esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Damage Level of Slab-Column Joints under Blast Load
## 4 Numerical Analysis Results and Discussion  
### Displacement  
Figure 4 shows the results of the maximum displacement of the slab and the column. The displacement for various scaled distances was observed by increasing the standoff distance of each TNT of 10 kg, 20 kg, or 30 kg. In Figure 4, the displacement represents the maximum displacement generated on the central axis of the cross-section of the slab and column. In addition, according to the results of preliminary numerical analysis, it was divided into a joint region and a single member region based on a scaled distance of 0.1 m/kg\({}^{1/3}\), and the maximum displacement in each region was investigated. Here, the distance from the column or slab surface of TNT 10 kg, 20 kg, and 30 kg corresponding to a scaled distance of 0.1 m/kg\({}^{1/3}\) was about 215 mm, 271 mm, and 311 mm, respectively. In Figure 4, it was noted that the maximum displacement was not a value at a fixed location but represented the maximum value among displacements that occurred in each region, such as joint and the single-member region, according to different scaled distances.  
In all cases, the displacement of the slab was greater than the displacement of the column. This was in line with the results of preliminary numerical analysis, which resulted in greater pressure and impulse in the slab. When the scaled distance was relatively small, the larger the TNT weight, the greater the amount of displacement. However, as the scaled distance became larger, the difference  
Figure 4: Maximum displacement of slab and column; (**a**) Slab; (**b**) Column.  
in displacement amount according to the TNT weight was not large. It appeared that the magnitude of shock wave parameters, such as pressure and impulse, transmitted directly to the member decreased significantly as the position of the explosion moved away from the member. When comparing the amount of displacement of the joint region and the single-member region, the slab generally had slightly larger deformation in the joint region, but the difference was not significant. Columns were also observed to have almost the same maximum strain in both regions. However, since there was no clear tendency in the relationship between the scaled distance and the displacement, it was difficult to predict the blast-resistant behavior of the joint based on this relationship.
## 2 Results and Discussion
### Characterization of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)
#### 2.1.4 Specific Surface Area Analysis  
To further investigate the entirely porous natures of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\), N\({}_{2}\) adsorption-desorption measurements were performed (as shown in Figure S1a). The N\({}_{2}\) adsorption isotherm accords with the type III isotherm. The test results showed that the specific surface area of this material was 372.00 m\({}^{2}\)/g. From the pore size distribution calculated by the desorption branch of the isotherm of the particles by the BJH method  
Figure 4: Magnetization curves of Fe\({}_{3}\)O\({}_{4}\) and C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) nanoparticles.  
Figure 5: X-ray photoelectron spectroscopy (XPS) analysis (**a**), the fit of the C1s XPS spectrum (**b**), the fit of the O1s XPS spectrum (**c**), the fit of the N1s XPS spectrum (**d**).  
(Figure S1b), the pore size range of the material was between 0 nm and 70 nm, and the mesopore of 3.0-4.0 nm was dominant, which showed that the material has a good pore structure (including micropores, mesopores, and macropores).  
In addition, it was compared with the materials without C18 modification (Figure S2). The specific surface area of PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was 415.25 m\({}^{2}\)/g, which was lower than that of C18 modified, which may be due to the blocking of some voids after C18 grafting.
