esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Residual Stress Analysis  
As shown in Figure 8, the heating rate at each point was significantly greater than the cooling rate in the laser processing process; a higher heating rate may lead to instability in the molten pool and splashing [42], and the cooling rate affects the residual stress distribution of the LPBF parts [43]. When the laser scanned the powder support zone, the temperature rose significantly, and the temperature gradient was greater than that in the solid support zone.  
Figure 12 shows the residual stress distribution of each layer. The residual stress distribution of the part was relatively uniform. The stress was concentrated inside the part, while the stress at the four corners of the part was obviously smaller, especially in the powder support area. This result conforms to the above analysis of the temperature field and temperature gradient. In the stress distribution diagram of the first layer in Figure 12a, the residual stress in the overhanging part was low. This is because the stiffness of the structure in the overhanging area was poor. Due to the large temperature gradient that occurred during the process, the residual stress exceeded the material strength, the deformation occurred at this position, and the residual stress was released. Therefore, the residual stress in this area was low. This same phenomenon also existed in the experiment. For the same overhang length group, the deformation of the first 20 layers (0.6 mm) was large. As the number of forming layers increased, the thickness of the overhanging part increased, and the structural stiffness was enhanced as well. The deformation diagram shows that the deformation of a subsequent layer was smaller than that of the previous layer. The residual stress in the overhanging part also increased with the increase from approximately 160 Mpa to 250 MPa in the forming layers, as shown in Figure 12b-d. In the stress distribution diagram, the stress was the largest in the contact area between the bottom of the part and the substrate. This was due to the stress concentration phenomenon in the corner position, where the print part and the substrate were in contact. At the edge, the temperature gradient was large because of the surrounding powder; therefore, the residual stress was also relatively large.
# Tourism-Induced Urbanization in Phuket Island, Thailand (1987-2024): A Spatiotemporal Analysis
## 2 Materials and Methods
### Data Collection  
Here, we use Google Earth Engine (GEE), a cloud-based platform that is a key component for data acquisition, preprocessing, and initial analysis [33]. We directly accessed life-long-spanning satellite Landsat imagery from the Landsat 4-5 TM, 7 ETM+, and 8-9 OLI sensors from GEE's extensive public repository. However, due to the high cloud  
Figure 2: Study area of Phuket, Thailand, showing the detailed coastal geography, tourist attractions, and its regional context.  
cover during the monsoon season, to guarantee low levels of cloud cover and stable observing conditions, target dates for each time point were chosen to coincide with the dry seasons (between January and July of each year). Hence, the process began with the creation of image collections for each five-year interval, incorporating a \(\pm 6\)-month window around the target date to account for seasonal variations. It should be noted that due to the data availability, we have made a modest adjustment to the analysis timeframe for the periods 1987-1990 and 2021-2024. The analysis for 1987-1990 was initiated from this year due to the insufficient satellite imagery available prior to 1987. In the same vein, the most recent data available for the period 2021-2024 only encompassed 2024. These changes were implemented to guarantee the inclusion of the most comprehensive data feasible for these timeframes while maintaining consistency. Next, automated cloud masking algorithms, tailored to each sensor, were applied to filter out cloud-covered pixels and atmospheric noise. Then, median composites were made to obtain clear, representative images for each time point, and GEE's radiometric calibration tools made sure that all the sensors worked together [34]. Besides these automated steps, we also export the processed Landsat images from GEE for LULC classification and more calculations, like the UEII and Shannon Entropy, which are important for the study's objective. This comprehensive data preparation methodology, supported by the computational framework of GEE, resulted in a high-quality time series of cloud-free Landsat imagery spanning nearly four decades. Figure 3 exhibits the number of scenes of Landsat images used in this study. This integration of automated tools with manual oversight ensured a robust and reproducible analysis framework. These datasets form the foundation for analyzing urban growth and land cover changes in Phuket, providing insights into the impacts of economic policies and tourism-driven development on the region's landscape.
