esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 5 Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
### System Parameters  
The system parameters used in the proposed scheme-II are as follows:  
* \(q\): Prime number
* \(H_{1}\): Hash functions, \(\{0,1\}^{*}\times\mathbb{G}\rightarrow\mathbb{Z}_{q}^{*}\)
* \(H_{2}\): Hash functions, \(\mathbb{G}\rightarrow\{0,1\}^{l_{1}+l_{2}}\) for some bit-length \(l_{1},L_{2}\in\mathbb{N}\)
* \(H_{3}-H_{5}\): Hash functions, \(\{0,1\}^{*}\rightarrow\mathbb{Z}_{q}^{*}\)
* \(H_{6}\): Hash functions, \(\mathbb{G}\rightarrow\mathbb{Z}_{q}^{*}\)
* \(Part_{i}\): System participant \(i\), \((Dev_{A},Dev_{B},Med,KRC,KRA\in Part_{i})\)
* \(s\): Master secret key of KGC, \(s\in\mathbb{Z}_{q}^{*}\)
* \(sk_{i}\): \(Part_{i}\)'s private key, \(sk_{i}=(d_{i},y_{i},z_{i})\)  
Figure 9: Scenario of proposed scheme-II.  
* \(pk_{i}\)_; Part\({}_{i}\)_'s public key, \(pk_{i}=(X_{i},Y_{i},Z_{i})\)
* _KRF_: Key recovery field, \(KRF=(KRF_{1},KRF_{2},KRF_{3},KRF_{4})\)
## 2 Electromagnetic Wave Propagation Behavior and the Principle of the Reflected Voltage
### Resolution of Electromagnetic Waves  
As electromagnetic waves are propagated in a medium, the frequency of the main reflected wave is slightly lower than the frequency of the center. Therefore, during the detection process of the ground penetrating radar, the limit of resolution is 1/2 of the wavelength of the central frequency. The higher the frequency of the electromagnetic wave, the greater the attenuation coefficient and the shallower the detection thickness. The resolution of the electromagnetic wave can be illustrated as follows.  
1. Vertical resolution  
The minimum resolution length of the ground penetrating radar is 1/4 of the wavelength of the electromagnetic wave in the medium. The vertical resolution is correlated to the thickness detected by the ground penetrating radar.  
Figure 1: Electromagnetic Wave Propagation in Medium.
## 6 Abuse Attack Techniques
### Steganography
#### 6.1.1 CPLS as a Primary C&C Communication Channel
[118] proposed a malware called SUbot that leverages blog websites for the creation of covert channel communications to evade detection. The author of SUbot implemented steganography and cryptography strategies, using RC4 to hide a secret message and appending the ciphertext message to the end of a JPG file. The modified JPG file containing an executable command was then uploaded to a blog. When the infected mobile device visits the URL of the blog site, it downloads and retrieves the plaintext commands from the JPG image.
## 2 Methodology and Flow of Research  
Regarding \"material information,\" it is necessary to obtain the \"CO\({}_{2}\)e reference value,\" which represents the standard CE for each type of construction material, as well as the \"CO\({}_{2}\)e actual value\", which reflects the actual emissions of the materials applied in the building. In terms of \"quantity information\", the amount of materials used can vary significantly depending on factors such as building size, function, location, and structural type. Therefore, a standardized \"reference quantity\" is required for accurate comparison and analysis. \"Greenhouse gas information\" refers to the environmental impact data used to calculate CE, CR, and ZCBI based on the collected material and quantity information. Specifically, for \"material information\", this study used certified products from the Environmental Product Declaration (EPD) system operated by the Korea Environmental Industry& Technology Institute (KEITI) [17]. For \"quantity information\", data were extracted from completed life cycle assessment (LCA) cases under Korea's Green Standard for Energy and Environmental Design (G-SEED), administered by the Korea Institute of Civil Engineering and Building Technology (KICT) [18]. \"Greenhouse gas information\" was also based on data provided by KEITI. The \"CO2e reference value\" was derived from Korea's national LCI database managed by KEITI, while the \"CO2e actual value\" was obtained from certified EPD product data [19]. A more detailed research flow is as follows.  
