esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 FE Models  
The TIRF method applied in this study is more efficient than the DIR as it uses a linear superposition method. However, it has a limitation as it can be used only under a linear system. As explained in the previous section, strictly speaking, TIRFs cannot be used in LNG systems because there are distinct non-linearities, such as geometry and boundary conditions. As computational efficiency is the first priority from the point of view of design applications, this study investigates whether the TIRF method can be used despite such nonlinearity. We intend to quantitatively examine the degree of error, which occurs when this method is applied to a nonlinear system, and to present a guideline that can be applied to the design. In this study, three models were investigated, as listed in Table 2. As the model number increases, i.e., from Model 1 to Model 3, the complexity and nonlinearity of the overall model increase. For all the models, geometric nonlinearity based on the large deflection theory was considered, and linear and nonlinear boundary conditions were applied, respectively. In all cases, the results derived by the method using the TIRF and the results derived through the DIR were compared. In Model 1, a simple uniform pressure was applied to the top of the model (Figure 4), while in Model 2 and Model 3, all 12 sloshing load conditions, as shown in Figure 5, were considered. Figure 5 shows the typical load-exposed areas, which can be divided into 24 areas [1]. This study selected 12 load areas considering geometrical symmetricity. For each case, five rise times, which were selected according to the study by [PERSON] and [PERSON] [1], were applied to consider the dynamic effect. The primary purpose of Model 1 was to confirm the feasibility of the dynamic structural responses using the TIRF method based on the linear superposition method. Therefore, two simple steel boxes were stacked on top of each other with linear and nonlinear boundary conditions, respectively. Material nonlinearity was excluded, and the element size of the FE model was approximately 24 mm \(\times\) 24 mm, which was determined based on the results of a mesh sensitivity study conducted by [PERSON] and [PERSON] [1]. The purpose of Model 2 was to verify the TIRF method to calculate the ultimate dynamic strength of the LNG boxes against LNG sloshing pressure. As shown in Figure 3a,
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 6 Feature Selection  
Feature selection (also called \"dimension reduction\") is a process performed to determine a smaller group of characteristics that help complete information analysis. This is prone to reduce the computing time in the classification process and remarks on the features that could improve the classifier algorithm accuracy. There are different procedures and methods for feature selection. Being among the most popular, the metaheuristic procedure tries to answer an optimization model through heuristics (a search algorithm). It is used for selecting features from large sets of features because it can manage the severe dimensionality problem [47].  
Likewise for the machine learning algorithms, the feature selection methods could be divided into unsupervised and supervised variants [48]. These methods are differentiated if there are labels available for characterized data. If the data possess a label, the feature selection could follow a supervised methodology; if not, it is considered an unsupervised feature selection. These methods are divided into four categories: filter, wrapper, hybrid, and embedded. The method selection depends on the data interaction in the algorithms, which define how the more critical features are extracted from the data.  
The filter approach is based on statistical measures. The objective of this technique is to rank the features and hence isolate the most important information in databases. On the other hand, the wrapper approach makes a combination of information and evaluates it to find the most suitable for the system classification or the most relevant.  
The hybrid methodology tries to combine a method that could generate a feature ranking and then use this list to evaluate the performance of the combination in the system. Finally, the embedded method integrates the selection process into the classification algorithm. The more visible algorithm is the decision tree, where the branches reflect the selection of certain information, and the growth process forms smaller subsets.  
In [47], the systems that include a selection procedure for salient features achieve higher accuracy than the BCIs cases that are not involved in this process. The algorithms used for selecting features could be used for classification [49]. Additionally, there exists the manual feature selection, performed by a human expert, through direct observation, to label and remark on an important feature for the system. A list with some of the most used algorithms divided by their approach is presented as follows (Figure 8).
