esa-sceva/satcom-chunk-collection huggingface dataset:
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
## 4 Flow Field and Pressure Fluctuation
### Flow-Induced Noise at Different Flow Velocities  
The RNL values for different flow velocities at a far-field monitoring point on the side of the fin and rudder are shown in Figure 23. The value in the upper right corner of the figure is the overall RNL of the monitoring point.  
Figure 23: RNL of the flow-induced noise at a monitoring point: (**a**) narrow band (20–1000 Hz); (**b**) third-octave band.  
Figure 22: Directivity of flow-induced noise at 5 m/s: (**a**) XY plane; (**b**) XZ plane; (**c**) YZ plane.  
Upon examining the figure, it can be seen that at high flow velocities, the magnitude of the sound pressure level is greater than that at low flow velocities. As the frequency increases, the RNL of flow-induced noise gradually decreases. This is consistent with the observed trends in the variations in pressure fluctuations at different flow velocities, as described above. In Figure 23b, the change in the sound pressure level of flow-induced noise is more clearly presented by the third-octave frequency.  
To investigate the overall noise characteristics, the values of sound pressure received by 108 far-field monitoring points positioned 50 m away from the model in the XY, XZ, and YZ planes were considered. By utilizing Equation (11) to calculate the RNL for each monitoring point, the overall RNL can be obtained as an indicator of the magnitude of flow-induced noise. The values reflecting different cases can be found in Table 10. By comparing the data in the table, it can be found that as flow velocity increases, the overall RNL of flow-induced noise also increases. The relationship between the radiated sound power of the fin and rudder and the flow velocity corresponds to a power of about seven.
## 3 Results
### Uncertainties of Crack Position and Progression on Total Strain Controlled Fatigue Results
Starting from the abscissa value, the crack propagation rate can be determined. This procedure is exemplified in Figure 13 with brown arrows. The process shown in Figure 13 is only a rough approximation since the crack propagation law may not lie exactly within the range considered. Figure 14 shows the function of load drops for a constant strain amplitude of \(0.3\times 10^{-2}\). The diagram also contains the crack sizes determined during the test, which are further used as input variables in the calculation, e.g., using the PROST analysis tool. Horizontal dashed lines indicate the geometrical estimation of the crack depth \(a\). The bars (slightly shifted to avoid overlapping) indicate the possible ranges of possible crack lengths depending on the extensometer clip position. The calculation results are focused on a load drop of 25%. Figure 14: Calculated incipient depth as a function of load drop. Figure 13: PROST diagram for determining the crack propagation rate, based on the analytical method according to [33] for air and \(R=-1\). For total strain amplitudes larger than \(1\times 10^{-2}\), it should be noted that the reliability of the present calculations is limited since the Chaboche parameters were adjusted for relatively small total strain amplitudes. Further investigations and results of this work are described in more detail in [31]. From the investigation of crack sizes associated with the fatigue life achieved, an estimation can be proposed that gives a probability of a certain crack size at the defined load drop. The relation is based on the range spanned by two idealized crack shapes related to the experimental findings. The following equation approximates the full span of values for possible crack sizes for an assumed load drop \(d_{L}\) (relative value), based on data of CATs with a total strain amplitude of 0.3 \(\times\) 10\({}^{-2}\) (Figure 14). \[a\in\left[4.7\;\text{mm}\times\sqrt{d_{L}}\times 4.4\;\text{mm}+6.4\times d_{L}\right] \tag{18}\]  
These values of a minimum and maximum envelope of the crack depths measured in the CATs are shown in Figure 15. Considering the possibilities, the experimentally observed cracks are within the range predicted by the numerical simulation, which allows possible corrections in terms of the scatter of fatigue life data with respect to a defined crack initiation criterion to be made.
# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 2 Related Work
### Zero-Shot Stance Detection  
Zero-shot stance detection (ZSSD) is an emerging task in natural language processing aimed at identifying stances toward unknown targets by leveraging stance information learned from known targets [15]. In contrast to traditional stance detection methods, ZSSD enhances model generality, making it particularly valuable in scenarios with scarce data or rapidly emerging new targets, such as rumor detection and public opinion analysis during sudden events.  