First, an A1-A3 CE database was established for building materials used in Korean construction that have obtained environmental product declaration (EPD) certification. Furthermore, a reference building for evaluating the A1-A3 ZCBI was developed by establishing a classification system based on region, building purpose, structural form, and scale. A1-A3 CR and A1-A3 ZCBI were derived by comparing the A1-A3 CE data from the Korean LCI database with A1-A3 CE-based data from the EPD database. This analysis considers the lowest, average, and highest values of A1-A3 CE from each EPD database, which may vary depending on the manufacturer and model, even for identical products. Finally, major construction materials were identified from the perspective of A1-A3 CE.  
The data collection and management framework, evaluation methodology, and analytical procedures developed in this study are considered to be broadly applicable through adaptation to systems currently implemented in other countries or regions. In particular, the global expansion and standardization of such an evaluation framework may contribute to the establishment of a universally applicable integrated system for assessing carbon neutrality in the future. Figure 1 presents the overall flow of this study.  
Figure 1: Research framework.
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
## 3 Numerical Simulation
### Structure Load
#### 3.2.1 The Selection of Seismic Wave  
In accordance with the current national standard \"Seismic Design Code for Urban Rail Transit Structures\", the relationship between seismic fortification intensity and design basic seismic acceleration values is considered. According to the existing research, when discussing the response of the structure under the seismic load, the type, frequency, and action time of the seismic wave will have an impact on the seismic response of the shield tunnel structure. Existing research shows that the response of seismic wave type is shear wave, and the seismic wave frequency is low frequency [31]. From the existing seismic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Uniaxial Compression**} & \multicolumn{4}{c}{**Uniaxial Tension**} \\ \hline
**Stress** & \multirow{2}{*}{**Inelastic Strain**} & \multirow{2}{*}{**Damage Factor**} & **Stress** & **Cracking** & \multirow{2}{*}{**Damage Factor**} \\
**/MPa** & & & **/MPa** & & \\ \hline
26.892 & 0 & 0 & 3.423939 & 0 & 0 \\
38.455 & 6.6 & 0.208666 & 3.257697 & 0.3 & 0.125466 \\
34.997 & 10.9 & 0.309287 & 2.701560 & 1.1 & 0.349736 \\
28.692 & 16.2 & 0.420016 & 2.169453 & 1.8 & 0.495345 \\
23.006 & 21.2 & 0.513588 & 1.561394 & 3.2 & 0.650423 \\
18.640 & 25.8 & 0.586791 & 1.045034 & 5.8 & 0.778459 \\
15.401 & 30.2 & 0.643377 & 0.675897 & 10.7 & 0.867185 \\
12.985 & 34.3 & 0.687565 & 0.430703 & 20.5 & 0.922839 \\
9.725 & 42.0 & 0.751014 & 0.272294 & 40.0 & 0.955944 \\
6.315 & 56.6 & 0.824267 & 0.171334 & 78.7 & 0.975075 \\
3.624 & 84.5 & 0.890016 & 0.107651 & 156.0 & 0.985949 \\
1.784 & 148.0 & 0.941518 & 0.067876 & 308.0 & 0.992065 \\  & & & 0.032288 & 953.0 & 0.996891 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uniaxial stress–strain relationship and concrete damage parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \multicolumn{1}{c}{**Young’s Modulus**} & \multirow{2}{*}{**Poisson’s Ratio**} & \multirow{2}{*}{**Mass Density**} & \multirow{2}{*}{**Damping Ratio**} \\
**Semi-Weathered granite** & & & & \\
**Concrete** & & & & \\
**Bolt** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters.  
wave, the Kobe seismic wave, as a kind of low-frequency seismic wave with higher content, will make the shield tunnel appear more obviously deformed [32]. Kobe waves with peak ground accelerations of 0.1 g, 0.3 g, and 0.5 g are selected for the study. The acceleration time history and Fourier spectrum curve for the seismic wave with a peak acceleration of 0.1 g are shown in Figure 6. The acceleration time histories for 0.3 g and 0.5 g can be obtained through proportional scaling. The seismic wave is assumed to have a bottom vertical incidence angle and a duration of 20 s, and the peak ground acceleration of the selected Kobe wave occurs at 3.46 s into the seismic wave.
