esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Program and Procedures
Five stress paths with loading angles of 56.3\({}^{\circ}\), 71.6\({}^{\circ}\), 90\({}^{\circ}\), 123.7\({}^{\circ}\) and 251.6\({}^{\circ}\) were chosen in SPTT, corresponding to BL, AL, CP, LU and AU tests, respectively, listed in Table 3. For BL (loading in both axial and lateral directions) tests, both \(\sigma_{1}\) and \(\sigma_{3}\) increase with \(\eta\) kept at 1.5. In AL (axial-loading) tests, \(\sigma_{1}\) increases with \(\sigma_{3}\) remains constant, and it is the most frequently-used stress path in triaxial tests. To implement CP (constant \(p\)) tests, \(\sigma_{3}\) decreases at half the speed of that of the increase in \(\sigma_{1}\). LU (lateral-unloading) tests were conducted to simulate the stress paths of soil elements in the side of foundation pits, by reducing \(\sigma_{3}\) and keeping \(\sigma_{1}\) constant. The purpose of AU (axial-unloading) tests was to study the mechanical behavior of soil elements in the bottom of foundation pits, as shown in Figure 1. It is worth noting that in AU tests, \(\sigma_{3}\) remains unchanged and \(\sigma_{1}\) is reduced to be less than \(\sigma_{3}\), so that negative \(q\) occurs. The first two series are increasing-\(p\)' stress paths, and the latter two belong to decreasing-\(p\)' stress paths, while the third represents the constant-\(p\)' stress path. In SPTT, soil specimens were consolidated anisotropically along the \(K_{0}\) line in order to simulate the in situ stress state. \(K_{0}\) was calculated according to the well-known [PERSON]'s equation [54], i.e., \(K_{0}=1-\sin\,\varphi^{\prime}=0.53\). During this stage, \(\sigma_{3}\) was increased at a rate of 10 kPa/h to ensure the dissipation of excess pore water pressure. When the target anisotropic stress state was reached, both \(\sigma_{1}\) and \(\sigma_{3}\) were kept constant for another 2 h until the drainage rate of pore water was less than 1 mm\({}^{3}\)/min. Afterwards, the anisotropically consolidated soil specimens were sheared along predetermined stress paths. To avoid the accumulation of pore water pressure, the tests were performed slowly enough, with \(q\) increased linearly at a rate of 8 kPa/h, which was determined according to the research of [PERSON] [19]. All the triaxial tests in this study were conducted using the TAS-LF (Triaxial Automated System--Load Frame) experiment system. The system consists of a load frame used to control axial load or displacement, two pressure volume controllers (PVC) for cell and back pressure or volume controlling, a data acquisition device, and the control software. The range of the submersible load cell for axial-loading is 10 kN with an accuracy of 0.01 kN. For PVC, the resolutions of measurement of volume and pressure are 1 mm\({}^{3}\) and 1 kPa, respectively.
# Tailored Nanoparticles as Vaccine Components
## 3 Types and Uses of Nanoparticles in Vaccines
### Inorganic Nanoparticles  
Inorganic nanoparticles that are made, for example, from gold, silica, or carbon, were used in the development of vaccines due to their rigid structure and tightly controllable synthesis [11; 19; 24]. Since they are not biodegradable, though, they might cause inflammation and even fibrosis in some organs such as the lower lungs [11; 17]. Inorganic NPs are usually isotropic (internally unstructured); they can be produced by first using a reducing agent to form small particles of the respective material, followed by using silver acetate and cetyltrimethylammonium bromide to bring them into the desired size and shape [19].  
Gold nanoparticles (AuNPs) can be easily manufactured in different shapes (cubic, spherical, etc.) with sizes between 2 nm and 150 nm, and thus, they are used in vaccine delivery with gold nanorods conjugating the protein antigen to the surface [44; 45]. They can be modified at their surface with multivalent carbohydrates that interact with lectins (carbohydrate-binding proteins of the cell surfaces), with a potential for tissue-specifictargeting [30]. Various types of AuNPs were used both to carry antigens derived from viruses, such as the influenza virus, and DNA of the human immunodeficiency virus (HIV) [19; 24].  