## 3 Results and Discussion
### Analysis of Numerical Simulation Results  
#### 3.2.1 Crack Extension Analysis  
The distribution of cracks inside the numerical model after experiencing different numbers of freezes and thaws is shown in Figure 11.  
Figure 11: Crack distribution for different freeze–thaw cycles. (**a**) 20, (**b**) 40, (**c**) 60, (**d**) 80.  
The crack distribution images show that tension cracks dominate the crack types in the early freeze-thaw stage. This phenomenon coincides with the conclusion of the Second Theory of Freeze-thaw Force [33]. The Second Theory of Freeze-thaw Force suggests that when a water particle freezes, there is a region with low water content and low moisture conductivity at the bottom of the particle. This region is called the frozen edge (Figure 12). Moisture migrates from the unfrozen region through the frozen edge to the freezing zone under the action of temperature gradients, gravitational potential energy, pressure potential energy, matrix potential energy, and other generalized traction forces. As moisture continues to migrate and accumulate in the freezing zone, the moisture inside the sandstone specimens gradually freezes into ice, forming freeze-thaw force. Under freeze-thaw forces, sandstone particles squeeze each other, making the pore space between the particles larger. More critically, the repeated phase transitions between water and ice are like cyclic loading and unloading on sandstone particles. These are subjected to cyclic loading and unloading and undergo cementation damage to form tiny cracks. Moreover, due to the characteristics of the rock's high compressive capacity and weak tensile capacity, the microcracks formed by the freeze-thaw process are mostly tension cracks.  
The changes in the number of different types of cracks throughout the freeze-thaw process are plotted in Figure 13 to visualize the trend of the cracks. In Figure 13, we divided the process into four stages. Stage I is 0-20 freeze-thaw cycles. Stage II is 20-40 freeze-thaw cycles. Stage III is 40-60 freeze-thaw cycles. Stage IV involves 60-80 freeze-thaw cycles. In Stages I and II, the number of cracks remained almost stable and showed no significant increase, indicating that the damage inside the model was mild. However, from Stage III onwards, the number of cracks starts to climb sharply, indicating that a large amount of damage is rapidly occurring inside the model. Upon entering Stage IV, the total number of cracks jumps to over 8000. Combined with the internal fragment view of the model (Figure 14), we can see that the prefabricated cracks in the central region were significantly affected. Expansion and particle spalling occurred in the prefabricated cracks themselves. At this point, the numerical model shows a 'frozen crisp' state, with powdery spalling. This phenomenon agrees with the experimental results.  
Figure 12: Freeze–thaw micro-mechanism diagram.  
Figure 14: Internal fragment view of the model. (**a**) 40, (**b**) 60, (**c**) 80.  
Figure 13: Crack changes during the process of freeze–thaw.
## 3 Experimental Results
### The Results of the Resonant Frequency by Using the Equivalent Parallel Circuit  
The magnetron is found to achieve the dominant mode in the frequency domain by using the equivalent parallel circuit in the one-section network of the magnetron. The structural dimensions and the presence of the electric and magnetic fields in the magnetron are also considered and used to analyze how this mode is achieved. The results of the resonant frequency indicate the frequency of each mode of operation of the magnetron between modes 1 to 11 and then sets the T\({}_{\mathrm{SLOT}}\) at 3.97 mm in the dominant mode or pimode operation (mode number = 6) as 2.99816 GHz. Compared with the target frequency of 2.9982 GHz, this equates to a difference of 0.04 MHz or a relative error of 0.0013%, and the mode of operation results for the magnetron are shown in Figure 15.
## 4 Experiments and Analysis
### The Performance Evaluation on Real-World Vulnerability Detection  
To address RQ3, we collected nine real CVE vulnerability functions from two software packages, as detailed in Table 9. Each software was compiled across three architectures (ARM, MIPS, X86) and four optimization levels (O0, O1, O2, O3), producing 12 versions that constituted a function pool of 27,714 functions. For each vulnerable function, a version for the X86 architecture at optimization level O1 was selected as the query function. Its similarity to other functions in the pool was assessed and ranked. The number of functions with homologous vulnerabilities within the top 10 highest similarities is illustrated in  
Figure 10: The performance of GMN+ variants with different blocks in the Graph Learner.  
