esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Intelligent Weather-Conscious AMC Scheme for Global Satellite-to-Ground Communications  
### Overview  
We propose an AMC method for satellite-to-ground based on DL and DRL, which fully considers satellite motion patterns, historical SNRs, and weather conditions. This method can identify transmitter and receiver characteristics, learn online, and adapt to highly variable radio communication scenarios. As shown in Figure 3, the intelligent weather-conscious AMC model processes the position and weather information from the environment. Furthermore, it estimates the state of the satellite-to-ground channel jointly with the past channel information. The coding scheme is then dynamically selected based on the estimation results. So the integrated AMC model can be represented as a DL-based estimation model and a DRL-based decision model. The estimation model makes full use of the historical data of the satellite-to-ground channel and takes the real-time global weather model into account. The decision model can input multi-dimensional information and identify the characteristics of different transmitters and receivers.  
First, the estimation model reads information from the environment, including the position of the UE at moment \(t\): \((lat_{t},lon_{t})\), the position of the satellite at moment \(t\): \((x_{t},y_{t},z_{t})\), the SNR for the past moments \(\{SNR_{t-1},SNR_{t-2},\ldots,SNR_{t-n}\}\), and the real-time weather conditions. We use one-hot encoding \((Sunny,Cloudy,Rainy)\) to describe weather; if it is currently sunny, the encoding should be \((1,0,0)\), and to write this conveniently, we will use \(w_{t}\) to represent weather at moment \(t\). In summary, the information read by the estimation model from the environment at moment \(t\) is \((lat_{t},lon_{t},x_{t},y_{t},z_{t},w_{t})\).  
Figure 3: Proposed intelligent weather-conscious AMC for global satellite-to-ground communications.  
Next, when the estimation model predicts the satellite-to-ground channel state at the moment \(t\) as \(S\hat{N}\hat{R}_{t}\), it is passed to the actor-critic network in the decision model to select the optimal encoding scheme. The actor-network is responsible for selecting the optimal encoding scheme, i.e., giving the selected action \(a_{t}\), Moreover, the critic network needs to score the selection of the actor-network, and the two enhance each other and work together to learn the optimal strategy for selecting the encoding. When the actor selects the action \(a_{t}\), it needs to be passed to the environment as the encoding scheme for the satellite-to-ground channel at time \(t\). Finally, the environment will pass back the reward \(r_{t}\), which is the actual throughput \(DR_{t}[1-PER(DR_{t},SNR_{t})]\) at moment \(t\). This concludes a complete interaction.
## 1 Introduction
### Research Gaps and Challenges  
Integrating digital twin software solutions with industrial automation systems, where a robotized system (industrial, collaborative, autonomous equipment, etc.) is usually a key part, has highlighted several research gaps and challenges in the field. These gaps primarily focus on data management, security, scalability, and interoperability between different systems [20]. One notable research gap is the optimization of the integration between digital twin software solutions and industrial robotics applications, which entails the development of efficient data exchange protocols, synchronization mechanisms, and real-time interaction algorithms. Additionally, effective data management poses a critical challenge, especially in large-scale systems with real-time data streams. Moreover, the security of digital twins is a significant concern due to their susceptibility to cyber threats. The development of robust security measures, authentication protocols, and encryption techniques is necessary to safeguard digital twin systems and their associated data. Scalability further complicates the implementation of digital twin software solutions in large-scale industrial settings. Lastly, interoperability between diverse systems and platforms represents a crucial challenge in fully harnessing the benefits of digital twin software solutions. Standardization of data formats and communication protocols and developing interoperability frameworks are vital to enable seamless fusion. By addressing these research gaps and challenges, the effective utilization and advancement of digital twin technology in conjunction with industrial systems can be achieved.
# Temperature Dependence of Density and Viscosity of Biobutanol-Gasoline Blends
## 3 Results and Discussion
### Kinematic Viscosity  
The mean values of the kinematic viscosity of the biobutanol-gasoline blends for the selected temperature range and concentrations are shown in Tables A5 and A6. The tables also show the results of the ANOVA test with respect to the multiple comparisons using [PERSON]'s HSD test in the similar indices (a, b, c, etc.). The same indices indicate that no statistically significant difference was demonstrated between the respective kinematic viscosity at the significance level of \(p=0.95\).  
