esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Latitude Model for the E Layer Dominated Ionosphere
## 3 Modeling Approach
### Model Structure  
Since we have had good experience using the multiplicative approach in previous modeling studies with respect to its robustness, small number of coefficients and real-time capability [19; 20; 21], we follow the same modeling approach in the present work as well. For a model observable \(F\), the associated equation \(F(x_{1},\ \ldots,\ x_{n})\) depends on a specific set of model drivers \(x_{1},\ \ldots,\ x_{n}\). Each dependency of the model observable on a single model driver is given by a separate model term \(F_{1}(x_{1}),\ \ldots,\ F_{n}(x_{n})\). Since the model equation follows the multiplicative approach, it can be computed as the product of all model terms as shown in Equation (1).  
\[F(x_{1},\ \ldots,\ x_{n})=F_{1}(x_{1})\cdot F_{2}(x_{2})\cdot\ldots\cdot F_{n}(x_{ n}) \tag{1}\]  
Each model term contains one or more base functions, which were chosen from trend analysis of the model observable with respect to the driver in such a way that they approximate the trend sufficiently well. Furthermore, each term includes internal coefficients located inside of the base functions and external coefficients located outside of them, all of which were determined numerically by means of nonlinear fitting. As an example, in Equation (2), we show a reduced version of the \(NmE\) model equation, which is given fully in Section 4.1. The first term models the dependency on the driver \(MLAT\) using a Gaussian as a base function, which contains the phase shift \(MLAT_{0}\) and the standard deviation \(\sigma_{MLAT}\) as internal coefficients. The second term models the dependency on the driver \(LT\) using two cosine functions. They represent the diurnal and the semidiurnal variations and contain the phase shifts \(LT_{D}\) and \(LT_{SD}\) as internal coefficients, respectively. The external coefficients are given by the amplitudes \(a_{1}\), \(a_{2}\) and \(a_{3}\), and the bias \(a_{4}\).  
\[\begin{split}& NmE(MLAT,\ LT)=\\ &\left(a_{1}\cdot\exp\left(-\frac{(MLAT-MLAT_{0})^{2}}{2\cdot \sigma_{MLAT}^{2}}\right)+1\right)\\ &\left(a_{2}\cdot\cos\left(2\pi\cdot\frac{1}{24}\cdot(LT-LT_{D}) \right)+a_{3}\cdot\cos\left(2\pi\cdot\frac{2}{24}\cdot(LT-LT_{SD})\right)+a_{ 4}\right)\end{split} \tag{2}\]
# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 2 Materials and Methods
### Satellite Images  
Sentinel-2 (S2) satellites images, acquired from November 2015 to October 2019 with cloud cover lower than 90%, were acquired for the 4 granules corresponding to the study area. The Multi-Spectral Instrument (MSI) sensor onboard S2 is characterized by a high spatial resolution (10 m, 20 m, and 60 m), a high revisit time (5 days with two satellites), and 13 spectral bands from visible to shortwave infrared. The spectral bands of the images in the MUSCATE format, distributed by Theia as the bottom of the atmosphere (BOA) reflectance, orthorectified, terrain-flattened, and atmospherically corrected with the MACCS-ATCOR joint algorithm (MAJA) [15], were processed for spatial resampling at 10 m masked for invalid pixels (cloud, cloud_cirrus, cloud_shadow, topographic_shadow, snow, edge, sun_too_low). A static mask, generated from Copernicus Land Monitoring Service datasets, was applied to mask out pixels not corresponding to croplands.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 3 Dataset and Methodology
### Evaluation Indicators Selection  
To evaluate the developed models and their predictive performances, in this paper, mean relative error (MRE), root mean square error (RMSE), and mean absolute error (MAE) are selected as the evaluation indicators of the network model. At the same time, this experiment adds additional model running time to the evaluation index to compare the model running efficiency.  
