esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.3 Correction Coefficient for Topographic Areas  
The terrain fluctuation can differentiate sunshine distribution in shady and sunny slopes, which affects the brightness of the ground objects on the remote sensing data [46]. Therefore, a correction for topographic area must be performed, as follows, to eliminate the uncertainty of vegetation evaluation caused by differences in absolute topography and different topographic factors [47]:  
\[K=\frac{S_{i\epsilon}/S_{\epsilon}}{S_{i}/S} \tag{5}\]  
where \(S_{i\epsilon}\) is the area of vegetation change type \(i\) in e-level topography, \(S_{\epsilon}\) is the topographic areas of level \(e\), \(S_{i}\) is the area of vegetation and change type \(i\) in the study area, and \(S\) is the study area. \(k>1\) indicates that vegetation change type \(i\) is dominantly distributed in topography at level \(e\), \(k=1\) indicates that vegetation change type \(i\) is smoothly distributed in topography at level \(e\), and \(k<1\) indicates that the \(i\)th vegetation change type is nondominantly distributed on topography at level \(e\).  
We used the correlation coefficient method to calculate the correlation coefficients of FVC data with the TEM, PRE, and SUN of the current month and the previous 1-6 months, respectively, as follows [48]:  
\[R_{xy}=\frac{\sum_{i=1}^{n}[\left(x_{i}-\overline{x}\right)(y_{i}-\overline{ y})]}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\left(\sum_{i=1}^{n} (y_{i}-\overline{y})^{2}\right.}} \tag{6}\]  
where \(R_{xy}\) is the correlation coefficient of two variables, \(x_{i}\) is the time series data of year/month, \(y_{i}\) is the time-series data of year/month, \(\overline{x}\) is the average of multiyear/month time-series data, \(\overline{y}\) is the average of multiyear/month PRE, and \(n\) is the number of samples. A significant correlation was considered to be at a 0.05 significance level. Each pixel selected the month with the maximum correlation coefficient value as the lag period. To analyze the instantaneous correlation and lag between FVC and climate, the MVC (maximum value composite) was used to calculate the spatial distribution of the lag period in the entire province.
## 1 Introduction
NDVI was one of the firsts VIs developed and remains widely used. Previous studies have identified the main plant traits driving VIs using observational data or Global Sensitivity Analyses (GSA) of radiative transfer models. Both data-based and GSA studies have shown that NDVI is particularly sensitive to LAI and chlorophyll density; NDVI saturates at high LAI and is also influenced by soil reflectance [19; 20]. Given these sensitivities, some degree of correlation between NDVI and GPP is expected because LAI and leaf chlorophyll largely determine the absorption of photosynthetically active radiation (PAR) radiation in canopies, and also because leaf chlorophyll is a driver of leaf-level photosynthesis [21]. Despite these potentially strong physiological linkages to GPP, NDVI has proven to be a poor predictor of GPP in some cases [10; 11]. While some studies assume this issue may be related to NDVI's saturation to LAI or undesired influence of environmental conditions [18], it is unclear whether this could be related to traits not captured by NDVI. EVI and NIR\({}_{\text{v}}\) were subsequently developed with the goals of decreasing saturation in dense canopies and reducing cross sensitivity to soil and atmospheric conditions [6; 12; 22]. EVI and NIR\({}_{\text{v}}\) share NDVI's sensitivity to LAI, while adding sensitivity to differences in reflectance in the NIR [6; 12; 20; 23]. Prior GSA studies have produced ambiguous relationships between plant traits, EVI and NIR\({}_{\text{v}}\), suggesting a strong sensitivity to either mean canopy leaf angle or to chlorophyll and leaf mass per area [20; 24]. NIR reflectance is related to scattering at air-water interfaces within leaves [25], and studieshave shown that NIR reflectance is correlated with LAI and a suite of leaf traits including leaf mass per area, nitrogen content, water content, and canopy traits such as mean leaf inclination angle and canopy shape [26; 27; 28; 29; 30]. Some of these leaf traits are correlated with each other, as well as leaf photosynthetic rate [27; 29; 31; 32]. However, we are unaware of previous studies that have quantitatively assessed which of these plant traits are the main drivers of EVI and NIR\({}_{\rm v}\) across a broader set of biomes, and how these traits mediate or affect the relationship between these VIs and GPP. We explored the temporal and spatial relationships between NDVI, EVI, NIR\({}_{\rm v}\), and GPP across 10 homogenously-instrumented eddy covariance sites in California that spanned a range of climatic conditions. These sites varied markedly in vegetation density and type, and sampled the local analogues of six major terrestrial biomes (grassland, subtropical desert, shrubland, woodland/savanna, temperate forest, and boreal/subalpine forest). We then explored these relationships using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. We focused on two questions: Which Vegetation Index or Indices are most generally correlated with GPP across temporal and spatial scales? What biophysical property or properties mediate the relationships between VIs and GPP?
