esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation for the Highway Subgrade Stability
### Numerical Results and Discussion
#### 4.3.2 Influences of ELB Width on the Subgrade Stability  
According to the Chinese Technical Code of Building Slope Engineering (GB50330-2013), the safety factor of the subgrade is 1.25. Figure 9 shows the relationship between the stability factors and the ELB width. It can be seen that the stability factors of the subgrade increase with the increase in ELB width. This is agreeable with the analysis results in Section 3.1. It is found that the stability factors of the subgrade with the ELB are greater than those of the subgrade without the ELB (\(F_{S}\) = 1.03). The stability factors of the subgrade with width \(B\) = 10 m and height \(H\) = 6 m increase by 22.33% compared with those of the subgrade without the ELB.  
Figure 8: Shear strain increment contour of the model with ELB (H = 6 m, B = 10 m).  
Figure 7: Shear strain increment contour of the model without ELB.  
A linear equation was utilized to fit the relationship between the stability factors and the ELB width. The fitting formula and correlation coefficient are shown in Table 2. When ELB height \(H=3\) m, the slope of the fitting formula is 0.014. When ELB height \(H=4\) m, 5 m, and 6 m, the slopes of the fitting formula are all 0.016. The correlation coefficients are higher than or equal to 0.98 for every fitting formula. The correlation coefficient is very high. It suggests that the stability factors of the subgrade increase linearly with the ELB width increase. This relationship is consistent with the analytical results in Section 3.1.
# Smart Facility Management System Based on Open BIM and Augmented Reality Technology
## 5 System Development and Validation  
### Development of AR-Based Smart FMS  
To validate the proposed AR-based smart FMS, the system was developed according to the aforementioned BPMN and functional display UI designs. The generation of AR visualized data and the simulation of data transmission were configured according to the maintenance scenario previously mentioned. AR-related software development kits (SDKs) and 3D rendering tools were needed to develop the AR application proposed in this study. The development environment for the application prototype was based on C# and utilized Unity and PTC Vuforia. In addition, Android-based mobile devices (e.g., smartphones and tablets) were adopted to run the developed application, and the IFC version of the open BIM model used in the system was IFC2 \(\times\) 4. The development process of the proposed AR-based smart FMS is shown in Figure 10. The BIM modeling platform was Autodesk Revit, and COBie data was automatically extracted using Revit add-ons. For AR visualization, marker-based technology was utilized, and QR codes were used to recognize spatial information. The information in this QR code was interfaced with the COBie datasheet to display maintenance attribute data to the user.  
Figure 9: System’s functional display configuration.  
As shown in Figure 11, when the field inspector visits the maintenance site and runs the application, he or she receives the spatial information and BIM data along with the IoT sensor information via a QR code. In addition, the attribute data as well as the maintenance history track record for the member being inspected is reviewed through web-based COBie data, and the inspector performs the inspection. In this process, the application manager is able to store and synchronize the inspector's work status and updated information on a real-time basis.  
The information exchange process created and transmitted during this maintenance work is shown in Figure 12. The system's underlying data utilizes BIM data at the time of handover. The BIM data in this step includes more detailed information about the space and interior finishes, and these spatial and attribute data are used through interfacing with COBie. The 3D shape information from the information required for AR visualization requires a separate extraction process. In addition, the modu  
Figure 11: On-site inspection process using AR-based smart FMSs.  
Figure 10: Development process for AR-based smart FMSs.  
AR marker information and to visualize the information are tracking, rendering and measurement modules that are implemented through separate application programming interfaces (APIs). In addition to the AR visualization function, interior navigation, data transmission, voice recognition, video streaming, auto-measurement and photography functions are required. When the inspection is completed, the resulting information is stored in COBie's 'Issue' sheet, while the progress and completion of the maintenance work is automatically updated with the relevant information on the 'Job' sheet.
# A Scalable Deep Network for Graph Clustering
## 7 Analysis of Transmission Probability \(\alpha\)  
The linear propagation layer adopts the propagation strategy defined by Equation (11). Owing to no parameters, it simply performs linear operations on the original matrix, which greatly improves scalability and reduces computational costs. We set \(\alpha=0,0.1,0.3,0.5,0.7\), and \(1\), respectively, on each dataset, and measure their final clustering effect. In this way, we obtain the most suitable transmission probability \(\alpha\) setting, and the experimental results are shown in Figure 3. The experimental results generally show a trend of increasing first and then decreasing with the increase in \(\alpha\). The transition probability essentially indicates the probability that the target node learns from itself or its neighbor nodes. Based on the experimental results, we can infer that learning from only one of them is not sufficient. Therefore, it is necessary to find a suitable \(\alpha\) to integrate the information of the target node and its neighbor nodes to obtain the deep embeddings.