\[\mathrm{MRE}=\frac{1}{\mathrm{m}}\sum_{i=1}^{\mathrm{m}}\frac{\left|y_{i}-y_{ \mathrm{pre}}\right|}{y_{i}} \tag{3}\]  
\[\mathrm{RMSE}=\sqrt{\frac{1}{\mathrm{m}}\sum_{i=1}^{\mathrm{m}}\left(y_{i}-y_ {\mathrm{pre}}\right)^{2}} \tag{4}\]  
\[\mathrm{MAE}=\frac{1}{\mathrm{m}}\sum_{i=1}^{\mathrm{m}}\left|y_{i}-y_{ \mathrm{pre}}\right| \tag{5}\]  
where \(y_{i}\) is the observed BHP values and \(y_{\mathrm{pre}}\) is the calculated BHP values, which are predicted by the built models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Var6(t\(-\)1)** & **var7(t\(-\)1)** & **var8(t\(-\)1)** & **var9(t\(-\)1)** & \(\dots\) & **var13(t\(-\)1)** \\ \hline
0.00038 & 0.957212 & 0.849603 & 0.009718 & \(\dots\) & 0.799 \\
0.00042 & 0.957229 & 0.879854 & 0.018351 & \(\dots\) & 0.799 \\
0.00046 & 0.957242 & 0.867551 & 0.016718 & \(\dots\) & 0.799 \\
0.00050 & 0.957263 & 0.855436 & 0.026837 & \(\dots\) & 0.799 \\
0.00058 & 0.95728 & 0.881510 & 0.020017 & \(\dots\) & 0.799 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample of multivariable sequence data.
###### Abstract  
The rail is an important factor in railway traffic safety. Surface defects in the rail head comprise a common type of rail damage, and magnetic flux leakage (MFL) technology is applied for its detection. MFL detection is influenced by various factors, resulting in high noise and a low signal-to-noise ratio (SNR) in the collected MFL signal, which influence defect assessment. This article improves the empirical wavelet transform (EWT) to apply it to rail surface-defect MFL signal filtering. A boundary optimization method based on mutual information (MI) is proposed to reduce the boundary redundancy caused by adaptive spectrum division. A method for component selection based on MI and kurtosis is proposed to select the suitable components from the decomposed components for signal reconstruction. The experimental results show that the method can effectively filter out the interference in the MFL signal, and the effectiveness is superior to the traditional methods, such as complementary ensemble empirical mode decomposition (CEEMD) and wavelet transform (WT).  
rail MFL detection; signal filtering; improved EWT; MI; kurtosis +
Footnote †: journal: Applied sciences  
Article An Improved Empirical Wavelet Transform Filtering Method for Rail-Head Surface-Defect Magnetic-Flux Leakage Signal
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Detection Features  
Techniques that identity deception detection models use depend on the social platform and the objective (e.g., detecting explicitly bots). For example, a malicious bot that is designed to make friends with as many accounts as possible will require a different detection approach than a bot that is designed to repeatedly post links to malicious websites. As such, our analysis identified two broad categories that we can divide models into: social context models and user behavior models. The two models accomplish the same task (i.e., detecting malicious accounts), but they use different sets of features. Many models also use some combination of both social features, as well as user behavior features. By features, we refer to the elements (or variables) in data that are used to differentiate between different data points (e.g., the number of posts can differentiate a malicious from a non-malicious user).
