esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Intelligent Inversion Method of the In Situ Stress Field
### Optimization Solution Based on PSO-BP  
#### 2.2.1 Coordinate Transformation and Orthogonal Design  
The in situ stress measurements are typically presented in the geodetic coordinate system. However, the inversion analysis for the in situ stress distribution in the underground cavern area is generally carried out in the computational coordinate system. Hence, before performing the inversion analysis, the stress state at the test points needs to be transformed from the geodetic coordinate system to the computational coordinate system, as demonstrated in Equation (1) [24].  
\[\begin{array}{l}\sigma_{\rm x}=L_{1}^{2}\sigma_{1}+L_{2}^{2}\sigma_{2}+L_{3} ^{2}\sigma_{3}\\ \sigma_{\rm y}=M_{1}^{2}\sigma_{1}+M_{2}^{2}\sigma_{2}+M_{3}^{2}\sigma_{3}\\ \sigma_{\rm z}=N_{1}^{2}\sigma_{1}+N_{2}^{2}\sigma_{2}+N_{3}^{2}\sigma_{3}\\ \tau_{\rm xy}=L_{1}M_{1}\sigma_{1}+L_{2}M_{2}\sigma_{2}+L_{3}M_{3}\sigma_{3}\\ \tau_{\rm yz}=N_{1}M_{1}\sigma_{1}+N_{2}M_{2}\sigma_{2}+N_{3}M_{3}\sigma_{3}\\ \tau_{\rm xz}=L_{1}N_{1}\sigma_{1}+L_{2}N_{2}\sigma_{2}+L_{3}N_{3}\sigma_{3} \end{array} \tag{1}\]  
where, \(L_{i}=\cos\beta_{i}\text{cos}\alpha_{ix}\), \(M_{i}=\cos\beta_{i}\text{sin}\alpha_{ix}\), \(N_{i}=\sin\beta_{i}\), \(\beta_{i}\) is the angle between \(\sigma_{i}\) and the horizontal plane, and \(\alpha_{ix}\) is the angle between \(\sigma_{i}\) and the direction of the x-axis.  
In order to conduct in situ stress inversion using neural network intelligence algorithms, it is necessary to generate sufficient numerical data as input and output samples. The orthogonal design method is used to design the training sample scheme and thus calculate more comprehensive and representative combinations of boundary conditions at a lower computational cost. This method utilizes orthogonal design tables to effectively address the challenges posed by multi-factor design and optimal level determination. Table 1 shows a typical four-factor and three-level L9(34) orthogonal design. The designed boundary condition scheme is then input into FLAC\({}^{3\text{D}}\) software, respectively. The simulated stresses at the in situ stress test points, together with the boundary conditions, serve as the training samples for the network.  
Figure 1: Different boundary conditions. (**a**) horizontal stress in the X-direction; (**b**) horizontal stress in the Y-direction; (**c**) gravity stress; (**d**) shear stress in the XZ-plane; (**e**) shear stress in the YZ-plane; and (**f**) shear stress in the XY-plane.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 5 Limitations and Future Plan  
Our method significantly alleviates the high-frequency information loss inherent to downsampling, as evidenced by its effectiveness in human pose estimation and object detection tasks. Currently, it has been optimized for CNNs with 2D feature representations and is not yet compatible with transformer-based models that rely on 1D token representations. Future work will focus on adapting our approach for transformer-based methods. We also found that high-frequency information loss is concentrated in the stem stage, and addressing this issue in the non-stem stages yields minimal improvement. Therefore, a key research direction is improving the performance of target detection models in the presence of significant high-frequency loss. One approach could involve integrating prior knowledge, such as spatial or kinematic structural information.
## 3 Results and Discussion  
### Transmission electron microscopy of GNPs  
Dimensional characteristics of GNPs were evaluated by transmission electron microscopy. According to the data obtained, the synthesized preparation had an average diameter of nanoparticles about 24.9 nm, the average deviation was 5.3 nm in a sample of 152 particles. The corresponding distribution is shown in Figure 1.  