From Tables A5 and A6, it can be seen that the kinematic viscosity values of the biobutanol-gasoline blends decrease with an increasing temperature. Also, the blends with a higher biobutanol content have higher kinematic viscosity values, and the RON 98 gasoline also has lower kinematic viscosity values than the RON 95 gasoline, similar to the bioethanol-gasoline blends [25]. The dependence of the kinematic viscosity on the concentration of the biocomponent in the fuel was also observed and described [8; 33].
## 3 Methodology
### Simulation of Outdoor Environmental Performance in Urban Blocks  
After completing the intelligent generation of parametric forms for elderly-oriented urban blocks, the subsequent step involved batch simulation of the outdoor environmental performance of the generated parametric models. Based on the relevant literature [71; 72; 73], two outdoor environmental performance indicators that significantly influence the outdoor activity experience of elderly individuals were selected as simulation targets: the Universal Thermal Climate Index (UTCI) at pedestrian height and sunlight hours (SHs). This study used the Ladybug Tools and Butterfly plugins, which are built-in tools of the Rhino/Grasshopper platform, to perform environmental performance simulations. As a widely recognized and accurate simulation engine in the field of building multi-objective optimization, the simulation results of Grasshopper [48; 68; 70] have been extensively validated and widely applied in urban environmental performance research. To further ensure the accuracy of the simulation results, the authors conducted detailed parameter tuning during the simulation process and employed targeted strategies to enhance the simulation  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Indicator Name** & **Indicator Symbol** & **Formula** & **Indicator Unit** \\ \hline Average Building Height & AH & \(AH=\dfrac{\sum_{i=1}^{n}h_{i}\times n}{n}\) & m \\ Standard Deviation of Building Height & StdH & \(StdH=\sqrt{\dfrac{1}{n}\sum(h_{i}-\overline{h})^{2}}\) & m \\ Distance Between Buildings & DB & \(DB=\dfrac{1}{n}\sum\limits_{i=1}^{n}d_{i}\) & m \\ Building Density & BD & \(BD=\dfrac{\sum_{i=1}^{n}f_{i}}{S_{T}}\) & \% \\ Floor–Area Ratio & FAR & \(FAR=\dfrac{\sum_{i=1}^{n}A_{f}}{S_{T}}\) & - \\ Volume–Area Ratio & VAR & \(VAR=\dfrac{\sum_{i=1}^{n}Volume_{i}}{S_{T}}\) & m \\ Space Crowding Density & SCD & \(SCD=\dfrac{\sum_{i=1}^{n}Volume_{i}}{H_{max}S_{T}}\) & - \\ Porosity & PO & \(PO=\dfrac{(H_{max}S_{T})-\sum_{i=1}^{n}Volume_{i}}{H_{max}S_{T}}\) & \% \\ Shape Coefficient & SC & \(SC=\dfrac{\sum_{i=1}^{n}C_{i}}{\sum_{i=1}^{n}h_{i}f_{i}}\) & - \\ Perimeter–Area Ratio & PAR & \(PAR=\dfrac{\sum_{i=1}^{n}P_{i}}{\sum_{i=1}^{n}f_{i}}\) & - \\ Mean Building Area & MA & \(MA=\dfrac{1}{n}\sum\limits_{i=1}^{n}f_{i}\) & m\({}^{2}\) \\ Standard Deviation of Building Area & SA & \(SA=\sqrt{\dfrac{1}{n}\sum(f_{i}-\overline{f})^{2}}\) & m\({}^{2}\) \\ Average Building Volume & AV & \(AV=\dfrac{1}{n}\sum\limits_{i=1}^{n}Volume_{i}\) & m\({}^{3}\) \\ Standard Deviation of Building Volume & SV & \(SV=\sqrt{\dfrac{1}{n}\sum(Volume_{i}-\overline{V})^{2}}\) & m\({}^{3}\) \\ Open Space Ratio & OSR & \(OSR=\dfrac{S_{T}-\sum_{i=1}^{n}f_{i}}{\sum_{i=1}^{n}f_{i}}\) & \% \\ Coefficient of Variation for Building Height & CVH & \(CVH=\dfrac{SH}{\overline{h}}\) & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formulas for morphological indicators.  
precision. The specific procedures and parameter settings will be presented in detail in this section. The specific flowchart can be seen in Figure 6.