Early studies on ZSSD primarily relied on transfer learning techniques. Researchers trained models on known source targets and transferred the acquired knowledge to unknown targets. [PERSON] and [PERSON] [5] were the first to explicitly propose the ZSSD task, designing a method based on target-invariant representation. This approach learns text features independent of specific targets, enabling stance prediction for unseen targets. With the development of large-scale pre-trained language models (PLMs), recent approaches have transformed ZSSD into a prompt-based or reasoning-driven task. For example, [PERSON] et al. [16] proposed Pattern-Exploiting Training (PET), which reformulates classification as a masked language modeling task guided by textual patterns. While effective, these methods often depend heavily on prompt quality and lack robustness in complex scenarios.  
To further improve reasoning and generalization in zero-shot stance detection, several representative approaches have been proposed in recent years. [PERSON] et al. [17] introduced the ANEK framework, which leverages adversarial training to extract transferable knowledge from seen targets, while incorporating sentiment information and commonsense knowledge to enhance contextual understanding. [PERSON] et al. [18] proposed StanceReasoner, a model that integrates pre-trained language models with chain-of-though reasoning to generate intermediate steps for interpretable stance prediction. [PERSON] et al. [19] developed a commonsense-based adversarial framework, which uses external commonsense graphs and a feature separation adversarial network to capture both target-invariant and target-specific features. Additionally, [PERSON] et al. [20] reformulated stance detection as a conditional generation task, combining target prediction and unlikelihood training to improve the semantic alignment between input text, targets, and stance labels.  
These methods demonstrate that ZSSD is an active and evolving field, where knowledge incorporation, prompt design, and reasoning strategies play increasingly important roles. Our work builds on this line of research by introducing a structured multi-agent debate framework that integrates external knowledge while enabling interpretable, dynamic argumentation for robust zero-shot stance detection.
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 2 Materials and Methods
### Related Work
Regardless of the weaknesses in the various modulation enhancement techniques, the authors in the literature reviewed above give useful insights on where to start and how to manipulate the conventional modulation techniques if one wants to significantly improve them and obtain better OFDM channel data throughput.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 3 Methodology  
This study can be divided into the following four main steps: (1) LiCSAR processing chain; (2) time series analysis (LiCBAS); (3) obtaining InSAR outputs (deformation products); (4) application of the proposed approaches, MIRA (multi-dimensional InSAR research and analysis) and InCROSS (Crosta's application in InSAR). Figure 3 shows all the steps followed during this study.  
\begin{table}
\begin{tabular}{c c c} \hline
**Direction** & **Ascending** & **Descending** \\ \hline Frame ID & 058A\_05279\_131311 & 065D\_05285\_111314 \\ Heading Angleo & 10.13 & 169.86 \\ Average Incidence Angleo & 33.8035 & 39.5272 \\ Azimuth Resolution & 13.965700 & 13.961460 \\ Range Resolution & 2.329562 & 2.329562 \\ Coherence Threshold & 0.05 & 0.05 \\ Unwrapped Coverage & 0.3 & 0.3 \\ Threshold & 171, 151 & 171, 151 \\ Size of Image (w,l) & 236 & 195 \\ Images to be Used & 232 & 181 \\ All Interferograms & 806 & 930 \\ Interferograms to be Used & 767 & 741 \\ Removed Interferograms & 39 & 189 \\ Threshold of Used & & \\ Unwrapped & 232 & 181 \\ Interferograms & & \\ Date Range of Used Images & 14/03/2015–29/03/2020 & 03/03/2015–21/08/2019 \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-1 SAR data and SBAS parameter details.  
Figure 2: Interferograms from ascending (**a**) and descending (**b**) orbits were analyzed for their perpendicular baselines. Red lines highlight the interferograms with coherence values below 0.05, which were not used in the analysis.  
