esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Feasibility and Evaluation
### Development and Deployment  
Besides some component engineering, the EDAS development involves a technology integration process. It utilizes the thing event framework included in almost all mature applications and devices as a logging, troubleshooting and debugging facility. However, local adopters, which are merely a string parser and API caller, must be developed and should have read and execute permissions in order to execute the call to a specific security component against a specified application. Local adaptation can also be performed via the application where the adaptation request received is passed as arguments to override the method that ensures security. As for the variety of technology used in the IoT, a platform-independent design, e.g., Java, can be opted to develop and integrate uniform local adopters across the monitored environment.  
As most devices in IoT have low computational power and sometimes perform only specific sensing or actuation routines, the analysis burden has been taken away from the thing level to a resourceful machine (EDAS platform). Event sources can be monitored from anywhere. If the event source is addressable in the environment and if it can generate and communicate events, no matter where it is located (remote or on-site), it can be integrated with EDAS. This implies that traditional systems, such as firewalls, databases, file/application servers and other critical information systems, with built-in well-defined event frameworks, can also be integrated in and monitored by EDAS, as they have key roles in the overall service delivery.  
Component-based architectures (CBA) and event-driven architectures (EDA) have the ability that their operational components can be distributed over the network as they are loosely coupled [8; 12]. EDAS can be used as a standalone platform to monitor, analyze and adapt to the risk faced. However, it inherits the CBA and EDA concepts, and thus, its major operations can be distributed over multiple locations in different hierarchical settings. For instance, in an IoT-eHealth perspective, a city hospital \(X\) can analyze the threats related to the environment it operates in by a local EDAS risk analyzer. Same settings can be established for a city hospital \(Y\). However, a principal adaptation engine in location \(Z\) governed by a central security policy can be deployed to decide a mitigation response based on _X's_ and _Y's_ risk information to reduce the risk level in either of the domains. Such distributed settings enable security analysts to isolate and focus on a set of threats concerning a particular location context and may add more to the precision of the risk analysis process.
## 4 The Fast Second-Order Blind Identification (SOBI) Method for Diagonalization of Average Matrix
### The SOBI Method for Joint Approximation Diagonalization
#### 4.1.2 Algorithm Steps for Joint Approximate Diagonalization SOBI Method Implementation  
The implementation algorithm flow of the SOBI method is as follows:Step 1: Estimate the sample covariance matrix \(\widehat{R}(0)\) from \(T\) sample data; use \(\lambda_{1},\cdots,\lambda_{n}\) to represent the \(n\) largest eigenvalues, and have \(h_{1},\cdots,h_{n}\) as the corresponding eigenvectors. Under the assumption of white noise, the noise variance estimate \(\overline{\sigma}^{2}\) is the average of \(m\)-\(n\) minimum eigenvalues of \(\widehat{R}(0)\). In the ideal case without noise, the last \(m-n\) singular values of \(\widehat{R}(0)\) are equal to 0. Therefore, the noise variance \(\overline{\sigma}^{2}\) is 0.  
Step 2: Obtain the whitening signal \(z(t)=[z_{1}(t),\cdots,z_{n}(t)]^{T}\), where \(z_{i}(t)=(\lambda_{i}-\overline{\sigma}^{2})^{-\frac{1}{2}}h_{i}^{\star}x(t)\); \(1\leq i\leq n\). Using \(\widehat{W}=[(\lambda_{1}-\overline{\sigma}^{2})^{-\frac{1}{2}}h_{1},\cdots,( \lambda_{n}-\overline{\sigma}^{2})^{-\frac{1}{2}}h_{n}]^{H}\) forms a whitening matrix.  
Step 3: Fix a set of delays \(\tau\in\left\{\tau_{j}\middle|j=1,\cdots,K\right\}\), estimate the covariance matrix \(\widehat{R}(\tau)\) of the samples, and obtain the whitened signal \(\widehat{R}(\tau)\).  
Step 4: Get the approximate diagonalizer \(\widehat{U}\) of the set \(\left\{\widehat{R}(\tau_{j})\middle|j=1,\cdots,K\right\}.\)  
Step 5: Calculate the estimate of the source signal \(\widehat{s}(t)=\widehat{U}^{H}\widehat{W}x(t)\), and the mixed matrix \(A\) estimates \(\widehat{A}=\widehat{U}^{\star}\widehat{W}\).