# Zero-Voltage Ride-Through Capability of Single-Phase Grid-Connected Photovoltaic Systems
## 1 Introduction
Thus, a lot of synchronization techniques can be found in the recent publications, such as the zero-crossing detection method [21; 22], the Kalman filter (KF)-based synchronization method [23], and the discrete Fourier transform method [24], and the phase-locked loop (PLL) methods. The synchronization based on PLL techniques is very attractive among the various synchronization methods because of its easy implementation, high robustness and cost-effectiveness [2; 25; 26; 27; 28; 29]. Moreover, due to harmonic resonances and grid voltage sags caused by the high penetration of the renewable energy system in low-voltage distributed networks, the more advanced and efficient control strategies in single-phase PV systems are desirable in order to ride through the grid faults. The conventional dual-loop control structure in single-phase PV systems could be applied under the grid fault by directly calculating the current references [30]. When the instantaneous power theory is adopted in single-phase PV systems, the single-phase instantaneous active and reactive powers (PQ) theory [31] can be used to independently control the active and reactive power under LVRT as reported in [11; 12; 13; 14]. In addition, the droop control methods could be considered as an alternative for regulating the active and reactive power under the grid fault [32], on condition that the grid is mainly inductive. Nonetheless, in light of the above, it becomes essential to combine a fast and accurate synchronization mechanism with a dedicated control strategy for the ZVRT operation. Thus, in this paper, the performance of single-phase PV systems under an extreme grid fault (i.e., the grid voltage dips to zero) is explored. First, an overview of the typical control structure of the single-phase PV system is presented. Furthermore, the performance of the commonly-employed and recently developed PLL methods are assessed in terms of accuracy and dynamics. A control strategy is then proposed for the single-phase grid-connected PV system in the case of ZVRT operation. Simulationsare performed in order to validate the performance of the proposed control strategy. Results have verified the effectiveness of the proposed method. Finally, concluding remarks are drawn.
## 3 Characterization of Synthesized Silica Nanoparticles
The XRF analysis provides the emission of characteristic secondary (or fluorescent) X-rays from a material that has been excited with high-energy X-rays or gamma rays, which leads to the elemental analysis and chemical analysis of the specimen under investigation and is a more effective way to identify the chemical components that make up the nanoparticles as constituents. Table 2 provides compositions of SNPs using X-ray fluorescence (XRF) showing the presence of SiO\({}_{2}\) as the highest compound present in the samples, with the percentage ranging from 96% to 93%, while other compounds, such as, K\({}_{2}\)O, SO\({}_{3}\), CaO, Fe\({}_{2}\)O\({}_{3}\), CuO, and ZnO were also present in trace amounts. \begin{table}
\begin{tabular}{c c c c c} \hline
**Silica Nanoparticle** & **FTIR** & **XRD** & **EDX** & **Particle Size** & **Reference** \\ \hline Barley-grass-synthesized & Si-O-Si \(\sim\)1076 cm\({}^{-1}\) & Amorphous & Si, O, and C & 70 nm & [31] \\ nano-amorphous silica & Si-O-Si \(\sim\)1076 cm\({}^{-1}\) & Amorphous & Si, O, and C & 102 nm & [31] \\ Barley-husk-grass-synthesized & Si-O-Si \(\sim\)1060 cm\({}^{-1}\) & Amorphous & Si, O, and C & 145 nm & [30] \\ Barley-grass-synthesized & Si-O-Si \(\sim\)1080 cm\({}^{-1}\) & Amorphous & Si, O, and C & 131 nm & [20] \\ nano-amorphous silica & Si-O-Si \(\sim\)1080 cm\({}^{-1}\) & Amorphous & Si, O, and C & 25 nm & [68; 69] \\ Rice-husk-synthesized & Si-O-Si \(\sim\)1080 cm\({}^{-1}\) & Amorphous & Si, O, and C & 25 nm & [68; 69] \\ nano-amorphous silica & \multicolumn{1}{c}{} & & & \\ \end{tabular}
\end{table} 
Table 1: Characterization results of nanoparticles from different sources. Figure 5: SEM images of synthesized silica nanoparticles for (**A**) amorphous NS barley; (**B**) amorphous NS wheat; (**C**) AFM results for NS barley; (**D**) magnetic NS barley; (**E**) hydrophobic NS barley; and (**F**) AFM results for NS wheat.