## 3 Probing the Metal-Support Interactions at the Interface
### Transmission Electron Microscopies and Other Imaging Techniques  
Transmission electron microscopy is a powerful technique that is able to disclose the structural and morphological features of the metal-support interface at the atomic scale. TEM images combined with energy-dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy (EELS) allowed appreciating the metal dispersion on support and individuating the presence of defects, unsaturation, heteroatoms, and functional groups. Therefore, TEM represents the ideal tool to unravel the effects of heteroatom-mediated metal-support interactions [91].  
In fact, the comparison of TEM images of metal nanoparticles supported on undoped and doped surfaces evidenced the fundamental role of homogeneously distributed heteroatoms in providing nucleation and anchoring sites and promoting a higher metal dispersion. High metal dispersion was detected for different metal species (Au [92], Pd [93], Pt [94; 95], Co [87], MoS\({}_{2}\)[96]) on several doped carbon surfaces (N and O-doped carbon nanotubes, N-doped mesoporous carbon, N-doped carbon shells, carbon nanosphere, polyaniline (PANI) fibers). The presence of nitrogen has been demonstrated to assure excellent metal dispersion, even at very high metal loading [93].  
Electron micrographs (Figure 8) unveiled the drastic difference between the intimate contact at the interface of Pd particles on defective carbon surfaces, including heteroatom and topological defects, and the undamaged grain boundary of Pd NP supported on an undoped graphitic surface [69].  
Figure 8: \(Cont\).  
Besides TEM, other imaging techniques have been successfully used to characterize supported metal nanoparticles. For instance, atomic force microscopy (AFM) revealed that Au nucleation occurs selectively at step edges in a freshly cleaned highly oriented pyrolytic graphite (HOPG) surface, whereas a homogeneous dispersion of Au nanoparticles in the middle of terraces has been attained on HNO\({}_{3}\)-treated surfaces due to the introduction of O functionalities and defects acting as preferential anchoring sites [81].  
Scanning transmission X-ray microscopy (STXM) uses a nanoscaled focused soft X-ray beam to provide microscopic measurements with high spatial resolution. It has been successfully applied to obtain the spatial mapping of reduced and stoichiometric Co\({}_{3}\)O\({}_{4}\) on nitrogen-doped graphene, demonstrating the nitrogen and oxygen sites are favored sites for the immobilization of Co\({}_{3}\)O\({}_{4}\) nanocrystals [90].
## 3 Results and Discussion
### Sensory Analysis of Cookies Fortified with Germinated  
In order to evaluate certain applications in the food industry of the analyzed sprouts, biscuits were obtained with composite flour (wheat flour/sprouts) in three different compositional variants (2.5, 5, 10% sprouts in wheat flour), according to Figure 3. Also, a control sample from wheat flour was analyzed for comparison.  
In order to evaluate the consumer acceptability of the cookies fortified with sprouts, a sensory evaluation was carried out by a panel ofasters who analyzed the following attributes: appearance, aroma, texture, taste, and general acceptability. The results of this evaluation are presented in Figure 10.  
Figure 10: Sensory analysis of cookies using a 5-point hedonic scale.  
The analysis of data presented in Figure 10 shows that the control sample obtained only from wheat flour is the most appreciated product, achieving the highest scores: 5.00 \(\pm\) 0; 4.55 \(\pm\) 0.52; 4.36 \(\pm\) 0.67; 4.73 \(\pm\) 0.65; 4.73 \(\pm\) 0.65 for appearance, aroma, texture, taste, and overall acceptability, respectively. This product was the most preferred by consumers. Among the analyzed germinated samples, it was observed that CPSF 2.5% (4.82 \(\pm\) 0.40; 4.18 \(\pm\) 0.60; 3.82 \(\pm\) 0.75; 4.27 \(\pm\) 0.47; 4.55 \(\pm\) 0.52) and CBSF 2.5% (4.73 \(\pm\) 0.47; 4.18 \(\pm\) 0.60; 4.36 \(\pm\) 0.67; 4.27 \(\pm\) 0.65; 4.27 \(\pm\) 0.47) received scores close to control, indicating they were also well-liked by consumers.  
