esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The potential, cost-effectiveness, versatility, and relative accuracy of the RTHS technique allow the concentration of resources on the assessment of the relevant components of interest within a dynamic system, which leads to reduced experimental setup and logistic cost, decreasing execution time of the tests, and increased productivity and efficiency compared to traditional large-scale testing. In this study, HSI effects on a footbridge with precise sensitivity in its dynamic properties affected by human-induced gait loads were evaluated using RTHS techniques. The implementation of this innovative methodology in the field of the experimental evaluation of HSI effects on footbridges allowed the division of the dynamic system into three principal components: a footbridge modeled using a fourth-order partial differential equation as the numerical substructure (NS), pedestrian walking as the experimental substructure (ES), and a multi-axial test framework developed and described by [PERSON] et al. [28; 84] as the transfer system (TS). This proposal allowed the assessment and measurement of the loads induced by human gait under laboratory conditions and simulated the complete states of the footbridge dynamic responses through a continuous model, which had an interaction in real-time to create a dynamic system with continuous feedback. The remainder of this paper is organized as follows. Section 2 provides details of the evaluated reference footbridge, a description of the general information of the test subjects (TSs), and a test description. In Section 3, the dynamic substructuring of the evaluation of the effects of human-structure interaction on a pedestrian bridge with precise dynamic sensitivity, the transfer systems used, and the experimental setup. A complete analysis of the dynamic performance of the reference footbridge under HSI effects of crowd-induced loads is presented and discussed in Section 4. In addition, the performance of the RTHS technique is determined. Finally, Sections 5 and 6 present the future works and conclusions of the study.
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 4 Results of Surface Discharge Simulations
### Effect of the Length of the UHF Sensor  
The length of the sensor was varied from 10 to 80 mm. The graphs in the bottom rows of Figures 9 and 10 show an increase in signal amplitude with UHF sensor length up to a certain value. The sensor length should be smaller than this, and in any case, it should be smaller than the minimum wavelength (\(\lambda_{min}\)) of the EM waves excited by the PD pulse. Since this is related to the PD pulse width, which is 0.4 ns in this case, the sensor length should be much less than \(\lambda_{min}=120\) mm. Clearly, satisfactory measurements can be achieved for lengths of 30 mm or less for all three positions. Beyond this length, there is no benefit in obtaining reflection-free signals.  
Figure 10: Results of parametric study, surface discharge, uniform field electrode configuration.
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy (FTIR) Test  
The silane coupling agent UP152 used in the study with the chemical name vinyltriacetoxysilane and the molecular formula C\({}_{8}\)H\({}_{12}\)O\({}_{6}\)Si was produced by the Nanjing Upchemical company. The chemical reaction mechanism between GF-WTB and UP152 is shown in Figure 8. The UP152 Si functional groups acetoxyl -OAc hydrolyze and generate silanol Si-OH, which are unsteady and generate Si-O-Si in further polycondensation, and the carbon functional group vinyl CH\({}_{2}\)=HC- in UP152 can react with some organic functional groups in asphalt materials. Therefore, the UP152 can couple the inorganic glass fiber recycled from WTB with the organic asphalt materials.  
Figure 7: Effect of GF-WTB content on BBR k-value under certain SBS dosage and temperature: (**a**) SBS2 + WTBx; (**b**) SBS3 + WTBx; (**c**) SBS4 + WTBx; (**d**) SBS5 + WTBx.  
The FTIR spectrograms of the untreated GF-WTB and UP152-modified GF-WTB are in Figure 9. Absorption peaks at 2929 and 2872 cm\({}^{-1}\) for the original GF-WTB respectively represent C-H stretching of CH\({}_{2}\) and CH in aliphatic of the polyester resin curing agent residues, corresponding to 2954 and 2842 cm\({}^{-1}\) for the modified GF-WTB [32]. The original GF-WTB and the UP152-modified GF-WTB show the band corresponding to the C=O stretching of the polyester resin curing agent residues respectively at 1728 cm\({}^{-1}\) and 1720 cm\({}^{-1}\)[33]. The new absorption peaks at 1635 and 1454 cm\({}^{-1}\) for UP152-modified GF-WTB respectively characterize the carbon functional group vinyl C=C stretching and the vinyl CH\({}_{2}\) scissor bending vibration. Absorption peaks at 1091, 699, and 479 cm\({}^{-1}\) for the modified GF-WTB characterize the antisymmetric stretching, symmetric stretching, and bending vibration of Si-O-Si, with the corresponding bands 1073, 699, and 477 cm\({}^{-1}\) for the original GF-WTB. The three absorption peaks all show the same trend; that is, the absorption peak of UP152-modified GF-WTB is stronger than that of original one. It shows that the chemical reaction between the coupling agent and the glass fiber surface produces new Si-O-Si bonds. The analysis of the FTIR results shows that the silicon coupling agent UP152 was successfully grafted to the surface of the recycled glass fiber from WTB.  
