esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Interconversion Relationship between Time-Domain and Frequency-Domain Functions
#### 2.2.1 Interconversion from Dynamic Modulus to Relaxation Modulus  
The dynamic modulus and relaxation modulus sheared same Prony series coefficients, thus, the Prony series coefficients of relaxation modulus can be directly acquired by fitting master curve of dynamic modulus. Direct fitting of dynamic modulus is extremely difficult since the equation is the sum of storage modulus and loss modulus which cannot be directly expressed. Fitting dynamic modulus includes fitting the two components of dynamic modulus: Storage modulus and loss modulus. The Prony series by fitting either storage modulus or loss modulus can be used as the Prony series for dynamic modulus since dynamic modulus, storage modulus and loss modulus shared the same Prony series as shown in Equations (1) and (11). However, as shown in Equation (11), the fitting process of storage modulus can give the result of \(Ee\) which is a required parameter to calculate relaxation modulus in Equation (3), whereas the fitting process of loss modulus cannot. Thus, the fitting of storage modulus was applied to obtain the Prony series of dynamic modulus. Here, the storage modulus in viscoelastic materials measures the stored energy, representing the elastic portion, and the loss modulus measures the energy dissipated as heat, representing the viscous portion. Equation (10) shows the relationship between complex modulus, storage modulus, and loss modulus.  
\[E^{*}=E^{\prime}+iE^{\prime\prime} \tag{10}\]  
In Equation (10), \(E^{\prime}\) is the storage modulus; \(E^{\prime}\) is the loss modulus.  
According to the representation of complex modulus in Equation (1), the storage modulus and loss modulus can be found as:  
\[E^{\prime}(\omega)=E_{e}+\sum_{i=1}^{m}\frac{\omega^{2}\rho_{i}^{2}E_{i}}{ \omega^{2}\rho_{i}^{2}+1},\ E^{\prime\prime}\left(\omega\right)=\sum_{i=1}^{m} \frac{\omega\rho_{i}E_{i}}{\omega^{2}\rho_{i}^{2}+1} \tag{11}\]  
Therefore, the procedure of interconversion from dynamic modulus to relaxation modulus is available:  
(1) Pre-smooth and fit the experimental data of dynamic modulus and phase angle;Calculate the storage modulus \(E_{t}\)\({}_{experiment}\) based on experimental data;
3. Make the storage modulus \(E_{t}\)\({}_{predict}\) determined by Equation (11) equal to the \(E_{t}\)\({}_{experiment}\) so as to obtain Prony series coefficients \(E_{e}\) and \(E_{i}\);
4. Substitute \(E_{e}\) and \(E_{i}\) into the relaxation modulus representation to plot its master curve over a wide time region.
## 5 Implementation and Results
### Occupant's Optimal Evacuation Route  
* System operation step: (1) internal structure of a building \(\rightarrow\) (2) AR-based drawing recognition \(\rightarrow\) (3) detects danger zone \(\rightarrow\) (4) select occupant's position \(\rightarrow\) (5) show a safe path guideline  
Figure 17 shows the system operation for occupants. Assuming a fire situation, the occupants quickly execute the AR-based Smart Building Disaster Management Service application and recognize the drawing of the building (building structure recognition in real situations) through the camera. The fire occurrence area is recognized by the smart sensor and displayed on the application screen based on the AR. The position of the occupants recognized by the smart sensor is displayed and its position is selected. A safe evacuation route from the selected occupant location to the exit is recommended and displayed on the smart pad screen.  
Figure 14: AR-based fire situation guideline screen UI: (**a**,**b**) AR based Safe Path Guideline UI; (**c**) Recognition of Dangerous Area; (**d**) Room Information View.
## 3 Security and Privacy of Computing Paradigms
### Fog-Related Aspects
#### 3.3.1 Fog Data Security  
Some attacks usually threaten private and government entities since they function in Cloud, Edge, and Fog computing. To offer a level of protection to the architecture, a Threat Intelligence Platform (TIP) is important to be developed [91]. Data security is the most prioritized aspect in the industrial sector, especially as information must be safeguarded. Intelligent equipment and sensor devices are deployed to reduce threats and security attacks extensively. The feature about heterogeneity and geographical sharing impacts the implementation of Cloud security frameworks into Fog computing systems [5]. Some of the considered security challenges are confidentiality, authentication, availability, and information privacy. These mentioned frameworks assist in creating and monitoring accesses to persons and organizations.  
