esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Post Processing for Wave Propagation Problem: Response Filtering Method
## 1 Introduction
The subsets have a specific number of stress values, which, in this paper, is called the filtering length. The averaged stress values are set as the filtered stress values, and the original stress values are replaced with the filtered values in the corresponding time domain, as shown in Figure 1. The RFM not only efficiently solves the oscillation problem in the numerical solution by adjusting the filtering length, but it also creates discontinuous solutions that converge to the analytical solutions. Alternatively, it can be considered that the RFM can create new frequency components of the responses (especially higher frequency components). In this study, the relationship between the RFM and frequency components is mathematically verified, and a formulation is introduced to predict the generated frequency components using RFM. Figure 1: Response filtering method (RFM) concept. This remainder of this paper is organized as follows. In Section 2, the concept, application, and limitations of finite element formulations for the mechanical wave problem and numerical integration scheme, i.e., [PERSON]'s scheme, are reviewed. In Section 3, to resolve the oscillation issue, i.e., Gibb's phenomenon in the numerical solution, the RFM is presented. In Section 4, several numerical examples are presented to demonstrate that the present RFM can be utilized to improve the accuracy of numerical solutions. Finally, Section 5 provides conclusions and directions for future research.
## 3 Results
### NEAM Use to Extract Physical Parameters  
Seeing the similarity between the NEAM model and the low-frequency approximation model, it should be possible to relate the physical material parameters using the coefficients obtained with NEAM. To verify this relation, Equations (3) and (4) have been programmed into the 1D FDTD scheme, creating a non-physical material to calculate the absorption coefficient. The proposed non-physical material characteristics are presented in Table 1. Results obtained from the 1D FDTD NEAM model for different PPWLs are then compared to the low frequency model and gathered in Table 2.  
In this example, as can be observed in Equations (4) and (19), when \(\Psi_{A}=1,~{}\Psi_{B}=0\) thermal effects are neglected, that the NEAM model simplifies to just viscous effects. Furthermore, as follows in the document, the low-frequency approximation of tortuosity is directly related to the \(\Omega_{A}\) NEAM coefficient, which should yield similar result, and the effective flux resistivity \(\eta\phi/\text{k}_{0}\) is also directly related to the \(\Omega_{B}\) NEAM coefficient. The low-frequency parameters were calculated with the 1D FDTD schemes with 40 PPWL, and the absorption coefficient solution, which is the input data for the NEAM model block, was considered with and without rounding decimals. The \"rounded\" data entry in Table 2 has been estimated with two significant figures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha_{\infty}\) [-] & \(K_{0}\) [Pa] & \(\eta\) [Pa-s] & \(\phi\) [-] & **th. [mm]** \\ \hline
1.0953957103 1  & \(9\times 10^{-10}\) & \(18\times 10^{-6}\) & 0.98 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties for a virtual material which are used to calculate the absorptive performance with the low frequency model in 1D FDTD scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _PPWL_ & \(\Omega_{A}\) **Rounded** & \(\Omega_{A}\) **Not** & \(\Omega_{B}\) **Rounded** & \(\Omega_{B}\) **Not** \\ \hline
6 & 1.0000017548 & 1.0000017548 & 13,833 & 13,790 \\
10 & 1.0390070343 & 1.0301187897 & 18,225 & 18,582 \\
20 & 1.0419824982 & 1.0337904358 & 18,395 & 18,728 \\
30 & 1.0826280975 & 1.0741842651 & 19,084 & 19,429 \\
40 & 1.1039675140 & 1.0953920746 & 19,450 & 19,800 \\ low. freq. model & & 1.0953957103 & & 19,800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained with 1D FDTD NEAM model using diverse PPWL. NEAM results are compared to the low frequency model results in order to extract the true values from Table 3.  
Figure 9: The 40 mm PET diffuse field calculated using oblique incidence absorption and Paris formulation.
