasca Consulting Group, Minneapolis, MN, USA) software. Most open-pit mines frequently produce blasting operations, and blasting seismic waves have a great impact on slope stability. A large number of experts and scholars have conducted relevant research on the impact of blasting vibrations on slopes. [PERSON] et al. [22] studied the influence of blasting amplitude and frequency on slope stability, based on the established slope instability mutation theory. [PERSON] et al. [23] studied the safety control standard value of blasting vibrations through analyzing slope stability during blasting. [PERSON] et al. [24] discussed the weakening law of blasting damage based on the disturbance factor D value in the Hoek-Brown strength criterion, and they discussed the influence of the D value on the slope safety factor. [PERSON] et al. [25] used PFC3D 6.0 (Itasca Consulting Group, Minneapolis, MN, USA) software to simulate the variation in internal cracks and the displacement of the slope with blasting height. With increases in mining depth, the operational costs of open-pit mines are also increasing. Most mines have changed from open-pit mining to underground mining or open-pit combined mining. The goaf left by underground mining seriously threatens the stability of the slope. Therefore, [PERSON] et al. [26] conducted research based on the strength reduction theory and elastic plastic mechanics; they used FLAC3D software to study the influence of the underground goaf on slope stability. [PERSON] et al. [27] studied the influence of the goaf on the instability of red clay slopesusing a numerical simulation and the limit equilibrium method. [PERSON] et al. [PERSON] et al. (2018) explored the characteristics and evolution of slope slip under different mining sequence combinations of open-pit and underground combined mining. [PERSON] et al. [PERSON] et al. (2019) carried out an analysis and evaluation of the potentially dangerous area division, slope stability characteristics, and isolation thickness of an open-pit slope according to the theoretical deduction of the goat and the results of borehole detection. A large number of experts and scholars have made fruitful studies on various factors affecting slope stability respectively, but due to the complex environment of open-pit mines, there are more factors that may cause slope instability, whether they are the preferred structure plane, production blasting vibration, or underground mining area. In order to solve this problem, all kinds of factors that may cause slope instability damage need to be analyzed.
## 2 Materials and Methods
### Image Treatment and Data Processing  
The first step of the data processing consists of identifying the pixels corresponding to each individual droplet in the recorded raw infrared images. The associated DL signals are then extracted, which allows to reduce the whole set of raw images to one single image by averaging the DL signals on time. This average image captures main information regarding the position of the droplets on the plate. Their segmentation by thresholding produces a new binary image in which all pixels unambiguously associated to the droplets are identified by a single value (1) and the rest of the pixels (background) are all 0-valued. The connected components of the binary image foreground are then split in a set of  
Figure 1: Sketch of the experimental setup.  
disjoined elementary objects (individual droplets). This is done using the general procedure outlined in Reference [11]. Once elementary objects have been conveniently labelled (each droplet on the plate is identified by a different arbitrary color), the DL signals associated to each droplet can then be easily retrieved. In the second step, a droplet-by-droplet analysis is carried out to identify the temperature at which each droplet undergoes a transition. As a droplet can have hundreds of pixels (depending on the size of the droplet), the disparity in the signal of each plotted pixel makes the determination rather difficult, as can be seen in Figure 2.  
To overcome this difficulty, a singular value decomposition (SVD) technique described in Reference [11] was applied to our data. The determination of the transition lines is now reduced to the identification of abrupt changes in three of the first four time-eigenfunctions \(Z_{i}(t)\) for each droplet, as can be seen in Figure 3.
## 1 Introduction
Using an SRR-loaded antenna has been shown to increase both gain and bandwidth, making it a promising candidate for WiMAX deployments [25]. The suggested design has multiband operating frequencies as well as a high gain. The proposed architecture is intended for fixed satellite services such as radio location identification and different wireless communication services. The antenna's performance has been enhanced by reflection coefficient, gain and bandwidth. The simulated and measured results of the suggested design are in excellent agreement. The presented article is structured as follows: Section 2 analyzes the proposed antenna's design concerns, including dimensions characterization, far-field resonance behavior and parametric research of several associated dimensional factors. Section 3 depicts the performance metrics. Section 4 reflects the design's uniqueness compared to previous works, and the Section 5 summarizes the conclusion.
