esa-sceva/satcom-chunk-collection huggingface dataset:
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 3 False Alarm Reduction Method for Reliability Analysis of Static Analysis Results
### Select Multiple Static Analysis Tools and Experiment
#### 3.2.1 CWE Mapping and Group  
For several weakness static analysis tools, the CWE report describes the relationship between unique identifiers and other types of weakness. However, different static analyses use different identifiers for the types of weaknesses that they support. Due to these different identifiers, it is difficult to automatically evaluate whether the static analysis tool refers to the correct type of weakness, which requires a more rigorous evaluation. As a result, we developed a mapping module to match each analyzer's weakness identifier to the corresponding CWE-ID; the output message for the weakness analyzed for each tool was defined based on CWE-ID. The weakness static analysis tools selected in this study can analyze 157 CWEs with differences in each tool.  
In the CWE system, the relationship of each CWE was designated as a child-parent hierarchy. This implies that a child CWE indicates a more specific instance of software weakness in the parent CWE. For instance, CWE-664 (Improper Control of a Resource Through its Lifetime) can be considered as a common denominator for all child CWEs (CWE-118, CWE-610, etc.). Group study of CWEs using child-parent relationships is necessary since only particular CWEs can be evaluated or output results may differ. Even if the exact CWE is not analyzed, the analysis is considered successful if the related CWE is analyzed. In this study, we created a CWE group referring to the CWE VIEW Research concept [43]. Figure 2 shows an example of the structure of the CWE group.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Tool Name \& Version** & **Static Analysis Method** & **Output CWE** & **No. CWE** \\ \hline \multirow{3}{*}{Cppcheck (CPP) 2.8} & Provides unique code analysis to & & \\  & detect bugs and focuses on & & \\  & detecting undefined behavior & & \\  & and dangers. & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Clang Static Analyzer (CLG) \\ 9.0 \\ \end{tabular} } & Path-sensitive, inter-procedural & & \\  & analysis based on symbolic & & \\  & execution. & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Flawfinder (FLF) \\ 2.0.19 \\ \end{tabular} } & Using a built-in database of & & \\  & C/C++ functions with & & Output & 17 \\  & well-known problems & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Infer (IFR) \\ 1.3 \\ \end{tabular} } & Using separation logic, and & & \\  & bi-abduction & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Frama-C (FRC) \\ 25.0 \\ \end{tabular} } & Runtime-error detection, value & & \\  & analysis, dependency analysis, & & \\ \cline{1-1}  & and slicing & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} PVS-Studio (PVS) \\ 7.16 \\ \end{tabular} } & Abstract syntax tree, & & \\  & pattern-based analysis, data-flow & & \\ \cline{1-1}  & analysis, symbolic execution, & & \\ \cline{1-1}  & and taint analysis & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of selected multiple static analysis tools.
## 3 Coordinate Transformation of Structural Plane Information
### Coordinate Sampling and Transformation  
The PNG files were parsed using a custom Python script to extract the pixel coordinates of structural lines. Based on a predefined number of sampling points, the line was evenly segmented, and the endpoints of each segment were used as discrete sampling points.  
Since the coordinates extracted from the image are in pixels, they must be converted into physical spatial coordinates using the following formulas:  
\[X_{\text{real}}=X_{\text{pixel}}\times R_{\text{X}} \tag{1}\]  
Figure 5: Simulated tunnel cross-section diagram.  
\[Z_{\text{real}}=Z_{\text{pixel}}\times R_{\text{Z}} \tag{2}\]  
where \(X_{\text{real}}\) and \(Z_{\text{real}}\) are the actual physical coordinates, \(X_{\text{pipe}}\) and \(Z_{\text{pixel}}\) are the coordinates in pixel units, and \(R_{\text{X}}\) and \(R_{\text{Z}}\) are the conversion ratios in the X and Z directions, respectively.  