Figure 8: Chemical reaction mechanism between GF-WTB and UP152.  
Figure 9: FTIR spectrograms of untreated GF-WTB and UP152-modified GF-WTB.
## 5 Results and Discussion  
Figures 6-9 display the final depth-velocity models produced from the seismic refraction tomography for the seventeen lines. These models demonstrated that the studied site was constituted primarily of three geoseismic subsurface layers based on the seismic inversion and the calibration with the pre-implemented geo-electric resistivity studies in this site [35]. Figure 10 illustrates an example of a two-dimensional ERT profile that included a well location. These findings were consistent with the results of nearby boreholes scattered near the study site. A careful examination of both the P- and S-wave velocities 2D seismic models revealed that the maximum derived depth was 50 m. The range of the P- and S-wave velocities for the derived shallow subsurface geoseismic layers were 356.38 m/s to 2821.81 m/s and 225.83 m/s to 1896.28 m/s, respectively. The V\({}_{\rm P}\) and V\({}_{\rm S}\) values of the first geoseismic surface layer changed from 356.38 to 618.2 m/s and 225.83 to 496.24 m/s, respectively. It was composed of wadi deposits, which were made up of gravel, sand, and silt, and its thickness varied between 1.5 and 4.8 m. The V\({}_{\rm P}\) and V\({}_{\rm S}\) values of the second geoseismic layer varied from 1064.44 to 1303.6 m/s and 833.24 to 1119.74 m/s, respectively. Its lithology showed intercalated sand and muddy sand deposits. The thicknesses of this layer ranged from 14.7 to 35 m. The V\({}_{\rm P}\) and V\({}_{\rm S}\) values of the third geoseismic layer varied from 1955 m/s to 2821.81 m/s and 1155 to 1896.28 m/s, respectively. Its lithological nature was clay deposits, with its thickness undefined. All 17 velocity-depth models are shown in Figures 6-9. The results of the 17 surveyed profiles are summarized in Table 1. A careful investigation of the previous models indicated that there was no fracturing or faulting in front of the Osirion site according to the interpretation of the seismic wave velocities and the final geoseismic models. Figure 6: Example of the seismic refraction profile 1: (**A**) travel time–distance curve derived from the P-wave profile; (**B**) 2D depth–velocity model derived from P-wave profile; (**C**) travel time–distance curve derived from the S-wave profile; and (**D**) 2D depth–velocity model derived from the S-wave profile. Figure 7: The 2D depth–velocity models for the P-wave and associated S-wave profiles along the NW-SE direction were produced. Figure 8: The 2D depth–velocity models for the P-wave and associated S-wave profiles along the NW-SE and SW–NE directions were produced. Figure 9: The 2D depth–velocity models for the P-wave and associated S-wave profiles along the NW-SE and SW-NE directions were produced. Figure 10: The 2D electrical resistivity model of profile 1. This model emphasized the distinctive geoelectric zones that were identified using the borehole and resistivity data that were available and it also showed the depth of the groundwater level. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Profile No.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 3 Method of This Article
### Fusion  
Multiscale fusion algorithms are commonly used in the fields of image processing and computer vision. They can fuse meaningful information from different scales together, enhance the features of input images, and thus improve the accuracy and efficiency of image processing and computer vision tasks. Multiscale fusion has shown excellent performance in applications such as remote-sensing image processing [33], defogging and rain removal [34], and high-resolution imaging. In this article, we generate a stable fusion framework that can increase image visibility and emphasize image details. Our framework is built on images with different contrasts generated from a single original image.  