Figure 9: The performance of GMN+ variants with different blocks in the Semantic Learner.  
Figure 11, where the horizontal axis represents specific CVE identifiers. The experimental results reveal that GMN+ surpasses all baseline methods in detecting real-world vulnerabilities. Specifically, GMN+ identified 61 vulnerable functions (within the top 10) in total, in contrast to 32 by FIT, 35 by GMN, and 8 by SAFE. The reasons for superior performance of GMN+ in real-world vulnerability detection align with the benefits seen in the function similarity detection task, underscoring the efficacy of our method.
## 3 Results and Discussion
### Evaluation of Intrinsic Parameters in Kinetic Models  
The parameters of the four kinetic models were assessed using their non-linear forms (see Table S3, Supplementary Materials), and the DEO method was implemented for the models. The efficacy of the optimal characteristic parameters of the four kinetic models is presented in Figure 7. Again, it can be observed that linearization undervalued the kinetic parameters. For all the kinetic models, the DEO using optimized kinetic parameters better predicted the experimentally obtained values. These scatter plots validated the non-linearity of the adsorption process; hence, the DEO-based non-linear model predictions better correlated with the experimental values. To further confirm the model predictions, non-linear statistical metrics were evaluated. The prominent statistical metrics were evaluated and compared for both linear and non-linear kinetic model expressions, as shown in Table 3 for C\({}_{0}\) of 10 mg/L. The linear plots of the pseudo first-order (PFO), pseudo second-order (PSO), and Weber-Morris kinetic models C\({}_{0}\) of 20, 30, and 40 mg/L are shown in Figures S4 and S5 (Supplementary Materials). For all the evaluated kinetic models, \(R^{2}\) was higher for non-linear models, thus confirming that these kinetic models better describe the MB adsorption onto nZVISLP. The \(R^{2}\) values were \(\geq\)0.98 for the pseudo second-order kinetic model, thus confirming that this model better explains the MB adsorption process. Also, all other prominent statistical metrics resulted in a similar trend. It can be observed from Table 3 that the optimal parameters derived from DEO not only provided lower values of RMSE and \(\chi^{2}\), but also provided improved values for other statistical metrics. Therefore, the optimal kinetic parameters estimated by DEO suitably captured the dynamics of MB adsorption onto the surface of nZVISLP.  
Figure 7: Scatter plots comparing the performance of DEO in terms of linear transformation for various kinetic models.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Models \(\rightarrow\)**} & \multicolumn{2}{c}{**PPO**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**Weber-Morris**} & \multicolumn{2}{c}{**Boyd**} \\ \hline
**Metrics \(\downarrow\)** & **Expressions \(\downarrow\)** & **L** & **NL** &
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### End of Life of a Wind Farm  
Artificial intelligence can play a significant role in the end-of-life phase of wind turbines. Here are some important areas where AI can contribute [85]:  
* Decommissioning planning: AI can analyze data to optimize the decommissioning process, ensuring that it is carried out efficiently and cost-effectively.
* Recycling and material recovery: AI can help identify and sort materials from turbines that are no longer in use, improving the efficiency of the recycling process and maximizing material recovery.
* Life extension analysis: AI can assess the condition of aging turbines and predict their remaining life, helping to make decisions about whether to refurbish, repower, or decompression them.
* Environmental impact assessment: AI can model and predict the environmental impact of decommissioning activities, helping to minimize negative impacts on the surrounding ecosystem.
* Resource allocation: AI can optimize the allocation of resources during the decommissioning process, ensuring that labor, equipment, and materials are used efficiently.
* Risk mitigation: Artificial intelligence tools can help identify potential risks associated with decommissioning, such as weather delays or logistical challenges. By quantifying these risks, operators can develop strategies to effectively mitigate them.  
