esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Iterated Savitzky-Golay Filter-Based VI Reconstruction  
Since VI products have been widely used in the RS community as time series data, many more differences in the growing patterns of different crops have been expressed, and such patterns are significantly important for distinguishing crops from each other. However, because of both the satellite observation plan and the influence of clouds and poor atmospheric conditions, it is unrealistic to build a daily RS data series (e.g., NDVI time series) without interpolation or any reconstruction process. Applying a reconstruction approach to the original observation time series data is required in some operational practice for two reasons: denoising and filling missing values. By contrast, to facilitate the consequent CWT analysis, our approach requires that the input of the CWT is a daily time series for a full year. Therefore, three original VIs are reconstructed by applying an iterated Savitzky-Golay (S-G) filter-based method [47], which was proposed to provide high-quality NDVI time series data, to meet the requirement of the CWT analysis. The S-G filter method was originally proposed by [PERSON] in 1964 [48], which has widely been used in data smoothing and denoising. It is a filtering method based on local polynomial least squares fitting in the time domain, and its biggest advantage is that it can ensure the shape and width of the signal while filtering out noise. Within the reconstruction approach, those positions that have an NDVI increase greater than 0.3 during 20 days are rejected and replaced by a null value before the iterated reconstruction process because such an increase cannot be caused by natural vegetation changes. As different pixels have different situations, in the most severe case, only 3 samples were set to null after applying this threshold. In addition to the reconstruction of the NDVI time series, the positions that are first replaced by a null value in the original NDVI time series are also replaced by a null value when the EVI and GCVI time series are reconstructed. During the reconstruction process, the width of the smoothing window and the degree of the smoothing polynomial are the two most important parameters; they determine the smooth extent of the reconstruction result. The wider the smoothing window, the smoother the result at sharp peaks, and the smaller the degree of the smoothing polynomial, the smoother the result, but bias may be introduced. Finally, we set the width of the smoothing window to 31 and the degree of the smoothing polynomial to 3 to obtain the optimal reconstruction result.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 3 Voucher-Based Addressing
### Link Voucher Structure and Rules
Any updates to vouchers henceforth are required to use this same public key, unless the most recent LV had expired--according to its Expiration field--without being refreshed or renewed. This \"lock-in\" process ensures that only the original VB providing LVs via NDP message attachments is authorized to provide further voucher information and updates. Securing against rogue LVs and unauthorized changes is beyond the scope of this paper and is left to other mechanisms that can prevent rogue Router Advertisement or Redirect messages in the first place.
## 5 Conclusions  
Broadcast storm problems and intermittent connectivity are the significant challenges of VANETs. The reduction in number of rebroadcasting vehicles can be achieved through clustering the vehicles  
Figure 16: Packet collision ratio versus vehicle density.  
Figure 17: Packet loss ratio versus vehicle density.  
into groups and selecting a minimal number of vehicles as next forwarders. A vehicle joins the cluster to which its degree of cohesion is higher which is computed based on the relative mobility among the neighborhood vehicles. The proposed technique provides a solution for reducing the broadcast storm problem by selecting cluster heads based on dominating set and cluster gateways based on the set covering problem. The stability of the cluster head is increased by allowing the state of a vehicle to be un-clustered temporarily if it can receive messages from any other gateway node in its transmission range. The reachability of the messages in the average and dense scenario is approximately 99% to 100%. The predominant decrease in packet loss ratio and packet collision ratio proves the reduction in broadcast storm. As future work, the performance of the proposed technique will be analyzed in a real-time scenario.  
Conceptualization, S.K. and V.S.S.S.; methodology, S.K.; software, S.K.; validation, S.K. and V.S.S.S.; formal analysis, S.K.; investigation, S.K.; resources, S.K. and V.S.S.S.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, V.S.S.S.; visualization, S.K. and V.S.S.S.; supervision, V.S.S.S.; project administration, V.S.S.S.  