The methodological framework of this study involves a sequential four-step process designed to ensure the accurate and comprehensive analysis of surface deformations in the study area. Initially, the LiCSAR processing chain was implemented to generate interferograms through procedures such as image co-registration, multilooking, and phase unwrapping. The second phase utilized the LiCSBAS2 version 1.9.1 software tools to perform a time series analysis on the unwrapped interferometric products, allowing for  
Figure 3: Schematic representation of the multi-temporal InSAR analysis workflow based on LiCSAR and LiCSBAS, including the implementation steps of the proposed methods.  
the detection of temporal deformation trends while minimizing atmospheric noise. In the third step, ascending and descending orbit data were decomposed into vertical and east-west components to obtain multi-directional deformation products. Finally, two advanced analytical approaches were applied as follows: MIRA used multidimensional visualization and support vector machine classification, while InCROSS employed principal component analysis and the Crosta technique to highlight and classify significant deformation zones. Detailed information about the processing steps are given in the next sections.
## 5 Performance Analysis and Simulation
### Security Performance Analysis
#### 5.1.1 Security Performance Analysis of EPUSBRF, RPBMP and Shortest Path Algorithms  
Suppose that the time for the attacker to trace back one hop along the routing path is \(T\). For the EPUSBRF scheme, let the hop count from the SoN to the PN be \(\text{Hop}_{\text{S,p}}\) and the hop count from the PN to the SN be \(\text{Hop}_{\text{P,sink}}\). For the RPBMP scheme, let the hop count from the SoN to the PN be \(\text{Hop}^{\prime}_{\text{s,p}}\) and the hop count from the PN to the SN be \(\text{Hop}^{\prime}_{\text{p,sink}}\). For the shortest path algorithm, let the hop count from the SoN to the SN be \(\text{Hop}_{\text{S,sink}}\).  
For EPUSBRF and RPBMP, the routing path hops are given by Equations (5) and (6), respectively:  
\[\text{Hop}_{\text{EPUSBRF}}{=}\text{Hop}_{\text{S,p}}{+}\text{Hop}_{\text{ p,sink}}, \tag{5}\]  
Figure 9: The flowchart of our proposed DPS scheme.  
\[\text{Hop}_{\text{RPBMP}}=\text{Hop}^{\prime}{}_{\text{s,p}}+\text{Hop}^{\prime}{}_ {\text{p,sink}}, \tag{6}\]  
For the shortest path algorithm, the routing path hops is:  
\[\text{Hop}_{\text{Shortest}}{=}\text{Hop}_{\text{s,sink}}. \tag{7}\]  
For EPUSBRF and RPBMP, the time required for the attacker to trace back to the SoN is given by Equations (8) and (9), respectively:  
\[\text{T}_{\text{EPUSBRF}}{=}\text{Hop}_{\text{EPUSBRF}}*\text{T}=(\text{Hop}_ {\text{s,p}}+\text{Hop}_{\text{p,sink}})*\text{T}, \tag{8}\]  
\[\text{T}_{\text{RPBMP}}=\text{Hop}_{\text{RPBMP}}{\text{*T}}=(\text{Hop}^{ \prime}{}_{\text{s,p}}+\text{Hop}^{\prime}{}_{\text{p,sink}})*\text{T}. \tag{9}\]  
For the shortest path algorithm, the time required for the attacker to trace back to the SoN is shown in Equation (10):  
\[\text{T}_{\text{Shortest}}{=}\text{Hop}_{\text{Shortest}}{\text{*T}}=\text{ Hop}_{\text{s,sink}}{\text{*T}}. \tag{10}\]
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
###### Abstract  
Hydroelectric dams are a major threat to rivers in the Amazon. They are known to decrease river connectivity, alter aquatic habitats, and emit greenhouse gases such as carbon dioxide and methane. Multiscale remotely sensed data can be used to assess and monitor hydroelectric dams over time. We analyzed the Sinop dam on the Teles Pires river from high spatial resolution satellite imagery to determine the extent of land cover inundated by its reservoir, and subsequent methane emissions from TROPOMI S-5P data. For two case study areas, we generated 3D reconstructions of important endemic fish habitats from unmanned aerial vehicle photographs. We found the reservoir flooded 189 km\({}^{2}\) (low water) to 215 km\({}^{2}\) (high water) beyond the extent of the Teles Pires river, with 13-30 m tall forest (131.4 Mg/ha average AGB) the predominant flooded class. We further found the reservoir to be a source of methane enhancement in the region. The 3D model showed the shallow habitat had high complexity important for ichthyofauna diversity. The distinctive habitats of rheophile fishes, and of the unique species assemblage found in the tributaries have been permanently modified following inundation. Lastly, we illustrate immersive visualization options for both the satellite imagery and 3D products.  