Figure 1: (**A**) TEM image of gold nanoparticles. (**B**) A histogram of the gold nanoparticles (GNPs) distribution by diameter (basing on TEM data). \(n=152\).
# Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features
## 4 Discussion
Concentric ring artifacts constitute this type of pattern, and, as such, the greater the radius, the more likely a human observer would be todetect it, even if it is subtle. An advantage of the polar coordinate transformations used in our study, and in the application of the Hough transform implemented by [PERSON] et al. [11], is the fact that non-uniformities tend to be concentric rings, so that ring artifacts of any size become mapped onto straight lines across the entire extent of the image matrix in polar coordinates. Polar coordinate transformations have the effect of bringing concentric artifacts that are near to the center-of-rotation up to an equal footing with those at a greater radius. This process depends on knowing the center of the reconstruction matrix, which generally corresponds to the center field of view in each slice. This implies that in order to compute radial features, the uniform section of the cylindrical phantom should be placed as close as possible to the center of rotation of the detector. In practice, the presence of a small offset between the phantom and rotation axes does not constitute a problem as long as this offset makes it possible toextract a radial profile with a sufficient number of pixels, which, from our experience, corresponds to an offset smaller than half of the phantom radius. The fact that the \(R_{Contrast}\) and \(R_{KMSC}\) parameters agreed more closely with our medical physicists' scores than any of the 163 radiomic features can likely be explained by the fact that the radial parameters make use of the a priori condition that these artifacts are circular patterns, whereas the radiomics features are more generic and shape-independent.
## 4 Materials and Methods
### Data Processing  
Untargeted metabolic profiling analysis was performed for the fungi volatile compounds. Raw data were processed with multiple procedures, containing filtering, feature detection, alignment and normalization, according to the pipeline described by [PERSON] and [PERSON] [55]. For this purpose, the freely available software XCMS online ([[http://xcmsonline.scripps.edu](http://xcmsonline.scripps.edu)]([http://xcmsonline.scripps.edu](http://xcmsonline.scripps.edu))) was introduced in our study [56]. Raw data were transferred to NetCDF files using the MSD ChemStation software (Agilent). Afterwards, data were extracted using the centWave algorithm, which collects regions including potentially useful mass information in the chromatographic data and applies continuous wavelet transformation (CWT) [15]. The advantage of this method is detection of both strong and weak peak responses while maintaining a high sensitivity and low false discovery rate (FDR) [57]. The XCMS online parameters were optimized to extract the maximum information possible according to the protocol described by [PERSON] et al. [21], According to the character of our data, the setting was S/N threshold 3 and minimum peak width was 3 s. The remaining parameters were set as default. Pre-processed data were then exported as.csv files for further analysis using chemometrics.  
The processing pipeline of quantitative analysis comprised the following steps: deconvolution, library-based identification, and alignment [58]. Identification and deconvolution comprise the main procedures of data processing, while alignment is a validation procedure for identification. For deconvolution, the open source software, automated mass spectra deconvolution (AMDIS) was used to process the GC-MS data. Next, alignment was performed relying on retention index (RI) similarity. RI data were calculated automatically by AMDIS software, with the help of performing a series of n-alkanes (C7-C40) under the same chromatographic conditions. Subsequently, MVOCs were determined according to RIs of available standards and obtained mass spectra compared with corresponding volatile standards in the NIST11 MS database. Considering those volatile compounds without reference standards, tentative identifications were conducted based on comparison of mass spectra with those of the NIST11 MS database with match quality higher than 700 [59] and RIs found in literature. For quantification, a specific ion was extracted for each volatile compound (Table 1), which was generally the most abundant. The respective area of the specific ion was then calculated. Afterwards, relative areas of volatile compounds were obtained compared to that of the \(m/z\) 57 ion of the internal standard (tetradecane).
# Similarity Model Test on Rainfall Scouring Mechanism of High-Speed Railway Subgrade Slope
## 3 Test Results and Discussions
### Occurrence and Development Process of Subgrade Slope Erosion of G5  
As the slope gradient of G5 increased significantly, local ponding occurred in 9 min and 54 s, and the overall ponding occurred in 12 min and 14 s, with a depth of 0.4 cm.  