This research received no external funding.  
This research was supported by Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (SR/FST/ETI-349/2013).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
## 4 Fluorescence Quenching of Pollutants  
This section presents the physical aspect of the sensing process of one of the water pollutants, such as hydrogen peroxides, which form radicals in water. The active sensing material, lanthanide oxide nanoparticles, was selected as cerium oxide nanoparticles (ceria NPs) according to its visible emission under UV or violet optical excitation along with its reduction-oxidation capabilities. Ceria NPs were synthesized via the chemical precipitation technique due to their cheap initial precursors and simplicity of operation [37]. The synthesized nanoparticles solution was exposed to the violet excitation of a 405 nm light-emitting diode (LED). The visible emission was scanned over the spectrum of 500-800 nm through consecutive monochromatic stages for scanning, a photomultiplier tube to amplify the optical emission, and optical power meter to detect the scanned signal. The setup schematic is clarified in Figure 3. The solution of nanoparticles was added with different concentrations of hydrogen peroxide; then, the emission intensity was detected at each added concentration of the pollutant.
## 2 Methods
### Validation Model  
#### 2.4.1 Numerical Model  
An acrylic rigid frame with beam elements consisting of 20,760 elements and 20,767 nodes was modeled in the commercial finite element software Abaqus. For the purpose of validation, we assumed nearly identical, to our best, geometric and material properties between the numerical model and experimental setup. A geometric model of the frame (Figure 2a) included six 400 mm \(\times\) 50 mm \(\times\) 5 mm acrylic slats fastened with stainless steel joints. The bottom of the frame was assumed fixed. The material properties of the acrylic and stainless steel included the following: [PERSON]'s modulus \(E=2.74\) and 200 GPa, mass density \(\rho=\) 1190 and 7930 kg/m\({}^{3}\), and Poisson's ratio \(\
u=\) 0.32 and 0.247. To emulate the scenarios of damage and no damage, the horizontal middle section was categorized into three types of slats (Figure 2b), namely intact (INT), mildly (DL1) and severely (DL2) damaged structures, indicated by no cracks and cracks of 5 mm and 10 mm in depth, respectively.  
Modal dynamic analysis was then performed on the model by applying a horizontal impact force (representing a hammer shock applied on the structure over a short time period) of 1 N at the top right corner of the frame to obtain the strain (node data). Strain data were calculated at the yellow and red points in Figure 2b, which were 8 and 9 cm away from the right boundary, respectively. Strain data obtained at the red points, defined as expanded points, were expanded to extract full-field strain over the entire frame by using the transformed matrix in (11). Then, \(D_{0}\) and \(D\) were obtained by comparing the data at the expanded point and corresponding measured data at the yellow point, which was defined as the measured point in (13). Furthermore, the \(DI\) in the DL1 and DL2 scenarios were obtained using (14).