conservation; Cubesat; satellite; UAV; PlanetScope; LiDAR; SfM photogrammetry; fish; freshwater; TROPOMI; GLAS; virtual reality; VR +
Footnote †: journal: 2021
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Application Scenarios  
As CPS technologies have been widely used in multiple scenarios, smart grids, health care, smart transportation, and smart homes are hot topics in research. There are also some studies on more general areas, including cloud servers, industrial control systems, and embedded devices.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 4 Results and Discussions
### Experimental Results on the SIGFloods Dataset  
The thorough assessment of the SIGFloods benchmark dataset reveals the outstanding performance of the CCT-U-ViT architecture, achieving leading results in all evaluation metrics and setting new benchmarks for satellite-based flood detection (Table 3). This model reached a top overall accuracy of 97.9%, marking a noteworthy 1.1% improvement over the second-best method (CNN-Transformer at 96.8%), a considerable leap given the already impressive baseline performance on this challenging dataset. The Kappa coefficient 0.969 suggests near-perfect agreement beyond mere chance, underscoring the model's exceptional reliability for operational flood monitoring applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Kappa** & **OA** & **\(F1\) Score** & **mIoU** \\ \hline CNN 2D-1 Layer & 0.872 & 0.937 & 0.911 & 0.843 \\ \hline CNN 2D-2 Layers & 0.908 & 0.956 & 0.933 & 0.877 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy assessment of the proposed and benchmark models for flood detection on the SIGFloods dataset.  
Figure 11: Selected zoomed areas for the error distributions of the flood detection for the proposed and baseline models.  
The performance hierarchy identified in the S1 GFloods dataset provides important insights into architectural design principles for flood detection tasks. Traditional CNN methods display a distinct performance gradient, with deeper architectures (CNN 2D-2 Layers: 95.6% OA) surpassing their shallower versions (CNN 2D-1 Layer: 93.7% OA) due to better feature abstraction capabilities and broader receptive field coverage. 3D CNN architectures demonstrate competitive performance (95.9% OA for a single layer, 95.5% OA for two layers), suggesting that the temporal dimension offers valuable information when ample training data are available, as evident with the extensive S1 GFloods benchmark. The Vision Transformer achieved excellent results (96.1% OA, 0.945 Kappa) by utilizing global attention mechanisms, although its computational complexity poses a practical challenge for large-scale use.  
The CNN-Transformer hybrid approach (96.8% OA, 0.958 Kappa) demonstrates the effectiveness of combining convolutional inductive biases with transformer attention mechanisms, achieving superior performance compared to pure CNN or transformer architectures. However, the proposed CCT-U-ViT surpasses all baseline methods with an \(F1\)-score of 0.966 and mIoU of 0.933, indicating exceptional precision-recall balance and spatial overlap accuracy. The consistent performance gains across all metrics (2.1% improvement in Kappa, 1.1% in OA, 1.4% in \(F1\)-score, and 1.8% in mIoU compared to the second-best method) provide strong empirical evidence for the architectural innovations incorporated in the CCT-U-ViT design, particularly the effective integration of multiscale feature fusion with hierarchical attention mechanisms optimized for satellite imagery analysis.
# Implementing CAD API Automated Processes in Engineering Design: A Case Study Approach
## 4 Case Studies
### Case Study 2: Automated Drawing Generation from CAD Models  
Goal  
The objective for Case Study 2 was to extend automation processes with the generation of completely annotated mechanical drawings from part files in CAD. Integrating drawing generation into the same automated chain that generates folder hierarchies and metadata, the objective was to save time in manual drafting and ensure that drawing output is within pre-specified standards.  