## 4 Results
### Analysis Results of LDA Topic Modeling
#### 4.4.2 Topic Classification Results
The third theme, \"Secure Software Development with AI\", addresses the security challenges inherent in developing software that utilizes AI technologies. Keywords like \"ChatGPT\", \"code\", \"model\", and \"software\" reflect research on AI's role in coding, the security of AI-generated code, and methods for enhancing security throughout the software development lifecycle. A study by [PERSON] et al. (2023) discussed the potential risks associated with AI code assistants such as GitHub Copilot, particularly the possibility of introducing unintended vulnerabilities. The paper proposed rigorous human review and secure software development practices as ways to mitigate these risks [47]. The fourth theme, \"Security and Risk Management of AI Systems\" (14%), focuses on the security and risk management of AI systems. Keywords like \"security\", \"system\", \"AI\", and \"technology\" indicate research into the inherent vulnerabilities of AI systems and the necessary security considerations during their development. [PERSON] et al. (2024) investigated the dual role of ChatGPT in cybersecurity, analyzing both its benefits and risks. The study recommended comprehensive threat analysis, the implementation of misuse prevention mechanisms, and the establishment of secure development environments as essential measures for managing the risks associated with AI systems [48]. The fifth theme, \"User Privacy and AI Security\" (9%), explores the security and privacy challenges that arise from user interactions with AI systems, as evidenced by keywords such as \"user\", \"factor\", \"analysis\", and \"perception\". In a comprehensive examination of public opinion regarding the privacy implications of ChatGPT, [PERSON] and [PERSON] (2024) identified significant concerns about data privacy and security. The study proposed countermeasures such as minimizing data collection, implementing rigorous data management practices, and incorporating feedback loops to prevent security threats and avoid misinterpreting user intent [49]. The final theme, \"Healthcare Security with AI\" (5%), addresses the security challenges posed by AI technologies in the healthcare sector, particularly regarding the protection of patient data and the security of AI-powered diagnostic systems. A study by [PERSON], [PERSON], and [PERSON] (2024) investigated the deployment of ChatGPT-like AI tools in healthcare, emphasizing the need to safeguard privacy, ensure confidentiality, and comply with regulatory norms. The study recommended encrypting patient data, conducting periodic security assessments, and validating AI models to strengthen healthcare systems against potential security breaches [50].
## 4 Measurements and Characterization
### Test Results Analysis  
Figure 10a presents the measured and simulated S-parameters. As shown in Figure 10a, the input return loss (\(S_{11}\)) remains below \(-14.7\) dB across the operating band, the small-signal gain (\(S_{21}\)) ranges from 32.4 to 34.2 dB, and the designed PA remains stable across all frequencies. The measured S-parameter results show a trend that is consistent with the simulation results, although there is a slight frequency shift toward higher frequencies. This minor discrepancy between the simulation and the measurements is likely due to  
Figure 9: (a) S-parameter measurement setup; (b) large-signal measurement setup.  
factors such as modeling, coupling, or thermal effects, which are common in MMIC design. Therefore, during the design process, the operational bandwidth was intentionally extended and a certain margin was included to ensure that the chip maintains good performance within the target frequency range, even with potential frequency offsets and performance deterioration.  
Figure 10b shows that the saturated output power ranges from 47.0 to 47.6 dBm across 8-12 GHz, with an in-band fluctuation of \(\pm\)0.3 dBm. The power gain varies from 27.0 to 27.8 dB, while the PAE remains between 40.0% and 45.5% over the same frequency band. The large-signal test results generally align with the simulations, although some performance degradation is observed. This is likely due to the use of continuous wave testing, where the characteristics of the GaN power devices under high power and temperature conditions differ from those assumed in the modeling process. Therefore, such a degradation is to be expected. Despite this, the large-signal performance of the proposed amplifier remains competitive.
## 3 Method  
### System Overview  
The dataset used in this approach is weakly labelled, and CNN can automatically extract and learn the features from the image, so the CNN network is applied in this work. In view of the previous work [1; 2; 3], because of the great differences between the background texture in the dataset, researchers have to use a lot of data to train the CNN network. However, In the field of deep learning, a large amount of training data is often difficult to get. Additionally, it will not only greatly increase the training time, but also the overall computation. To solve the above problems, this approach uses wavelet transform in the data preprocessing phase. The low-frequency part obtained after the image wavelet transform is the compression of the original image, which is only a quarter of the size of the original image, but the defects in the original image are still visible. Using small images can speed up model training and save resources. In order to speed up the training speed, reduce the training data and improve the detection accuracy, this approach uses the information in the wavelet domain as model input. In the model training phase, in order to overcome the challenge that the defect area is small, this paper proposes a random-window method to extract patches from the training images, and then uses the patches to train the CNN network. In the defect detection phase, this paper uses the sliding-window method and judgment strategy to solve the problem of background interference. First, we extract patches from the image to be detected using the sliding-window method. Then, all the patches are sent to the CNN network to judge whether there are defects. Finally, after considering the defect condition of the original image, the step of the sliding-window and other factors, we set a threshold. When the number of defective patches exceeds the threshold, the image is considered defective. By adjusting the threshold to find the best average accuracy, the best threshold is determined. Figures 4 and 5 show the process of this approach in the model training phase and defect detection phase, respectively.