In this study, the image scale was kept consistent in both directions, so Rx = Rz. To assess the accuracy of pixel-to-physical coordinate conversion, we compared several manually annotated reference points with their transformed spatial coordinates. The mean absolute error between measured and converted coordinates was 1.87 cm, confirming the reliability of the transformation approach. Examples of the sampling result with 10 points on a structural plane curve are shown in Figure 6. All sampled coordinates were stored in.txt files to ensure reproducibility and support further processing.  
Uniform distribution of sampling points is essential for maintaining interpolation accuracy and computational stability. An automated uniform sampling strategy was implemented in this study, which calculates segment length based on the total curve length and the desired number of sampling points. The program then samples evenly from the start to the end of the curve. Figure 7 presents the sampling results with 20, 50, and 80 points, confirming that the approach ensures evenly distributed points for accurate geometric reconstruction.  
Figure 6: Sampling results of structural lines.  
Figure 7: Uniform sampling results with different point numbers.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 5 Discussions
### Scalability and Real-World Performance  
Compared to traditional batch processing methods, the HiGoReg algorithm demonstrates significant advantages in scalability. The experimental results show that as the data size and number of groups increase, the efficiency gains of HiGoReg become increasingly pronounced, particularly when handling large-scale point cloud datasets. Moreover, the consistent and stable registration accuracy observed in both synthetic and real-world datasets further validates the robustness and broad applicability of the proposed framework, highlighting its reliable performance across diverse scenarios. However, HiGoReg currently does not incorporate an outlier rejection mechanism during adjustment. Consequently, the presence of erroneous point correspondences may lead to registration failures or degraded results. Addressing this potential limitation, future work should focus on enhancing the identification and removal of outliers to improve the stability and accuracy of the algorithm, ensuring its effectiveness in more complex and noisy real-world environments.
# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 4 Specific Routing and Traffic-Related Algorithms in SDN
### Load Balancing Algorithms
Dynamic Load Balancing: Algorithms in this category continually monitor the state of the network and adapt their decisions based on current load conditions. Techniques such as machine learning can be employed to predict future traffic patterns and adjust allocations accordingly. The implementation of dynamic load balancing in SDN-based data center networks, as described in the article [28], involves the real-time management of network traffic by shifting loads among various paths. The SDN controller plays a crucial role in this process by dynamically rerouting traffic based on the congestion status of network links. This method helps to optimize network efficiency and performance by reducing congestion and evenly distributing the load across the network. The implementation of dynamic load balancing in [23] involves leveraging the central control capability of SDN to actively monitor and manage network traffic. This is achieved by continuously analyzing traffic flows and network conditions, and then dynamically adjusting routes to optimize network performance. The system is designed to be responsive to changes in network load, automatically redistributing traffic to avoid congestion and ensure the efficient utilization of network resources. This approach improves network flexibility and can adapt quickly to varying network demands. Table 4 represents a summary of the Algorithms/Techniques discussed in the articles above while Table 5 summarizes the datasets and tools used. In SDN architectures, load-balancing algorithms are implemented at the controller level, enabling centralized decision making that takes into account the global state of the network. This centralized perspective allows for more sophisticated load-balancing
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## 3 Results  
Results of the Quantitative Data Analysis--Characterisation of the Acoustic Environment per ISO/TS 12913-2:2018  
The participants were exposed to acoustic environments recorded using the FOA microphone on-site and then reproduced via the SOA system in the laboratory. Exposure conditions were matched by SPL, using a SLM positioned in the'sweet spot'.  
As observed by the researchers, recordings at the location in Zadar (A) captured sounds from ship/sea traffic, road traffic, people talking and walking, church bells, music from stands, sound of the waves hitting the waterfront, sound of the Sea Organ installation. Recordings at the location in Graz (B) captured sounds created by road traffic, people talking and walking, sound of the river and sound of the electroacoustic installation Time Piece Graz. Recordings at the location in Zagreb (C) captured sounds from people walking, talking and clapping, echo, sounds of birds chirping, and sounds of road traffic.  