## 3 Results and Discussion  
### Results of Sensory Evaluation  
#### 3.1.1 Results of Traditional Sensory Evaluation  
The sensory evaluation of tea plays a crucial role in scientifically assessing sample quality and guiding tea production [32]. The evaluation factors of the four groups of samples were comprehensively assessed, as shown in Table 1. Among the four groups, in terms of appearance, the samples of the Cl and CMI groups, which underwent a cold fermentation process, exhibited a tighter and firmer appearance, while the samples of the DI and DMI groups, which underwent a hot fermentation process, appeared looser. The color of the appearance and leaf base varied among the four groups, with the Cl and DI groups, which were not inoculated with _M. purpureus_, displaying a brownish color, in contrast to the CMI and DMI groups, which were inoculated with _M. purpureus_, showing a reddish-brown color. Studies have shown that Aspergillus can produce a variety of enzymes closely related to the theabrownin biotransformation during dark tea fermentation, such as glycosyltransferase, glycoside hydrolase, and tannase. These enzymes effectively catalyze the hydrolysis, transformation, oxidation, and biodegradation of tea polyphenols, leading to an increase in theabrownin content and a deepening of the dark tea color. This finding is consistent with the results of this study [33]. In terms of taste, the Cl and DI groups weredescribed as rich and full-bodied with a sweet aftertaste, while the CMI and DMI groups, which were inoculated with _M. purpureus_, exhibited a sweet and mellow taste with a sweet aftertaste, albeit CMI displayed a thicker and smoother taste. In terms of aroma, each of the four groups of samples possessed distinct characteristics, with the exception of an overall stale essence. Specifically, the Cl group displayed a stale aroma with medicinal notes, the CMI group had a robust woody, stale, and fruity aroma, the DI group featured a stale, floral, and fruity aroma, and the DMI group exhibited a strong stale aroma along with woody, flowery, and fruity notes, all of which were lasting. The appearance, infusion color, aroma, taste, and leaf base were scored comprehensively, with the DMI group receiving the highest score and being deemed the best in quality. These results indicate that the inoculation and fermentation of _M. purpureus_ were more conducive to producing the reddish-brown color of Liupao tea, while also enhancing taste and aroma. Samples inoculated and fermented by _M. purpureus_ exhibited a sweeter and mellow taste, intensified aroma, and enriched aroma varieties. The results are consistent with those obtained by [PERSON] et al. (2015).
## 4 Experiment Arrangement and Results
### Experiment III: Comparison of Different Data Decomposition Algorithms  
In Experiment III, the prediction performance of five models, MPSO-BPNN, EMD-MPSO-BPNN, EEMD-MPSO-BPNN, CEEMDAN-MPSO-BPNN, and CEEMDAN-VMD-MPSO-BPNN, is compared by combining the R\({}^{2}\), MAPE, RMSE, MAE, U1, and U2 evaluation indicators. It was found that the values of the above six metrics were 0.912, 8.23, 5735, 3677, 0.047, and 0.173 for the MPSO-BPNN, 0.923, 7.65, 5213, 3260, 0.044, and 0.165 for the EMD-MPSPO-BPNN, 0.929, 5.98, 4766, 2388, 0.041, and 0.156 for the EEMD-MPSO-BPNN, 0.946, 5.38, 4133, 1892, 0.033, and 0.142 for the CEEMDAN-MPSO-BPNN, and 0.978, 4.13, 3172, 2036, 0.025, and 0.113 for each of the six indicator values for the CEEMDAN-VMD-MPSO-BPNN, respectively. Taking the two dimensionless metrics of MAPE and R\({}^{2}\) as an example, Figure 6 visualizes the prediction performance of the five models. Comparing the five models, the MPSO-BPNN is found to be the least effective, and integrating data decomposition techniques with the MPSO-BPNN significantly enhances prediction accuracy compared to the standalone MPSO-BPNN model. In addition, among all the optimization algorithms, the prediction effect of the CEEMDAN-VMD-MPSO-BPNN is better than the other compared models, which indicates that CEEMDAN-VMD can eliminate the adverse effects of noise and capture the internal features of the data and, at the same time, that a two-layer data decomposition algorithm can improve the prediction accuracy of the model substantially.  
Figure 5: The diagrams of MAPE and R\({}^{2}\) for the four models.