## 3 Case Study: Experimental Verification and Comparison Results
### Comparison Studies
#### 3.2.3 Comparison of Model Performance According to Domain Optimization Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \begin{tabular}{c} **Number of** \\ **Training Data** \\ \end{tabular} & \begin{tabular}{c} **No** \\ **Optimization** \\ \end{tabular} & \begin{tabular}{c} **Target** \\ **Dropout** \\ \end{tabular} & \
\begin{tabular}{c} **KLD** \\ **Instance** \\ **Weights** \\ \end{tabular} &
\begin{tabular}{c} **MMD** \\ **Instance** \\ **Weights** \\ \end{tabular} \\ \hline S1 & 180 & 92.11 & 96.33 & 97.11 & 97.33 & 98.33 \\  & 170 & 96.33 & 97.11 & 97.44 & 97.44 & 98.89 \\ S2 & 160 & 89.33 & 92.78 & 95.22 & 92.56 & 96.44 \\  & 150 & 92.56 & 93 & 95.89 & 95.89 & 97.78 \\  & 140 & 94.11 & 94.67 & 95.67 & 96.56 & 97.33 \\  & 130 & 93.33 & 95.78 & 90.56 & 94.78 & 96.22 \\ \hline \multirow{4}{*}{S2} & 120 & 88.89 & 92.11 & 93 & 91.78 & 96 \\  & 110 & 92.33 & 93.44 & 94.56 & 92.78 & 95.56 \\  & 100 & 90.44 & 92.67 & 90.67 & 93 & 95.44 \\  & 90 & 86.67 & 86.22 & 86.78 & 90.45 & 91.89 \\  & 80 & 88.22 & 85 & 83.89 & 87.11 & 91.11 \\  & 70 & 89.22 & 86.89 & 85.22 & 90 & 93.11 \\ \hline \multirow{4}{*}{S3} & 60 & 83.22 & 85.33 & 81.33 & 86.44 & 88.56 \\  & 50 & 77.78 & 81.44 & 77.44 & 82.56 & 85.11 \\ \cline{1-1}  & 40 & 78.78 & 81.89 & 75.44 & 81.89 & 83.22 \\ \cline{1-1}  & 30 & 69 & 75.56 & 72.78 & 73.89 & 75.56 \\ \cline{1-1}  & 20 & 65.56 & 70 & 69.44 & 75 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy comparison results of different domain optimization methods. Figure 7: Comparison results between different domain optimization methods.
# Enhancing Cloud Security--Proactive Threat Monitoring and Detection Using a SIEM-Based Approach
## 4 Results and Discussions
### Challenges, Limitations, and Recommendations  
While our system demonstrated promising results, it is essential to acknowledge the limitations encountered during the experiment. Firstly, we strategically deployed cloud resources within our budgetary limits, allowing us to initiate the implementation of solutions while considering further scalability enhancement. To ensure comprehensive evaluation, future research should aim to incorporate large-scale deployments that align more closely with organizational-level scenarios, ensuring a greater relevance and applicability of findings. Secondly, we must emphasize that our study primarily focused on exploring a cloud-native SIEM solution. Nevertheless, it is worth acknowledging the availability of various third-party SIEM solutions that offer promising opportunities for effective integration within the cloud environment, broadening the range of options for potential users. Exploring these alternative solutions can provide valuable insights and expand the scope of research in this domain.  
Thirdly, while it is feasible to integrate data sourced from third-party cloud service providers (CSPs) to evaluate pertinent compliance scores, this could be a daunting task for organizations that adopted multi-cloud environments--signifying entities that have implemented two or more distinct CSPs. As of the time of composition, the sole third-party CSPs supported in Sentinel are AWS and GCP. Furthermore, it was apparent that for large-scale deployments, the mismanagement of cloud resources could potentially result in inflated charges, which may prove burdensome to organizations. In this light, it is significant to have proficient individuals who can govern cloud expenditure to circumvent unnecessary expenses. As there are various security tools, a comprehensive assessment may be required to judge which one best fits organizational demand. It is equally important to leverage the inherent features of cloud computing, such as rapid elasticity, which allows cloud resources to be shrunk and/or grown to accommodate organizational needs. Likewise, with the metered services characteristic of cloud computing systems, customers can monitor and regulate the utilization of provisioned resources in real time, thereby providing transparency between the CSPs and the customers.  
Nevertheless, we maintain that the advantages of implementing a SIEM system outweigh the associated challenges. Integrating such a system within a cloud environment enables the automation of security controls, reducing risks and mitigating the impact on cloud resources. Once the requisite configurations are in place, the system alleviates the burden on security professionals, who are typically accountable for cybersecurity incidents. This can promote a more resilient security framework and enhance an organization's overall security posture in the cloud environment.
## 4 Results and Discussion
### Simulation Results
#### 4.1.2 Sinusoid Track  
A sinusoidal signal with an amplitude of 20 N and frequency of 0.2 Hz was set for the simulation, and the response curves of the three controllers were obtained, as shown in Figure 13.  
All three controllers could smoothly follow the setting curve but had different degrees of lag. BPNN-SP-PID produced the smallest lag, followed by BPNN-PID, and the worst was from the PID method. The difference in response time also brought about a difference in control accuracy. The maximum force error of PID reached 3.22 N, and the average error reached 2.09 N. The maximum error of BPNN-PID was 2.65 N, and the average error was 1.68 N. The maximum error of BPNN-SP-PID was 2.63 N, and the average error was 1.59 N.  