Considering the medical field, we see that patients' health history involves classified information and the Fog architecture has several nodes that might present some vulnerabilities. These vulnerabilities can be unpermitted access to information when stored or at the time of transfer, untrustworthy insiders, and during system distribution of information. Fog system by means of cable or wireless network consistently receives information transferred from sensors of medical devices. Tampering with patients' personal data, integrity, and device availability is obvious and can occur when communication systems and sensors are targeted. Some through channels as Denial of Service (DoS) can easily be perpetrated due to the vulnerabilities found in wireless networks. On the other hand, the absence of proper frameworks to control access to the Fog nodes that process important information can compromise information through leakage because of account theft, unpermitted access, and possibly some unsafe passage. The mentioned problems can be mitigated through thorough analysis and stringent rules and regulations to establish standard control mechanisms such as personal systems, selective (limited) encryption, and reciprocated authentication [92].  
Overall, Fog provides Edge-like challenges while bridging those even more towards the decentralized and distributed environment.
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Survival Analysis
#### 3.1.3 Survival Trees  
Survival trees (ST) are decision trees that are specifically designed for analyzing time-to-event data. In the context of survival analysis, the goal of building a survival tree is toidentify subgroups of instances which differ in their risk of experiencing the event, based on their baseline characteristics [25].  
The basic idea of a survival tree is to recursively partition the sample into increasingly homogeneous subgroups with respect to the event of interest. The partitioning is achieved by repeatedly splitting the data based on the values of one or more predictor variables, such that the within-node heterogeneity in terms of the survival outcomes is minimized [25]. At each step of partitioning, the algorithm selects the variable and the split point that maximally differentiates the survival outcomes of the subgroups defined by the split. This process continues until no further improvement in within-node homogeneity is achieved or a stopping criterion is met. The result is a tree structure where each terminal node represents a distinct subgroup with a unique risk profile for the event of interest.  
Once the tree is built, it needs to be pruned to prevent overfitting and improve its generalizability to new data. The most used method for pruning is cost complexity pruning, which involves adding a penalty term to the impurity measure to favor simpler trees that avoid overfitting. Other methods, such as cross-validation or resampling techniques, can also be used for pruning.  
Survival trees have several advantages over traditional regression models, including their ability to handle nonlinear and interactive effects, identify distinct subgroups with different risk profiles, and provide easily interpretable results in the form of a decision tree. However, they also have some limitations, such as their tendency to create overfit trees, sensitivity to the choice of split criterion and stopping rule, and inability to handle time-varying covariates or competing risks.
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## Appendix A Appendix A  
### Appendix A.1  
Appendix A.1 details the optimization of linear antenna array radiation patterns using the Taguchi method. This optimization is applied to an array with ten elements, achieving a radiation ratio (_rr_) of 0.9 and a sidelobe level (\(SLL\)) of \(-20\) dB. The parameters optimized to minimize sidelobes while maximizing the main lobe gain include the excitation amplitudes and phases of the array elements. The results show significant performance improvements in the antenna array, demonstrating the effectiveness of the Taguchi method for this application.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### PDR Positioning  
Nowadays, most smartphones have various built-in sensors, such as accelerometers, magnetometers, gyroscopes, light sensors, microphones, barometers, etc. These sensors can measure acceleration, orientation, and various environmental conditions. Among these sensors, the IMU sensor, consisting of an accelerometer, magnetometer, and gyroscope, can be used to perform PDR. PDR is a relative navigation technique that uses IMU sensor measurements to estimate the current position based on the previous position by calculating the distance and the heading angle. Concretely, the position \(\mathbf{x}_{k+1}=\left[x_{k+1},y_{k+1}\right]^{\mathrm{T}}\) can be real-time updated by adding an amount of displacement to the previous position \(\mathbf{x}_{k}=[x_{k},y_{k}]^{\mathrm{T}}\):  
\[\begin{bmatrix}x_{k+1}\\ y_{k+1}\end{bmatrix}=\begin{bmatrix}x_{k}\\ y_{k}\end{bmatrix}+l_{k}\begin{bmatrix}\cos\varphi_{k}\\ \sin\varphi_{k}\end{bmatrix}, \tag{5}\]  
where \(l_{k}\) is the distance traveled on step \(k\) and \(\varphi_{k}\) is corresponding heading direction. In the following, we discuss the process in detail.