# Incoherent Digital Holography: A Review
## 4 Special Topics and Applications
### Reduction of Scanning Speed and Recorded Data in OSH  
Although OSH has some unique merits, the shortcoming is clear: the demand of 2D raster scanning limits the recording speed of OSH, and thus dynamic optical scanning holography is still a challenge. So far, there have been some trials to shorten the recording time in OSH. The goal is usually to reduce the number of rows of scanning, because the scanning time is proportional to the number of rows, which is related to the vertical pixel pitch. The first trial was proposed by [PERSON] et al., in which a vertical-bandwidth-limited scanned hologram was recorded using a coarse sampling pitch without aliasing [60]. The recording time was reduced to only about 1/10 that of conventional scanning. This method, however, sacrifices the vertical resolution of the whole hologram. For this reason, [PERSON] et al. proposed a novel technique called adaptive optical scanning holography (AOSH) [72]. As shown in Figure 7, in recording the \(j\)th row of the hologram, the row data \(r(j)\) together with the data from the previous row \(r(j-1)\) stored in a line buffer are sent to the predictor. If the variation between \(r(j-1)\) and \(r(j)\) is small, the separation of next scanning, \(\Delta_{j}\), is increased. In this way, the recording time as well as the size of hologram data can be reduced by up to an order of magnitude. On the basis of a similar concept, not only the vertical pixel pitch but also the horizontal pixel pitch can be adjusted dynamically. In compressive optical scanning holography (COSH) [57; 73], only a portion of the hologram pixels of a row are selected and converted into a 1-bit binary representation. Therefore, the resulting hologram data size can be about 1% of that of the original hologram. Recently, [PERSON] et al. proposed subsampled scanning holographic imaging (SuSHI) to record a scanned hologram along a low-density spiral trajectory [74]. Subsequently, the hologram has been reconstructed by a compressed sensing (CS) approach. In this way, both the recording time and the data size can be reduced to up to 4% of that of raster-scanned OSH. The shortcoming of SuSHI, however, is that the CS reconstruction is time-consuming, and thus the scanned holograms cannot be reconstructed on the fly of holographic recording. The use of COSH- or SuSHI-based OSH would make dynamic holographic imaging possible.  
Figure 7: Schematic of the concept of adaptive optical scanning holography (AOSH).
## 3 Models
### Equally Spaced Obstacles Model  
#### 3.2.1 Continuous Model  
For the equally spaced obstacles model, the circular obstacles (radius \(R_{\text{obs}}\)) are centered on the subdomain intersection points. The particle radius is fixed at \(R_{\text{part}}=1.0\), and the obstacle radius is varied from 0.5 to 3.0 by increments of 0.5. This range was chosen to enable particle movement between obstacles; an obstacle radius greater than 3.0 inhibits transitions between subdomains.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 3 Experiments and Results
### Performance of Fine-Tuned DNN Models without Adversarial Attacks  
To evaluate the performance of different DNN models without adversarial attacks, the apple leaf disease dataset (2-class or multi-class) is divided as a training set and a testing set with an 80/20 ratio. For each DNN model, a leaf image is resized as the standard input size required by a pre-trained DNN model. In the fine-tuning phase, each DNN model is fine-tuned from weights pre-trained using ImageNet dataset [27]. The models are trained with a stochastic gradient descent (SGD) optimizer with an initial learning rate of 0.001 and momentum of 0.9. The learning rate decays with a rate of 0.1 every 7 epochs. Our shallow training has 100 epochs with an early stopping of 7-epoch tolerance. The test accuracy of the four fine-tuned DNN models are presented in Table 2. It can be seen that without adversarial attacks all models perform very well on both 2-class and multi-class disease identification.
# Cycle-to-Cycle Variation of a Diesel Engine Fueled with Fischer-Tropsch Fuel Synthesized from Coal
## 4 Conclusions  
The main objective of this study was to research the performance characteristics when using Fischer-Tropsch (F-T) fuel in diesel engines and highlight the importance of a short ignition delay and a long combustion duration on cyclic variation. The following conclusions can be drawn compared to 0# diesel.  