It can be seen that the BPNN-SP-PID controller had the best dynamic adjustment performance among the three controllers.  
Figure 12: Comparison of step responses of the three controllers in the simulation.  
Figure 13: Comparison of sinusoidal tracking response curves in the simulation.
###### Abstract  
The gallium-nitride (GaN) high electron-mobility transistor (HEMT) technology has emerged as an attractive candidate for high-frequency, high-power, and high-temperature applications due to the unique physical characteristics of the GaN material. Over the years, much effort has been spent on measurement-based modeling since accurate models are essential for allowing the use of this advanced transistor technology at its best. The present analysis is focused on the modeling of the scattering (\(S\)-) parameter measurements for a 0.25 \(\upmu\)m GaN HEMT on silicon carbide (SiC) substrate at extreme operating conditions: a large gate width (i.e., the transistor is based on an interdigitated layout consisting of ten fingers, each with a length of 150 \(\upmu\)m, resulting in a total gate periphery of 1.5 mm), a high ambient temperature (i.e., from 35 \({}^{\circ}\)C up to 200 \({}^{\circ}\)C with a step of 55 \({}^{\circ}\)C), a high dissipated power (i.e., 5.1 W at 35 \({}^{\circ}\)C), and a high frequency in the millimeter-wave range (i.e., from 200 MHz up to 65 GHz with a step of 200 MHz). Three different modeling approaches are investigated: the equivalent-circuit model, artificial neural networks (ANNs), and gated recurrent units (GRUs). As is shown, each modeling approach has its pros and cons that need to be considered, depending on the target performance and their specifications. This implies that an appropriate selection of the transistor modeling approach should be based on discerning and prioritizing the key features that are indeed the most important for a given application.
## 4 Threat Model and Assumptions  
The present paper aimed to address the challenges associated with the certificate chain verification process, which is a critical aspect of ensuring data authenticity and integrity. The proposed solution focuses on enabling consumers to authenticate and verify the content they receive, regardless of their location, while avoiding the overhead associated with validating each certificate involved in the received content. Unlike existing solutions, our approach does not require consumers to have prior knowledge of the trust schema used for data authentication. Instead, we assumed that consumers must possess the blockchain before requesting and verifying data, and each consumer in the network can request and receive the blockchain.  
To account for potential adversarial nodes, we made several assumptions. Firstly, we assumed that attackers have the capability to initiate multiple protocol sessions simultaneously. Secondly, we assumed that attackers can access all public data related to the protocol, which gives them the ability to read, store, and block any sent messages. Furthermore, attackers can intercept, construct, and resend messages to their desired destinations. We also assumed that no key breaches occur. To ensure the effectiveness of our proposed mechanism, we needed to develop robust strategies to mitigate the impact of such attacks and enhance the overall security of the system. To this end, we investigated several potential security threats to the system, including impersonation attacks, blockchain tampering attacks, man-in-the-middle attacks, and (D)DoS attacks. In an impersonation attack, the adversary tries to masquerade as a legitimate producer. The blockchain tampering attack, on the other hand, involves the insertion of false or modified data into the blockchain by a malicious node. The man-in-the-middle attack, as the name implies, occurs when an attacker intercepts and alters the communication between two parties, often for the purpose of stealing or manipulating sensitive information. Finally, the (D)DoS attack involves flooding the system with _Interest_ packets in an attempt to overwhelm it, and thereby prevent legitimate requests from being satisfied.
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 2 Methods
### Material Selection  
It is very important to select the piezoelectric material with material properties suitable for measuring the vehicles' axle loading. In many studies, the piezoelectric charge constant and the piezoelectric voltage constant has been approved to be highly related to the measurement accuracy [24; 25; 29]. In 33-mode, these two material properties are express as d\({}_{33}\) and g\({}_{33}\), which is directly related to the current and voltage output under the vehicles' load. In these studies, piezoelectric materials with lower elastic compliance are recommended due to their better electrical output performance under the same load. The quality factor, Q, has been investigated in [PERSON] et al.'s study as another important factor to the error and variance of the measuring results [30]. During the measuring process, the piezoelectric material will endure less damping with a higher quality factor. As a conclusion, P-WIMs are fabricated using a piezoelectric material with higher piezoelectric coefficients, lower elastic compliance, and higher quality factor.
