esa-sceva/satcom-chunk-collection huggingface dataset:
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 4 Results and Analysis
### Antenna Misalignment and Realignment Due to Gust Events  
Sudden changes in RSSI, such as a rising or dropping of up to 1-2 dB, have been observed occasionally after gusty periods. We have attributed this effect to misalignment and realignment caused by wind gusts. The definition of such misalignment is the decrease or increase in RSSI beyond the threshold (\(\Delta P_{th}\)) within a 5-min period. We calculated the probability of misalignment and realignment as functions of the maximum wind speed within the period. The algorithm searches the data to find the mis- and re-alignment events in each 5-min period where \(\Delta RSSI\geq\Delta P_{th}\), while rain rate remains below \(R_{\text{th}}\) within the same period. \(R_{\text{th}}\) is the rain rate in mm/h, averaged over 1 min, such that the attenuation caused solely by rain is equal to \(\Delta P_{th}\) according to ITU-R models. The exclusion of events where the rain rate exceeded the threshold is to ensure that the RSSI shifts were caused by wind gusts.  
Figure 16: Expected attenuations as functions of the link distance under (**a**) low rain conditions and (**b**) high rain conditions.  
As seen in Figure 17a,b, some instances of wind gusts worsen the antenna alignment (misalignment events), while the other instances improve the antenna alignment (realignment events). For a fixed value of RSSI shift threshold \(\Delta P_{th}\) (1 dB, 1.5 dB, 3 dB or 5 dB), the probability of misalignment events increases with the maximum wind speed, as shown in Figure 18. However, no RSSI shifts of more than 5 dB threshold were observed. Hence, mechanical realignment may be required following strong wind.  
Figure 17: Examples of misalignment and realignment events caused by gust of wind causing either an increase or decrease in RSSI. (**a**) Examples of misalignment events, only RSSI drops while the rain rate is below \(R_{\text{th}}\) are counted. (**b**) Examples of realignment events, only RSSI increases while the rain rate is below \(R_{\text{th}}\) are counted.
## 2 Results and Discussion  
### GC-MS-Based Metabolite Profiles  
To understand the volatile compound composition of _C. sinensis_, the volatiles in different tissues including, the bud, the first leaf (FL), the second leaf (SL), mature leaf (ML) and stem of tea plants were examined using gas chromatography-mass spectrometry (GC-MS). As shown in Tables 1 and 2, 41 terpenoids including, 26 monoterpenes (17 acyclic, six monocyclic and three bicyclic monoterpenes), 14 sesquiterpenes (three acyclic, five monocyclic, four bicyclic and two tricyclic) and one triterpene squalene were identified. Of the 41 identified compounds, 25 were verified based on the authentic standards, the others were identified via their retention index constructed by even-numbered chain n-alkenes (C10-C40) and mass spectrum. The metabolites were differentially produced by stem and leaves of different developing stages, and some metabolites can only be detected in certain tissues at specific positions. The terpenoid metabolites separated by GC-MS were further used for principle component analysis (PCA) and clustering analysis. It can be seen from the PCA plot (Supplemental Figure S1) that the accumulation patterns of metabolites from five different tissues could be separated. Moreover, a heatmap coupled with clustering analysis was generated to further understand metabolic differentiation in the five tissues (Figure 2). The compounds from bud and FL were clustered together, closer to SL, and separated from the stem and ML, which was consistent with the PCA results indicated that the patterns of metabolite profiles are affected by the tissue and developmental stage in the tea plants. In most cases, the tissue accumulated terpen are consistent with the expression of the _TPS_ gene [21]. According to the heatmap, farnesene exists in the bud and first leaves, consistent with aphids attack. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sesquiiterpenenes(14)** & **Chemical Formulas** & **Tissues** & **Types and Number** \\ \hline \((Z)\)-Nerolidol * & C15H26O & All tissues & \\ \(\alpha\)-Farnesene * & C15H24 & All tissues & Acyclic(3) \\ \((E)\)-Nerolidol * & C15H26O & All tissues & \\ \hline \((E)\)-Farnesene epoxide * & C15H24O & Bud, FL, SL, Stem & \\ Humulene * & C15H24 & Bud, FL, SL, Stem & \\ \(\alpha\)-ionone * & C13H20O & ML, Stem & Monocyclic(5) \\ \(\beta\)-ionone * & C13H20O & All tissues & \\ \hline \((E)\)-\(\gamma\)-Bisabolene * & C15H24 & Bud, FL, Stem & \\ \(trans\)-Bergamotene & C15H24 & All tissues & \\ \(\alpha\)-Cadinol & C15H26O & Bud, FL, SL, Stem & \\ \(T\)-Cadinol * & C15H26O & Bud, FL, SL, Stem & \\ Caryophyllene * & C15H24 & Bud, FL, SL, Stem & \\ \hline \(\alpha\)-Cubebene & C15H24 & All tissues & Tricyclic(2) \\ \(\alpha\)-Copaene & C15H24 & Bud, FL, SL, Stem & \\ \hline Squalene & C30H50 & Bud, FL & Tricperone(1) \\ \hline \hline \end{tabular} Note: * were identified using authentic standard compounds.  