## 4 Results and Discussion
### Results Using TF-IDF Features and the SMOTE Technique
Results indicate that the use of SMOTE to balance the dataset leads to higher classification accuracy both with BoW and TF-IDF. In this study, different machine learning models are used with two different feature extraction techniques such as BoW and TF-IDF. These feature extraction techniques have been applied with a combination of SMOTE. Analysis of experimental results proves that the SVM model can achieve the highest accuracy among all the models with different features. The accuracy of SVM is as high as 95% with BoW and TF-IDF features without using any statistical techniques and 94% with BoW and TF-IDF features when applied along with SMOTE. Table 15 shows the number of CP (correct predictions) and WP (wrong predictions) for machine learning models with both features with the combination of using SMOTE and without SMOTE. The highest number of CP is achieved by SVM using TF-IDF and SMOTE, which is 5610 with only 183 wrong predictions. Using BoW with SMOTE, the highest number of CP is 5440 by the SGD classifier. Although these classifiers perform better on the original dataset as well, the number of correct predictions is high when they are used on SMOTE balanced data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Without SMOTE**} & \multicolumn{4}{c}{**Using SMOTE**} \\ \cline{2-9}  & \multicolumn{2}{c}{**BoW**} & \multicolumn{2}{c}{**TF-IDF**} & \multicolumn{2}{c}{**BoW**} & \multicolumn{2}{c}{**TF-IDF**} \\ \cline{2-9}  & **CP** & **WP** & **CP** & **WP** & **CP** & **WP** & **CP** & **WP** \\ \hline LR & 4013 & 276 & 3889 & 400 & 5395 & 398 & 5471 & 322 \\ \hline SVM & 4047 & 242 & 4032 & 257 & 5428 & 365 & 5610 & 183 \\ \hline RF & 4042 & 247 & 4030 & 259 & 5010 & 783 & 5462 & 331 \\ \hline DT & 4018 & 271 & 4000 & 289 & 4818 & 975 & 5506 & 287 \\ \hline KNN & 4018 & 271 & 4000 & 289 & 4818 & 975 & 5506 & 287 \\ \hline AdaBoost & 3354 & 935 & 3316 & 973 & 4015 & 1778 & 4407 & 1386 \\ \hline GNB & 2910 & 1379 & 2911 & 1378 & 4501 & 1292 & 4126 & 1667 \\ \hline ETC & 3861 & 428 & 3918 & 371 & 4705 & 1088 & 5169 & 624 \\ \hline SGD & 4061 & 228 & 3982 & 307 & 5440 & 353 & 5536 & 257 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Confusion Matrix of a model using TF-IDF and BoW without SMOTE and using SMOTE.
## 5 Limitations and Future Research Directions  
This study offers valuable insights into the integration of AI-based digital twins (DTs) within Saudi Arabia's construction industry; however, it is subject to several limitations that should be addressed in future research. First, the study's reliance on survey data introduces the potential for respondent bias, as participants' subjective perceptions and experiences may influence their responses. Second, the sample size of 106 responses, while sufficient for an exploratory analysis, may not fully capture the heterogeneity of the architecture, engineering, and construction (AEC) sector, potentially limiting the generalizability of the findings. Third, the study primarily identifies benefits and challenges without extensively examining the organizational and cultural dynamics that influence the adoption of AI-based DTs.  
Future research should adopt a multi-method approach incorporating longitudinal studies, qualitative investigations, and comparative analyses across different construction contexts to advance the understanding of AI-based DT adoption. A multi-case study design would provide a more nuanced understanding of how organizational structures, workforce capabilities, and institutional policies shape the implementation of AI-based DTs. Moreover, integrating theoretical frameworks such as the Diffusion of Innovation theory could offer deeper insights into technology diffusion mechanisms and the factors that facilitate or hinder its widespread adoption. A systematic literature review focusing on AI, DTs, and the construction industry over the past five years could further elucidate research gaps and emerging trends. Additionally, future research should explore the long-term impacts of AI-based DTs, including their role in enhancing decision making, optimizing project performance, and mitigating risks in construction.  
Additionally, qualitative research exploring organizational and cultural factors, such as resistance to change, workforce training, and institutional support, would offer valuable context-specific strategies for overcoming adoption barriers. Comparative studies across different regions and construction sectors could also highlight best practices and unique challenges, fostering a more global perspective. Furthermore, research into developing tailored training programs and educational initiatives could address the skill gaps and enhance the understanding of AI capabilities within the construction workforce. Investigating the intersection of AI-based DTs with emerging technologies such as blockchain and advanced analytics could also unlock new dimensions of innovation, further strengthening the digital transformation of the construction sector, as shown in Figure 8.
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 2 Vibration Signal Detections
### Signal Analysis Methods
#### 2.1.2 Short-Time Fourier Transform (STFT)
Additionally, the fixed window size in STFT may not be optimal for capturing rapid changes or transient events in signals containing superimposed frequencies. These limitations hinder the ability of STFT to precisely resolve and separate individual frequency components in scenarios involving the superposition of extremely close frequencies.