# A Strain-Transfer Model of Surface-Bonded Sapphire-Derived Fiber Bragg Grating Sensors
###### Abstract  
An improved strain-transfer model was developed for surface-bonded sapphire-derived fiber Bragg grating sensors. In the model, the core and cladding of the fiber are separated into individual layers, unlike in conventional treatment that regards the fiber as a unitive structure. The separation is because large shear deformation occurs in the cladding when the core of the sapphire-derived fiber is heavily doped with alumina, a material with a high Young's modulus. Thus, the model was established to have four layers, namely, a core, a cladding, an adhesive, and a host material. A three-layer model could also be obtained from the regressed four-layer model when the core's radius increased to that of the cladding, which treated the fiber as if it were still homogeneous material. The accuracy of both the four- and three-layer models was verified using a finite-element model and a tensile-strain experiment. Experiment results indicated that a larger core diameter and a higher alumina content resulted in a lower average strain-transfer rate. Error percentages were less than 1.8% when the four- and three-layer models were used to predict the transfer rates of sensors with high and low alumina content, respectively.  
strain transfer; sapphire-derived fiber; Bragg grating; core radius; alumina content +
Footnote †: journal: Applied Sciences
# GPS Results from Long Time Monitoring of Geodynamic Processes in South-Western Bulgaria
## 3 Obtained Horizontal Velocities

Table 3: Estimated horizontal velocities relative to Eurasia, ETRF2014. All obtained horizontal velocities clearly exceed 1.5-2 mm/year, reaching up to 3-4 mm/year. As a general tendency, the velocities tend to increase from north to south. The northernmost area (N-NW of Sofia) sees a majority of velocities of about 1.5 mm (stations BERK, VLAD, BUHO, and SLIV). In the area to the south of Sofia, velocities of 2-3 mm/year (stations DSEC, CARV, BOGO, VERI, PADA, VETR, and MALA) dominate. Further south, the velocities of DOB1 and SATO are almost 4 mm/year, which is the high \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & **Lat** & **Long** & **Ve** & **SVe** & **Vn** & **SVn** \\ \hline
**ID** & [°] & [°] & [mm/year] & **[mm/year]** & **[mm/year]** & **[mm/year]** \\ \hline VERI & 42.48 & 23.73 & 0.49 & 0.11 & \(-\)2.28 & 0.09 \\ VETR & 42.29 & 24.06 & 0.22 & 0.18 & \(-\)2.15 & 0.15 \\ VITI & 42.78 & 23.80 & \(-\)0.34 & 0.13 & \(-\)2.53 & 0.11 \\ VLAD & 42.87 & 23.36 & 0.12 & 0.05 & \(-\)1.60 & 0.10 \\ ZEME & 42.50 & 22.70 & 0.61 & 0.10 & \(-\)2.03 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 2: Long-term repeatability of the horizontal station positions. est in the area. The behavior of some stations with velocities from earlier campaigns [6] (e.g., VLAD and BUHO), that were showing significant deviation now fit in the general trend. This shows that the reason was the lack of accuracy due to the short period between observations, but not to tectonic reasons.
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
## 4 Simulation and Result Analysis  
In the simulation part, low-orbit satellites were utilized with a distance of 1200 km from the ground near Earth's surface.The advantage of low-orbit satellites are obviously Doppler integral changes, which are beneficial to improve the positioning accuracy of single-satellite navigation. The designed run period was 2.13 h; orbit inclination was \(\pi/6\); right ascension point was \(\pi/3\); the eccentricity of the elliptical orbit was 0.1; and perigee angle was \(pi/6\).  
The main sources of error for single-satellite positioning include orbital, pseudorange, satellite-velocity and INS errors. These are considered in single- and comprehensive-factor simulations. Due to INS errors existing in all target-displacement data, every single-factor simulation includes INS errors. At the same time, our proposed algorithm was compared with Doppler single-satellite positioning method based on the least-square Newtonian iteration and EKF single-satellite Doppler pseudorange positioning methods.