\end{table} 
Table 2: Sesquiterpenes and triterpenenes detected in tea plants.
## 3 Results and Discussion
### Tortuosity Comparison Across Streamline  
The streamlined geometric tortuosity values in Figure 6 were used as a reference (ground truth) and plotted on the \(y\)-axis against various tortuosity estimates obtained from alternative methods.  
The tortuosity values based on the streamline method range from 1.0 to 2.5, generally reflecting the complexity of fluid flow paths within the rock pores. Notably, the Dijkstra method and the semiempirical approach formulated in Equation (17) are consistent with the streamline values, with most data points closely distributed around the 1:1 reference line, depicted by the solid line in Figure 6. This indicates that both approaches can accurately represent the actual flow paths in porous media.  
In contrast, the tortuosity estimates derived from Equation (13) significantly deviated from the ground truth, with widely scattered values reaching 5.0, suggesting a high sensitivity to the input parameters used in that formulation. Specifically, Equation (13) relies solely on the effective porosity of the rock samples, which appears insufficient to capture the geometric and topological complexities of the pore pathways in heterogeneous porous media. By comparison, the formulation in Equation (17) is more comprehensive, integrating several key microstructural parameters known to influence transport behavior.  
These parameters include effective porosity, the average coordination number \(Z_{avg}\), the maximum coordination number relative to the average (\(Z_{max}\)), and the minimum throat length (\(l_{min}\)) within the pore network. Following the approach proposed by [PERSON] et al., the empirical coefficient C is set to \(0.85\times l_{min}\)[44] to align the estimation with local geometric characteristics represented by the minimum throat length, which physically corresponds to the lower bound of flow resistance within the rock pores. Overall, these results underscore the importance of a multiparametric approach for accurately estimating Dijkstra tortuosity. Incorporating additional pore network structural information enhances prediction accuracy, ultimately providing a more realistic depiction of fluid flow dynamics within complex porous media. This is especially relevant at the microscopic scale, which is critical for transport studies in geological systems and reservoir engineering.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 3 Methods and Materials
### Base Neural Network  
The use of recurrent neural networks is proposed to obtain temporal and spatio-temporal predictions, that is, to determine the possible values that a variable can obtain at a certain number of hours ahead, or/and at a certain spatial point. To achieve these predictions, the recurrent neural networks learn through historical information, generating relationships between the delivered variables. For this particular case, it is very important to study how the prediction of meteorological and pollutant variables are directly related to the values of these variables in a time \(t-1\). The pre-processing of data will allow one to choose which variables will be used in the models.  
We propose the use of long short term memory (LSTM) [29], which has been established as an efficient and scalable model for various types of problems. For this investigation, the LSTM network had additional time distributed in which it not only related the values recorded in a single time \(t-1\) but also generated a relationship between all the inputs \(t-1\). For example, to obtain the value at time \(t\), it depends on the observations recorded at time \(t-1\), \(t-1^{\prime}\), \(t-1^{\prime\prime}\), where each registered value corresponds to a different spatial site but which also influences the prediction of the time variable \(t\).  