Carbon nanoparticles were studied for vaccine delivery because they are biocompatible and can be produced in different shapes, e.g., nanotubes and mesoporous spheres. Carbon nanotubes lead to intense IgG responses since they can carry several copies of protein and peptide antigens [24].  
Nanoparticles based on silica (SiO\({}_{2}\), SiNPs) are biocompatible and water-soluble, with long-lasting blood circulation and vaccine delivery, which make them one of the most encouraging inorganic materials used in nanovaccines and delivery system design [26; 46]. They can be mesoporous and even hollow, with very high antigen loading capacity. The surface silanol groups allow functionalization with antibodies or other proteins for cell recognition, absorption of additional biomolecules, and strengthened cellular uptake [47; 48]. SiNPs can be fabricated with a luminescent core and a paramagnetic coat, allowing for multimodal imaging by microscopy and magnetic resonance [11; 49].
# A Lightweight Multi-Source Fast Android Malware Detection Model
## 3 A Lightweight Multi-Source Fast Android Malware Detection Model
### Android Application Structure and Its Feature Selection and Feature Extraction Scheme  
As shown in Figure 1, Android application package is a ZIP file with the extension of apk, which contains all the contents of the Android application. Assets stores the static resource files required by the application, such as images, etc. The resources files in the res directory are compiled into binary to generate the corresponding index IDs in the R.java file. lib directory stores the library files written in C/C++. META-INF stores the signature information of the application, which will be checked before installing the application to ensure the integrity and security of the Android application package. resources.arsc file is used to record the correspondence between the resource files and IDs in the res directory. This model mainly uses the classes.dex and AndroidManifest.xml files.  
classes.dex is an executable file for the Android Runtime (ART) and Dalvik virtual machines. The compact Dalvik executable format is designed to work on limited memory and processor speed systems.  
classes.dex is essentially all the program logic of an Android application, given that Android applications are typically written in Java and compiled to bytecode by the dx tool. The Java compiler compiles all Java source files to Java bytecode (class files), and then the dx tool converts them from Java bytecode to Dalvik-compatible bytecode Dex files. The dx tool eliminates the redundant information present in classes, and in dex files, all.class files are wrapped into one file, merging the.class header information and sharing a pool of constants and indexes as in Figure 2. As a result, the vast majority of the code logic of an Android application is contained in the classes.dex file.  
Mobile apps frequently request access to sensitive information, such as unique device ID, location data, and contact lists. Android currently requires developers to declare what permissions an app uses [26]. AndroidManifest.xml is the manifest file of the Android application, which describes each component of the application and defines the manifestation of the component such as component name, theme, launch type, operation of the component, launch intent, etc. The manifest file also declares the application properties and permission information.  
Figure 1: The Android application package with the file extension apk is a container file in which all parts of the Android application are packaged.  
In summary, the classes.dex and AndroidManifest.xml files contain most of the features of Android applications, so this paper ignores the other files in the APK file and only takes the classes.dex file and AndroidManifest.xml file to extract the features.
## 2 Materials and Methods
### Data Analysis  
The traits and SRI data were analyzed according to a randomized complete block design using the Proc Mixed procedure in SAS software (v9.4, SAS Institute, Inc., Cary, NC, USA). Data were analyzed in each growing year and growth stage, and in combined years and growth stages. Mean squares of combined analyses were obtained using the Proc Mixed procedure following the TYPE1 method. Pearson's correlation coefficients (r) were used to test the relationships of plant traits with individual wavelengths in the full-spectrum range and different SRIs at each growth stage and across different growth stages. Principal component analysis (PCA) was conducted to detect the interrelationships between plant traits and all SRIs at each growth stage and across all stages using data from all genotypes over two years. PLSR combined with leave-one-out cross-validation was applied to relate plant traits with all SRIs. The prediction accuracy of the PLSR was evaluated by selecting the optimal number of latent factors (ONLVs). The best ONLVs were those yielding the smallest root mean squared error (RMSE) and largest coefficient of determination (R2). Correlation, PCA, and PLSR analyses were conducted using the XLSTAT statistical package (v2020.1, Excel Add-ins soft, SARL, New York, NY, USA).