## 1 Introduction
To do so, we _predict_ the reflectances of missing bands with nonlinear regression algorithms that use the other spectral bands as _predictors_. In order to produce results that are representative of real settings and are generalizable, we build a dataset by sampling pixels from Sentinel-2 acquisitions with a wide variety of geographic areas and dates. We therefore take an empirical, data-driven approach. We choose not to leverage the spatial and the temporal dimensions and carry out a mono-date, pixel-based analysis. We understand that temporal and spatial correlations would reduce the errors in the reconstruction of missing bands. The goal of the work is not to propose an optimal regression algorithm, but rather to show that band reconstruction is possible using regression. The results of this work can be seen as a lower bound in terms of reconstruction quality and therefore encourage the pursuit of further studies.
## 1 Introduction
The integration of fixed-position and pan-tilt-zoom video cameras has enabled advanced multi-camera analyses of human movement in snowboarding and skiing maneuvers [16, 17, 18]. Machine vision technology further enhances the precise measurement of kinematic parameters such as joint angles, velocity, and acceleration [19], making it possible to identify the critical factors that influence maneuver success. High-difficulty maneuvers demand precise joint angle adjustments and greater speeds to achieve the intended rotations and flips. By assessing how rotational parameters change with increasing difficulty, athletes can optimize their use of rotational inertia while minimizing energy expenditure. This study examines key rotational parameters during the takeoff phase in snowboarding, including moment of inertia, angular momentum, and rotational energy. Through systematic analysis of these factors, our research offers a comprehensive understanding of the biomechanical characteristics that define this crucial performance phase. By focusing on the takeoff phase, we are able to identify the principal biomechanical factors that directly influence elite performance, thus providing a foundation for targeted training strategies. This study aims to quantitatively analyze the takeoff phase of SBA by comparing performance, kinematic, and rotational kinetic parameters across tricks of increasing difficulty (720\({}^{\circ}\), 1080\({}^{\circ}\), 1440\({}^{\circ}\), and 1800\({}^{\circ}\)). We hypothesize that higher-difficulty maneuvers are characterized by increased takeoff velocity, greater joint extension, and higher angular momentum. The objective is to identify key biomechanical adaptations that enable successful execution of high-difficulty tricks and to offer targeted recommendations for athlete training and coaching.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 2 The Electric System of El Hierro  
The Canary Islands face structural limitations in their energy generation due to their isolation and the small size of their electrical systems, with only Lanzarote and Fuerteventura having an interconnection. This has led to heavy reliance on fossil fuels to ensure reliability. El Hierro, which is the smallest grid of the Canary Island systems, has historically relied on small diesel generators, which were in a redundant arrangement to provide high security of supply. The Gorona del Viento plant was commissioned in 2014 to achieve energy self-sufficiency while maintaining reliability, but after a decade, fully renewable energy has only been achieved for less than 100 days a year, with an annual renewable energy share of around 50%.  
Island systems require significantly higher power reserve margins compared to continental grids, with peak demand-to-installed power ratios reaching 25% and actual available power margins fluctuating between 40% and 70% [44]. These systems prioritize supply security, leading to different operational and economic considerations, including higher costs. The strong seasonal demand, driven by tourism, further complicates energy planning. Electricity costs are mainly driven by fuel prices and transportation expenses, with variable costs accounting for approximately 75% of total expenses. To gain deeper insights into the complexities of decarbonizing small and isolated energy systems, this study conducts a comprehensive analysis of El Hierro's energy infrastructure and electricity demand.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 3 System Model and Problem Formulation
### Channel Modeling for UAV-Assisted Non-Terrestrial Networks
#### 3.3.3 Interference and Noise  
The received signal at the user is affected by additive white Gaussian noise (AWGN) with variance \(\sigma^{2}\). External interference is modeled as a random variable \(I\), which depends on the scenario (e.g., high interference in urban environments).  
Our system model assumes that the RIS controller estimates the relative propagation delays of the direct and reflected paths based on real-time location and channel measurements. The proposed RIS-assisted NTN architecture employs a single intelligent surface comprising passive reflecting elements subject to the unit-modulus phase constraint \(\phi_{m}\in[0,2\pi)\). Operating in the sub-6 GHz band (2.4 GHz), the system assumes block fading channels where coherence time exceeds the optimization interval. There is perfect synchronization between the NTN infrastructure and the RIS controller, with user trajectory and velocity obtained via onboard sensors or cooperative localization. Interference is modeled as a log-normal random variable \(\mathcal{I}\sim\mathcal{LN}(\mu,\sigma^{2})\), where environment-dependent standard deviations follow empirically validated values. Doppler shifts are estimated through consecutive CSI samples at 1 ms intervals. These well-justified assumptions establish a reproducible evaluation framework for joint RIS-UAV optimization.  