The following parameters were calculated as suggested by ISO/TS 12913-2:2018 (Table 2): L\({}_{\text{Aeq},T}\), L\({}_{\text{Ceq},T}\), loudness (per ISO 532-1), sharpness (per ISO 532-1) and roughness. It should be taken into account that psychoacoustic parameters were initially conceived with the primary purpose of describing single sound sources [48,80], while urban open spaces are extremely complex acoustic environments.  
Variation in the \(L_{\text{Aeq},5 min}\) between all sound excerpts was 27.6 dB(A), with large variations at the locations A (17.9 dB \(L_{\text{Aeq},5 min}\)) and C (15.2 dB \(L_{\text{Aeq},5 min}\)), while the location B was characterized by more consistent \(L_{\text{Aeq},5 min}\) (variation of 3.1 dB(A)).  
Accordingly, the overall variation in average loudness was 27.7 some, while the highest variation in average loudness was measured between the points C1 and C2 (15.3 some). Also, the highest difference in N\({}_{5}\) and N\({}_{95}\) values was measured at the location C2 (25.2 some).  
High sharpness (3.1 acum) and roughness (0.214 asper) values were measured at the measurement point C2, which can be explained by the same signal being quickly repeated as part of the investigated flutter echo effect and the high frequency content preserved due to many flat surfaces in the pavilion.  
All the measures observed were within the values usual for urban open spaces [29,81], with the exception of the before mentioned sharpness and roughness at C2.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 4 Discussion
The use of visual images post facto as evidence of genocide, population displacement, environmental damage, the supplementation of the results with eyewitness and independent reports, and the versatility of the use of data are all examples of how important remote sensing has become in the domain of global peace and security. Though it is not novel for use as military means, it has only recently been incorporated into other areas, such as quantifying conflict zones. More research is needed regarding how to use remote sensing imagery as an early warning tool for conflict prevention. The credibility of remote sensing information and its application in court or by policymakers is still a grey region. In the field of peace and security, the need for strong communication between scientific applications and policymakers is crucial. The _Eyes on Pakistan_ project is one of the successful examples of how remote sensing data documenting human rights violations have been used and publicized through an open-source platform accessible to public and government officials [95; 114]. \begin{table}
\begin{tabular}{c c c c c c} \hline
\begin{tabular}{c} Visual image \\ interpretation \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Major case studies associated with applications of remote sensing data in peace and security.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 4 Spatiotemporal Traffic Prediction Models
### The ST-Kriging Approach
One of the more significant aspects of this study is modeling traffic data that have a high ratio of missing values collected from 1000 road networks. To identify the most information-rich segments, the authors used a method called reduced measurement space [25]. The study indicates the ability of ST-Kriging methods in handling missing observations, where they recommend modeling the road networks as one connected spatial component. This approach helps in reducing the impact of the missing observation on the prediction accuracy, but it does increase the computational overhead. In contrast, the prediction accuracy was reduced when each road network was considered separately. Therefore, the authors suggested using a distributed approach with a central control unit in future work. Another study by [PERSON] et al. [26] applied ST-Kriging methods to handle road segments that take into consideration spatial characteristics and spatial homogeneity. Unlike other approaches, point-based Kriging considers the road segments as a single point and ignores these two factors, despite their importance in building more accurate traffic prediction. Their study proposes a segment-based regression Kriging (SRK) method to predict the traffic volume with a comparison between heavy vehicles, such as trucks, and light vehicles. There was a slight improvement in the prediction accuracy compared with point-based Kriging prediction. In the case of heavy vehicles, the prediction accuracy improved by 0.67%, whereas the uncertainty estimation showed significant results and improved by 53.63% compared with point-based Kriging. On the other hand, there was no increase in the prediction accuracy of the light vehicle, where the prediction accuracy results were less than the prediction accuracy in the point-based ST-Kriging approach. Much of the usage of the ST-Kriging approach in traffic modeling research to date has been for improving the traffic system by modeling traffic conditions, such as by analyzing traffic congestion [27] or predicting traffic speed and travel time [28; 29].