In the first 12 min, there was no obvious erosion. Until 13 min 2 s, water flow transported soil particles locally, causing flake erosion with a large erosion area but slight depth, as shown in Figure 13. Water flow scoured and transported R1 particles of G5 first to the slope foot before R2 and R3 were scoured. Water flow started to transport and erode R1, R2 and R3 at 13 min 33 s, 14 min 4 s and 15 min 21 s, respectively. Due to the large particle size, R3 had only a small amount of scouring under a rainfall intensity of 1.0 mm/min. Pits of G5 appeared at 14 min 45 s, finally forming nine pits of different sizes, with a maximum diameter of 6 cm and a depth of 2.3 cm, as shown in Figure 14.  
R1 was the first soil particle size group to be scoured on the slope with a gradient of 1:1.25, decreasing the cohesion of the slope surface, and leading to the erosion of other particle sizes. However, the R4 and R3 groups did not experience much scouring or transportation due to their large particle sizes and some residual soil particles of R1 that still provided shear strength. The erosion and collecting condition of G5 is summarized in Table 7. Compared with R1 and R2, R2 and R3, the incipient flow velocity increased by 56.9% and 47.3%, respectively, while the erosion quantity decreased by 65.8% and 44.5%, respectively.  
Figure 14: Pits after Erosion of G5.  
Figure 13: Sheet erosion occurs after erosion of G5.  
Compared with G2, the larger slope gradient of G4 leads to faster flow acceleration, shorter po-nding time, more erosion quantity of R1 and R2, larger erosion area, and less quantity and depth of sheet erosion. Compared to R1 of G2 and G5, the incipient flow velocity decreased by 20.5%, while the erosion quantity increased by 258%. Compared to R2 of G2 and G5, the incipient flow velocity decreased by 19.7% while the erosion quantity decreased by 32.1%. Due to the larger gradient, water flow could scour R3 of G5 at a low velocity, but not R3 of G2. Therefore, reducing the slope gradient appropriately can enhance the anti-scour ability and improve the starting velocity of the same particle size.  
The start-up time and ponding depth of each particle size on the slope surface under various working conditions are summarized in Figure 15. Under the same conditions, the smaller particle size starts first, but when the rainfall intensity is high, the starting time of the particle size below 2.5 mm is almost the same. Compared with G2 and G4, after removing the R1, the permeability coefficient increases, causing the soil to saturate faster. Compared with G2 and G5, when the slope gradient increases, the drag force of soil particles along the slope direction increases, which not only accelerates the start-up time but also promotes the start-up of the R3 group.
## 8 AI Applications in Smart Grids
### Protection in Smart Grids  
AI greatly enhances the defensive capabilities of smart grids in security applications. It quickly detects and diagnoses malfunctions, isolates impacted regions, and expedites restoration procedures. Artificial intelligence algorithms can identify irregularities in the flow of power, voltage levels, and other crucial factors, indicating possible problems before they escalate into significant issues. Early detection is crucial for preventing power outages and ensuring the integrity of the grid. Furthermore, AI can examine patterns in past power outages to improve future strategies for safeguarding the grid, thereby increasing its ability to withstand faults and external disruptions. Incorporating artificial intelligence (AI) into intelligent grid protection not only enhances the dependability of the power supply but also augments the overall safety and sustainability of the energy system. AI has the potential to resolve many problems that have resisted the best attempts of conventional mechanism-based approaches, with favorable performance, as indicated in Table 8. Most papers reviewed in this current study mentioned a few common challenges. We will talk about the challenges in the following paragraphs. In order to overcome the difficulties and close the gap between research and practice, we also offer recommendations for possibly significant future research directions. Numerous AI applications are data-driven and primarily rely on the quantity and quality of data, especially machine learning (ML) and deep learning (DL), and thus using AI on electrical grids may present problems. Many AI algorithms need data before producing accurate and helpful results. The current power system applications typically require thousands of instances. Additionally, the more instances are required, the more complicated the AI agent. Simulation can fill the gaps left by traditional modeling methods, providing a more dynamic and flexible approach to understanding complex system behaviors under varied conditions.