After the above processing, we obtained two enhanced image versions: \(V_{1}\) and \(V_{2}\). Because light exposure significantly affects image quality, we fused image regions with good brightness through exposure weight maps.  
\[w_{i}(x,y)=\exp\left\{-\frac{\left(V_{i}(x,y)-0.5\right)^{2}}{2\times 0.25^{2}}\right\} \tag{22}\]  
The formula for calculating the normalized weight map for fusion is as follows:  
\[\overline{w}_{i}(x,y)=\frac{w_{i}(x,y)}{\sum_{i}w_{i}(x,y)} \tag{23}\]  
where \(V_{i}(x,y)\) represents the \(i\)th input image at \((x,y)\). We use the pyramid fusion method. The resulting enhanced image is obtained as:  
\[V_{\rm es}(x,y)=\sum_{l}\left(\sum_{i=1}^{2}G^{l}(\overline{w}_{i}(x,y))L^{l} (V_{i}(x,y))\right) \tag{24}\]  
where \(l\) represents the number of layers of the pyramid, \(G(\cdot)\) represents Gaussian operators, and \(L(\cdot)\) represents Laplacian operators, respectively.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 1 Introduction
### Contribution and Paper Structure  
State-of-the-art work in AISs such as [3, 9] demonstrates the potential of AISs in IoT malware detection; however, no existing work validates this approach in a realistic implementation. In this work, we present the first study that examines the applicability of AIS methods in an IoT-like architecture under different constraints and datasets:  
* We use multiple state-of-the-art datasets with different types of malware attacks in the IoT to run AIS solutions for malware detection. We benchmark the results against the state-of-the-art intrusion detection methods that use the same datasets.
* We propose an AWS-enabled validation framework for the evaluation of AIS malware detection solutions, under realistic architecture and characteristics.
* The proposed framework is used to evaluate the performance of two leading AIS solutions under constrained systems. The memory size is found to be the most limiting factor that results in under-par performance compared to the reported simulation results for both solutions.
* We propose the first trial of transfer learning within IoT systems to combat the constrained memory in IoT devices. We demonstrate the transfer learning effectiveness of AIS solutions in securing the IoT.  
In Section 2, we present relevant research concerning malware detection in IoT systems utilizing AIS methodologies. In Section 3, we present using multiple datasets to run AIS solutions for malware detection. In Section 4, we present the AIS solutions' implementation in a realistic setup. We describe the IoT systems' architectures and the problem formulation. In Section 5, we present the implementation results, performance analysis, and discussion. We finally conclude in Section 6.
## 3 Results and Discussion
### Analysis of Molecular Structural Characteristics
#### 3.3.2 Deacetylation Degree  
The DD can significantly influence chitosan physiochemical properties and biological activity, which is attributed to with DD raised, the -NH\({}_{2}\) group of chitosan molecules protonated in acidic environment to form NH\({}_{3}\)\({}^{+}\) ions, and the charge density on the carbon chain increased [36]. The variation of chitosan DD could help to define the different properties of the following chitosan samples. It was found that the M\({}_{\text{W}}\) of CSS (3122.3 kDa) is higher than that of CSC (728.1 kDa) (not listed) when the reaction time is shortened to make their DD approximate to CS, which was similar to the result of [PERSON] et al. [37]. Therefore, under high temperature and pressure can reduce the effective breaking degree of \(\beta\)-1,4-glycosic bond and obtain high M\({}_{\text{W}}\) chitosan when the DD of CSC and CSS near commercial chitosan(CS). With the reaction time was prolonged, the DD of CSS and CSC were 97.8% and 92.7% respectively, which were significantly (\(p\) = 0.03) higher than that of CS (83.4%) (Table 3). The DD of prepared chitosan was similar to that reported by [PERSON] et al. [38] And [PERSON] et al. [39]. M\({}_{\text{W}}\) of CSC decreased and CSS decreased sharply, indicating the molecular chain of chitosan broken with the DD deepening, and CSS was more degraded than CSC may be related to their different molecular crystalline structure.