The feasibility of propagation delay estimation at the RIS controller stems from deterministic NTN geometry and modern localization capabilities. In UAV-/ satellite-assisted deployments, RIS and NTN node positions are either fixed or tracked via GPS/inertial navigation systems. The location and velocity data of the users are acquired by the following:  
* Direct feedback channels;
* Integrated sensor fusion;
* Cooperative localization protocols.  
Leveraging this spatial awareness, geometric transmitter-RIS-receiver distances are approximated with sub-wavelength accuracy, enabling precise delay estimation. Further refinement is achieved through the following:  
\[\hat{\tau}=\operatorname*{arg\,min}_{\tau}\left\|\mathbf{y}-\sum_{k=0}^{K-1} \alpha_{k}\mathbf{s}(\tau-\tau_{k})\right\|^{2} \tag{6}\]  
which is applied during channel sounding, where \(\mathbf{s}(\tau)\) denotes the pilot waveform. Recent RIS positioning studies report sub-nanosecond estimation errors (<1 ns) in LoS conditions, confirming practical viability for real-time adaptation. Residual errors remain within tolerable margins for sub-6 GHz NTN reconfiguration.  
Given the deterministic geometry in NTN deployments (e.g., known locations of the RIS and the satellite), estimating path length differences and applying a timing advance or phase precompensation at the RIS or transmitter side is feasible.
## 3 Results
### Heat-Transfer Analysis by the Thickness of Layers  
The heat-transfer efficiency was compared, as shown in Figure 8, by changing the thickness of each layer in the multilayer structure mold. To improve the heat-transfer efficiency of the multilayer structure mold and find the optimal thickness of each layer, comparisons of heating rates with varying material thickness and simulation analysis were conducted using the Ansys simulation tool. The detailed simulation setup is listed in Table 3. To verify the simulation model before optimization analysis, one-cycle heating performance was verified by comparing analysis and experimental data (see Figure 8e). The simulation model presented a maximum error of 12.6% during heating, but the maximum heating temperature was very similar, with an error of approximately 0.5%. The simulation model was verified with an average error of 9.4%. Based on this simulation model, the heat-transfer performance of each layer in the multilayer structure mold was analyzed. In Figure 8b, it can be seen that as the thickness of the CNT web film decreases, heat-transfer performance reaches its maximums at 10 \(\upmu\)m and 5 \(\upmu\)m. In Figure 8c,d, it can be seen that the heat-transfer performance increases as the insulator thickness increases and the thickness of the electrical insulator decreases, respectively. The maximum heat-transfer performance was achieved when the insulator thickness was over 5 mm and the electrical insulator thickness was 0.25 mm. Finally, the thicknesses of the multilayer structure mold's CNT web film, insulator, and electrical insulator were selected as 10 \(\upmu\)m, 5 mm, and 0.5 mm,  
Figure 6: Measuring points (**a**) 4 measuring points of CNT web film, (**b**) Detail points in P1.  
Figure 7: Results of Raman spectroscopy of CNT web film.  
respectively. In the case of electrical insulators, thinner thicknesses improve heat-transfer performance, but for safety reasons, a thickness of at least 0.5 mm should be applied for electrical insulation. Additionally, the one-cycle heating performance of the RHCM mold with optimal thickness was compared to the CIM mold applied with cartridge heaters. It was confirmed that RHCM heats approximately four times faster than CIM.  
Figure 8: Temperature response of multilayer structure mold: (**a**) total data of various thicknesses, (**b**) CNT web film thickness variation, (**c**) insulator thickness variation, (**d**) electronical insulator variation, (**e**) verification of simulation model and comparison of RHCM and CIM.
# Calibration of a Low-Cost 8\(\times\)8 Active Phased Array Antenna
###### Abstract  
This paper presents the calibration process involved in a planar active phased array antenna operating in the K-band (17.7-20.2 GHz). The array consists of eight columns, each containing a 1 \(\times\) 8 subarray of patch antennas. To enhance the antenna bandwidth, a double-stacked patch structure is employed. We analyze the challenges encountered when measuring active antennas. Additionally, we discuss the solutions and calibration techniques used to improve the array performance. Finally, we present the results of the optimal calibration approach, comparing simulated and measured data, both with and without calibration, to evaluate the improvements achieved.  