## 2 Materials and Methods
### IoT Framework Design and System Setup
#### 2.1.2 Cloud Layer  
Data Storage  
The sensor data were pushed to the MongoDB database hosted on the cloud server. MongoDB is an open-source that is used to store semi-structured data (NoSQL): a database that saves and retrieves documents in either JavaScript Object Notification (JSON) or extensive Markup Language (XML). Mongo is recommended to be adapted to big data management due to its characteristics [38]. In this research, MongoDB was chosen due to its capability to store and process data in real time. The application implemented with Mango had capabilities such as scalability and a high processing speed [39].  
The MQTT (Message Queueing Telemetry Transfer) is a communication protocol that can be adapted at the application layer. MQTT Serving, as a client-server, publishes/subscribes to the messaging protocol designed for machine-to-machine communications in a low bandwidth environment [40]. MQTT has been adopted in many IoT-based applications, such as manufacturing process management and healthcare [41; 42], energy generation and trading [43], and agricultural environmental monitoring [26]. Developing an IoT system involves IoT data transmission and thus requires IoT communication protocols. MQTT presents a remarkable contribution to IoT applications due to low power consumption, a small bandwidth, and less memory, which are common in IoT systems [44].  
Figure 5 displays the sensor node publishing data to the Mongo database. These data were sent to the MQTT broker, followed by the MQTT client subscriber, as shown in Figure 6.  
Cloud Web Platform  
The web application is a major part of the IoT system; it enables intended users to view and understand the status of the biogas station in real time. ReactJS Programming language was adapted to develop the front-end and the back-end web services, which were developed using the Laravel PHP framework. The web application is currently running on the cloud server. Table 4 presents the summarized functionalities of the application.  
Figure 4: IoT kit design.
## 2 Materials and Methods
### SEM Test and Image Process  
The macroscopic mechanical behavior of soil reflects its underlying microstructure. Microscopic analysis was performed using a JSM-7610F ultra-high-resolution thermal field emission scanning electron microscope (FESEM) from Japan Electronics Corporation (JEOL)(Tokyo, Japan). This instrument has a secondary electron image resolution of 1.0 nm and a magnification range of \(\times\)250 to \(\times\)10,000.  
For the ring-sheared samples with different initial moisture contents and subjected to varying numbers of F-T cycles, small 10 mm \(\times\) 10 mm \(\times\) 5 mm specimens were extracted from the shear zone. The fresh structural surfaces of the specimens were used for SEM analysis. A total of 18 groups of samples with different moisture contents and F-T cycles were prepared. The samples were processed according to specific test requirements to optimize their conditions for SEM analysis.  
The basic parameters, including the structure, number, and distribution of the particles and pores in the Hipparion laterite samples, were observed at magnifications of \(\times\)250, \(\times\)500, \(\times\)1000, and \(\times\)2000. Subsequently, the SEM images at different magnifications were processed and analyzed using the software of Image-Pro Plus (IPP) 6.0 to characterize and quantify these parameters. After importing the SEM image into the software, the first step is to calibrate the scale bar to ensure consistency with the original image. Next, set the grayscale threshold to distinguish soil particles (in black) and pores (in white) by adjusting the threshold values in RGB mode, thereby completing image binarization and segmentation (Figure 7). Manually fill in pore areas lost during the automated quantification process to reduce experimental errors. After pore labeling, statistical criteria should be defined to filter qualified pore regions. Statistical analysis and quantitative evaluation of the extracted pore parameters within these regions were then conducted (using the same software as employed for error analysis and curve fitting in the ring-shear tests). It is crucial to note that microstructural parameters are typically extracted and analyzed from three distinct locations of the same test sample to ensure the scientific integrity and comprehensive representation of the results regarding the impact of F-T cycles on the pore structure of Hipparion laterite.  