The least appreciated products were CRSF 2.5%, 5%, and 10%, which received low scores. For CRSF 2.5%, the scores were 4.27 \(\pm\) 1.01; 2.28 \(\pm\) 0.98; 3.36 \(\pm\) 1.03; 1.73 \(\pm\) 0.90; 2.09 \(\pm\) 0.54, while for CRSF 5%, they were 3.82 \(\pm\) 0.98; 2.91 \(\pm\) 1.14; 2.73 \(\pm\) 1.10; 2.27 \(\pm\) 1.27; 2.59 \(\pm\) 1.28, and for CRSF 10%, they were 3.64 \(\pm\) 1.21; 2.55 \(\pm\) 1.37; 2.91 \(\pm\) 1.04; 1.55 \(\pm\) 0.82; 1.68 \(\pm\) 0.64. These samples were not well-received by consumers, likely due to their slightly spicy taste.  
The obtained results confirm what other authors have reported regarding the possibility of using some legumes, either as such or in the form of sprouts, as functional ingredients in baking, namely that only in small percentages, up to 10%, flour products based on sprouts are acceptable from a sensory point of view [21,40]. The study on the sensory properties of biscuits with sprouted lupin added in the form of flour or in raw form highlighted that fortification with flour sprouts was more appreciated compared to the addition of green sprouts, and a percentage of 5% proved to be optimal for most respondents to the study.
## 4 Results and Discussion
### Discussion
#### 4.2.5 Sensitive of the Dimension of Feature Representation  
In Figure 8, the accuracy change curve can be separated into three phases as a whole. In the first stage (T/8 to 2T), classification accuracies improved as dimensions rose. This demonstrated that higher dimensions might capture more complete temporal changes in crop growth. In the second stage, from 2T to 9T, classification accuracy increased gradually. In the third stage (from 9T to 20T), classification accuracy remained consistent at the highest level, with an OA score of 84.78% and an F1 score of 0.7624. Thus, the 480-dimensional feature representation space may completely describe inherent crop characteristics. Here, we argue that the number of raw timestamps is an excellent candidate for determining the dimensions of represented features in crop classification.
## 4 Experiments and Result Analyses
### Datasets
#### 4.1.2 Magnetic Tile Defect Dataset  
[PERSON] et al. [27] produced the magnetic tile defect dataset, including 1344 magnetic tiles images cropped according to the ROI with defects. The defects of this dataset were divided into six categories, namely, blowhole, crack, break, fray, uneven, and free, as shown in Figure 9.
In particular, light-gas guns (LGG) allow launching impactors at speeds in the high-velocity and the hypervelocity ranges [35; 36; 37]. In this context, the Malta College of Arts, Science & Technology (MCAST) is developing a novel LGG facility [38] with operational ranges that will allow testing velocities typical of GEO impacts. In particular, system designs from similar facilities [39; 40] have been adopted to reduce the maintenance requirements of such a facility and increase the number of tests that can be performed in a single day. In the development of a hypervelocity impact facility, significant attention is focused on several crucial components, including the launch package and the sabot-stopping system. The launch package serves as a protection for the projectile during acceleration, while the sabot-stopping system is responsible for halting the sabot. Typically, launch-gas guns (LGGs) employ an expandable sabot system to house the projectile. Subsequently, after the acceleration phase, the sabot disengages from the projectile, fragmenting to prevent target contamination. However, this approach can cause damage to the sabot-stopping system, necessitating either complete replacement or time-consuming maintenance [2]. This research paper tackles this issue by presenting an innovative solution: a reusable and modular design for the sabot-stopping system. The objective is to develop a design that ensures operational safety, requires less maintenance compared to conventional LGG designs, and minimizes the transmission of impact forces to the LGG structure. The subsequent sections provide an overview of MCAST's LGG and a description of the sabot-stopping system, followed by an explanation of the advantages offered by this solution.