active phased array antenna; array calibration; antenna design; patch antenna; antenna measurement +
Footnote †: journal: Applied sciences
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 3 IoTR-DS Dataset Creation and Attacks Modeling
### Attack Implementation
It is open-source and can run on top of different operating systems such as Linux, Windows, and MAC. It has the capability of implementing and simulating RPL and other routing protocols at a larger scale. We applied the attack algorithms mentioned earlier. Table 3 shows the configuration parameters for the implementation of attack scenarios. \begin{table}
\begin{tabular}{l l} \hline \hline
1 & Begin \\
2 & If Malicious node and Start Attack \textless{} time \textless{} End Attack then \\
3 & Replay and Multicast DIO on 802.11 interface every t time \\
4 & If Malicious node receiving DIO on 802.11 interface then \\
5 & Recalculate Preferred\_Parent (to be the Malicious node) and this Node Rank \\
6 & Trickle reset DIO timer, Send DIO (with new rank) on 802.11 to all reachable nodes on \\
7 & If a node receiving DIO and Sender Rank less than Preferred\_Parent (default route) Rank then \\
8 & Recalculate Preferred\_Parent and this Node Rank \\
9 & Trickle reset DIO timer, Send DIO to all reachable nodes on next interval \\
10 & End \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation Parameters For Normal and Attack Traffic Scenarios. Table 4 describes the five sub-datasets' results from the simulation scenarios. For the normal dataset, it contains only normal traffic samples without any attack behaviors. The DIS, Rank, and Wormhole datasets contain a mixture of both normal and abnormal (attack) traffic samples. The combined dataset represents the concatenation of the four datasets in one larger dataset. Notice that the number of samples varies from one dataset to another to give some samples diversity.
# A Multibody Model Calibration Method for Vehicle Misuse Testing
## Appendix A  
We will demonstrate that the fast vibration component of the acceleration response cannot be the opposite eigenmode with a higher frequency.  
As depicted in Figure 11, there is a faster vibration component with a half-period of approximately 0.0058 s present in the wheel's center acceleration response. This faster component begins at stage 1 and continues into the first part of stage 2. Although the slower vibration component represents a vibration mode with a smaller frequency, the faster vibration component is not indicative of another vibration mode with a larger frequency. The proof is as follows.  
The frequency is at least ten times that of the slow vibration component. For a simple harmonic vibration \(z(t)=A\sin(\omega t+\varphi)\) with an amplitude \(A\), a circular frequency \(\omega\), and a phase angle \(\varphi\), the relationship between the displacement and acceleration is  
\[\frac{z(t)}{\dot{z}(t)}=-\frac{1}{\omega^{2}}. \tag{11}\]  
Therefore, the displacement of the fast component is less than 1% of that of the slow component and is imperceptible in the displacement responses or comparisons.  
The half-period of the slower vibration component in stage 2 can be read from the displacement response, as shown in Figure 7, as 0.0675 s, whereas the faster component can be read from the acceleration response in Figure 8 as 0.0058 s. By comparing the slower and faster components with the theoretical circular frequencies in Equation (17), the following algebraic equation is obtained:  
\[\frac{\left(c_{\text{u}}+c_{\text{e}}\right)-\sqrt{\left(c_{\text{u}}-c_{ \text{e}}\right)^{2}+4c_{\text{u}}}}{\left(c_{\text{u}}+c_{\text{e}}\right)+ \sqrt{\left(c_{\text{u}}-c_{\text{e}}\right)^{2}+4c_{\text{u}}}}=\frac{(\pi/ 0.0675)^{2}}{(\pi/0.0058)^{2}}=\frac{58^{2}}{675^{2}}\triangleq p=0.01. \tag{12}\]  
wherein \(p\) represents the square of the ratio of the low frequency to the high frequency.  
Equation (12) can be rewritten as  
\[q(c_{\text{u}}+c_{\text{e}})=\sqrt{(c_{\text{u}}-c_{\text{e}})^{2}+4c_{\text{ u}}}, \tag{13}\]  
with \(q=\frac{1-p}{1+p}\approx 1-2p=0.98\). Solving the algebraic Equation (13) to obtain  
\[c_{\text{e}}=\frac{(q+1)c_{\text{u}}\pm 2\sqrt{(qc_{\text{u}}+q-1)c_{\text{u} }}}{1-q}. \tag{14}\]  
We substitute \(q=0.98\) and \(c_{\text{u}}=10\) into Equation (12), either \(c_{\text{e}}=1.06\) or \(1978.90\), both of which conflict with the value of \(11.00\) for \(c_{\text{e}}\) calibrated by the slower vibrationcomponent. Thus, the fast vibration component of the acceleration response cannot be the opposite eigenmode with a higher frequency.