The sample preparation procedures and experimental instruments/equipment discussed in this section are presented in Figure 8.  
Figure 7: SEM image processing.
By generating high-resolution density maps and analyzing their contour lines, we can identify regions with unusual density gradients that are likely to signal panic zones. In these zones, individual trajectories are analyzed to detect irregularities, such as sudden changes in direction or speed. Additionally, semantic recognition of verbal cues enabled by speech-to-text conversion via the Baidu AI interface [15] enhances our ability to detect distress signals or panic-related language. Unlike introducing fundamentally new model architectures, this work focuses on the systematic integration of spatial, temporal, and semantic cues through a contour-triggered progressive fusion strategy and an attention-weighted multimodal aggregator, tailored for real-time panic detection in dense pedestrian networks. This proposed approach represents a significant advancement in crowd safety management, offering a more comprehensive and accurate method for detecting panic behaviors in real-time. By integrating these diverse data sources, we provide a holistic view of crowd dynamics that accounts for spatial and behavioral factors, enabling timely and effective interventions in high-risk scenarios that occur in crowded micro-road networks. The remainder of this paper is organized as follows: Section 2 reviews related work in the fields of crowd density and panic detection. Section 3 presents the methodology, including the data collection process and the integration of density maps, trajectory analysis, and semantic recognition. Section 4 discusses the experimental results and evaluation. Finally, Section 5 concludes with a discussion of the potential applications of the proposed framework and directions for future research.
# Spatial Data Analysis for Deformation Monitoring of Bridge Structures
## 5 Conclusions  
This paper deals with the deformation monitoring of bridge structures by non-conventional surveying methods: terrestrial laser scanning and ground-based radar interferometry. The basic principles of deformation monitoring using the mentioned methods are described with a special focus on the data analysis approaches. The paper describes the experimental deformation monitoring of the Liberty Bridge (Bratislava, Slovak Republic).  
The results of the case study presented in this paper show that both methods described are highly effective for the deformation analysis of engineering structures. The accuracy of 3D coordinate measurement of points by state-of the-art laser scanners is in the range of a few millimeters. The precision can be increased using the data processing approach described in the paper. In addition to the described geometry-based approach for deformation analysis, information concerning the behavior of the reflected laser beam is needed, as shown in [30].  
The G-B radar interferometry is an innovative contactless technique that allows the monitoring of the entire structure's deformation synchronously with high frequency. The accuracy of the results depends on the system configuration and on the quality of the reflected signal. The results obtained by G-B radar interferometry were verified by accelerometer measurements. Comparison of the result shows close compliance between the results of both methods. The measured deformations allow for the prediction of possible structural failure, often before an accident occurs.  
The results of the measurements performed and described in the paper confirmed the assumptions about the static and dynamic responses of the structure being monitored, as defined in the technical documentation of the bridge construction project.  
Conceptualization, J.E. and A.K.; methodology, J.E., A.K., and P.K.; investigation, J.E., A.K., and P.K.; resources, J.E.; writing--original draft preparation, J.E.; writing--review and editing, A.K.; visualization, P.K.; project administration, J.E.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Slovak Research and Development Agency under Contract no. APVV-18-0247. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
###### Abstract  
Various control methods and applications have been introduced to power systems in order to alleviate the uncertainties of relying on renewable sources. This paper, focusing on a reactive power allocation process, discusses the development of a self-modification algorithm and its introduction to an online reference management system. The algorithm is designed to predict unexpected situations occurring in real-time operations, when, due to technical issues, the control system becomes unavailable. A test system has been formed to check the feasibility of the composed method. As the main purpose of the feasibility study is checking availability with a complex solution, case studies are designed to adapt a reactive power allocation method based on loss minimization. When unexpected systemic errors were induced, the proposed modification plan changed order automatically with maintaining previous objective function. The improved effects, caused by realistic responses, are classified according to EMTDC (Electro-Magnetic Transient Design and Control) analysis.  
online management; WFMS; reactive power allocation; automatic response; voltage sourced converter 2018 8, 68; doi:10.3390/app8010068