## 3 Proposed Design Method for Dual-Mode PUF
### Problem Formulation  
Based on the above analysis, the design space of an SPUF is shown in Figure 13. The objective of the design problem is to find an optimal point \((\textbf{X},\textbf{M})\) that combines the key parameters **X** (e.g., \(W\) of its transistors) associated with SRAM cell dimension and the aggressiveness **M** (e.g., by changing the voltage \(V_{X}\)) of WLM, DVS, NBL adaptation, to minimize  
\[F(\textbf{X},\textbf{M})=\min_{\textbf{X},\textbf{M}}\textbf{Pr}(\text{RF}, \text{WF}), \tag{9}\]  
subject to the following constraints:  
\[\left\{\begin{aligned} &\text{reliability}(\text{ECC}[n,k,t])\geq \text{reliability}_{\text{min}},\\ &\text{area\_cell}\leq\text{area\_cell}_{\text{max}},\\ &\text{leakage\_cell}\leq\text{leakage\_cell}_{\text{max}},\\ &\text{power\_cell}\leq\text{power\_cell}_{\text{max}},\end{aligned}\right. \tag{10}\]  
where \(\text{ECC}[n,k,t]\) is the error correction code specified by the code word length \(n\), number of information bits \(k\) and number of correctable errors \(t\). The PUF reliability is measured by the complement of BER. The area_cell, leakage_cell and power_cell refer to the area, standby power and dynamic power dissipated during the read and write operations of the SRAM cell.  
The design problem can be solved by the design flow described in Figure 14. It should be noted that the ABB technique is not considered at design time, but it is applied in the post-silicon tuning to calibrate against inter-die variation to reduce degradation of cell reliability [18]. Its application alongside with other techniques is limited mainly by the constraint of hardware resources and target fabrication process. In applying circuit-level techniques, combinations of multiple techniques should be considered if one technique alone is inadequate to reduce **Pr**(WF,RF) to meet the target. If **Pr**(RF)  
Figure 13: Design space exploration of SRAM cell for dual-mode SPUF design.  
dominates \(\mathbf{Pr}(\text{WF,RF})\), priority should be given to WLM. Otherwise, if \(\mathbf{Pr}(\text{WF})\) dominates, NLB should be included primarily. If \(\mathbf{Pr}(\text{RF})\) and \(\mathbf{Pr}(\text{WF})\) are comparable, then DVS should be chosen. The aggressiveness of the three techniques are increased by tuning their control knobs differently. For WLM, it means decreasing \(V_{WL}\) in read operation. For DVS, it means increasing \(V_{DD}\) in read operation and decreasing \(V_{DD}\) in write operation. For NBL, it means decreasing the bit-line voltage.  
The variables and constraints involved can be evaluated as follows.
# Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis
## 5 Conclusions  
In this article, a novel approach for spatial SPC based on wavelet spectrum analysis was proposed. With a brief review of existing approaches, we suggested an automated and quantified control method using an image processing method to detect spatial fault images in the manufacturing process without any assumptions about pre-determined values. In this procedure, 2D DWT and wavelet spectrums were utilized to calculate the degree of defects via Hurst exponents for divided windows. After the construction of the distance matrix derived from Hurst exponents was completed, the statistics and control limits were defined by the values and densities of matrix elements. The construction of the spatial control chart provided computational efficiency in diagnosing the status of images from both the spatial and real-time perspective. Through the applications of the proposed method to real-world WBMs in the semiconductor industry, the quantitative evaluation criteria for fault detection were provided in order to represent the degree of faults in WBM. Moreover, the spatial monitoring for detailed fault types led to real-time diagnosis and detection during the operation, where the cost for damage delays could be reduced and the most frequent fault type could be investigated.  
For future research, classification based SPC would be possible since the defective patterns of images have typical shapes or positions, where the eventual cause of specific defects is prevented. In addition, faults in hidden layers that consist of the WBMs could be detected; while the modeling of defects in hidden layers is a valuable topic for additional research, this paper only covered the faults of image-type data that could be observed from the WBMs. It is also necessary to determine an effective control limit for automated detection, instead of the pre-specified threshold. Another approach applied to other signal processing methods could be also an interesting topic for study.  
Conceptualization, M.L. and S.J.B.; methodology, M.L. and S.J.B.; software, M.L.; validation, M.L. and S.J.B.; formal analysis, M.L. and S.J.B.; investigation, M.L.; resources, S.J.B.; data curation, M.L.; writing,  
Figure 6: Wavelet spectrum-based 3D spatial control chart for the half-defective WBM in Figure 5.  
original draft preparation, M.L.; writing, review and editing, M.L. and S.J.B.; visualization, M.L.; supervision, S.J.B.; project administration, S.J.B.; funding acquisition, S.J.B. This paper was prepared with the contributions of all authors.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2018R1D1A1A09083149). This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20174030201750).  