## 5 Applications  
The integrated geological information combined within the 3D model has become a useful tool to visualize the depth and geometry variations below ground to characterize a large variety of properties that the units contain and hence to evaluate the underground geological conditions, for instance, to optimize the planning and development of subsurface structures in urban areas. Typically, 3D subsurface modeling outputs are not utilized directly in the urban planning process but form the basis for applied models [57], for example, dynamic monitoring models, and for information on geotechnical and geochemical subsurface properties. Geological and geotechnical applications based on the attributes each unit was parametrized with are mainly discussed in this work.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 4 Overview of Indoor Localization Accuracy Based on the RBF Method
### Localization Errors from the All (Ceiling and Wall) GW Combinations  
The combined configurations, employing both ceiling-mounted and wall-mounted receivers, demonstrate improved localization accuracy compared to the separate wall and ceiling configurations previously discussed. The enhancements are especially significant in the front pocket, back pocket, top pocket, and wrist positions across varying numbers of gateways. This combination offers more comprehensive and robust coverage, leading to improved accuracy in indoor localization. Figure 15 illustrates the localization errors for all possible combinations of wall and ceiling gateways. Table 5 presents the best localization accuracies achieved using both ceiling-mounted and wall-mounted gateways in configurations ranging from 2 to 10 gateways.  
Figure 14: Best localization errors found and their corresponding ceiling GW combinations.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **2 GWs** & **3 GWs** & **4 GWs** & **5 GWs** & **6 GWs** & **7 GWs** & **8 GWs** & **9 GWs** & **10 GWs** \\ \hline Back Pocket & 1.2611 & 0.79428 & 0.79073 & 0.79873 & 0.84529 & 0.79335 & 0.78744 & 0.82037 & 0.99724 \\ \hline Front Pocket & 1.3633 & 1.2513 & 1.0562 & 0.9921 & 1.0104 & 1.0608 & 1.0626 & 1.0854 & 1.1946 \\ \hline Shoulder & 0.78079 & 0.75438 & 0.60427 & 0.55268 & 0.48302 & 0.50486 & 0.54446 & 0.63895 & 0.74947 \\ \hline Top Pocket & 1.2281 & 0.95874 & 0.87405 & 0.93692 & 0.9524 & 1.0238 & 1.0905 & 1.1378 & 1.2553 \\ \hline Wrist & 1.3756 & 1.0343 & 0.84019 & 0.74182 & 0.72923 & 0.71245 & 0.75607 & 0.77685 & 0.90835 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The best localization accuracies using all gateway receivers. The configuration is evaluated by combining both ceiling-mounted and wall-mounted receivers. The results in red boxes are depicted in detail in Figure 16. Highlighted boxes indicate the best errors for each quantity of GWs.  
Figure 15: Indoor localization errors using all possible combinations of wall and ceiling GWs.  
The shoulder position retains its low error rates in the combined configurations, with improvements becoming more pronounced as the number of gateways increases. The combined setup enhances the reliability of the shoulder position for precise indoor localization. These results indicate that integrating both ceiling and wall-mounted gateways yields a more robust and accurate indoor localization system across various body positions. Figure 16 illustrates the best localization errors for the respective ceiling and wall combinations.  
Figure 16: Best localization errors found and their corresponding ceiling and wall GW combinations.