## 2 Design Principles
### Topology Optimization Model of the Three-Dimensional Seismic Isolation Device  
Passive seismic isolation devices include various types, such as groove track isolation devices, ball-type isolation devices, sliding rail isolation devices, spherical track isolation devices, load-bearing isolation devices, and combined rubber isolation devices [25]. The lead-core rubber seismic isolation bearings are commonly used in bridge engineering [26], as shown in Figure 2.  
Based on existing rubber seismic isolation bearings, a new type of three-dimensional seismic isolation bearing with adjustable stiffness and improved isolation performance is proposed.  
Firstly, the topology optimization model of the rubber seismic isolation bearing under specified working conditions is obtained using the Evolutionary Structural Optimization (ESO) method [27]. The fundamental idea is to use stress as the optimization criterion: under loading conditions, the lowest 1% of stressed elements are removed; then, the structure is reloaded, causing a redistribution of stress, and the process continues by removing the lowest 1% of stressed elements repeatedly until the total number of removed elements reaches a predetermined ratio. The initial topological shape of the rubber seismic isolation bearing is designed as a cylinder, and its topology is optimized under compressive shear loads.  
The topology optimization result when the element removal rate reaches 85% is shown in Figure 3.  
Figure 2: The lead-core rubber seismic isolation bearing.  
The topology optimization model obtained from Figure 3 is then converted into a parametric three-dimensional seismic isolation bearing, with hollow cross-sectional dimensions \(h_{1}\), \(h_{2}\), \(l\), and \(r\) as the parameters to be optimized, as illustrated in Figure 4. Subsequently, optimization methods will be employed to obtain the optimal parameters that enable the rubber seismic isolation bearing to achieve the best seismic isolation performance.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 1 Introduction
In addition, not all edge information is needed for evaluation methods and few scholars distinguish the extracted edge information. In this paper, we focus on the abovementioned problems. Firstly, a Canny edge detection algorithm with excellent comprehensive performance was improved to enhance the edge detection effect of the measured images. Then, we proposed an eight-neighborhood grayscale difference method to achieve a rapid and efficient determination of the edge points' four edge directions. Finally, by comparing three distance factors based on the histogram of the edge width, the image sharpness evaluation method proposed in this paper was obtained. With the abovementioned improvements, our proposed method has excellent performance in terms of content irrelevance, subjective-objective consistency, and computational speed, especially in the real-time evaluation of image sharpness, which has great potential for application.
## 2 Methodology
### FFT with A-CFS Method
Employ \(F_{m}\) as the cutoff frequency, and add a buffer bandwidth above the cutoff frequency to preserve the main frequency and its neighboring frequency components. This approach minimizes the attenuation of the primary frequency information. Subsequently, execute an inverse transformation of the signal from the frequency domain back to the time domain to acquire the final post-processed signal \(D\_s^{\prime}(t)\).
## 2 Coding Metamaterials for RCS Reduction
### Development of Unit-Cell Metamaterial Structures
#### 2.1.1 Circular Split-Ring Resonator  
The monostatic RCS values happened between a common range from \(-110\) dBm\({}^{2}\) to \(-40\) dBm\({}^{2}\). Moreover, only minor differences were visible between all the substrate materials in each design structure. It was revealed that better RCS-reduction values are possible when utilizing the second-highest dielectric constant substrate material. This indicates that the dielectric properties of material structure do not influence the outcomes and the unique metamaterial properties are possible by adopting a distinct design structure. Meanwhile, the Rogers RT6002 substrate material produces the second better RCS values, although it has a 2.94 dielectric constant. In this circular metamaterial structure, the thickness of substrate material influences the outcomes, whereas the RCS value increases as the thickness of the substrate material decreases. The thicknesses of the substrate materials adopted in this work were 1.6 mm, 1.524 mm, and 0.25 mm, respectively.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Designs** & **FR-4** & **Rogers RT6002** & **Rogers RO3006** \\ \hline Design 1 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bistatic scattering patterns of circular structure with the various substrate materials.