# Go Source Code Vulnerability Detection Method Based on Graph Neural Network
## 1 Introduction
The system employs graph random walk networks and Transformer models with multi-head attention mechanisms to extract both graph-level and token-level features of Go source code. The integration of both dimensions enhances detection effectiveness. (3) Validation on Real-World Vulnerabilities: To validate the effectiveness of our method, we conducted comprehensive experiments on a real-world dataset. We selected one vulnerability detection tool and three GNN-based detection models (RATS, CSGVD [14], VDoTR [15], and AMPLE [16]) for comparison. Experimental results show that our method achieves an F1-score of 91.35% for Go source code vulnerability detection. The rest of the paper is organized as follows: Section 2 reviews the related works while Section 3 shows preliminaries. Section 4 represents the methodology, and Section 5 discusses the results. Finally, the conclusions and future work directions are summarized in Section 6.
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 2 Related Work
### Application of Deep Learning in Turbulence Modelling  
In recent years, the utilization of machine learning methodologies has surged within the realm of fluid mechanics, especially in areas such as turbulence modeling. These techniques have been pivotal in refining conventional RANS models to better forecast fluid flow. Methods employed span from Bayesian approaches [26], which fine-tune model parameters for heightened accuracy, to neural networks that introduce correction factors for turbulence production and even techniques incorporating spatially distributed correction fields via field inversion and Gaussian processes [27]. However, in spite of their efficacy in enhancing predictive capabilities, the interpretability of the adjustments made to traditional RANS models often remains challenging.  
Apart from simply adjusting existing model parameters, there have been more extensive endeavors to devise new Reynolds stress closures by using physics-informed machine learning. For instance, [PERSON] [28] employed a random forest method to train Reynolds stress discrepancy functions, aiming to address modeling discrepancies based on DNS data. However, further validation is needed to ascertain the performance of mean quantities. Similarly, [PERSON] [29] used physics-informed neural networks to model turbulent flows, assimilating experimental data and solving the Reynolds-averaged Navier-Stokes equations for mean flow fields of sparse data.  
Despite the growing trend of utilizing machine learning for turbulence model development, challenges persist in training and implementing these models for engineering applications. Enhancing traditional turbulence models by using machine learning to achieve improved flow prediction accuracy still remains an area necessitating ongoing and meticulous exploration.  
In the field of turbulence model mapping, we have previously proposed a two-dimensional flow field mapping model for mapping between RANS and LES based on deep learning [30]. However, there has been no existing work using deep learning to extract features from the flow field data of RANS and regressing these features to more accurate LES data. Nevertheless, this research topic remains crucial because deep learning regression and mapping have the potential to rapidly and accurately obtain complex and precise flow field data and thus can significantly reduce the computational time and resources required for traditional fluid dynamics computations. In addition, it provides a novel approach for generating LES data from the corresponding RANS data in a more efficient way than the traditional LES model.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 4 Collision Avoidance System
### Evaluation Function
#### 4.4.2 Safety Subfunction \(f_{2}(X_{pi},S_{i})\)  
The safety of the CAS is extremely important for the USV. The safety subfunction should ensure that the vessel is away from all obstacles in the surrounding environment. In Section 3, the value of potential map \(V\), which is generated by the TFMS method, can describe the security of each pixel point from 0 to 1. Hence, the sum of the potential values \(V_{pk}\) corresponding to all the predicted states \(X_{pi}\) on \(i\)th trajectory is the safety evaluation of the trajectory. The safety subfunction is  
\[f_{2}(X_{pi},S_{i})=\sum_{k=1}^{n}V_{pk} \tag{16}\]  
where \(n\) is the number of states on \(i\)th trajectory. Figure 10 explains the safety subfunction in a potential map \(V\) with one obstacle point.