## 4 Proposed Approach and Experiments
### Performance Metrics  
This study uses various metrics to evaluate the performance of malware detection models. Accuracy (Equation (17)) determines the overall success of the model. At the same time, Recall (Equation (18)) and (Equation (19)) Precision metrics are used to measure how much malware was correctly detected and how much of the detected malware was malicious, respectively. F-measure (Equation (20)) is a measure obtained by combining recall and precision in a balanced manner, indicating the balanced success of the model. [PERSON]'s Kappa (Equation (21)) evaluates the reliability of the model against random guesses. At the same time, Mean Absolute Error (MAE) (Equation (22)) is used to measure how much the model's predictions deviate from the true values. Using these metrics together provides a more comprehensive assessment of the model's performance in malware detection.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{17}\]  
Figure 4: Overall design of the proposed framework.  
True Positive (\(TP\)) are true positive predictions and are cases where the model correctly detects malware; False Negative (\(FN\)) are cases where the model misses the malware, i.e., does not detect it despite being malicious; True Negative (\(TN\)) are cases where the model correctly classifies a benign software as benign; and False Positive (\(FP\)) are cases where a benign software is incorrectly classified as malicious.  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{18}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{19}\]  
\[\text{F-Measure}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{20}\]  
\[\text{Cohen's Kappa}\left(\kappa\right)=\frac{P_{0}-P_{e}}{1-P_{e}} \tag{21}\]  
\(P_{0}\) represents the observed agreement between raters or classifiers. \(P_{e}\) represents the expected agreement between raters or classifiers based on chance. \(\kappa=1\) indicates perfect agreement between raters or classifiers. \(\kappa=0\) indicates agreement equivalent to what would be expected by chance. \(\kappa<0\) indicates agreement worse than what would be expected by chance.  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y}_{i}\right| \tag{22}\]  
where \(N\) is the total number of samples, \(y_{j}\) is the actual value and \(\hat{y}_{j}\) is the value predicted by the model.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 4 Analysis and Discussion  
The precise position details of the customer must be secured from unwanted individuals. The history of the consumer including the geographic details of the existing and historical positions of the consumer, including points of interest, shall not be released to unauthorized persons. In Vehicular Ad-hoc Networks (VANETs) it is possible and a great problem for vehicles to be tracked while in transmission with other vehicles or during communication with road-side infrastructure which can become a dangerous situation, this type of threats can lead to location privacy of vehicles users. Table 4 shows VANETs location privacy schemes used in different papers.  
The following conclusions can be drawn from the analysis of these state-of-the-art, discussed above.  
* The choice of the best location privacy technology depends largely on the standard and type of service and the computing resources available on the VANETs node.
* Obfuscation is a good choice if the sponsored LBS is for a personalized, user-specific history-driven recommendation service.
* If you need an accurate LBS service, then choosing a group-based or group-mixed approach may produce the desired results.
* As far as mixed party strategies are concerned, group-based authentication is preferred for VANETs' low-traffic or comparable speed and traffic trends as seen on highways.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Ref** & **Computation Overhead** & **Communication Overhead** \\ \hline
[81] & Medium & \(\times\) \\ \hline
[82] & High & High \\ \hline
[83] & Medium & Medium \\ \hline
[84] & \(\times\) & \(\times\) \\ \hline
[85] & Low & \(\times\) \\ \hline
[86] & Low & Medium \\ \hline
[87] & Low & High \\ \hline
[88] & Medium & High \\ \hline
[89] & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 4: VANETs location privacy schemes used in different papers.  
* Due to frequent changes in pseudonyms, mixed group authentication schemes introduce more overhead computation relative to community-dependent schemes.  