# X-Reality Museums: Unifying the Virtual and Real World Towards Realistic Virtual Museums
## 2 Related Work
### Summary of Future Challenges  
As XR technologies gradually become more and more immersive and realistic, they are empowered to transform the way that information is delivered and consumed. Put simply, in order to successfully combine the real and virtual world and deliver a unified experience that cannot be derived exclusively either from the real or the virtual content, three technological challenges need to be resolved: (i) hide some parts of the real world (diminished reality), (ii) superimpose realistic virtual scenes and characters (true mediated reality), and (iii) interact with the virtual agents in a natural manner (natural multimodal interaction). The relevant current technological challenges and future directions, as they have been discussed in this section, are summarized in Table 1.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{34.1 pt} p{34.1 pt}} \hline \hline \multicolumn{3}{c}{**Diminished Reality**} \\ \multirow{2}{*}{Challenges} & \multirow{2}{*}{\(\bullet\)} & Shortcomings regarding the realistic and real time elimination of obstacles and their substitution by plausible background. \\ \hline \multirow{2}{*}{Future Directions} & \multirow{2}{*}{\(\bullet\)} & Introduce methods based on 3D geometry and Deep Learning \\  & & Address other aspects of the real environment beyond visual, such as sound or human motion. \\ \hline \multicolumn{3}{c}{**True Mediated Reality**} \\ \multirow{5}{*}{Challenges} & \multirow{2}{*}{\(\bullet\)} & Realistic 3D reconstructions appropriate for real-time XR environments. \\  & & Antiquities, and especially statues, are often incomplete (e.g., broken, or partly recovered). \\  & & Need for realistic, high fidelity and high refresh rate rendering of the deformable 3D characters in low CPU power devices (e.g., untethered headsets). \\  & & Interactive virtual characters supporting realistic deformation. \\  & & Incorporate in virtual characters components of realistic behaviour, including verbal and non-verbal communication attributes. \\ \hline \multirow{5}{*}{Future Directions} & \multirow{2}{*}{\(\bullet\)} & Advance 3D reconstruction techniques. \\  & & Develop semi-automated approaches for 3D modelling of incomplete physical objects, through an interdisciplinary approach involving the fields of archaeology, museology and curation, as well as computer science. \\ \cline{1-1}  & & Advance techniques for the realistic rendering of deformable objects and characters, as well as for real-time animation interpolation. \\ \cline{1-1}  & & Develop new models to support improve the virtual agents’ behaviour naturalness, by following the perception-attention-action approach. \\ \cline{1-1}  & & Deploy convincing, real time head mounted display removal for photo-realistic digitized 3D characters. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technological challenges and future directions towards unified XR experiences in virtual museums.
## 2 Methods
### Angle Measuring Using the Wireless Electronic Component
The rotation of the previous gravity vector is performed and has been described in [42]. The pitch, roll, and yaw angles are obtained using Equations (5)-(7), respectively. \[Pitch=\arctan\left(\frac{a_{y}}{\sqrt{a_{z}^{2}+a_{x}^{2}}}\right) \tag{5}\]  
\[Roll=\arctan\left(\frac{a_{x}}{a_{z}}\right) \tag{6}\]\[Yaw=\arctan\left(\frac{X_{h}}{Y_{h}}\right) \tag{7}\]  
These angles are calculated according to the axis represented in Figure 7, which depicts a left-handed coordinate system. The orientation of the sensorial modules in space is thus represented with a forward orientation along the y axis, with pitch defining the rotation on the x-axis, roll on the y-axis, and finally yaw on the z-axis. The vector \(\overrightarrow{v}_{i}\) in Equation (8) is a representation of the module's orientation after its rotation. Now that the orientation of each module is known, the lower angle between the vector of the module and the z-axis, represented by the vector \(\overrightarrow{u}\) in Equation (9), is calculated using the inner product space, as seen in Equation (10). The angle \(\theta_{i}\), formed during the gait cycle, is the subtraction of the angle measured by the lower and upper parts of the leg, namely the femur and tibia, respectively. This method is more tolerant regarding the placement of the modules in the user's body, since it accounts for any kind of tilt which influences the axis we want to measure (z-axis), in contrast with, for example, measuring only the roll angle of both modules. \[\overrightarrow{v}_{i}=[x_{i},y_{i},z_{i}] \tag{8}\]  