###### Abstract  
The Junggar Basin, where twenty-seven oil fields and five gas fields have been discovered, is a typical \"oil basin\" with proven ratio of natural gas of 5.3%. The amount of natural gas from Permian source rocks has been found in the western margin of the basin, but no large-scale natural gas reservoir has been discovered. The key to natural gas exploration is whether Permian oil-prone source rocks have large gas generation potential. Based on the comprehensive analysis of geochemical features and hydrocarbon generation simulation experiments, it is proved that the gas generation intensity could meet the standard of medium to large gas-fields (20 \(\times\) 10\({}^{8}\) m\({}^{3}\)/km\({}^{2}\)) at a depth of more than 6500 m. In the Penyijing and Shawan Sags, the burial depth of the Fengcheng Formation source rocks is between 8500 m and 10,000 m, respectively. It could be concluded that the Permian source rocks in the Penyijingxi and Shawan Sags have relative higher gas generation potential. In addition to high natural gas generation potential, two sets of effective reservoirs (wreathing volcanic rocks and secondary dissolution of sandy conglomerates) and thick caprocks with overpressure are developed in the most areas of Junggar Basin. Recently, natural gas reservoirs have been discovered and industrial natural gas had been obtained around the Penyijingxi Sag, Shawan Sags, and the Southern margin of the Junggar Basin. Our research results and natural gas exploration practice show that the Junggar Basin have relatively large natural gas exploration potential.  
oil-prone source rock; natural gas generation potential; natural gas exploration; Fengcheng Formation; Junggar Basin
## 3 Strength Test
### Flexural Tensile Strength  
Flexural tensile strength tests were carried out according to the Chinese Test Methods of Materials Stabilized with Inorganic Binders for Highway Engineering (JTG E51-2009) [36]. The formed large-scale beams specimen with a length of 550 mm and a width and height of 150 mm each were cured in a standard curing room for 3, 7, 14, 28, 60, and 90 days. In addition, strength tests were carried out at different curing times on the MTS at a loading rate of 50 mm/min. Five parallel tests were carried out at each curing time. In addition, the variation pattern of the flexural tensile strength was described by a logarithmic equation. The strength test results at different curing times were fitted by Equation (1). Figure 4 shows the fitting results for flexural tensile strength, where \(R^{2}\) is the correlation coefficient of fitting; \(S_{c}\) is the flexural tensile strength; \(t\) is the curing time; and \(a\), \(b\), and \(c\) are the fitting parameters.  
From the fitting results, it can be noted that  
1. A large variation coefficient for the flexural tensile strength of the large-scale beams specimen was obtained, which was difficult to control within 15%, and discreteness monotonously increased with the cement content. With the same cement content, the flexural tensile strength increased with the curing time. Before 28 days, the growth rate was relatively rapid, the strength value increased 2-fold (from 0.6 to 1.15 MPa), and after 28 days, the growth rate tended to be nearly stable, and the strength value increased by 0.85 times (from 1.15 to 1.35 MPa).
2. The flexural tensile strength of cement-treated aggregate base materials also monotonously increased with the curing time and cement content. The result was consistent with the growth trend of unconfined compressive strength and indirect tensile strength. The flexural tensile strength for the sample cured for 90 days tended to be stable, and with the increase in the cement content by 1%, the flexural tensile strength was multiplied, the flexural tensile strength at 3%  
Figure 4: Variation patterns of flexural tensile strength with curing time and cement content.  
cement content and 28 days is \(\sim\)0.4 MPa, the flexural tensile strength at 5% cement content and 28 days is \(\sim\)1.15 MPa. Simultaneously, the effect of the cement content on the strength of the cement-treated aggregate base material is larger than the curing time. The flexural tensile strength was also consistent with the growth trend of unconfined compressive strength. The Chinese Specification for Design of Highway Asphalt Pavement (JTG D50-2017) [37] has used the flexural tensile strength as the strength index of the inorganic binder. The flexural tensile strength at 90 days is \(\sim\)1.35 MPa which is very close to the requirements.  