## 5 Results and Discussion
### Model Training and Testing
The PCA application contributed to the accuracy of the results by efficiently factorizing the input data to zero in on the most important contributors to the output and provide a high degree of predictability in the matches. Artificial intelligence approaches, including petrophysical logs, proved useful for estimating illite wt.%. Our findings prove the usefulness, speed, and low cost of employing ANNs to estimate illite wt.% in the absence of core samples. The model's high level of predictability can be used for a wide range of Montney plays and even other plays.
## 2 The Scenario, Architecture and Optimization Strategy of the UEMS for ADR  
### The Scenario of the UEMS  
Due to serious air pollution in the north of China, the government is vigorously promoting the electric-heating-reform-project and many electric heaters are put into service. The widely used electric heaters only have two running states, on and off, and cannot adjust their power continuously. Due to the absence of temperature sensors, these electric heaters cannot change their working state according to the indoor temperature automatically. Obviously, the manually controlled electric heaters cannot precisely guarantee the comfort of users, let alone optimally response to the real-time electricity price. To minimize the electricity bill while guarantee user comfort, a UEMS that executes an optimal strategy according to the indoor temperature and electricity price is indispensable.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 1 Introduction
It is termed \"integrated photocatalyst adsorbent\" (IPCA), and is based on an adsorbent which has the ability to degrade organic matter in the presence of sunlight [18]. The investigation of different catalyst materials and the comparison of homogeneous vs. heterogeneous catalysis routes are also crucial points, and are hence the object of intense discussion. This paper presents an up-to-date review of the solar collector systems and of the related catalysts applied to wastewater purification. In particular, we bring together all the collector designs which have been employed for solar photocatalysis to date. To the best of our knowledge, this was done only partially in previous reviews. In addition, we organize the reviewed literature according to the pollutant or pollutants investigated, and to whether the application is to model or real wastewaters. This is a novel angle of analysis; it seeks to highlight the importance of moving towards the investigation of real wastewaters. Specifically, the first part details solar collector designs and their components. The second part focuses on the catalyst materials employed for solar AOPs. As the development of photocatalysts is a vast field in itself, we decided here to consider only those materials which have already been investigated in combination with a solar collector. Finally, a last section reviews a number of case studies on both model solutions and on real wastewater samples.
# Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis +
## 5 Reliability-Oriented Sensitivity Analysis
### Statistical Estimation of ROSA Sensitivity Indices  
To practically estimate the ROSA indices \(\hat{\eta}_{i}\) and \(\delta_{i}^{f}\), the following steps are required [22]:  
1. Obtain \((V_{1},\ldots,V_{n})\) approximately i.i.d. from \(f_{U|Z\
ot\in\mathbf{L}(t)}\) and their corresponding value \(Z_{k}=\mathcal{M}(V_{k})\) by \(\mathcal{M}\). From \(Z_{k}\), the value of \(\hat{Z}_{k}\) is then easily computed with \(\hat{Z}_{k}=||Z_{k}||_{2}\).
2. Use the sample \(((V_{k}^{(i)},\hat{Z}_{k}),k=1,\ldots,n,i=1,\ldots 5)\) where \(V_{k}^{(i)}\) is the \(i^{th}\) component of \(V_{k}\), to get estimates \(\hat{f}_{U^{(i)}|Z\
ot\in\mathbf{L}(t)}\) and \(\hat{c}_{i}\) of the density \(f_{U^{(i)}|Z\
ot\in\mathbf{L}(t)}\) and of the copula density \(c_{i}\) respectively. In this article, they are both estimated with the non-parametric method [20; 24], but any other efficient density and copula estimation techniques can be chosen.