The F-T diesel has the properties of high CN and low boiling point, which led to a short ignition delay and a long combustion duration. Compared to 0# diesel, owing to these superior characteristics that are beneficial to improve the air-fuel mixture and reduce the gas turbulence during the kernel formation phase, the cyclic variation and fluctuation intensity of F-T diesel were smaller. To a certain extent, cyclic variation was improved with increased speed, and then the average fluctuation intensity decreased when the engine was fueled with the test fuels. A strong linear relationship was observed between cyclic variation and ignition delay. The cyclic variation linearly increased with ignition delay because pre-mixed reactivity increased with long ignition delay. Moreover, the influence was more obvious under high speed conditions. Compared to ignition delay, the combustion duration made less impact on cyclic variation, which resulted in a smaller value of R at the same speed. Furthermore, the cyclic variation linearly decreased with long combustion duration; the F-T diesel showed lower R than 0# diesel under all the test conditions. Moreover, the combustion duration had a significant influence on cyclic variation at low speeds due to the large absolute slope of linear regression.  
In all, in-cylinder pressure fluctuations and the effect of short ignition delay and long combustion duration on cycle-to-cycle variations were further analyzed. These effects can be used to improve combustion stability and control the harmful damage caused by cyclic variation. Furthermore, the diesel engine can run stably and effectively without any modifications with F-T diesel.  
Data curation, Z.Z. ([PERSON]) and Z.Z. ([PERSON]); Writing--review & editing, S.J., W.T., and W.Z.  
This research received no external funding.  
The authors acknowledge the Lu'an group for providing the F-T diesel and the specifications of CTL.  
The authors declare no conflict of interest.  
Figure 10: The correlation between peak in-cylinder pressure and combustion duration with (a) F–T diesel, (b) 0# diesel.
## 3 Results and Discussion
### Potentially Toxic Elements
crystalinum_ \\ \hline Na & 420 \(\pm\) 54.60 \({}^{\ast}\) & 1120 \(\pm\) 145.60 \({}^{\ast}\) & 600 \(\pm\) 78.00 \({}^{\ast}\) & 309 \(\pm\) 40.17 \({}^{\ast}\) & 570 \(\pm\) 74.10 \({}^{\ast}\) & 520 \(\pm\) 67.60 \({}^{\rm{h}}\) & 391 \(\pm\) 50.81 \({}^{\rm{h}}\) \\ Ca & 45.00 \(\pm\) 5.40 \({}^{\ast}\) & 36.10 \(\pm\) 4.33 \({}^{\rm{h}}\) & 9.20 \(\pm\) 1.10 \({}^{\ast}\) & 41.00 \(\pm\) 4.92 \({}^{\ast}\) & 30.70 \(\pm\) 3.70 \({}^{\rm{h}}\) & 27.60 \(\pm\) 3.30 \({}^{\rm{h}}\) & 11.70 \(\pm\) 1.40 \({}^{\ast}\) \\ K & 194 \(\pm\) 40.70 \({}^{\ast}\) & 400 \(\pm\) 84.00 \({}^{\ast}\) & 186 \(\pm\) 39.00 \({}^{\ast}\) & 72 \(\pm\) 15.00 \({}^{\ast}\) & 159 \(\pm\) 33.50 \({}^{\rm{h}}\) & 110 \(\pm\) 32.10 \({}^{\rm{h}}\) & 58 \(\pm\) 12.18 \({}^{\rm{h}}\) \\ Mg & 45.60 \(\pm\) 6.38 \({}^{\rm{h}}\) & 55.60 \(\pm\) 0.17 \({}^{\ast}\) & 66.00 \(\pm\) 9.34 \({}^{\rm{h}}\) & 18.30 \(\pm\) 2.65 \({}^{\rm{h}}\) & 13.30 \(\pm\) 18.65 \({}^{\rm{h}}\) & 16.70 \(\pm\) 2.23 \({}^{\rm{h}}\) & 6.80 \(\pm\) 1.20 \({}^{\rm{h}}\) \\ Cu & 0.008 \(\pm\) 0.0007 \({}^{\ast}\) & 0.110 \(\pm\) 0.0009 \({}^{\ast}\) & 0.120 \(\pm\) 0.0010 \({}^{\ast}\) & 0.008 \(\pm\) 0.0007 \({}^{\ast}\) & 0.007 \(\pm\) 0.0006 \({}^{\ast}\) & 0.016 \(\pm\) 0.0010 \({}^{\rm{h}}\) & 0.006 \(\pm\) 0.0005 \({}^{\rm{h}}\) \\ Fe & 0.48 \(\pm\) 0.007 \({}^{\ast}\) & 1.42 \(\pm\) 0.19 \({}^{\ast}\) & 0.71 \(\pm\) 0.09 \({}^{\ast}\) & 0.22 \(\pm\) 0.01 \({}^{\ast}\) & 0.73 \(\pm\) 0.10 \({}^{\rm{h}}\) & 0.41 \(\pm\) 0.05 \({}^{\rm{h}}\) & 0.32 \(\pm\) 0.05 \({}^{\rm{h}}\) \\ I & -0.03 \({}^{\ast}\) & -0.03 \({}^{\ast}\) & -0.03 \({}^{\ast}\) & -0.03 \({}^{\ast}\) & -0.03 \({}^{\ast}\) & -0.03 \({}^{\ast}\) & -0.03 \({}^{\ast}\) \\ Mn & 0.16 \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Heavy Metals** & \multicolumn{1}{c}{_I. crithmoides_} & \multicolumn{1}{c}{_S. fritcosa_} & \multicolumn{1}{c}{_S. rannossissima_} & \multicolumn{1}{c}{_D. crass/golumn_} & \multicolumn{1}{c}{_C. maritiumum_} & \multicolumn{1}{c}{_M. nod/gmm_} & \multicolumn{1}{c}{_M. crystallum_} \\ \hline _Aggby DW_ & & & & & & & & \\ \hline As & 0.057 \(\pm\) 0.004  & 0.310 \(\pm\) 0.001  & 0.130 \(\pm\) 0.001  & \(<\)0.03  & 0.090 \(\pm\) 0.002  & 0.170 \(\pm\) 0.004  & \(<\)0.03  \\ Cd & \(<\)0.10  & 1.22 \(\pm\) 0.38 & \(<\)0.10  & \(<\)0.10  & \(<\)0.10  & \(<\)0.10  \\ Hg & \(<\)0.03  & \(<\)0.03  & \(<\)0.03  & \(<\)0.03  & \(<\)0.03  \\ Pb & 1.02 \(\pm\) 0.05  & 0.73 \(\pm\) 0.02  & 0.42 \(\pm\) 0.03  & 1.95 \(\pm\) 0.05  & \(<\)0.03  & 0.44 \(\pm\) 0.05  & 2.99 \(\pm\) 0.04  \\ \hline \end{tabular}  
* Results are expressed as means values \(\pm\) standard deviation (n = 3). \({}^{\ast}\) LCO limit of detection ug/g. The letters (a=) correspond to the significant difference between halophytes using Tukey’s lost (\(p<0.8\)).  
\end{table} 
Table 4: Potentially toxic metallic elements content in the halophyte species.
# Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile
## 2 Literature Review
### Statistical-Based Methods  
The authors of one study [11] proposed a statistical-based detection mechanism based on continuously ranked probability scores and exponentially smoothing to detect denial of service (DoS) and DDoS attacks efficiently. The finding shows that the suggested method is suitable for large-scale DDoS attacks only. The results show that the accuracy of the proposed model in medium or low traffic is not sufficient to detect DDoS attacks.  
In another study [26], the authors demonstrate a model for a smart home that could be a target for a DDoS attack. The authors claim that their proposed model provides comfort and a secure system for the smart home. The paper suggests a VPN to detect an active attack in IoT devices using Snort tools and Mikrotik Routers. This study aims to analyse network traffic to detect any possible intruders in the IoT devices.  
The authors of one study [27] investigate the possibility of utilizing a lightweight statistical approach for DDoS detection methods. The algorithm is designed to be used at the Internet Service Provider (ISP) level. The suggested algorithm involves minimal computational complexity. The proposed technique monitors traffic and uses the calculated Z-score and covariance values to detect anomaly traffic (DDoS) attacks. The authors claim that their method can detect up to 100% of attacks within 30 s.