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 2 Materials and Methods
### Temporal Feature Extraction
To evaluate the similarity measurement between the template beat and ECG signal heartbeat (PCC), features are extracted for our classifier as follows:  
* **PCC** (_Template beat_, _heart beat_) is defined as the Pearson's correlation coefficients between the template beat and ECG signal heartbeat. * **PCC** ( _Template beat_\({}^{\prime}\)_, _heart beat_\({}^{\prime}\)) is defined as the Pearson's correlation coefficients between the derivatives value of the template beat and ECG signal segmented beat. * **PCC** (_Template beat_\({}^{2}\)_, _heart beat_\({}^{2}\)) is defined as the Pearson's correlation coefficients between the squares ratio of the template beat and ECG signal segmented beat. * **PCC** \(\left((\mathbf{Template beat}^{\prime})^{2},(\mathbf{heart beat}^{\prime})^{2}\right)\) is defined as the Pearson's correlation coefficients between the squares of the derivatives of the template beat and ECG signal segmented beat. * **The Average of Template beat**\(\left|\overline{\mathbf{Template beat}}\right|\) is defined as the average of the template beat. Figure 5: Template matching of segmented heartbeat where red is the original abnormal beat and blue and black is the original normal beat and template beat, respectively. The Average Derivate of Template beat \(\left|\overline{Template\ beat}^{\prime}\right|\) is defined as the average derivatives of the template beat. To this end, the total 11 features are selected for the study proposed classifier in which five were based on temporal features of ECG morphology and the rest of the features were based on (PCC) by using the template matching approach. All these features are stored in the matrix denoted as \(\mathbf{F}(\mathbf{n})\).
## 3 Case Study
### Project Specification
#### 3.2.3 The Multi-Dimensional Joint Distribution Model for the Duncan-Chang Model Parameters
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameters** & \(\varphi\varphi^{0}\) & \(R_{f}\) & \(K\) & \(K_{b}\) & \(m\) & \(n\) \\ \hline \(\varphi/^{\circ}\) & 1.00 & 0.03 & 0.57 & 0.40 & 0.19 & \(-\)0.21 \\ \(R_{f}\) & & 1.00 & 0.20 & 0.27 & 0.06 & 0.21 \\ \(K\) & & & 1.00 & 0.87 & 0.11 & \(-\)0.17 \\ \(R_{b}\) & & & & 1.00 & 0.08 & 0.06 \\ \(m\) & & & & & 1.00 & 0.14 \\ \(n\) & & & & & & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 15: The correlation coefficient among the parameters in the Duncan–Chang E-B model in the overburden. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameters** & \(\varphi\varphi^{0}\) & \(R_{f}\) & \(K\) & \(K_{b}\) & \(m\) & \(n\) \\ \hline \(\varphi/^{\circ}\) & 1.00 & 0.03 & 0.57 & 0.40 & 0.19 & \(-\)0.21 \\ \(R_{f}\) & & 1.00 & 0.20 & 0.27 & 0.06 & 0.21 \\ \(K\) & & & 1.00 & 0.87 & 0.11 & \(-\)0.17 \\ \(K_{b}\) & & & & 1.00 & 0.08 & 0.06 \\ \(m\) & & & & & 1.00 & 0.14 \\ \(n\) & & & & & & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 14: The correlation coefficient among the parameters in the Duncan–Chang E-B model in the rockfill. better in describing the multi-dimensional joint probability distribution of Duncan-Chang E-B model parameters for core wall, rockfill, and dam foundation overburden materials. Additionally, the parametric samples effectively cover the original samples (see Figure 8), indicating that the multi-dimensional joint distribution model for Duncan-Chang model parameters based on the \(t\) Copula function can accurately capture the nonlinear correlations between parameters and their joint probability distribution. Based on the statistical analysis of Duncan-Chang model parameters, the random characteristics of parameters in the dam and foundation are shown in Tables 17 and 18, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{2}{c}{**AIC**} & \multirow{2}{*}{**Number of \(t\) Copula \textless{} Gaussian Copula**} \\ \cline{2-3}  & **Gaussian Copula** & & \(t\)**Copula** \\ \hline Core wall & \(-139.95\) & \(-164.10\) & \(6720\) \\ Rockfill & \(-251.10\) & \(-264.43\) & \(8333\) \\ Overburden & \(-137.02\) & \(-171.37\) & \(6282\) \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Test results of the Copula function based on the AIC. Figure 8: The joint sampling results of typical parameters. and the multi-dimensional joint probability density functions of Duncan-Chang model parameters can be described by Equation (15).