Location privacy schemes can be classified into two types, the disturbance of ID (anonymization) and the disturbance of position (obfuscation). Both approaches are similar from a security and operational perspective, as location privacy can be increased at the expense of service quality. However, their implementation is very different. Anonymization focuses primarily on protecting the identity of the user by protecting the Mapping between users and locations monitored. User mappings are switched on and off to inhibit and disable statistical analysis. The statistical analysis in networks uses a continuous time series that may match the user's previous history. On the other side, obfuscation-based techniques ensure the secrecy of locations by intentionally sharing incorrect knowledge of the location over the network.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 5 Enhanced SCAFFOLD Protocol
Performance metrics quantify the security, overhead, and efficacy to facilitate analysis under different scenarios. By comprehensively addressing IoT threat vectors, the Enhanced SCAFFOLD protocol represents a systematic, expandable framework for end-to-end security in modern and emerging IoT systems. Random forests (RF), support vector machines (SVM), and recurrent neural networks (RNN) are used in the Enhanced SCAFFOLD framework to identify IoT network traffic abnormalities and threats. Ensemble learning improves detection robustness and accuracy by combining several classifier predictions [1]. Random forests, an ensemble of decision trees, prevent overfitting and increase generalization via feature randomization and bagging [2]. SVMs find suitable hyperplanes in high-dimensional feature spaces to distinguish anomalous and normal traffic [3]. Temporal correlations in network traffic and attack patterns may be detected using LSTM RNNs [4]. Enhanced SCAFFOLD integrates several classifiers' complementing properties to detect DDoS, MITM, reconnaissance, and malware. RF and SVM excel at finding spatial abnormalities, while RNNs catch seasonal patterns. The method also employs correlation analysis to find negative communication flows for the company. This simplifies identifying coordinated attacks and the security events' causes. By examining the links between the payload features, protocols, and source and destination IP addresses, Enhanced SCAFFOLD may improve attack pattern knowledge and mitigation tactics [5].
## 3 Results
### Spatiotemporal Patterns of Forest Biophysical Differences in Disturbance and Non-Disturbance  
Both \(\Delta\) NDVI and \(\Delta\) LAI exhibited an overall increasing trend from 1986 to 2016, displaying similar temporal patterns within the 1986-2000 and 2001-2016 sub-periods (Figure 2). \(\Delta\) NDVI and \(\Delta\) LAI showed a significant increase (\(p\) < 0.05) in the former period, with slopes of 0.003 and 0.034, respectively. While, their trends stabilized during the 2001-2016 period, showing no statistically significant change (\(p\) > 0.05) (Figure 2a,c). Although \(\Delta\) GPP also increased overall (from \(-\)1.93 \(\pm\) 9.5 g C m\({}^{-2}\) in 1986 to 1.72 \(\pm\) 6.41 g C m\({}^{-2}\) in 2016), no significant changes (\(p\) > 0.05) were detected within either sub-period (Figure S4).  
In contrast, \(\Delta\) Albedo and \(\Delta\) ET demonstrated opposing dynamics between 1986 and 2016 (Figure 3). \(\Delta\) Albedo showed a significant overall decrease, declining from 0.02 \(\pm\) 0.01 in 1986 to \(-\)0.01 \(\pm\) 0.01 in 2016 (Figure 3a). This decrease became more pronounced during 2001-2016 (slope = \(-\)0.001) compared to 1986-2000 (slope = \(-\)0.0002). Conversely, \(\Delta\) ET exhibited a significant overall increase, rising from \(-\)27.66 \(\pm\) 27.42 mm in 1986 to \(35.65\pm\) 42.01 mm in 2016 (Figure 3c). This increase was substantial and statistically significant (\(p\) < 0.05) between 1986 and 2000 (slope = 1.937), after which no significant change (\(p\) > 0.05) was observed (post-2001).  
Figure 2: Spatiotemporal patterns of \(\Delta\) NDVI and \(\Delta\) LAI in Europe between 1986 and 2016. (**a**) \(\Delta\) NDVI time series. (**b**) Spatial patterns of \(\Delta_{\rm s}\) NDVI. (**c**) \(\Delta\) LAI time series. (**d**) Spatial patterns of \(\Delta_{\rm s}\) LAI. Orange and green dashed lines in (**a,c**) represent linear regression lines fitted to data for 1986–2000 and 2001–2016 periods, respectively.  