## 6 Simulated Experiments  
In order to analyze the effect of the method proposed in this paper, we used the SCP prototype to conduct simulated experiments. Due to the uncertainty of the Web services, under the premise of the validity of experiment, we made the following settings for the experimental conditions and related parameters.  
We used seven FRCPN graphs to simulate the Web service compositions; the number of Web services in the FRCPN graphs is 10, 30, 50, 70, 90, 110, and 130, respectively. We used five kinds of production rules to represent FRCPN, but kept the ratio between Web services and transitions is 1:5, so the length of SLLFRV is respectively 2, 6, 10, 14, 18, 22. The similarity threshold of all the transitions is set to the same value. After Algorithm 1 is finished, we assumed that the reliability values of transitions are divided into three ranges, i.e., \(\left(0.95,\ 1.0\right),\left(0.90,\ 0.95\right),\)and \(\left(0.85,\ 0.90\right]\). According to the three ranges, we can get three sets of data; the similarity thresholds of the three sets are 0.95, 0.90, and 0.85, respectively.  
The purpose of the experiment was to simulate the reliability values of FRCPN in different reliability ranges of transitions. The reliability values of Web service compositions were unlimited. The experimental results are shown in Figure 8. In the figure, the horizontal axis represents the number of Web services in FRCPN; the vertical axis represents the reliability values of Web service compositions.  
According to Figure 8, we can know the relationships among the reliability values of Web service compositions, the number of Web services, and the reliability ranges of transitions. The following relationships can be obtained:  
1. Under the same reliability range of transitions, if the number of Web services is greater, then the length of SLLFRV is longer and the reliability values of the Web service compositions are smaller.
2. Under different reliability ranges of transitions, if the minimum range is smaller, then as the number of Web services increases, the reliability values of the service compositions decrease more rapidly.
3. We should proper control the scale of Web services in FRCPN. For example, if we set the reliability threshold of Web service compositions as 0.6, then in the reliability range (0.95, 1.0) of transitions, the number of Web services should be controlled below 30 and the length of SLLFRV is below 6.
## 3 Material and Methods  
### Constructs  
The pGEX4T1 encoding an N-terminal glutathione S-transferase (GST) fusion protein was used to overexpression human IQGAP1 (accession number P46940) GRD1 (aa 962-1345), C794 (aa 863-1657) and CT (aa 1576-1657); human IQGAP2 (accession number Q13576) GRD2 (aa 875-1246) and C795 (aa 780-1575); human IQGAP3 (accession number P60953) GRD3 (aa 942-1330); human CDC42 (accession number P60953; aa 1-178). All constructs and related variants are list in Supplementary Table S1. For purification of these proteins, pGEX-4T1 constructs were transformed in _Escherichia coli_ and proteins were isolated via affinity chromatography using a glutathione Sepharose column on a KTA start protein purification system (Cytiva, US) [71]. GST-cleavage was carried out by incubation with thrombin (#T6884-1 KU, [PERSON], Taufkirchen, Germany) at 4 \({}^{\circ}\)C until full digestion of the fusion protein. Quality of the proteins were checked via SDS-PACE and Coomassie staining. CDC42 variants were further verified for their activity in HPLC by determining the amount of bound nucleotide [71]. Nucleotide free proteins were prepared by incubating the proteins with alkaline phosphatase (#P0762-250 UN, Sigma Aldrich, Germany) and phosphodiesteraserase (#P3243-1 VL, Sigma Aldrich, Taufkirchen, Germany) at 4 \({}^{\circ}\)C [71]. CDC42 variants were labelled with either GDP (#51060, Sigma Aldrich, Taufkirchen, Germany), GppNHp (#NU-401, Jena Bioscience, Jena, Germany), mant-GDP (#NU-204, Jena Bioscience, Jena, Germany) or mant-GppNHp (#NU-207, Jena Bioscience, Jena, Germany).
# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 2 Methodology
### Regression Analysis  
In order to estimate the magnitude of the linear relationships between the different observed and simulated variables analysed in this study, we fitted linear models to the datasets using Orthogonal Distance Regression (ODR) [66]. By carrying out the linear model parameter estimation using this method the uncertainties in both the dependent and independent variables were taken into account thus producing more realistic estimates for the linear model parameters than by using the ordinary least squares fitting [67]. In practice, the observations with smaller uncertainties constrain the linear model more than the observations with larger uncertainties. Furthermore, the used approach enabled computation of confidence intervals for the estimated parameters. The ODR analysis was carried out using the Python package scipy.odr, which utilizes the FORTRAN-77 library ODRPACK [68].  
As discussed in previous sections, the satellite products have uncertainty/variability estimates that can be used in the fitting of the linear models. The climate model data, on the other hand, do not include such information. Therefore, we used the variability between the daily values within the summer months to estimate the representability of the seasonal averages for each simulated pixel using the bootstrapping method [69]. Bootstrapping is a commonly used statistical method that may be used to assign measures of uncertainty to sample estimates. In practice, we constructed 1000 data point sets by randomly sampling from the daily averages and calculated averages for these new sets. Furthermore, the standard deviations of the set averages were computed and used as the measure of variability in the analysis.
## 3 Results and Discussion
### Nanocoated Fabric Pull-Out Properties
#### 3.2.2 Single and Multiple Nanocoated Fabric Crimp Extension and Pull-Out Force Results
\begin{tabular}{c} **Single-Yarn** \\ **Crimp** \\ **Extension** \\ **(mm)** \\ \end{tabular}  & \begin{tabular}{c} **Single-Yarn** \\ **Pull-Out Force** \\ **(mm)** \\ \end{tabular}  & \begin{tabular}{c} **Two-Yarn** \\ **Crimp** \\ **Extension** \\ **(mm)** \\ \end{tabular}  & \begin{tabular}{c} **Two-Yarn** \\ **Full-Out** \\ **(mm)** \\ \end{tabular}  & \ \begin{tabular}{c} **Three-Yarn** \\ **Crimp** \\ **Extension** \\ **(mm)** \\ \end{tabular}  & \begin{tabular}{c} **Three-Yarn** \\ **Pull-Out** \\ **(mm)** \\ \end{tabular}  \\ \hline \multirow{10}{*}{KPO} & 10 & 0.324 & 4.290 & 0.355 & 4.420 & 0.407 & 5.230 \\  & 20 & 0.649 & 5.640 & 0.711 & 5.630 & 0.814 & 7.380 \\  & 40 & 1.298 & 7.920 & 1.4  
Figure 7: Single and multiple (two yarns and three yarns) crimp extensions and pull-out forces corresponding to the fabric lengths for nanocoated p-aramid fabric structures. (**a**) Single-yarm crimp extension pull-out forces. (**b**) Two-yarn crimp extension pull-out forces. (**c**) Three-yarn crimp extension pull-out forces. It has been deduced that prior to the initial interlacement rupture phase (stage I, which encompasses initial friction, crimp extension, and progresses up to the maximum pull-out force), an increase in the number of yarn ends results in a corresponding enhancement in both pull-out force and crimp extension. This increase is further amplified with the incorporation of 0.3% nano-hexagonal boron carbide and multiwall carbon nanotubes into the para-aramid fabric structure. The findings suggest that the pull-out force exhibits a higher-order proportionality with the number of yarn ends, while crimp extension demonstrates a proportional relationship with the crimp ratio--a fundamental structural formation parameter of the fabric--and to a degree, with yarn elongation. Moreover, it is evident that the range of variation in pull-out force and crimp extension should also be evaluated considering the breaking strength and elongation at break of individual yarns. The primary objective here is to enhance frictional interactions within the fabric structure without compromising the integrity of the fibers, thereby enabling an additional energy absorption mechanism. This is particularly significant for soft ballistic applications [65]. In this regard, the type and concentration of nanomaterials, as well as the applied processes and their optimization, hold critical importance.