\[\overrightarrow{u}=[0,0,1] \tag{9}\]  
\[\theta_{i}=\arccos\left(\frac{\overrightarrow{v}_{i}\bullet\overrightarrow{ u}}{\left|\overrightarrow{v}_{i}\right|\left|\overrightarrow{u}\right|}\right) \tag{10}\]
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 3 Satellite Formation and Interferometry Baseline
### Along-Track Baseline and Time Decorrelation  
Different from single-platform interferometry, the triangulation of satellite formation is formed by two different time-space planes (except when the satellite formation is at the equator), as shown in Figure 5. At \(t-\Delta t\) moment, Sat1 is ahead of Sat2, right in the triangulation area (the gray shaded area); after \(\Delta t\), Sat2 reaches to the same area, therefore the along-track baseline of LB-IRA is inevitable during OST altimetry.  
The orbital velocity of ocean surface wave will aggravate the time decorrelation considerably, eventually decreasing the altimetric accuracy. The echoes received by two antennas of LB-IRA are from a combination of multiple wavelike scatterers within the same specific region, however with different  
Figure 4: LB-IRA satellite formation and the helix orbit configuration. (**a**) Maximum separation at equator, intersects near the poles, (**b**) maximum separation at equator, the small eccentricity offset causes different heights of perigee, snd (**c**) optional rotation of the perigee to achieve proper baseline configuration near the high-latitude region.  
Figure 5: Interferometry by LB-IRA.  
time moments due to the along-track separation. Because of the randomness of wave orbital velocity of the scatterers, the doppler spectrum of two received echoes statistically deviate from each other [26]. The ocean coherence time can be used to quantify the statistics of doppler spectrum of ocean surface wave, and higher sea state (usually determined by surface wind velocity) and radar waveband lead to shorter ocean coherence time. The time decorrelation can then be expressed as [27]:  
\[\gamma_{time}=\exp\left(-\frac{\Delta t}{2\tau_{c}{}^{2}}\right) \tag{11}\]  
where \(\tau_{c}\) is the ocean coherence time, \(\Delta t=\frac{B_{tt}}{V_{sat}}\) is the time-lag determined by the along-track baseline, and \(V_{sat}\) is the satellite velocity. In consideration of other technical and engineering issues, the waveband of LB-IRA is chosen to be Ku-band (center carrier frequency is about 13.5-13.6 GHz). The surface wind velocity is set to be 7.4 m/s (1\(\sigma\) result) based on one year of statistical data from Cross-Calibrated Multi-Platform [25]. According to the evaluation method from Reference [28], assuming surface wave propagates along the orbit track, the ocean coherence time is about 8 milliseconds at the Ku-band and under 7.4 m/s of wind velocity. According to Equation (11), the interferometric coherence will decrease to 0.79 due to time decorrelation, which corresponds to about 40 m of the along-track separation at the altitude of 891 km. Since IRA interferometric data will suffer from all kinds of decorrelations, we decided that 0.8 should be the lower bound for time decorrelation.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 3 Materials and Methods
### Arima-Wnn  
Actual time series data often have both linear and nonlinear characteristics, and ARIMA or WNN alone cannot reflect the dual linear and nonlinear characteristics of time series. In order to simultaneously utilize the good linear fitting ability of the differential autoregressive moving average model and the powerful nonlinear relationship mapping ability of the wavelet neural network model, we combine ARIMA and WNN methods.  