3. Use the estimates \(\hat{f}_{U^{(i)}|Z\
ot\in\mathbf{L}(t)}\) and \(\hat{c}_{i}\) to compute \(\hat{\eta}_{i}\) and \(\delta_{i}^{f}\) as follows: * for \(\hat{\eta}_{i}\), estimate the one-dimensional integral \(\|f_{U^{(i)}|Z\
ot\in\mathbf{L}(t)}-f_{U^{(i)}}\|_{L^{1}(\mathbb{R})}\) either by direct numerical approximation, or if \(f_{U^{(i)}}\) can be sampled from, by Monte Carlo method via \[\hat{\hat{\eta}}_{i}=\frac{1}{N^{\prime}}\sum_{k=1}^{N^{\prime}}\left|\frac{f_ {U^{(i)}|Z\
ot\in\mathbf{L}(t)}(U_{k}^{(i)})}{f_{U^{(i)}}(U_{k}^{(i)})}-1\right|\] (14) where the \(U_{k}^{(i)}\) are i.i.d. with common distribution \(f_{U^{(i)}}\); * for \(\delta_{i}^{f}\), generate \(((H_{1k},H_{2k}),k=1,\ldots,N^{\prime})\) i.i.d. uniformly distributed on \([0,1]^{2}\) and estimate \(\delta_{i}^{f}\) by \[\hat{\delta}_{i}^{f}=\frac{1}{2N^{\prime}}\sum_{k=1}^{N^{\prime}}\left|\hat{c }_{i}(H_{1k},H_{2k})-1\right|.\] (15)  
The estimates \(\hat{\eta}_{i}\)\(\hat{\delta}_{i}^{f}\) can be computed for different failure events \(Z\
ot\in\mathbf{L}(t)\) for different values of \(t=t_{\alpha}\) that correspond to several minimum volume sets of probability \(\alpha\). \(N^{\prime}\) can be taken as large as possible, as it does not imply any calls to \(\mathcal{M}\) and thus we set to \(N^{\prime}=10^{4}\).
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Preprocessing Approaches  
Preprocessing of the spectral data is an integral part of the development of parsimonious and stable predictive models. The purpose of this task is to mitigate/remove physical phenomena in the spectra unrelated to the target responses, including, in this work, the size and curvature of the grape berry [7; 13; 14]. The methods adopted in the preprocessing step belong to the reference-independent class of preprocessing methods, as they strictly involve the spectra data. The literature on preprocessing methods is extensive and various techniques have been applied for spectroscopic data in food/fruit analyses [21; 22; 23; 24; 25]. Thus, representatives of the most well-known spectra reference-independent preprocessing techniques were considered, which can be allocated into two categories: scatter correction methods and spectral derivatives. For the current purpose, multiplicative scatter correction (MSC), standard normal variate (SNV), and normalization techniques (auto-scaling) were selected from the first category, while Savitzky\(-\)Golay (SG) with first and second derivatives were chosen from the second group. SG employs a second order polynomial with a window size of fifteen points. The rationale for choosing these preprocessing methods was based on the fact that, according to the scientific literature, these have been the most commonly applied approaches for predicting the enological parameters of grape berries from spectroscopic data (see Table 1), as well as for the treatment of spectroscopic data to assess the quality of various fruits [21; 22; 23; 24; 25]. In addition, normalization (auto-scaling) of the spectra was selected due to the similarity with SNV. Mathematically, auto-scaling performs a column-wise normalization with the column mean and standard deviation, whereas SNV performs the same operation row-wise. Details on the spectra preprocessing techniques are available in the literature [26; 27; 28; 29; 30; 31; 32; 33; 34].
## 1 Introduction
Studies on the monitoring of the livestock state had been carried out on the IoT-based cattle biometric information collection terminal [13], cattle health monitoring system based on biosensors [14], design and implementation of livestock disease prediction system [15] and etc. However, the application of these systems is limited to a narrow field of livestock sheds, so it is difficult to be applied in the free-range grazing system. [PERSON] et al. [16; 17; 18] proposed a method of using the accelerometer sensor of the smart phone to recognize user situations (i.e., still or active) and developed a cloud-based smart phone application supported by a web-based interface to visualize the sedentary behavior. Complex and large-scale sensor communication [19] is needed under these circumstances. In this paper, we propose a momentum data collection system to provide ecological information and situations of livestock in grazing. In order to operate an unmanned ranch, it is difficult to apply high price sensors with high performance for economic benefit. We constructed a solar power supply system for the outdoor data repeater with low power consumption using solar panel and designed a data transfer platform for the analysis and storage of physical activity information of targets. We renewed the format of the communication protocol and data analysis method. The system and platform were applied to an actual grazing field and tested in National Rural Development Administration in Republic of Korea.
## 3 Computational Method
### Validation and Verification of CFD Results  
The computational results are validated through comparison with experimental results. A scaled model test was conducted at Pusan National University's towing tank to evaluate the resistance performance of the initial hull form [13]. The model-ship scale ratio \(\lambda\) was set to be 7.0. The design speed for the model \(V_{M}\) based on the Froude similarity scaling was set as \(V_{M}=V_{S}/\sqrt{\lambda}=3.111\) m/s. Here, the subscript S and M refer to the ship and model scale, respectively.  