## 4 Results
### Further Robustness Analysis: Estimations on Panel B (Excluding Broadband Access)  
We conducted a complementary analysis using Panel B, which spans the extended period 2014-2024 but excludes broadband availability as a predictor, to assess the robustness of our findings over time and to examine the implications of infrastructure data loss. The tuned Random Forest (RF) model trained on this temporally broader dataset achieved a Root Mean Squared Error (RMSE) of 0.515, a Mean Absolute Error (MAE) of 0.358, and an R\({}^{2}\) of 0.737 on the test set. While this represents a moderate decline in predictive accuracy compared to Panel A (RMSE = 0.436, R\({}^{2}\) = 0.854), it still indicates strong generalization and confirms the relevance of key predictors even in the absence of broadband metrics.  
As shown in Figure 11, ICT specialists remained the most influential factor (%IncMSE = 50.9), further emphasizing the pivotal role of digital workforce capacity in driving cloud adoption. Higher education (19.8%), unemployment rate (17.8%), and GDP per capita (16.4%) followed in importance, with a notable increase in the role of human  
Figure 10: Hierarchical Clustering of EU Countries Based on Digital and Socio-Economic Indicators (2014–2021). Source: Authors’ estimations in R software.  
capital variables. The exclusion of broadband slightly reshaped the relative contribution of remaining features, highlighting how digital infrastructure and skill development can act as partial substitutes in explaining adoption variation.  
To enhance interpretability, Figure 12 presents a SHAP-based feature importance visualization for Panel B. The SHAP values reinforce the dominance of ICT specialists and higher education, with ICT contributing over four times more than any other predictor to the model's predictive loss reduction. The tight confidence intervals across features suggest stable contributions across observations and support the robustness of the identified drivers.  
Figure 11: Variable importance scores for Panel B (Random Forest model). Source: Authors’ estimations in R software.  
Figure 12: SHAP-based feature importance (Panel B, without broadband). Source: Authors’ estimations in R software.  
Taken together, the Panel B results confirm the resilience of human capital indicators in the predictive framework and further demonstrate that, even in the absence of broadband data, structural readiness remains explainable through education, labor force, and economic indicators. However, the mild performance degradation confirms the added explanatory value of infrastructure variables, underscoring the necessity of including both human and physical capital in policy design.
# Autostereoscopic 3D Display System for 3D Medical Images
## 2 Methods
### Light Field 3D Subpixel Rendering  
A 3D light-ray image is created by applying a 3D rendering algorithm, which utilizes information on the global parameters of the 3D display and eye positions. Many prior works have been investigated to achieve 3D rendering [33; 34; 35]. Most of these methods assumed a fixed viewpoint, which is not suitable to support changing eye positions in real time. Therefore, our method employs a direct rendering method based on lumigraph [28]. The basic idea of this method is as follows.  
* All light rays passing through the slit (lens) are represented as lines in ray space.
* All light rays passing through the eye pupil are also represented as lines in ray space.
* The light ray we see is the intersection of these two straight lines.  