By using AI in these end-of-life areas, wind turbines can be managed in a more sustainable and efficient manner, reducing costs and environmental impact.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 6 Conclusions  
This paper presented a new object-level RGB-D dense SLAM approach for accurate motion estimation of an RGB-D camera in the presence of moving objects, while constructing an object-level semantic map of the scene. There are three important innovations in this method. First, object motion detection is performed in 3D space. Compared with the method in 2D space, the 3D object tracking method against the camera pose makes full use of the depth and gray texture information stored in the object and is more robust for detecting potential moving objects following the camera movement. Second, the method of establishing an association between instance segmentation and an object database with motion information could accurately find the dynamic part of the current frame. Third, our method fully utilizes the static data in the current frame as a data source for camera pose tracking, eliminating the interference from the dynamic data and thereby improving the camera pose accuracy. The quantitative and qualitative results demonstrate that our method can accurately detect dynamic objects in a scene and eliminate the effects of dynamic objects on camera pose tracking and map construction. The camera positioning accuracy can reach the level of state-of-the-art techniques when the tested sequences include several moving objects. We found that small objects are potentially difficult to track, and it remains a topic for future work to optimize the method for according data.  
Conceptualization: J.L. and X.K.; methodology: X.K.; software: X.K.; validation: X.K.; formal analysis: X.K. and H.J.; investigation: X.K., C.X., and J.L.; resources: [PERSON] and X.F.; data curation: X.K.; writing--original draft preparation: X.K.; writing--review and editing: [PERSON]; visualization: X.K.; supervision: [PERSON] and [PERSON]; project administration: [PERSON] and X.F.; funding acquisition: [PERSON] and X.F. All authors have read and agreed to the published version of the manuscript.  
This work was funded and supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19080101). This work was also partially supported by Key Deployment Program of Chinese Academy of Sciences (no. KFZD-SW-317). This research was funded by the National Key Research and Development Program of China (no. 2019 YFC1511304), and the National Natural Science Foundation of China (no. 41101436). This research was also partially funded by the National Natural Science Foundation of China (No. 41901328).  
Not applicable.  
**Data Availability Statement:** I use public dataset to verify my algorithm, and the public dataset link already writed in reference [52].  
The authors declare no conflict of interest.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 6 Security Analysis
### Application Structure  
The Android mobile application is built upon the Model-View-ViewModel (MVVM) architectural pattern and implemented using modern technologies such as the Kotlin programming language, local Room database, and Jetpack Compose framework for rendering the user interface. The application is composed of user interface elements, database access classes, and support classes providing communication and cryptographic core functions. The interrelation of these elements is illustrated in Figure 8.  
The user interface elements include the following classes:  
* **Models**: Classes containing data structures that hold the data of different parts of the application.
* **Views**: Classes defining the graphical environment using the Jetpack Compose framework. These classes may include basic logic for controlling the rendering of individual elements.
* **View Models**: Classes containing models that affect the displayed values of the graphical interface of Android application activities.  
Additionally, the application includes a persistent data model consisting of classes that offer basic operations over the database store and the definition of individual data entities, such as certificate data items. The last component of the application comprises supporting classes, mainly implementing the cryptographic core using the MCL library,  
Figure 8: Android user application class diagram.  
followed by the implementation of the communication stack through BLE. The BLE Stack is based on the standard functions provided by the Android operating system Application Programming Interface (API).
## 2 Omega-3 Polyunsaturated Fatty Acids
### Characteristics of n-3 PUFA In Silico  
Important n-3 PUFAs have been extensively studied using in silico methods to understand their various properties and potential therapeutic applications, which constitutes a significant contribution to drug design and development.  