## 3 Results and Discussion
### Effects on Durability Performance
In addition, it was found that the slag replacement concrete was more resistant to freezing/thawing than cement concrete. All of the aforementioned contents were summarized, and the optimal CaO content range to secure durability performance is shown in Figure 16. For concrete mixed with GGBS to secure durability performance, it is important to minimize the carbonation depth and chloride penetration depth and maximize the relative dynamic elastic modulus and weight reducing ratio in the concrete mix design. The results indicated that the optimal CaO content that can satisfy all four conditions ranged between 53% and 56%. This corresponded to the GGBS replacement ratio ranged from 27.5% to 47.1%. In the study, the ready-mixed concrete specification was set to 25-24-180. When the CaO content ranged from 53% to 56%, the compressive strength at 28 d of age was observed as between 31.3 MPa (130.5%) and 33.0 MPa (137.6%), thereby confirming that the target compressive strength (f\({}_{\text{ck}}\) = 24 MPa) was secured. Figure 16: Optimal CaO content range for enduring durability performance. Figure 15: (**a**) Relative dynamic elastic modulus and (**b**) weight reducing ratio relative to the replacement ratio.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### System-Level Architectural Models in VBDA
Hybrid cloud-edge architectures are best suited to meet the evolving demands of VBDA. Their adaptability, scalability, and built-in support for privacy-preserving, AI-driven video analytics make them the most promising foundation for future intelligent video systems.
## 4 Experimental Results
### Classification Results
#### 4.3.3 Deep Learning Baselines
The CNN model, implementing EarlyStopping and ReduceLROnPlateau callbacks, halted training early at epoch 24 with a learning rate of \(1\times 10^{-6}\). Table 3 presents a comprehensive comparison of various deep learning models on the testing subset, highlighting their performance in the task of classifying suicidal ideation in text content. The baseline deep learning models, including LSTM-CNN, LSTM, and BiLSTM, showed moderate performance with scores ranging from 0.7534 to 0.7752. However, the CNN model showed lower performance in accuracy and precision compared to the others. When compared to these models, our proposed LSTM-Attention-BiTCN model demonstrated exceptional performance, markedly outperforming the deep learning baselines. It achieved scores above 0.91 across all metrics. This superior performance underscores the effectiveness of the LSTM-Attention-BiTCN model in accurately detecting and classifying suicidal content in social media posts, affirming its potential as a valuable tool in mental health monitoring and intervention efforts.
# Calculation of Theoretical Travel Time and Automatic Picking of Actual Travel Time in Seismic Data
## 1 Introduction
[PERSON] and [PERSON] proposed the direct correlation method, but the study of the direct correlation method was carried out in a high time resolution, low noise signal obtained in the laboratory, so the scope of its use needs further study [27]. [PERSON] and [PERSON] proposed a multi-channel cross-correlation technique (MCCC) to achieve automatic acquisition of first arrival data, which not only greatly reduces human capital, but also improves data accuracy [28]. Therefore, the multi-channel cross-correlation technique has been widely used in the field of seismic time-dependent data processing. However, the biggest drawback of the MCCC method is that the system of overdetermined equations must be solved when calculating the absolute travel time data, which inevitably reduces the accuracy of the travel time data. Then, the best absolute and relative travel time data are obtained by iteratively using a selected phase arrival time as the reference time [29]. In this study, the theoretical travel times of different seismic phases propagating in the Earth's interior are numerically simulated using a ray-tracing technique based on the IASP91 Earth velocity model. The measured travel times are quickly picked using a modified cross-correlation method, and the relative travel time residuals can be determined directly. The whole calculation process is completed automatically by the computer program, which not only saves human resources, but also improves the working efficiency. The waveform data received by the station in Guangxi area is selected as an example for analysis and discussion.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 4 System Design
### Scalability for Municipal Operations  
Enhancing municipal scalability requires optimized resource management, improved inter-agent communication, and automation. The system expands across organizing large events, agricultural wastewater use, and financial audits, using specialized agents and distributed computing.  
For large events, the EventManagementAgent adjusts resources dynamically, while the TicketingAgent automates reservations and access control. The EmergencyResponseAgent enhances security through AI-driven surveillance, and CoordinationAgents streamline communication among security, coordination, and medical teams. A ChatbotAgent improves attendee interaction and emergency coordination.  