## 5 Conclusions  
In this work, a novel MGA method for deriving parameters from SD and DD solar PV cell/panel models of RTC France and Kyocera KC200 GT PV modules is given, taking into account PV manufacturing technology and solar cell modeling. The goal of the recommended MGA technique is to optimize the parameters for the best performance in terms of RMSE values. By obtaining the least four RMSE indicators with larger improvement percentages, the suggested MGA technique achieves significant superiority and statistical robustness in comparison to the recently described techniques. When determining the relevant PV parameters under fluctuating temperature and irradiance, the suggested MGA approach performs better than commercial PV systems. This shows the efficacy of the methodology and its capacity to produce fitted I-V data that are highly consistent with the manufacturer's data sheets. The exact solution of PV cell/panel models provides additional proof that the proposed MGA outperforms the optimizers recently published in the literature. The capacity of the suggested MGA technique to determine unknown PV properties is further illustrated by accounting for a range of operational conditions, including temperature and irradiance variations. The MGA optimizer consistently demonstrates superior performance in reducing RMSE compared to other algorithms across a range of weather conditions, with percentage improvements ranging from 8.07% to 90.29% for the SD and DD models.  
Conceptualization, W.A.; Methodology, M.G. and A.R.G.; Software, A.M.S.; Validation, A.M.S.; Formal analysis, W.A. and S.S.; Investigation, W.A. and M.G.; Data curation, M.G.; Writing--original draft, A.M.S. and A.R.G.; Writing--review & editing, S.S., A.M.S. and A.R.G.; Visualization, A.R.G.; Supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
King Abdulaziz University.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (GPIP: 1439-612-2024). The authors, therefore, acknowledge with thanks DSR for technical and financial support.  
The authors declare no conflicts of interest. There are non-financial competing interests.
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 3 Forecasting the Risk of Forest Wildfires Using a Case-Based Approach
### The Study Area  
The study area is Irkutsk Oblast--the largest oblast of the Russian Federation. Its area is 774,846 km2. (4.52% of the territory of Russia), with a population of 2,357,134 people (2022) and population density of 3.04 people/km2 (2022). Irkutsk Oblast is located in Eastern Siberia. The extreme southern point of the region is located at 51\({}^{\circ}\) north latitude, and the northern tip almost reaches the 65 th parallel. From north to south, the region stretches for almost 1450 km and from west to east for 1318 km. The southeastern border of Irkutsk Oblast runs along Lake Baikal. The region occupies the southeastern part of the Central Siberian Plateau, the plateaus and ridges of which have heights from 500 to 1000 m. Forests occupy 78% of the territory [13]. There are 32 administrative districts in Irkutsk Oblast. In recent years, there has been an increase in the area of wildfires in the region (Figure 1). Mass wildfires have caused a significant change in the landscapes of the catchment area of Lake Baikal and a decrease in forest reserves. The largest areas of wildfires for an average of 10 years are distinguished in Katanga, Kien, Mamsko-Chui, Bodaibinsk, Bokhansk, Ust-Kut, and Chunsk districts [59].  
Further in the paper, the research results are considered for the territory of Bodaibinsk and Kazachinsk-Lena districts. Bodaibinsk district, with an area of 92 thousand km2, is located on the Vitimo-Patomsky Highlands, in the northeastern part of Irkutsk Oblast, in an area equated to the regions of the Russia's Extreme North. The Visimskiy Nature Reserve with an area of 11.61 thousand km2 is located in this area. The Kazachinsk-Lena district occupies most of the Pre-Baikal depression and the northern part of the Baikal ridge (1200-2000 m above sea level). The natural zone is the taiga, an area of 33 thousand km2.