## 2 The MPwise System
### Hardware  
The system is designed in a modular manner, hence allowing different configurations of sensors and communication interfaces to be employed, depending on the case at hand. Such an arrangement would also help in future developments when the system is expanded from seismic to other hazard types. The current MPwise hardware (Figure 1) consists of three main parts, as shown in Figure 2, which presents a schematic overview of the system's architecture, namely the digitizer board (see Table 3), the microcomputer and communications board (see Table 4), and the external sensors. * The digitizer board (Table 3), i.e., an analogue-to-digital converter (ADC), was designed as a low-power consumption solution and considered special seismic requirements such as a high resolution (number of bits) in the analogue to digital conversion, exact time marks (using a (Global Navigation Satellite System--GNSS--module which is also used for locational information) and minimal drift in the time accuracy included in its design. The board is equipped with a 6-channel ADC which has a resolution of 24 bits, with the sampling rate selectable from 50 to 400 samples per second (sps). * The microcomputer (Table 4) board has three roles: (1) acquisition and storage of data; (2) data processing and analysis; and (3) communications, i.e., it includes the periphery for data transmission. It is made up of an embedded 8-core ARM processor ([[http://www.arm.com/](http://www.arm.com/)]([http://www.arm.com/](http://www.arm.com/)); see Table 4) that uses a micro SD card (currently 32 GBytes, but easily expandable) as a local data
## 3 Proposed VC-FFs
### VC-DFF Circuit Architecture and Operations  
The architecture of the VC-DFF is illustrated in Figure 3. It mainly consists of two VC-SOT MTJs (M\({}_{\text{data}}\) and M\({}_{\text{ref}}\)), four control transistors (N\({}_{2}\)\(\sim\)N\({}_{5}\)), five transmission transistors (N\({}_{1}\) and N\({}_{6}\)\(\sim\)N\({}_{9}\)), three inverters (INV\({}_{1}\)-INV\({}_{3}\)) and a pre-charge sensing amplifier (PCSA) [20; 23]. In this architecture, D represents the input data. CLK and CLKB are two complementary clock signals. Q and QB represent two complementary output signals. M\({}_{\text{data}}\) and M\({}_{\text{ref}}\) are data cell and reference cells, respectively, used to store one single bit. Cr represents the read control signal, which is employed to read the data stored in the MTJ device. It is defined by the following formula:  
\[\text{Cr}=\left\{\begin{array}{ll}0,&0\ll t<\frac{\mathrm{T}}{\mathrm{d}}\\ 1,&\frac{\mathrm{T}}{\mathrm{d}}\ll t<\frac{\mathrm{T}}{\mathrm{d}}\ \mathrm{a}=1,\\ \text{CLKB},&t\gg\frac{\mathrm{T}}{\mathrm{d}}\end{array}\right. \tag{1}\]  
where T represents a clock cycle. The control signal Cr is always consistent with CLKB, except for the first half of the clock cycle. The VC-DFF uses a symmetric complementary scheme, where two MTJs are used to store one bit. As shown in Figure 3, both the T3 terminals of M\({}_{\text{data}}\) and M\({}_{\text{ref}}\) are shorted to ensure the opposite direction of the writing current. It is noted that M\({}_{\text{data}}\) and M\({}_{\text{ref}}\) are in complementary states. This complementary approach for data storage improves the read margin of the PCSA. The increase in the read margin greatly improves the speed and reliability. The working status of the VC-DFF can be divided into normal mode, standby/power off mode, and recovery mode, based on the power supply status of the circuit.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 1 Introduction
### Iterative Steganography  
Iterative steganography is a unique approach to information hiding, distinct from traditional techniques that encode data within a single, complete unit, such as individual images [15] or text documents [16]. This method is designed for covers with an inherent iterative structure, consisting of multiple repeated elements over time. In iterative steganography, information is encoded incrementally, leveraging the cover's natural division into homogeneous segments. The full retrieval of the hidden message is only possible after multiple iterations.  
Prominent examples of iterative steganography include video steganography [17], where individual video frames naturally serve as iterations for encoding information, and network steganography [18], which utilizes repeated network packet calls as iterations in the steganographic process.  
A key feature of iterative steganography is its temporal dimension, which organizes consecutive iterations over time. Any repetitive phenomenon with intervals of information transmission can serve as an effective cover for iterative steganography.  
In steganographic methods based on iterative algorithms, each iteration involves two counteracting processes:  
* Information increment: arising from the decoding of data embedded within the steganogram,
* Information degradation: caused by transformations or interference affecting the steganogram.  
The successful retrieval of hidden information depends on the rate of information gain surpassing the rate of degradation, and the decoded information reaching a critical threshold necessary for complete message recovery.  