## 3 Experimental Section
### Comprehensive Performance Analysis of UOCC System
#### 3.5.1 Performance Analysis When the Air Pump Is Stationary  
The BER performance in saline water under various settings (when the position of the air pump was fixed) is shown in Figure 12, where the red line indicates the use of traditional CR decoding. Since the amount of data sent from the transmitter (Tx) was limited to \(10^{6}\), it could be considered an error free transmission when the BER was \(10^{-6}\). As observed, at the payload bit lengths of 60 bits and 80 bits, the BER performances of all the situations were error free when the proposed two methods were applied. As the payload bit length was increased, fewer pixel rows represented one logic bit. Hence, the bad BER performance was obtained in the case of dense bubbles and \(K\) = 5, where \(K\) is the distance between the air pump and the camera. We also found that when data rate and \(K\) were fixed, the BER became poor with the increase in bubble density. Conversely, if the data rate and bubble densities remained unchanged, the decrease in \(K\) led to the deterioration of the BER. It is worth pointing out that BER performances cannot satisfy the 7% forward error correction (FEC) limit without the proposed methods even at a low data rate of 3.6 Kbit/s. Experimental results show that the proposed schemes can significantly improve the BER performance and resist various bubble environments compared to the traditional CR scheme, whether the bubbles are sparse or dense.
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 3 Proposed Framework
### Isoline Tracking in Basic Particle-Based Fluids  
The level-set method is used in various fields like Fluid Dynamics, Computer Graphics, and Computer Vision. The essence of this method is encoding fluid surfaces implicitly, rather than representing them explicitly, by defining a level-set function across the entire simulation domain. Implicitly represented fluid surfaces are suitable for handling topological changes like the merging or splitting of surfaces. This approach allows for the natural representation of complex shapes and boundaries that change over time without the need to reproduce meshes or reparameterize. In this paper, the level set was calculated using the implicit function proposed by [PERSON] and [PERSON] [28].  
Surface tracking identifies and represents the boundary between the surface and the air layer. The method used for surface tracking determines the boundaries of the surface, so it is crucial to choose a method suitable for the fluid characteristics to naturally represent fluid surfaces. When tracking fluid surfaces using the methods mentioned earlier by [PERSON] [27] and [PERSON] [10], noise can sometimes appear on stable fluid surfaces (see Figure 1).  
To mitigate this issue, [PERSON] and [PERSON] [28] proposed the following level-set function (see Equation (6)).  
\[\phi(x)=|x-\bar{x}|-\bar{r} \tag{6}\]  
In traditional methods, \(\bar{x}\) and \(\bar{r}\) were the position and radius of a single particle, respectively. These were replaced with \(\bar{x}\) and \(\bar{r}\), the average position and radius weighted by neighboring particles, to reduce the influence of high-density areas and enable smooth surface representation. The equation was designed as follows to control the influence of particles (see Equations (7)-(9)).  
\[\bar{x}=\sum_{i}w_{i}x_{i} \tag{7}\] \[\bar{r}=\sum_{i}w_{i}r_{i}\] (8) \[w_{i}=\frac{k(|x-x_{i}|/R)}{\sum_{j}\left(|x-\bar{x}_{j}|/R \right)} \tag{9}\]  
where \(w_{i}\) represents the weight, and \(k\) is defined as \(k(s)=max\left(0,\left(1-s^{2}\right)^{3}\right)\). A notable aspect of this equation is the significant computational cost involved in calculating the weights \(w_{i},\bar{x},\bar{r}\). To compute \(\bar{x}\) and \(\bar{r}\), the values of \(w_{i}\) must be determined first, and to know \(w_{i},k(s)\) needs to be calculated. Especially in high-resolution simulations or large scenes, the requirement for frequent re-initialization and advection steps leads to increased simulation time and memory demand. Therefore, to represent high-quality fluid surfaces in real time, the burden of resources needed to compute the level set must be reduced.