## 2 Computed Tomography and Aortic Valve
### CT Assessment of the Aortic Valve for Transcatheter Procedures
#### 2.3.3 Aortic Leaflets
TAVI, calcium score significantly correlated with 30-day major adverse cardiac events (MACE) and 1-year mortality and with the incidence and severity of post-procedural aortic regurgitation. Patients with calcium scores > 750 experienced a significantly lower 1-year survival rate than those with <750 [47]. [PERSON] et al. confirmed these findings in a series of 120 patients receiving an Edwards SAPIEN prosthesis via a transapical approach [48]. Extensive calcification of the aortic cusps also represents a risk factor for obstruction of the left main coronary artery during THV deployment [31]. Additionally, [PERSON] et al. found that subjects with at least mild-moderate post-procedural aortic regurgitation had higher AVC scores than patients with no PAR on follow-up [49]. [PERSON] et al. evaluated the amount of calcifications of the aortic landing zone, including the left ventricle outflow tract (LVOT), aortic annulus, and valvular cusps in 307 patients undergoing transapical TAVI, using a semiquantitative (4-point scale) and quantitative (Agatston calcium score) CT method. They found that an increased risk of PAR was strongly related to each 100-unit increment of the Agatston calcium score. Furthermore, a higher risk of AR was reported in patients with asymmetric cusp calcification and severe calcification of the landing zone [50]. Interestingly, [PERSON] et al. recently showed that paravalvular AR increased with the protruding rather than the \"adherent\" calcifications of the aortic valve and root [51].
## 4 Results
### Test Setup  
Figure 10 illustrates the block diagram of the test setup used to apply live signals to the proposed receiver. The antenna captures GNSS signals from the sky. A variable attenuator is manually adjusted to decrease the carrier-to-noise density ratio (\(C/N_{0}\)) of the received signals down to 20 dB-Hz. Achieving a uniform \(C/N_{0}\) of 20 dB-Hz for all signals proves challenging due to variations in the received signal power from different satellites. Splitter 1 divides the received signals into two paths: one leading to the device under test and the other to a commercial receiver, which is used to validate the \(C/N_{0}\) of the received signals. The receiver introduced in this paper uses splitter 2 to divide the incoming signal internally, distributing it between the two RF inputs of the AD-FMCOMMS5-EBZ analog front-end. RF1 is tuned to the E1/L1 frequency band, while RF2 is tuned to the E5a/L5 band.  
The test setup involves two splitters in the signal path of the high-sensitivity GNSS receiver (splitter 1 and splitter 2). Similarly, the signal path of the commercial receiver incorporates splitter 1 and splitter 3. Each splitter introduces a 3-dB attenuation in the received signal. This arrangement allows us to maintain equal signal conditions, as detailed in the following explanation.  
The \(C/N_{0}\) observed by both receivers can be computed using the Friis formulas for noise [2]. Without the variable attenuator, the \(C/N_{0}\) seen by the receivers is mainly influenced by factors like received signal strength, antenna noise temperature, and the noise figure of the Low Noise Amplifier (LNA) in the active antenna. The splitters in the signal path, including splitter 1, splitter 2, and splitter 3, have negligible effects on the \(C/N_{0}\). However, when the variable attenuator introduces substantial attenuation, the \(C/N_{0}\) observed by the receivers is primarily determined by the total attenuation from the antenna to the analog front-end. This total attenuation encompasses both the variable attenuator and the attenuation introduced by the splitters. Splitter 3 introduces an attenuation of 3 dB, ensuring that the commercial receiver perceives the same \(C/N_{0}\) as the receiver under test when the variable attenuator introduces significant attenuation.  
The authors validated the \(C/N_{0}\) of received Galileo E1b/c signals using a U-blox NEO-M8T receiver [45]. The variable attenuator was adjusted to decrease the carrier-to-noise density ratio (\(C/N_{0}\)) of the received signals. This adjustment aimed to reduce the \(C/N_{0}\) of the weakest Galileo E1b/c signals to as low as 20 dB-Hz, while maintaining consistent attenuation levels across other signals.  
The rooftop GNSS antenna facility of the GESTATI testbed [46] was utilized to enhance satellite visibility during the tests. Additionally, the tests evaluating the receiver's ability to process Galileo E1b/c, Galileo E5a, GPS L1 C/A, and GPS L5 signals (Section 4.3)  
Figure 10: Test setup.  
were conducted using both the rooftop GNSS antenna facility and the receiver's TW8825 active antenna.