esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Immersive Analytics (IA)  
IA is a research field of computer science that studies \"how new interaction and display technologies can be used to support analytical reasoning and decision-making\" [18]. It covers topics such as data visualization; visual analytics; virtual, augmented, and mixed reality; human-in-the-loop algorithms; and human-computer interaction to design more natural interfaces that harness immersive technologies to better understand data and support information processing [2,18].  
IA presents six features for designing more appropriate human-computer interfaces, as described by [PERSON] et al. [2] and summarized below.  
1. _Situated analytics_. Relates to the use of data representations associated or organized in relation to objects, persons, or locations in the physical world to support in situ sense-making and decision-making.
2. _Embodied data exploration_. Addresses the use of natural interaction techniques (e.g., touch, gesture, voice, gaze, and tangible interaction) in the design of user interfaces that allow more intuitive and engaging data exploration in immersive environments.  
3. _Collaboration._ Addresses how immersive technologies and visual analytics techniques can support shared interaction to enable different types of collaboration (i.e., co-located or remote, synchronous or asynchronous).
4. _Spatial immersion._ Addresses the visual aspects of 2D and 3D data representations within immersive environments, as well as the use of the real-world environment as a workspace for immersive visualizations and interactions.
5. _Multi-sensory presentation._ Relates to the use of other sensory channels (i.e., audio, haptic, smell, and taste) to present data as complements to vision for supporting technological immersion or as substitutes when vision is unavailable or not applicable.
6. _Engagement_ (of stakeholders). Refers to the use of immersive technologies to generate immersive interactive narrative visualizations that deeply involve the users to support data understanding and more informed decision-making.  
Research in the different areas (features) of IA has been applied across various domains. For instance, in the health sector [29], collaborative immersive analytics enabled synchronous interaction across multiple remote locations by using display wall systems and handset devices, allowing a collaborative work environment for specialists situated in different places. In architecture [30], IA spatial immersion through an MR environment facilitated the on-site visual analysis of different facade alternatives, aiding architectural designers to evaluate the impact of their designs. Similarly, in construction [31], IA spatial immersion through an AR environment supported multi-criteria decision-making by providing digital representations of various construction alternatives and their attributes, enabling visual comparative analysis for more informed decision-making.
## 4 Experiments and Results
### Ablation Experiments
#### 4.5.2 The Effect of Transformer Attention  
To assess the efficacy of diverse fusion module strategies on model performance, we undertook ablation studies utilizing the DeepGlobe dataset, with a continued focus on mIoU, GPU memory usage, and the frames per second (FPS) metric. Table 6 encapsulates the findings, which indicate that the GL-Deep Fusion module not only boosts mIoU by 1% and trims GPU memory usage by 96 MB when contrasted with Attention (DANet) but also realizes a noteworthy FPS of 1.14. This enhancement is markedly superior to the baseline GLNet's 0.05 FPS and Attention (DANet)'s 0.02 FPS, highlighting the transformer attention mechanism's role in achieving both efficiency and precision in segmentation. The FPS metric accentuates the model's practical viability, suggesting that the GL-Deep Fusion strategy is particularly fit for settings that necessitate swift processing capabilities.
## 2 State-of-the-Art
### Standard Practices for Structural Stability Monitoring  
Most wind turbine manufacturers offer tools for operational management of their wind turbines; however, the focus of these tools is mostly on the rotating components (gearbox, generator, etc.), whereas the evaluation of structural stability of the wind turbine pylons are rarely addressed in these tools [15].  
The default inspection mode for structural stability of offshore wind turbines, are visual (RGB-camera-based) inspections and flooded member detection. For submerged parts, this is traditionally performed by divers or remotely operated vehicles, with a frequency between one and five years. These types of inspections and full scale post construction impact evaluation campaigns [16]; however, can be costly in terms of vessel time, and involve hazards to personnel [17].  
Therefore, in more recent constructions, manufacturers or operators often embed sensors to detect and quantify structural loading, flooding or corrosion effects, for example accelerometers, saline senors, displacement measurements or acoustic emission systems.  