Process  
Building on the folder-generation routine of Case 1, the engineer first completes the initialization form (Figure 3) and selects the CAD STEP option. This triggers the API script to open the existing.ipt part file and automatically invoke the drawing environment. The routine generates a new.idw (or.dwg) drawing file in the project's \"Engineering Drawings\" folder, populating three standard views--left, top, and isometric--and inserting the title  
Figure 5: Organization of data archiving in the form of an MS Excel index spreadsheet.  
block information captured in the form. Basic dimensions are added to the primary view according to a template, and prompts notify the user of the drawing's save location.  
Result  
The automated drawing procedure reduced the time required to create and annotate a part drawing from over an hour to under five minutes while guaranteeing consistent formatting and eliminating transcription errors. Figure 6 illustrates the resulting project folder containing both the.ipt model and.idw drawing files. Figure 7 presents a representative output, showing the three views, populated title block, and template-driven dimensions. By integrating drawing generation into the broader automation framework, this case demonstrated both a substantial productivity gain and a robust mechanism for maintaining drawing quality and traceability.
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
###### Abstract  
The shear strength and bearing characteristics of lunar soil have a strong connection with its compactness. The compactness varies significantly with depth and has an important effect on engineering activities on the lunar surface. In this study, lunar soil simulant samples of four compactness levels were prepared to explore the relationship between compactness and cone tip resistance in static cone penetration tests (CPTs). The compactness values at different depths were measured layer by layer, and CPTs were carried out. The results indicate that the cone tip resistance continuously increases with the increase in the penetration depth until it reaches a peak, and then remains constant for a certain depth. The cone tip resistance after the normalization of the overburden stress gradually increases and then decreases after reaching the peak. Models of the relationship between cone tip resistance before and after normalization and compactness were constructed using a regression algorithm. The variation in lunar soil compactness with depth can be determined by measuring cone tip resistance with this model. The research findings can provide a theoretical basis for in situ testing, site selection for lunar bases, and other related aspects on the lunar surface.  
lunar soil simulant; static cone penetration test; cone tip resistance; compactness; construction on the lunar surface
## 3 Methodology of Complementary Filter Calibration
### Data Collection  
The first step was data recording in a static position of the system, at various pitch and roll angles. The following attitude angle values were chosen for both channels: 0, 5, 10, 15, 20, 25, and 30 degrees. Fourteen data sets were recorded, each corresponding to a specific attitude angle. The duration of each experiment was 300 s, and the data transmission frequency was 50 Hz (every 0.02 s). There were registered acceleration signals, \(a_{x}\), \(a_{y}\), and \(a_{z}\), as well as angular rates, P, Q, R. For each angle, a set of 105,000 data points was recorded during each experiment, resulting in a total of 1,470,000 data points. Subsequently, the data were introduced into a simulation system in Matlab Simulink, where a model of the AHRS system with complementary filters was implemented. For each experiment,  
Figure 3: Complementary filter for attitude estimation.  
a set of attitude and heading angles was calculated, containing 45,000 additional data points for each analyzed angle and individual time constant. A total of 126 different time constant values were simulated for each attitude, which means 1764 simulation experiments for data acquisition. In total, there were 264,600,000 analyzed attitude data points. The experiments were conducted under controlled conditions to ensure objectivity and repeatability. Data acquisition was fully automated, and all recorded measurements were verified for consistency. The angular rates and acceleration data were obtained using an inertial measurement unit (IMU) and processed according to a predefined procedure. The determined attitude data were used to select the optimal time constant value for the complementary filter. Figures 4-6 show example time series of the obtained data.  
Figure 6 shows influence of the complementary filter time constant for the calculated angle. The results obtained show the possibility, and even the need, to select the time constant using a specific optimal criterion or based on the frequency analysis of the signal. Below, the methodology for selecting the optimal time constant will be presented.