# Design of a Laser Driver and Its Application in Gas Sensing
## 2 Structure of the Laser Driver  
A flow chart of the code for regulating the laser driver is shown in Figure 1. The executable code is running on STM32H743, which is a 32-bit microcontroller produced by STMicroelectronics. Keil RTX version 5 (RTX5), which implements the CMSIS-RTOS2 as a native RTOS interface for the Arm Cortex-M processor, is embedded in STM32H743 to assist the CPU in scheduling the tasks and thus enhances its ability in real-time processing. The working of our laser driver is divided into six tasks: display refresh, touch scan, data save/load, current set, threshold current set and actual current monitor. The emWin GUI is configured for building a friendly and intuitive interactive interface. The RTX5 operating system is responsible for the scheduling of the execution of these tasks as well as the communications between them. A 32 GB SDHC card is connected with STM32H743 via the secure digital input/output (SDIO) port to store data and images. A RGB LCD screen is driven by STM32H743 through the built-in LCD-TFT display controller (LTDC). By calling the driving functions of SDHC card and LCD, the human-device interaction and the data exchanging are realized in Task #1, Task #2 and Task #3. The output of laser current source is controlled by the voltage converted from DAC2 of STM32H743, and the maximum allowable output current is set by DAC1. The callings of the DACs are executed in Task #4 and Task #5. In Task #6, the analog voltage proportional to the output of the current source is 12-bit digitized by ADC1 for monitoring purposes.
# Collision Frequency and Energy Transfer Rate in e-He Scattering
## 3 Results and Discussion
### Phase Shifts and Momentum Transfer Cross-Sections  
The scattering phase shifts, as defined in Equation (3), were calculated based on the optical potential given in Equation (6). Figure 2 shows the results for various angular momentum quantum numbers \(l\) as a function of electron energy \(E\). Positive phase shifts (\(\delta_{l}>0\)) indicate an attractive interaction potential. Our results align well with both the theoretical predictions of [PERSON] et al. [37] and the experimental findings of [PERSON] et al. [38].  
For the \(s\)-wave, as shown in Figure 2, [PERSON]'s theorem [39] is satisfied, indicating that the scattering phase shift at zero energy is \(\delta_{l}(0)=\pi n\), where \(n\) is the number of bound states.  
Figure 3 shows the momentum transfer cross sections for electron scattering on a helium atom, compared with theoretical and experimental data from other authors. In our study, we used an optical potential approach similar to that presented in [35; 40], where the electron-atom interaction is modeled by combining Coulomb and  
Figure 1: Potential energies of the e–He interaction. The optical potential is represented by a solid line, showing contributions from static (\(V_{DHFS}\)), polarization (\(V_{P}\)), and exchange (\(V_{\rm ex}\)) potentials.  
Figure 2: Phase shifts of electron scattering on a helium atom for \(s\)-wave (\(l=0\)), \(p\)-wave (\(l=1\)), and \(d\)-wave (\(l=2\)). The results align with theoretical predictions by [PERSON] et al. [37] and experimental findings by [PERSON] et al. [38].  
account for interactions at short and long distances, respectively. The exchange contribution is represented by an effective local field defined using the static exchange approximation. Our numerical results are in good agreement with experimental data [41; 42; 43], as well as with the results of [PERSON] [44]. The Dirac equation was solved to determine \(V_{S}(r)\) in [PERSON]'s work, whereas we used a model potential, as noted in Equation (2), which allowed us to perform calculations more efficiently without solving complex quantum mechanical equations.