This study seeks to develop a formal method for quantifying the balance between information gain and loss in each iteration of iterative steganographic techniques. The aim is to determine whether the incremental accumulation of information over successive decoding iterations provides meaningful insights into the characteristics and performance of these methods. By introducing the Incremental Information Function (IIF), this research establishes a comprehensive framework for analyzing information increments in iterative steganographic processes. In contrast to traditional steganography, which focuses on single instance encoding, iterative methods require a distinct approach to understanding how information is progressively embedded and extracted over multiple iterations. Practical examples are presented to illustrate the applicability of the proposed method, providing a robust tool for evaluating and optimizing the efficiency, robustness, and capacity of iterative steganographic systems.
# The Biocompatibility of Wireless Power Charging System on Human Neural Cells
## 2 Material and Methods
### Cell Cultures and Cultivation Conditions  
Three commercially available human neural cell lines were used in the present study. Human astrocytes HA (ScienCell, Carlsbad, CA, USA) were grown in astrocyte medium (AM, ScienCell) supplemented by 2% fetal bovine serum (FBS, ScienCell), 1\(\times\) astrocyte growth supplements (AGS, ScienCell) and 100 I.U./mL of penicillin and 0.1 mg/mL of streptomycin (P/S, ScienCell). The neuroblastoma cell line (SH-SY5Y, ECACC, Salisbury, UK) was grown in Dulbecco's modified Eagle's medium/Ham's nutrient mixture F12 (DMEM/F12 = 1:1, Sigma-Aldrich, Darmstadt, Germany) supplemented by 10% fetal bovine serum (FBS, Sigma Aldrich) and 100 I.U./mL of penicillin and 0.1 mg/mL of streptomycin (P/S, Biosera). Glioblastoma cell line (T98G, ECACC) was grown in DMEM high glucose (Sigma-Aldrich) supplemented by 10% fetal bovine serum (FBS, Sigma-Aldrich) and 100 I.U./mL of penicillin and 0.1 mg/mL of streptomycin (P/S, Biosera, Nuaille, France). Cells were cultivated at standard conditions (5% CO\({}_{2}\), 37 \({}^{\circ}\)C, humidified atmosphere). HA and T98G were plated at the density of 5 \(\times\) 10\({}^{3}\) cells/cm\({}^{2}\), SH-SY5Y was plated at the density of 1 \(\times\) 10\({}^{4}\) cells/cm\({}^{2}\). Cell morphology was analyzed by light microscope under an inverted phase contrast (Optika XDS-2, Ponteranica, Italy), magnification 100\(\times\).
## 2 Results
### Metabolite Changes in P. Acerifolia Leaves in Response to C. ciliata Feeding Damage  
To analyze the changes in metabolite profiles that occur in response to _C. ciliata_ feeding, we compared the metabolic compounds isolated from _P. acerifolia_ leaves after _C. ciliata_ adults were allowed to feed for 24 and 48 h. We detected 50 metabolites in these samples and performed Principal component analysis (PCA) (Figure S4) and Partial least squares discriminant analysis (PLSDA) (Figure 3A) analyses. As shown in Figure S4 and Figure 4A, the _C. ciliata_ feeding treatments and the controls can be distinguished by their metabolite profiles. After ANOVA analysis, we found that the levels of nine metabolites were significantly different (p <0.05); these included glycoside, cinnamate, indole- 3- acetamide, L-tryptophan, epigallocatechin, L-fucitol and three amino acids (L-lysine, L- valine, and serine) (Figure 4B). Flavonoids are often closely related to insect interactions in plants. In this study, we detected one flavonoid chemical, epigallocatechin, which is increased at 24 h and decreased at 48 h. Combining the RNA-seq datasets, we detected seven key genes for epigallocatechin biosynthesis, including three genes involved in the biosynthesis of delphinidin from leucodelphinidin, and four genes that catalyze the biosynthesis of epigallocatechin from delphinidin (Figure 5). In addition, the relative expression values of these genes are consistent with the observed changes in epigallocatechin levels (Figure 5). These genes should be candidates for the key genes in the epigallocatechin biosynthesis pathway.  
Figure 3: MapMan visualization of DEGs in the _P. acerifolia_ response to _C. ciliata_ feeding damage. The up- and down-regulated genes are indicated in red and blue colors, respectively. The log\({}_{2}\)FC (treatment TPM/mock TPM) are shown in the scale bars. (**A**) 24-h treatment. (**B**) 48-h treatment.