In Figure 2, we can see an operating scheme of the recurrent neural network LSTM. In a simplified way, an LSTM neural network assumes a multidimensional input \(x_{t}\) that interacts with the previous network state \(r_{t-1}\), where it first passes through the input gate having \(i_{t}\) as output, which enters the memory cell that is affected by the forget gate output given by \(f_{t}\) in order to generate the memory cell output \(c_{t}\). This, in turn, is fed to the hidden state variable \(h\), which passes by the output gate originating the output variable \(o_{t}\). Finally, the output \(o_{t}\) in conjunction with the hidden state of the network \(h\) determines the current state of the network \(r_{t}\), which eventually can feed back the previous gates a predefined number of times. This recurrent network is based on sequential processing of information, where particularly long-term information can be controlled through the forgetting gate.  
Figure 1: Temporal prediction vs. spatio-temporal prediction. Green points indicate the sites of monitoring stations where historical information (pollution and meteorological variables) is available, \(p(\mathbf{s}_{1},t-m),\ldots,p(\mathbf{s}_{1},t)\) for a target point \(\mathbf{s}_{1}\), and temporal predictions at time \(t+1,\ldots,t+n\) can be made at the same points. The red triangle indicates a point where the is not a monitoring station. In this case, the proposed model uses the past (temporal and spatial) information of the other stations for providing the spatio-temporal prediction \(p(\mathbf{s}_{0},t+1),\ldots,p(\mathbf{s}_{0},t+n)\).
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 4 Simulation and Result  
In this section, to evaluate the performance of the proposed algorithm, RJ-RRT is compared with Basic-RRT and RRV through a series of experiments. Simulation experiments were tested in three groups of different environments. As shown in Figure 7, three representative maps are designed in two-dimensional space: complex environment (containing many obstacles), narrow passage, and bug trap environment. The map size of each environment is \(10^{\bullet}10\). The initial configuration in the first two groups is \([0.5,0.5]\), the last group's initial configuration is \([2,3.5]\), and the goal configuration uniformly is \([9.8,9.8]\). The black area represents obstacles, the green line represents the expansion state of the random tree, and the red line represents the final planned path. Each set of experiments is performed 50 times to collect experimental data. In order to clearly describe the differences in the per  
Figure 6: Schematic of SubTree expansion.  
formance of different algorithms, the average and maximum and minimum execution times of different algorithms in 50 experiments were calculated, and the number of generated nodes was used to reflect the memory usage. The number of collision detections for RRV and RJ-RRT includes additional nodes required for environmental judgment. All-time units are seconds. All algorithms use Gilbert-Johnson-Keerthi (GJK) [30] for collision detection, and the maximum number of iterations is 50,000. To speed up the planning, the three algorithms uniformly set the sampling probability in the goal space as 0.1. The expand size in the first two sets of experiments was 0.1. In our RJ-RRT, R1 is set to 0.5, N1 is set to 15, R2 is set to 0.7, L1 is set to 3, L2 is set to 1.5, and D1 and D2 are set to 0.7 and 0.5, respectively. All experiments were run on Windows 10 with Intel I7-7700, 3.6 Ghz 8 GB RAM processor, and using MATLAB 2019b as a software platform.
# Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems
## 3 The Proposed Hybrid-Based Analysis for Ransomware Detection  
This section presents the proposed hybrid Android ransomware detection system. This system utilizes both static and dynamic analyses to ensure efficient ransomware detection with high accuracy. The hybrid system starts running the static analysis first, as the dynamic analysis is more critical, complex, and time consuming. Based on the static analysis results, it is decided whether to proceed with the dynamic analysis or not. More than 70 well-known antivirus engines execute the static analysis. During this type of analysis, different features are extracted and saved. Moreover, the percentage of engines that classify the app as Ransomware is calculated. The app is forwarded to the dynamic analysis based on a specific threshold that the system admin can choose, considering the number or/and the types of antivirus engines. During the dynamic analysis, the app runs on a virtual or isolated environment, such as a sandbox, to check its behavior and functionalities and confirm whether the app is ransomware. At the same time, several features are extracted and later compared with the ones generated from the static analysis. All features resulted  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Year** & **Static** & **Dynamic** & **Features Used** & **Available** \\ \hline
[17] & 2015 & ✓ & & Natural Language Processing (NLP) features. & ✓ \\ \hline
from both types of analysis feed the construction of a dataset that can be used for further analysis by different ML (machine learning) or DL (deep learning) techniques. These techniques can build predictive models to be integrated with the ransomware detection systems (RDS) to detect ransomware apps with high accuracy. The proposal is that only the app's information is sent from the Android device user to the RDS. If this app was analyzed before, then the analysis result is transferred directly to the user. If not, the app is examined on the cloud (statically and dynamically), and then the analysis results are passed to the Android device user. Moreover, the apps' database is updated at the RDS server-side.  
Figure 1 shows the overall flow of the proposed hybrid detection system. The details of the static and dynamic phases are elaborated in the following subsections.
## 2 Enhancement Methods for High-Resolution SAR Ship Wake Processing
### Evaluation Method for Enhanced Ship Wake Detection Processing  
The Radon transform and its various improved algorithms have long been significant methods for detecting ship wakes in spaceborne SAR, receiving extensive attention from scholars worldwide. The detection method for SAR wakes can be summarized as identifying wakes based on the energy difference between wake targets and sea surface backgrounds. Therefore, this paper evaluates the effectiveness of wake enhancement processing using the Radon algorithm, comparing the changes in wake linear features in the Radon transform domain before and after enhancement to verify the effectiveness of the enhancement algorithm.  
The Radon transform is an integral transform that projects the image in space, mapping all pixels in the same direction to the Radon transform domain. The definition of the two-dimensional Radon transform on the plane is as follows [6]:  
\[R(\theta,\rho)=\iint\limits_{D-M}f(x,y)*\delta(\rho-x\cos\theta-y\sin\theta)dxdy, \tag{5}\]  
Here, \(D\) represents the entire image plane range, \(M\) is the mask, \(f(x,y)\) represents the grayscale value of each pixel, \(\delta(\cdot)\) is the Dirac function, \(\rho\) is the intercept from the origin to the line, and \(\theta\) is the angle between the normal direction of the line and the \(x\)-axis.  
In SAR images, various wakes have grayscale values that differ from the background. The linear features in the original image appear as points in the transform domain. This processing result transforms narrow V wakes and Kelvin wakes in the original image into bright points, while darker turbulent wakes become dark points. Thus, the wake detection problem in SAR images is transformed into the detection of peak/valley points in the transform domain, simplifying the processing process and improving detection accuracy.  
For peak detection in wake detection, the search area is typically limited, and a reasonable interval is selected based on the wake direction. A sliding window approach is used, with window size determined by the size of the original image. Setting the window size too small introduces false peak points, while setting it too large may miss true wake peak points. The difference between the detected pixel and the mean of all pixels in each window in the Radon transform domain is calculated, and a threshold is used to determine whether it is a peak value. The threshold can be set to twice the image variance. By traversing the entire image, all peak sequences are obtained, and the corresponding \((\rho,\theta,A_{P})\) represent distance, angle, and peak amplitude. By analyzing the differences in the Radon transform domain before and after enhancement, the effectiveness of the wake enhancement algorithm can be quantitatively assessed, demonstrating its ability to improve the detection of ship wakes in high-resolution spaceborne SAR images.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 5 Threat Analysis and Risk Assessment
### Mitigation Strategies  
A combination of technical, organizational, and procedural measures is necessary to mitigate the identified threats. The DMS must implement strong authentication mechanisms to prevent Spoofing. Multi-factor authentication (MFA) and biometric verification ensure that only legitimate users and components can access the system, thereby reducing the risk of impersonation. These methods are crucial for protecting sensitive data and ensuring only authorized entities interact with the system.  