## 2 Methodology
### The Experimental Techniques  
The mineral composition of initial materials and AAM hardened pastes was carried out by using X-ray powder diffraction analysis. Data were collected by DRON-6 X-ray diffractometer with Bragg-Brentano geometry using Ni-filtered Cu K\(\alpha\) radiation and graphite monochromator, operating with the voltage of 30 kV and emission current of 20 mA. The step-scan covered angular range of 2-70\({}^{\circ}\) in steps of 2 = 0.02\({}^{\circ}\). The powder X-Ray diffraction patterns were identified with references available in PDF-2 data base [22].  
The chemical composition of BBA and PG was evaluated by using XRF analysis. For this purpose, a Bruker X-ray S8 Tiger WD using a rhodium (Rh) tube, an anode voltage Ua up to 60 kV and an electric current I up to 130 mA were used. The compressed samples were measured in a helium atmosphere [23]. The hydration water (loss on ignition, %) in phosphogypsum was calculated after heating the material at the temperature 400 \({}^{\circ}\)C. The pH phosphogypsum was measured with the AD8000 professional multi-parameter pH-ORP-Conductivity-TDS-TEMP bench meter, with a measuring range of \(-\)2.00 to 16.00 pH, a resolution of 0.01 pH and an accuracy of \(\pm\)0.01 pH. The pH measurements of water suspensions were conducted when the ratio of water (W) and solid material (S) W/S was 10.  
Microstructure investigation of BBA, PG and hardened pates was performed using a high-resolution scanning electron microscope ZEISS EVO MA10 [24]. The resolution of the images (of secondary electrons in a high vacuum) of this microscope is at least 3 nm with 30 kV and at least 10 nm with 3 kV. In the performed analysis, the acceleration voltage was 5 kV.  
A laser particle size analyzer (CILAS 1090 LD) was used for the evaluation of the particle size of the BBA and PG. The distribution of solid particles in the air stream was 12 wt%-15 wt%. Compressed air (2500 mbar) was used as a dispersing phase [25].  
The compressive and flexural strength of hardened AAM paste and fine-grained concrete samples were determined by using hydraulic press Toni Technik 2020 according to the EN 196-1. The size of hardened AAM paste samples was 20 \(\times\) 20 \(\times\) 20 mm. Fine-grained concrete samples were \(40\times 40\times 160\) mm prisms. For each data point at least tree samples were tested. The compressive strength of hardened AAM paste was evaluated after 2 and 28 days of hydration. The mechanical properties of fine-grained concrete samples were tested after 7 and 28 days.  
The total and open porosity of alkali activated fine-grained concrete samples was evaluated by water absorption according to [PERSON] et al. [26].
## 3 Results and Discussion
#### 3.1.3 Establishment of the CSFM Flow Performance Evaluation Model  
In order to more accurately evaluate the flow properties of the slurry, the macroscopic flow properties (such as expansion, slump, and bleeding rate) and microscopic rheological properties (such as yield stress and consistency coefficient) of the slurry are comprehensively considered, which are collectively referred to as the flow characteristics of the slurry. Through the standardized processing and weight distribution of the experimental data, the mapping model between the fractal dimension and the comprehensive score of the flow characteristics is established, which provides a scientific basis for evaluating and optimizing the slurry ratio. The construction steps of the model are as follows.  
1. By fitting the experimental data, a relationship between the fractal dimension of CSFMs and the flow characteristic index is established. The detailed fitting results are presented in Table 3. The fitting results show that the correlation coefficient R\({}^{2}\) of each index is  
3. As shown in Figure 8, based on the different fractal dimensions and the corresponding flow performance index data collected by the experiment, the input and output relationship of the model is constructed using MATLAB (version 2021a) code. If the output slurry is not available, the fractal dimension can be adjusted by changing the particle distribution, and the comprehensive score \(F_{\text{S}}\) of the flow characteristics can be brought in and calculated again. The score serves as an indicator of the slurry's flow characteristics, with higher scores representing better performance. At the same time, when the comprehensive score \(F_{\text{S}}\) of the flow characteristics of the slurry is more significant than 0.80, the flow performance of the slurry can be evaluated as excellent.