## 2 Results and Discussion
### Method Validation and Real Sample Analysis  
To explore the practicability of the established method, it was applied to detect 11 food additives in environmental water samples. Under optimal conditions, standard solutions of food additives synthesized in 11 were added to all samples to evaluate the matrix effects. Four groups of experiments were set up; the first group was the original water sample, the standard solution addition in the second group was 1.6 \(\upmu\)g/L, the standard solution addition in the third group was 16 \(\upmu\)g/L, and the standard volume addition in the fourth group was 80 \(\upmu\)g/L. The obtained results are shown in Table 3. The recoveries were 86.53-106.32%, the inter-day RSD was 0.8-8.8%, and the intra-day RSD was 0.4-5.9%. Furthermore, Benzoic acid, temptation red, and erythrin were positive in the river water samples. The detected concentrations were 4.1 \(\upmu\)g/L, 1.2 \(\upmu\)g/L, and 7.0 \(\upmu\)g/L, respectively. Therefore, the detection of food additives in environmental water samples is very necessary.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Analytes**} & **Sample Concentration** & **Added** & \multirow{2}{*}{**Recovery \%**} & \multicolumn{2}{c}{**RSD \%**} \\  & **(\(\upmu\)g/L)** & **(\(\upmu\)g/L)** & & **Inter-Day** & **Intra-Day** \\ \hline \multirow{4}{*}{Acesulfame} & \multirow{4}{*}{—} & 1.6 & 97.60 & 1.5 & 2.3 \\  & & 16.0 & 96.45 & 3.8 & 5.8 \\  & & 80.0 & 99.21 & 2.9 & 0.4 \\  & & 1.6 & 98.02 & 5.8 & 3.8 \\ Benzoic acid & 4.1 & 16.0 & 101.53 & 0.8 & 4.2 \\  & & 80.0 & 99.63 & 6.5 & 5.5 \\  & & 1.6 & 98.25 & 2.9 & 1.9 \\ Sorbic acid & — & 16.0 & 95.65 & 3.2 & 3.6 \\  & & 80.0 & 96.02 & 1.8 & 1.1 \\ Dehydroacetic & — & 1.6 & 102.31 & 2.9 & 1.6 \\ acid & — & 1.6.0 & 106.32 & 3.7 & 4.2 \\  & & 80.0 & 105.27 & 4.1 & 5.9 \\ Saccharin & — & 1.6 & 98.24 & 5.8 & 1.3 \\ Sodium & — & 16.0 & 97.35 & 1.9 & 5.1 \\  & & 80.0 & 100.62 & 4.1 & 4.8 \\  & & 1.6 & 99.32 & 3.2 & 3.7 \\ Tartrazine & — & 16.0 & 101.52 & 6.2 & 1.2 \\  & & 80.0 & 99.61 & 4.7 & 1.4 \\  & & 1.6 & 99.45 & 2.3 & 3.7 \\ Amaranth & — & 16.0 & 102.90 & 1.5 & 2.2 \\  & & 80.0 & 104.80 & 6.9 & 4.1 \\  & & 1.6 & 91.40 & 8.8 & 2.7 \\ Sunset Yellow & — & 16.0 & 90.55 & 4.7 & 5.6 \\  & & 80.0 & 93.93 & 3.6 & 0.5 \\  & & 1.6 & 93.90 & 1.1 & 5.5 \\ Allura Red & 1.2 & 16.0 & 94.77 & 3.9 & 3.8 \\  & & 80.0 & 95.63 & 2.3 & 1.2 \\  & & 1.6 & 95.10 & 3.8 & 0.8 \\ Brilliant Blue & — & 16.0 & 88.23 & 4.1 & 0.7 \\  & & 80.0 & 90.52 & 2.6 & 4.2 \\  & & 1.6 & 89.71 & 1.3 & 4.8 \\ Erythrosine & 7.0 & 16.0 & 86.53 & 1.7 & 0.9 \\  & & 80.0 & 87.84 & 7.5 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Content, recovery rate, and relative standard deviation of 11 food additives in environmental water samples (\(n=6\)).