## 2 Example Bridge
### Uncertainty of the Structural Parameters Considering Chloride Ion Erosion
#### (1) Diffusion Stage  
In this paper, the corrosion process of chloride ions to the interior of the pier concrete is simulated by employing [PERSON]'s second diffusion law [25]. To begin, the corrosion is considered with continuous diffusion when the chloride ion concentration on the outer surface of the reinforcement reaches a threshold. The corrosion starting time (\(T_{corr}\)) can be calculated by employing the following formulas [26]:  
\[T_{corr}=\frac{{d_{c}}^{2}}{4D}\bigg{[}erf^{-1}\bigg{(}1-\frac{C_{cr}}{C_{s}} \bigg{)}\bigg{]}^{-2} \tag{1}\]  
\[C_{s}=A_{cs}(\omega/c)+\varepsilon_{cs} \tag{2}\]  
Figure 4: Division of the cap beam and pier section: (**a**) cap beam and (**b**) pier.  
Figure 5: The process of chloride ion corrosion in reinforced concrete structures.  
where \(d_{c}\) is the thickness of the concrete cover, \(D\) is the diffusion coefficient, \(erf^{-1}\) is the inverse function of the Gaussian error, \(C_{cr}\) is the threshold for the chloride ion concentration, \(C_{s}\) is the concentration of chloride ions on the concrete surface, \(\omega/c\) is the water-cement ratio, and \(A_{cs}\) and \(e_{cs}\) are the calculation parameters. Previous papers have demonstrated that the parameters \(D\), \(C_{cr}\), \(A_{cs}\), \(e_{cs}\) are approximately subject to normal distributions; the statistical results are shown in Table 1[27; 28; 29]. Here, \(T_{corr}\) is calculated as 19.40 years by taking the respective mean values and substituting the relevant parameters of the example bridge.
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
###### Abstract  
Cellular materials such as aluminum and polyurethane foams are recognized for their effectiveness in energy absorption. They commonly serve as crushable cores in sacrificial cladding for blast mitigation purposes. This study delves into the effectiveness of autoclaved aerated concrete (AAC), a lightweight, porous material known for its energy-absorbing properties as a crushable core in sacrificial cladding. The experimental set-up features a rigid frame made of steel measuring 1000 \(\times\) 1000 \(\times\) 15 mm\({}^{3}\) with a central square opening (300 \(\times\) 300 mm\({}^{2}\)) holding a 2 mm thick aluminum plate representing the structure. The dynamic response of the aluminum plate is captured using two high-speed cameras arranged in a stereoscopic configuration. Three-dimensional digital image correlation is used to compute the transient deformation fields. Blast loading is achieved by detonating 20 g of C4 explosive set at 250 mm from the plate's center. The study assesses the mineral foam's absorption capacity by comparing out-of-plane displacement and mean permanent deformation of the aluminum plate with and without the protective solution. Six foam configurations (A to F) are tested experimentally and numerically, varying in the foam's free space for expansion relative to its total volume. Results show positive protective effects, with configuration F reducing maximum deflection by at least 30% and configuration C by up to 70%. Foam configuration influences energy dissipation, with an optimal lateral surface-to-volume ratio (\(\zeta\)) enhancing protective effects, although excessive \(\zeta\) leads to non-uniform foam crushing. To address the influence of front skin deformability, a non-deformable front skin has been adopted. The latter demonstrates an increased effectiveness of the sacrificial cladding, particularly for \(\zeta\) values above the optimal value obtained when using a deformable front skin. Notably, using a non-deformable front skin increases maximum deflection reduction and foam energy absorption by up to approximately 30%.  
structural dynamics; blast mitigation; sacrificial cladding; mineral foam; numerical analysis; dynamic simulation; LS-DYNA