Results and Analysis of Fatigue Tests of Cement-Treated Aggregate Base Materials under Different Stress States
## 3 Dataset and NN Training  
The training case study was the Mw 7.0 earthquake that hit Haiti on 12 January 2010. The epicenter was located about 25 km west-southwest of Port-au-Prince city. The disastrous shock caused the collapse of a huge number of buildings and widespread damage.  
Table 1 describes satellite dataset used which consists of GeoEye-1 optical images (one pre- and one post-seismic), and three TerraSAR-X SAR images (two pre- and one post-seismic).  
Optical images inputs, both for pre- and post-, consisted of TOA reflectances at 2 m spatial resolution for RGB spectral bands; starting from these reflectances, the intensity values are derived (as described in Section 2).  
As regards SAR data, intensity images have been obtained by multi-looking 3 \(\times\) 3 m TerraSAR-X applying a re-sampling at 10 \(\times\) 10 m.  
The computation of change indexes based on EO imagery has been performed at object scale by considering a set of polygons, extracted from the open source Open Street Map (OSM) geo-database over Port-au-Prince. A total of 1513 polygons corresponding to city blocks of the affected areas have been considered. All the features (NDI, KLD, MI and ICD) are grouped in a unique dataset necessary for the Neural Network approach.  
In addition to satellite data, a Ground Truth (GT) survey for Port-au-Prince town, expressed in terms of collapse ratio (CR), has been used (see Figure 1). The CR has been calculated by using GT information collected during a post-earthquake survey and available from the JRC (Joint Research Center) database, and considering the same polygons (city blocks) used for the satellite features calculation.  
In this work, Back-Propagation Neural Network (BPNN) has been used [23]. The BPNN to model CR was implemented using, as input, the features extracted from satellite images (see previous section, e.g., NDI in Figure 2), and CR values as target output. The 1513 samples used for NN training were split in Training, Cross Validation, and Test datasets of 65, 20 and 15%, respectively.  
A first network topology (Exp 1) consisted of only optical features as input (NDI, KLD and MI), five hidden layers with variable number of neurons [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 2  
Figure 4: Regression curves for training, cross validation, test and total sets, considering Exp 1. Red squared regression curves describe the result obtained when an independent dataset is applied.  
Figure 5: Regression curves for training, validation, test, and total sets, considering Exp 2. Red squared regression curves describe the result obtained when an independent dataset is applied.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 3 Experiment and Results
### Experimental Results
#### 3.3.2 Comparative Analysis of Memory Efficiency for the Two Strategies  
To evaluate the memory efficiency of the two data organization strategies based on the ODC grid and scene [49], we recorded the memory consumption and memory efficiency of the two strategies for calculating NDVI and NDWI, as shown in Figure 6. The red bars and green bars in Figure 6a indicate the memory consumption of the ODC grid- and scene-based approaches for calculating NDVI. The red bars and green bars in Figure 6b indicate the memory consumption of the ODC grid- and scene-based approaches for calculating NDWI. The curves in Figure 6c,d show the memory efficiency improvement of the ODC grid-based approach over the scene-based approach for calculating NDVI and NDWI, respectively. As shown in Figure 6a,b, the ODC grid-based approach consumed less memory than the scene-based approach. Taking the year 2016 as an example, it took 531.9 MB of memory to calculate NDVI with the ODC grid-based approach and 843.2 MB with the scene-based approach, i.e., the proposed framework improved the memory efficiency by 58.5%. According to the curves in Figure 6c, d and their trends, the space complexity for calculating NDVI and NDWI was consistent. Therefore, the ODC grid-based framework consumed less memory than the scene-based approach in terms of NDVI and NDWI calculation in the study area.  
Figure 6: Memory consumption and memory efficiency improvement of the scene-based and ODC grid-based approaches. (**a**) NDVI calculating memory consumption (MB) based on ODC grid-based approaches (red bars) and scene-based approaches (green bars). (**b**) NDWI calculating memory consumption (MB) based on ODC grid-based approaches (red bars) and scene-based approaches (green bars). (**c**,**d**) Memory efficiency improvement percentage (%) of the ODC grid-based approach over the scene-based approach for calculating NDVI and NDWI, respectively.