Tampering, which involves the unauthorized modification of data or system components, can be addressed through robust cryptographic measures. All data in transit and at rest should be encrypted using advanced encryption protocols, ensuring that they cannot be altered or misused even if the data are intercepted. Additionally, tamper-resistant hardware can protect critical components such as sensors and processors, making it significantly more difficult for attackers to modify the system's behavior.  
To safeguard against Information Disclosure, all sensitive data must be encrypted during transmission and stored within the system. This prevents unauthorized access and ensures that only those with the appropriate credentials can retrieve sensitive driver and vehicle data. Furthermore, implementing strict access control measures ensures that only authorized users are granted access to specific system components or data, limiting the risk of exposure.  
DoS attacks, which seek to disrupt the system's availability by overwhelming it with malicious requests, can be mitigated through rate-limiting and redundancy strategies. By applying rate-limiting mechanisms, the system can prevent excessive requests from overloading the system. Additionally, the system architecture should incorporate failover mechanisms and redundant components to ensure continued operation in case of an attack, thus maintaining DMS availability and reliability.  
Finally, role-based access control (RBAC) and the principle of least privilege should be enforced across the system to prevent the Elevation of Privilege. Each user and process must only have access to the resources and functions necessary for their roles. This limits the potential damage an attacker can cause if they gain unauthorized access to the system, as their privileges would be restricted to a minimal set of actions.  
Together, these strategies form a comprehensive approach to securing the DMS against the full range of identified threats, ensuring the system operates reliably and securely.
## 2 Literature Review and Hypothesis Development
### The Theory of Planned Behavior (TPB)  
[PERSON] and [PERSON] [61] first suggested the theory of reasoned action (TRA), and [PERSON] [18] expanded this theory to the TPB. The TRA explains the individuals' volitional behavior with two predictors, which include attitude and subjective norm [62; 63]. The concept of attitude (AT) is the degree of the positive or negative evaluation of a particular object/behavior, which is explained above [18]. Subjective norm (SN) is defined as \"the perceived social pressure to perform or not to perform a behavior\" [18] (p. 188). The TPB suggests the concept of perceived behavioral control (PBC) as a predictor of behavioral intentions (BI) in addition to AT and SN. This concept is the degree of ease/difficulty in regard to performing a specific action, and this explains an individuals' non-volitional behavior [18]. In summary, the TPB explains the causal effects of the three variables, which include AT, SN, and PBC, on an individual's behavioral intention.  
Previous studies adopted the TPB and successfully investigated consumer behavior in the context of new technology-based services, such as delivery drones/robots and robotic restaurants [64; 65; 66]. For instance, [PERSON] et al. [64] discovered that AT, SN, and PBC positively affect behavioral intentions in the context of robotic restaurants. [PERSON] et al. [65] also proved the effects of AT, SN, and PBC on behavioral intentions in the context of drone delivery services. [PERSON] et al. [66] investigated the consumers' acceptance behavior of autonomous delivery robots in urban cities. Their study indicated that AT, SN, and PBC play a crucial role in regard to forming the consumers' behavioral intentions. The current study hypothesized three causal relationships in the TPB context.  
**Hypothesis 2 (H2)**.: _AT positively affects BI._  
**Hypothesis 3 (H3)**.: _SN positively affects BI._  
**Hypothesis 4 (H4)**.: _PBC positively affects BI._  
The extant studies also suggested and proved the causal effect of SN on AT because other people's perceptions of a specific behavior can affect an individual's evaluation of a behavior [67; 68; 69]. [PERSON] and [PERSON] [70] also emphasized the important role of SN in the context of the technology acceptance behavior, and they proved the causal effect of SN on AT towards using new technology. In other words, if individuals perceive that it is socially acceptable for others to adopt new technologies, they tend to have a favorable AT towards using the new technologies themselves. The current study hypothesized the effect of SN on AT, which is based on the discussions above.  
**Hypothesis 5 (H5)**.: _SN positively affects AT._