Figure 4 displays the total resistance coefficient (\(C_{TS}\)) and residuary resistance coefficients (\(C_{R}\)) of the initial hull form obtained from the model test. The \(C_{R}\) was calculated based on the 2-D analysis method \(C_{R}=C_{TM}-C_{FM}\). The \(C_{R}\) continuously increases up to \(Fn=0.52\) (\(V_{S}=12.7\) knots), which is typical behavior for a displacement ship. Beyond \(Fn=0.52\), \(C_{R}\) continues to decrease with increasing \(Fn\), which is consistent with the behavior of a planning vessel. In other words, this result indicates that the initial hull form represents the features of the semi-displacement vessel. At the design speed (\(Fn=0.645\), \(V_{S}=16.0\) knots), the \(C_{R}\) takes up approximately 77% of the \(C_{TS}\), which is a considerable portion. Since \(C_{R}\) is closely associated with the waemaking resistance and viscous pressure resistance, the efficient hull-form design should focus on reducing those resistance components. The former part can be reduced by bow hull-form design, while the latter is controllable by appropriate stern appendage. It is worthwhile to mention that these two approaches are employed in the present study.  
Figure 4: Total resistance coefficient \(C_{TS}\) and residuary resistance coefficient \(C_{R}\) obtained from the model test.  
Figure 3: Computational grid of overlapping and background region (medium mesh).  
The total resistance of model \(R_{TM}\) at design speed predicted by CFD is compared with that measured in the model test in Table 2. To ensure mesh independency, three different grids are created and simulated for the initial hull. Considering the results and computation time of numerical simulation, the medium mesh is selected as the basic grid system. The error between \(R_{TM}\) is only 1.6%, which corroborates the reliability of the present CFD prediction. In addition, the trim and sinkage values predicted by CFD are in close agreement with those values measured by the model test.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 6 Feature Selection
### Embedded Approaches  
* Linear discriminant analysis (LDA). This algorithm is a statistical method defined by the probability of the joint discrete/categorical variables on their distribution. This algorithm [53] is the evolution of a statistical method designed to distinguish between classes of plants. It is defined by the probability of the joint discrete/categorical variables on their distribution.
* K-nearest neighbors (KNNs). The KNN algorithm classifies new values based on similarity [14]. This similarity is achieved using two centroids, and the distance is measured for all the observations. This is how the cluster is made; this centroid moves until the process end. This algorithm is used for data reduction because of its clustering ability.  
Some studies of EEG signal classification use the cited algorithms in different combinations as input, selection, or classification algorithms. In addition, feature selection has been performed on raw EEG in the time domain. For example, the CNN algorithm [54] could be fed with signal values, calculated features, or images. This application provides a range for using this algorithm to find relationships between the signal information. Another algorithm that serves as an example is the ICA [47]. Some studies are mentioned as feature itself [55], and others as a part of the selection algorithms. Even though ICA is also used for noise reduction as a pre-processing algorithm, depending on the focus of the study and the type of information analyzed, recall that some studies of motor/imagery identification use raw signal information and other preferred calculated features. Additionally, the methodology of some studies, such as [56], refers to the absence of a normalization process or a feature selection process. The information considered in Table 2 refers to some studies and their connections with these techniques.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Author(s)**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Feature(s)**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Feature Selection**} & \multirow{2}{*}{**Quality Measures \%**} \\  & & & & **Extraction Domain** & & & \\ \hline [PERSON] et al. [57] & 2016 & BCI & Time- & \multirow{2}{*}{DWT} & No & Accuracy = 93.30 \\  & & Competion & Frequency & & & \\  & & 2003 Dataset & & & & \\ [PERSON] et al. [58] & 2017 & Previous & Time- & \multirow{2}{*}{DWT, RAMP} & Yes & Accuracy = 89.3-93.3 \\  & & & work and & & & \\  & & Public & & & & \\ [PERSON] et al. [20] & 2018 & Physionet & Time & Raw Data & No & Accuracy = 89.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature extraction and feature selection techniques.