Assuming \(y_{t}(t=1,\,2,\ldots,\,L)\) is the actual value of the time series, L is the number of sample points, and \(\hat{y}_{t}\) and \(\hat{y}_{it}(i=1,\,2,\ldots,\,N,\,t=1,\,2,\ldots,\,L)\) is the predicted value of the combined model and the first single method, respectively; then:  
\[\hat{y}_{t}=\sum\limits_{i=1}^{N}\lambda_{i}\hat{y}_{it} \tag{8}\]  
where \(\lambda_{i}\) is the weight of the prediction method, and \(\sum\
olimits_{i=1}^{N}\lambda_{i}=1\). The weight coefficients of each of the component models in the combined model are determined by solving the following optimization problems:  
\[Min\sum\limits_{t=1}^{L}\left(y_{t}-\hat{y}_{t}\right)^{2},\ \ \ s.t.\ \sum\limits_{i=1}^{N}\lambda_{i}=1,\ 0\leq \lambda_{i}\leq 1 \tag{9}\]  
On this basis, we propose a new combination model composed of the ARIMA model and WNN [23]. The optimal weight of a single model is obtained by the bat algorithm, and the predicted value of the combination model is expressed as follows:  
\[P_{\Sigma}=\lambda_{1}P_{WNN}+\lambda_{2}P_{ARIMA} \tag{10}\]  
where \(P_{\Sigma}\) is the final predicted value; \(\lambda_{1},\ \lambda_{2},P_{WNN},\ P_{ARIMA}\) are the weight coefficients and predicted values of WNN and ARIMA models, respectively; and \(\lambda_{1}+\lambda_{2}=1\), as shown in Figure 2.
## 5 Use Case
### Functional Safety Concept  
The functional safety concept aggregates functional safety requirements, defining the measures needed to reduce ITEM risks to acceptable levels. It serves as the foundation for specifying ITEM functions via technical safety requirements within the technical safety concept. Functional safety requirements, derived from safety goals, are implemented using ISO 26262:2018 [9] strategies such as fault prevention, detection, or driver warnings. Each requirement is assigned a single safety strategy and describes the functions necessary to achieve safety goals without specifying technical solutions. Requirements are allocated to ITEM elements or external vehicle components. Further specification uses predefined categories, with updates required for values determined in later development phases. The functional safety concept collectively underpins the technical safety concept. The diagrams of the model are shown in Figures A24 and A25.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMC and PDSMC
#### 3.1.2 Sliding Surface Response  
Figure 7 shows the sliding surface behavior of both controller schemes. It can be seen that there was a big overshoot in SMC (red line) as compared to PDSMC (blue line), which had smaller overshoot. Chattering was also in the sliding surface of SMC (solid red line), and on the other side, PDSMC had a small amount of chattering.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 4 System Model
### Security Requirements  
The provided points outline essential security and privacy requirements for the proposed protocol. These requirements collectively emphasize the importance of maintaining data privacy and integrity and ensuring secure ownership transitions in the proposed protocol. The detailed requirements are as follows:  
* Data privacy: Data privacy means that the actual content of the data should be protected from unauthorized access, ensuring that sensitive information within the data remains confidential. The original data remain inaccessible to cloud storage, the CS, and unauthorized clients.
* Data integrity: Data integrity involves ensuring that the data stored in the system remain unaltered and reliable. Both the cloud storage and the CS must have mechanisms in place to verify the purity and correctness of the data before storing them or transferring ownership.
* Forward security: Forward security is a concept where clients whose ownership has expired must be prevented from accessing data stored on the cloud storage. This ensures that, even after losing ownership rights, clients cannot access data that they previously owned. It aims to prevent unauthorized access, protect the integrity of data, maintain a clear separation of ownership, and ensure that clients cannot access data outside their current ownership scope.
* Backward security: Backward security is a concept where clients who have uploaded data to the cloud storage should not be able to access data that were stored before they gained ownership. In other words, even after acquiring ownership rights to certain data, clients should not have access to the historical data records from previous owners.