In silico pharmacokinetics predictions suggest that DHA can enhance the overall permeability of drugs, such as doxorubicin, in cancer cells. Doxorubicin, a chemotherapeutic agent known for its high cardiotoxicity and drug-resistance issues, may benefit from being conjugated with other molecules to optimize its potency. DHA has been shown to improve the absorption of doxorubicin in cancer cells. Computational methods based on structure-based design were used to explore the potential of a conjugated drug, predicting the pharmacokinetic properties of each component and identifying the best binding mode and energy to enhance the selectivity of the doxorubicin-DHA conjugate. These in silico approaches indicate that DHA might significantly improve the overall permeability and efficacy of doxorubicin in targeting cancer cells [41]. Some of the other drug interactions will be discussed in Section 4.  
Moreover, in silico studies related to COVID-19 have shown that DHA has moderate binding affinities and interactions with the active sites of interleukin-6 (IL-6) and high binding affinity with optimal interactions at the active sites of angiotensin-converting enzyme 2 (ACE2). These findings suggest that DHA could play potential roles in modulating inflammatory responses and possibly influencing viral entry mechanisms, acting as a potent inhibitor of both ACE2 and IL-6 [42].  
During the metabolic process, ALA is biotransformed into EPA and DHA. In silico ADME-Tox and pharmacokinetic profiling using artificial intelligence and machine learning approaches have shown that ALA is safe as a dietary ingredient, as it does not produce serious health problems. This essential fatty acid could be used as a nutraceutical and pharmacological food ingredient. However, the overall evidence on the association of ALA with health risks remains inconclusive [43].  
To fully understand the potential and application of DHA in the treatment of melanoma, further in silico research is essential. Current studies indicate that DHA can modulate various molecular pathways and immune responses, which are crucial in the context of melanoma. For instance, DHA has been shown to induce apoptosis in cancer cells, regulate the cell cycle, and enhance the effectiveness of the immune system against tumor cells. Additionally, DHA can impact signaling pathways such as PI3K/Akt and modulate the expression of immune checkpoints [44; 45; 46; 47]. However, while these findings are promising, comprehensive in silico analyses and simulations are needed to explore the specific mechanisms on melanoma cells.
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 4 Numerical Analysis of the Fire Risk Assessment  
In this study, risk is represented as \(f(s,p)\)[46], which is a function of the probability and severity of the consequences of a certain hazardous event. In particular, \(R\) represents the total fire risk of the overload-induced early cable fires, \(R_{i}\) is the risk index of the \(i\)-th risk factor, \(S_{ij}\) represents the quantitative value of the \(j\)-th severity in the \(i\)-th risk factor, and \(w_{i}\) represents the weight of the \(i\)-th risk factor. Then, the cable overload fire risk is:  
\[R_{i}=f_{i}(s,p)=\sum_{j=1}^{n}S_{ij}P_{ij}\ \left(i=1,\ 2,\ 3\right) \tag{15}\]  
\[R=\sum_{i=1}^{m}w_{i}R_{i},\ (m=3) \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parent Node State/C**} & \multicolumn{2}{c}{**Insulation Breakdown Risk**} & \multicolumn{2}{c}{**Parent Node State/C**} & \multicolumn{2}{c}{**Material Pyrolysis Risk**} \\ \hline \hline
**Cable** & **Cable** & **Cable** & **Slight** & **Serious** & **Cable** & **Aging** & **Slight** & **Medium** & **Serious** \\
**Damage/\%** & **Temperature/C** & **None** & **Slight** & **Serious** & **Temperature/C** & **Time/Month** & **Slight** & **Medium** & **Serious** \\ \hline
0 & [0,40] & 99.9 & 0.1 & 0 & [0,40] & 0 & 99.9 & 0.1 & 0 \\
0 & [40,70] & 90 & 9 & 1 & [0,40] & [0,6] & 95 & 4.99 & 0.01 \\
0 & [70,90] & 50 & 40 & 10 & [0,40] & [6,  
Based on the risk-establishing BN, the probabilities of different risk factors corresponding to each risk state were obtained. Then, the risk indices of risk factors were obtained from Equation (15), and the overall risk index was computed using Equation (16).