In agriculture, the SmartIrrigationAgent optimizes irrigation based on weather and soil data, while the WaterQualityAgent ensures wastewater safety. A Geo-Monitoring Agent uses drones and satellites for real-time tracking, and a centralized data hub, managed by the SecretariatAgent, promotes transparency. The RegulatoryComplianceAgent enforces policies, and the PartnershipAgent fosters collaboration for wastewater reuse.  
For financial audits, the FraudDetectionAgent monitors transactions, while the TreasuryAgent and AutomatedReportingAgent streamline financial reports. A Blockchain-AuditAgent secures transactions, and a DataIntegrationAgent ensures seamless cross-departmental collaboration. The PerformanceTrackingAgent monitors financial efficiency, and the ScalabilityAgent expands audit systems as municipal finances grow.  
These specialized agents enable scalable operations, enhancing decision-making, automation, and transparency. The system fosters collaborative governance, policy enforcement, and financial accountability, ensuring an efficient, adaptive municipal administration.
## 2 Investigated Battery Module
### Experimental Setup  
#### 2.1.1 Battery Pack  
The battery pack in the experimental setup was designed and built from medium fiberboard (MDF) of 18 cm thickness with four vent holes of 0.07 m diameter (Figure 6a). The pack had dimensions (\(30\times 25\times 10\) cm) and was built slightly larger than the exact volume of the battery module (\(24\times 24\times 10\) cm). This extra volume in the MDF box was designed to create a partition that would accommodate the excess length of the thermocouple sensor wires attached to the cells in the battery module (see Figure 6b). Glass fiber, (a nonconductive and nonflammable material) was placed between the battery module top cover of the MDF board as a protective measure to prevent possible electrical and fire hazards present during the experiment and most importantly prevent a low-resistance path for air flowing into the battery pack (Figure 6c).
## 1 Introduction
As mentioned, in addition to monitoring land conditions and events on the Earth's surface, it is also possible to use satellite data to monitor atmospheric events and the atmosphere's composition. For the atmosphere's composition or the aerosol contamination monitoring, often data recorded by the MODIS instrument [21; 22] and the TROPOMI instrument [23] are used. The TROPOMI instrument is installed on the European Space Agency's (ESA) satellite mission Sentinel 5p [24]. Satellite data usually contain information about the aerosol layer height or optical depth, aerosol absorbing index, ozone, nitrogen dioxide, sulfur dioxide, methane, etc. [23; 24]. Among the other parameters, the concentration of PM\({}_{10}\) and PM\({}_{2.5}\) particles are usually measured at surface air-quality stations [2], but it can also be calculated from the information about aerosol optical depth or layer height and aerosol absorption information. A comparison of the computed data and data measured using surface air-quality stations showed a significant correlation [25; 26]. In the energy industry, aerosol contamination can be significant at various levels. Power companies need to evaluate the Site Pollution Severity (SPS) before installing new power network components directly exposed to aerosol contamination and other atmospheric conditions. It is possible to measure SPS through direct and indirect aerosol contamination measurements (IEC 60815-1 and IEC 60815-4) [27; 28]. SPS estimation can be conducted in several ways, e.g., considering the highest recorded value, the average of several recorded highest values, or simply following the IEC 60815-1 recommendations that require the inclusion of additional data [29]. Aerosol contamination monitoring and modeling using satellite data can also be used for planning activities through the creation of contamination maps [30] and the selection of overhead lines [31]. Some authors have compared satellite data recorded by various instruments with ground truth results for different parts of the world [26; 32], but there is no research for Croatia and its complex geographical and climatological characteristics. This article's objective is to research the possibilities and limitations of using satellite data, focusing on Sentinel 2, 3, and 5p, in the power system control when forest fires develop and/or various aerosols highly contaminate the atmosphere. A detailed investigation of the satellite data application in the power system control enables other scientific research and new models and modules development. After the newly developed models and modules are implemented in the power system control on the operational and/or the planning level, the system's functionality will be optimized, and various resources will be saved (e.g., adequate materials selection for the insulators will be made). Consequently, the availability of human resources will be optimized because the need for such frequent insulator monitoring and cleaning will be decreased, etc.