The authors declare no conflict of interest.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 4 Location Selection Model of 5G Micro Base Station
### Location Selection Model Construction
#### 4.2.2 Location Selection Index
on whether the coupling effect can be generated between the mounted devices and is calculated as follows:  
\[\alpha_{m}=\sum_{i=1}^{SL_{\text{h}}}\frac{d_{i}}{\sum_{j=1}^{SL_{\text{o}}}d_{j}} \tag{2}\]  
where \(SL_{\text{h}}\) is the number of light poles carrying the target equipment, the number of which is the same as \(g_{\text{h}}\). \(SL_{\text{o}}\) is the maximum number of light poles with coupling equipment, in theory, that is, among all the light poles in the site selection area, the number of light poles carrying the equipment with the coupling relationship with the target equipment. \(d_{i}\) and \(d_{j}\) are the number of coupling devices of \(i\)th and \(j\)th light poles, respectively. (3) Unification ratio \(\alpha_{\text{t}}\)  
Due to the different infrastructure attributes installed in the smart street lighting system, such as coverage radius and interface specifications, from the perspective of the top-level design, the different equipment installed in the actual project increases production costs and affects the aesthetics of the city. Therefore, the unification ratio of equipment is also an essential factor in location selection. The unification ratio describes the consistency of the type and quantity of equipment carried by each street light pole in the actual project. It is measured by the standard deviation of the number of different equipment types carried by each pole, and the smaller the standard deviation, the higher the degree of uniformity. The unification ratio \(\alpha_{\text{t}}\) is defined as the ratio of the degree of equipment unity before and after the pole location selection. The calculation formula is as follows:  
\[\alpha_{\text{t}}=\frac{\sqrt{m^{\prime}\cdot\sum_{f=1}^{m^{\prime}}\left(r_{ f}^{\prime}-\overline{r}^{\prime}\right)^{2}}}{\sqrt{m\cdot\sum_{k=1}^{m} \left(r_{k}-\overline{r}\right)^{2}}} \tag{3}\]  
where \(m^{\prime}\) and \(m\) are the number of equipment types before and after the location selection, respectively, \(r_{f}^{\prime}\) and \(r_{k}\) are the numbers of the fth and kth equipment types before and after the location selection, respectively, \(\overline{r}^{\prime}\) and \(\overline{r}\) are the average values of different equipment types before and after location selection. \(f\in\{1,2,\dots,F\}\) and \(k\in\{1,2,\dots,K\}\) are the collections of smart light pole terminals before and after location selection.
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 3 Results and Discussion
### Spatial and Annual Variation in OMI AAOD
High values of AAOD were mainly distributed near dust sources as well as BC sources. The AAOD across China increased by a factor of nearly 1.5, especially 2-fold in the northwest during the studied period, implying a severe situation of dust and/or BC aerosol pollution in the recent years of 2016-2018 (Figure 5). In brief, the high-value center of OMI AAOD is predominantly scattered over the northwestern TB region, the BTH region and the surrounding northern part of Henan province, and the SB in China.
## 3 Results
### Characterization of The Synthesized AgNPs
#### 3.3.5 DLS Characterization and Zeta Potential Determination  
DLS analysis was used to determine the electrical charge of the surface (Figure 14) and the hydrodynamic diameter of AgNPs.  
Figure 14: Zeta potential of AgNPs-H\({}_{2}\)O (**a**) and of AgNPs-EtOH (**b**).  
Figure 13: EDX spectra of AgNPs-H\({}_{2}\)O (**a**) and of AgNPs-EtOH (**b**).  
The average diameter value obtained for AgNPs-H\({}_{2}\)O was 104 nm, while for AgNPs-EtOH the hydrodynamic diameter was 123 nm. The Zeta potential of AgNPs-H\({}_{2}\)O was \(-27.74\) mV, while for AgNPs-EtOH the obtained value was \(-26.46\) mV.  
A Zeta potential value of \(\pm\) 30 mV is believed to indicate the existence of a stable suspension [23].