## 3 Results
### Atmospheric Correction Assessment  
Satellite-in situ matchups of 40 stations were used to evaluate the accuracy of the 6S atmospheric correction and then to develop and validate the satellite retrieval algorithm. By comparing the in situ measured \(R_{\rm rs}\) and the OLCI-derived \(R_{\rm rs}\) (Figure 4a), the 6S atmospheric correction model is found to perform well on the Sentinel-3A-OLCI data (RMSE \(<0.0100\) sr\({}^{-1}\), MNB \(>-15.00\)% and NRMS \(<50.00\)%). Whereas the values obtained after the correction are generally lower than the actual measurements (MNB \(<0\)), the systematic and random errors are both within acceptable ranges. In particular, the accuracy is high in the 681-nm and 754-nm bands (RMSE \(<0.0060\) sr\({}^{-1}\), MNB \(>-15.00\)% and NRMS \(<35.00\)%), which are the main bands used by the \(K_{\rm d}\) algorithm.  
Figure 3: Relationships between \(K_{\rm d}\)(490) and the bio-optical parameters of the water: (**a**) SPIM; (**b**) SPOM; (**c**) Chla; (**d**) \(a_{\rm g}\)(440).  
The spectral shape of the OLCI-derived \(R_{\rm rs}\) is generally consistent with that of the in situ measured \(R_{\rm rs}\) (Figure 4b). Overall, the 6S model-based atmospheric correction method is able to acquire relatively high-accuracy \(R_{\rm rs}\) data from OLCI data and can be further employed to estimate such water parameters as \(K_{\rm d}\)(490) and the Chla and SPM concentrations based on remote sensing data.
## 5 Evaluation
### Experimental Results
#### 5.3.5 The Cost with OT Protocol  
We estimated the overhead of the OT protocol using standard assumptions.  
Calculation cost: When there are \(d\) keys, \(2\ell\) encryptions are performed, where \(d=2^{\ell}-1\). If decoding one encryption key costs \(0.1s\), the computational cost is \(0.1s\cdot 2\ell\). The computational cost with respect to the number of keys is shown in Figure 10(a).  
Communication cost: Assuming that the communication amount of one cipher text is \(N\), the user sends the server \(2\ell\cdot N\) data. The server returns \(\ell\cdot N\) data to the user. Finally, the user sends \(2\ell\cdot N\) of cipher text to the server.  
The total communication cost is \(5\ell N\). If \(N=2048\) [bit] and communication speed is \(100\) Mbps, the communication will be \(5\ell\cdot 2048/(100\cdot 10^{6})\). The communication cost are shown in Figure 10(b).  
Figure 11: Costs of OT protocol. (**a**) Calculation cost. (**b**) Communication cost.  
Figure 10: Mean gain of poisoning attacks with OT. (**a**) Synthetic data. (**b**) MovieLens.