The \(\Delta_{\rm s}\) NDVI, \(\Delta_{\rm s}\) LAI, \(\Delta_{\rm s}\) Albedo, and \(\Delta_{\rm s}\) ET of the 35 European countries were not spatially homogeneous (Figures 2b,d and 3b,d). The countries with larger \(\Delta_{\rm s}\) NDVI values are primarily located in the central and southern regions (Figure S3c). The countries with larger \(\Delta_{\rm s}\) LAI values are situated in the western region (Figure S3d). The countries with larger \(\Delta_{\rm s}\) Albedo values are situated in the central and southern regions (Figure S3e). The regions with larger \(\Delta_{\rm s}\) ET values are situated in the southeastern region (Figure S3f). The mean value of the \(\Delta\) Albedo for all the countries was negative (\(-\)0.01), and the others were positive: \(\Delta_{\rm s}\) NDVI (0.04), \(\Delta_{\rm s}\) LAI (0.30), and \(\Delta_{\rm s}\) ET (33.74 mm) (Figure S5; Table S1).
## 3 Results
### Pre-Flowering  
Pre-Flowering Stage Plant Growth Metrics (Data Collection 3)  
Statistical differences were observed in both pre-flowering height and diameter. Plants displayed an increase in plant height with increasing P concentrations, with the greatest plant height being observed at the highest concentrations. The shortest plant height was observed at the lowest concentration and was 18.7% smaller than the largest plants (Table 5). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**P Fertility**} & \multicolumn{3}{c}{**Iron \({}^{\gamma}\)**} & \multicolumn{3}{c}{**Manganese \({}^{\gamma}\)**} & \multicolumn{3}{c}{**Zinc \({}^{\gamma}\)**} & \multicolumn{3}{c}{**Copper \({}^{\gamma}\)**} & \multicolumn{3}{c}{**Boron \({}^{\gamma}\)**} \\ \cline{2-10}  & **Mean** & **SD \({}^{\star}\)** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
3.75 & 93.85 & 6.45 & 159.50 & 35.29 & 43.33 & 8.44 & 5.04A & 0.96 & 99.29 & 11.64 \\  & AB & 10.00 & 102.44 & 35.37 & 42.96 & 6.76 & 3.88 & 0.44 & 87.65 B & 10.61 \\
7.50 & 101.82 & 10.00 & B & 35.37 & A & 6.76 & 3.88 & 0.44 & 87.65 B & 10.61 \\
11.25 & 94.51 & 3.02 & 78.61 B & 19.54 & 41.28 & 4.22 & 4.22 & 4.22 & 0.50 & 72.13 C & 7.94 \\  & AB & 10.00 & 107.67 B & 17.57 & 40.53 & 4.18 & 2.91 C & 0.78 & 63.17 & 6.82 \\
15.0 & 89.01 B & 6.71 & 70.67 B & 17.57 & ABC & 4.18 & 2.91 C & 0.78 & 63.17 & 6.82 \\  & & & & & & & & & CD & \\ \hline
22.50 & 76.30 C & 3.72 & 80.74 B & 13.75 & 33.86 C & 3.23 & 3.48 & 0.73 & 60.43 & 4.19 \\  & & & & & & & & BC & & D & \\ \hline
30.0 & 72.20 C & 5.57 & 82.05 B & 31.67 & 34.39 & 3.24 & 3.10 C & 0.88 & 57.13 & 3.88 \\  & & & & & & & & & & \\ \hline Significance \({}^{\rm w}\) & *** & *** & *** & *** & *** & *** & *** \\ \hline \hline \end{tabular} \({}^{\rm x}\) P fertility concentrations based on mg-L\({}^{-1}\). \({}^{\rm y}\) All micronutrient concentrations are listed as ppm or mg-kg\({}^{-1}\). \({}^{\rm x}\) All standard deviation (SD) values assumed to be \(\pm\) of the given value. \({}^{\rm w}\), \({}^{\star}\), or *** indicate statistically significant differences between sample means based on _F_-test (proc GLM) at \(p\leq 0.05\), \(p\leq 0.01\), or \(p\leq 0.001\), respectively. NS (not significant) indicates the _F_-test difference between sample means was \(p>0.05\). Values with the same letter indicate a lack of statistical significance while values with different letters indicate statistically significant results.  
\end{table} 
Table 4: The impacts of phosphorus (P) fertility on micronutrient accumulation in leaf tissue of _C. sativa_ “BaOx” after eight weeks of vegetative growth.
## 3 MG Communication Protocols and Standards
### IEEE Standards  
#### 3.2.1 IEEE 1547  
IEC 1547 is a series of standards facilitating operation, control, monitoring, and integration of MG for utility networking for the provision of AS. The main drawback of this standard is the detail of establishing communication of MG with the supervisory level of the power system, i.e., TSO/DSO is unclear [43; 44].