We can reconstruct light fields sampled by 3D display on ray space using global parameters of the device. A set \(\mathcal{M}=\{\mathrm{m}_{1},\mathrm{m}_{2},\ldots,\mathrm{m}_{N}\}\) of lines on the ray spaces which pass the slits (lens) and each line \(\mathrm{m}_{\mathrm{n}}\) can be represented as:  
\[\mathrm{u}=\bigg{(}1-\frac{d}{p_{n}^{z}}\bigg{)}s+\frac{d}{p_{n}^{z}}p_{n}^{x},\ \ n=1,\ldots,N, \tag{1}\]  
where \(\mathrm{p}_{n}=(p_{n}^{x},p_{n}^{z})\) means the world coordinate of \(n\)-th slit (lens), and \(d\) is the distance of s-t and u-v planes (Figure 3). Likewise, A set \(\mathcal{N}=\{\mathrm{n}_{1},\mathrm{n}_{\mathrm{K}}\}\) consists of lines on the ray space that shows the rays passing through the eyes and \(\mathrm{n}_{\mathrm{k}}\) is defined as:  
\[\mathrm{u}=\bigg{(}1-\frac{d}{q_{k}^{z}}\bigg{)}s+\frac{d}{q_{k}^{z}}q_{k}^{x},\ \ k=1,\ 2 \tag{2}\]  
where, \(\mathrm{q}_{k}=\big{(}q_{k}^{x},q_{k}^{z}\big{)}\) means the world coordinate of the eyes. In this study, we assigned \(k\) values of 1 and 2 for the left and right eyes, respectively. We also define \(\mathrm{T}^{\mathrm{ij}}=\big{(}t_{u}^{ij},t_{s}^{ij}\big{)}\) as the intersected point of line \(\mathrm{m}_{\mathrm{i}}\) and \(\mathrm{n}_{\mathrm{j}}\) on ray space (Figure 4), where \(i\) and \(j\) are an index of lines passing through slits and eyes, respectively. The 3D light-ray image is rendering a 2D image, of which pixels are from the same location of either the left or right image. This problem can be simplified as whether to select each pixel value of the rendering image from the left or the right image. We can determine which eye is closest to the pixel that passed the slit by finding the nearest intersected points as:  
\[\mathrm{I}_{\mathrm{3D}}(u,v)=\left\{\begin{array}{ll}I_{left}(u,v),&\min \lVert u-t_{u}^{\mathrm{i1}}\rVert^{2}<\min\lVert u-t_{u}^{\mathrm{i2}}\rVert^ {2}\\ I_{right}(u,v),&\mathrm{otherwise}\end{array}\right., \tag{3}\]  
where \(\mathrm{I}(u,v)\) means the value of the pixel at \((u,v)\) on the left, right, and 3D rendering image. The advantage of our method is that each pixel value is independently calculated through a simple operation, which can be processed in parallel, thereby ensuring real-time rendering.
###### Abstract  
Two unique approaches were proposed to strengthen the bond between the glass fiber reinforcement polymer (GFRP) and the RC concrete surfaces. The two bonding materials are epoxy (EP) and geopolymer (GPP) with different ratios of short glass fibers (SGF). The experimental program includes seven reinforced concrete (RC) beams that have the same cross-section (150 mm \(\times\) 200 mm) and are 1500 mm in length. The first beam is the control beam (B0-Control). The next three beams B1-0-GPP, B2-0.6-GPP, and B3-1.2-GPP have GPP with SGF ratios of 0%, 0.6%, and 1.2%, respectively. The last three beams B4-0-EP, B5-0.6-EP, and B6-1.2-EP have EP with SGF ratios of 0%, 0.6%, and 1.2%, respectively. The results show that the failure loads of beams B1-0-GPP, B2-0.60-GPP, and B3-1.2-GPP are greater than the control beam B0-Control by approximately 20.80%, 25.60%, and 31.40%, respectively, whereas the failure loads of beams B4-0-EP, B5-0.6-EP, and B6-1.2-EP are greater than the B0-Control by approximately 16.90%, 26.90%, and 26.10%, respectively; it is also noted that debonding occurs. In addition to the adhesive material, GPP has a great effect on increasing the beam's failure load capacity due to the enhanced interfacial bond shear strength. Additionally, a finite-element program ABAQUS is performed to verify the experimental results.  
geopolymer paste; flexural behavior; GFRP; epoxy; short glass fibers; strengthening 2022, 12, 1444. [[https://doi.org/10.3390/buildings12091444](https://doi.org/10.3390/buildings12091444)]([https://doi.org/10.3390/buildings12091444](https://doi.org/10.3390/buildings12091444))
# Victim Detection and Localization in Emergencies
## 5 Victim Detection and Localization Method
Currently, this precise localization is obtained with GNSS receivers in the UEs and is reported through NRPPa as estimated coordinates. To fuse different ranges and different reference points, including temporary ones, the following messages should be included [30] as an extension to NRPPa:  
Reference point identifier;  
Reference point location;  
Technology type;  
Timestamp;  
Round-trip time. Figure 1: Illustration of the method.
## 3 Results and Discussion
### Complex Measures
The change in the DFS of signals as they pass through the ionosphere was also investigated and found to be non-chaotic.