## 5 Evaluation Method of Wind Barrier Arrangement  
### Evaluation Indicators Definitions  
The characteristics of the flow field in the vicinity of the bridge pylon zone and the quantitative description both supply the basic information and data for setting up the evaluation method of wind barrier arrangement. In order to arrange the wind barrier in an effective way and improve driving safety near the bridge pylon zone, the influence coefficient ratio \(R\) and the safety factor of response time \(K\) are defined in Equations (12) and (13) to quantitatively evaluate the effect of wind barriers:  
\[R=\lambda_{smax}/\lambda_{stand} \tag{12}\]  
\[K=\frac{L_{d}/V}{T_{s}}, \tag{13}\]  
where \(\lambda_{smax}\) is the maximum influence coefficient at the pylon zone and \(\lambda_{stand}\) is the standard influence coefficient far from pylon area. \(L_{d}\) is the distance along the axial direction from the center of the pylon to the point where \(\lambda_{smax}\) shows up, \(V\) is the vehicle limit speed on the bridge, and \(T_{s}\) is the reaction time for a driver to deal with emergencies.  
\(R\) reflects the correlative relationship of wind speed between the area near and far from the pylon. If \(R\) is close to 1, it indicates that the wind velocity is almost the same both near and far from the pylon, which is conducive to maintaining straight driving. Therefore, a smaller \(R\) means a better effect of the wind barriers in reducing the wind speed.  
\(K\) reflects the relationship between the time drivers have and the time drivers need to deal with the sudden change in wind speed. In previous experiments, the shortest reaction time for the driver to correct deflection after feeling a crosswind was 0.2 s. However, vehicles actually get the reaction from drivers 0.8 s later, since steering involves a change in gap, elastic force, and lateral force [30].  
Figure 17: Comparison of influence coefficient for the experimental and numerical results.  
Considering the discrete and stochastic nature of reaction time, it is considered safe when \(K\) is larger than 2.5, which is a common number for the safety factor in many standards.
# Application of Virtual Reality for Learning the Material Properties of Shape Memory Alloys
## 4 Teaching Experiment
### Questionnaire Results  
The questionnaire results by the experimental group are derived by a statistical analysis to understand the attitudes of students after using the teaching module. For every question, the average score, as well as the number and percentage of each selected item, are calculated and shown in Appendix B. The questionnaire adopts a five-point Likert scale [35] (strongly agree: 5 points; agree: 4 points; neutral: 3 points; disagree: 2 points; strongly disagree: 1 point) to measure students' attitudes after using the teaching module. The reliability analysis is performed using the statistical software SPSS and the Cronbach's \(\alpha\) = 0.95, showing a high standard of reliability.  
* Learning contents Most students thought the contents of the teaching module is easy to understand and enables them to learn the properties and applications of the SMA. They agreed that the teaching module could be used to learn the related knowledge and basic concepts of the SMA and it enabled them to understand the SMA's crystal structure may change with the temperature. The questionnaire results show that most students gave positive feedback on the learning contents. The average scores ranges from 4.27 to 4.40, and the overall average score in this category is 4.33.
* Interface design Most students considered that the operation of the teaching module is easy and the simulation of the SMA properties and applications is close to the real situation. They thought the teaching module provides a friendly user interface as well as proper font sizes and clear images. However, the score for the operational speed is comparatively lower because the tablet PC needs more computation time in processing the 3D interaction but its CPU is not very powerful. Basically, the questionnaire results show that students were satisfied with the interface design. The average scores ranges from 3.63 to 4.37, and the overall average score in this category is 4.12.
* Operational experience Most students could actively complete the learning activities and the online test by following system instructions. They felt that the teaching module could help them understand the SMA's properties and the shaping process, and they could also understand that the crystal structure of SMA may change with temperature. Students thought the teaching module could increase their learning interest and they would like to use it for learning the related knowledge and basic concepts of the SMA. The questionnaire results show that students were satisfied with their learning experience after using the teaching module. The average scores ranges from 3.94 to 4.25, and the overall average score in this category is 4.1.