Figure 1: Some commercial offshore wind turbine structures: (**a**) monopile wind turbine; (**b**) floating wind turbine; (**c**) lattice type wind turbine. Images are courtesy of [12].  
In this data-rich environment, it is not the data capture that is the main difficulty. Rather, the robustification of the measurements, the detection of all failure modes and the subsequent evaluation and analysis of the captured data is what leads to understanding and effective decision making.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 8 Wi-Fi Installations Used for AP Localization
For improved accuracy, the final AP locations were based on data collected during several such data collecting sessions.
###### Abstract  
This study evaluates the potential of natural and thermally modified starches from Kazakhstan, including cassava, potato, wheat, corn, pea, and rice, for the production of biodegradable films. Key physicochemical properties were analyzed using Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and a Rapid Visco Analyzer (RVA). The results show that cassava starch, with the highest magnesium content (43.07 mg/100 g) and peak viscosity (1300 RVU), exhibits superior mechanical strength and elasticity, making it ideal for durable agricultural films. Corn starch, with high crystallinity and moderate viscosity (1150 RVU), exhibits excellent stability for long-term applications. In contrast, wheat and rice starches, with lower viscosities (750 and 650 RVU, respectively) and high biodegradability, are more suitable for short-term eco-friendly applications. Modification processes improved moisture resistance and reduced retrogradation tendencies, particularly in cassava and corn starches. SEM analysis revealed that modified starches from cassava and corn have dense and uniform surface structures, enhancing film durability and flexibility. These findings highlight the potential of utilizing Kazakhstan's starch resources for localized biodegradable film production, reducing reliance on imports while promoting sustainable agriculture.  
Keywords:biodegradable films; natural starches; starch modification; mechanical strength; thermal stability; sustainable agriculture +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Content of Bioactive Compounds in Seed Powders
According to the literature data, a higher total polyphenol content and higher antioxidant activity are obtained when acetone, methanol or a mixture of water and acetone are used for extraction, while they are lower when water is used alone as an extractant [8,46]. This is confirmed by the fact that water allows for the extraction of polar compounds, including polyphenols and vitamin C. The mixture of water with acetone or methanol also allows the determination of compounds of a non-polar nature, including carotenoids and vitamin A and E [47,48]. The results obtained are also affected by the time and temperature of sample storage and extraction, or the contact of the powders with atmospheric oxygen [49]. Compared to the powders obtained from berries, considered to be a rich source of bioactive components, mango seed powder was characterized by a higher content of total polyphenols and higher antioxidant potential. According to the literature data [36], the total polyphenol content for freeze-dried chokeberry powder was 22.56 mg GAE/1 g d.m., with an antioxidant potential of 58.36 mmol TEA/100 g d.m. Convection-dried chokeberry powder had a lower polyphenol content of 21.47 mg GAE/1 g d.m. and lower antioxidant activity of 48.95 mmol TEAC/100 g d.m., compared to freeze-dried chokeberry powder [36]. Freeze-dried mango seed powder contained an almost 5-times higher total polyphenol content and had almost 2.5-times higher antioxidant activity compared to freeze-dried aronia powder. Rambutan seed powder had the lowest polyphenol content, regardless of the drying method. Nevertheless, the antioxidant potential of rambutan seed powders dried by both sublimation and convection is high, comparable to that of chokeberry powders dried by both methods discussed above. Plant tannins, which are macromolecular polyphenolic compounds containing numerous hydroxyl groups, are responsible for the asstringent taste of fruits and infusions from plants containing them [50]. The tannin content in the tested mango and rambutan seed powders varied and ranged from 0.3% in mango seed powder to 2.3% in rambutan seed powder. The level of tannins in the studied powders was comparable to the content of these compounds in green tea, ranging from 0.18% [48] to 3.1% [51]. In dried fruits, the tannin content is varied, but usually lower--ranging from 0.67% in quintec to 2% in chokeberry [52]. It is noteworthy that rambutan seed powders were characterized by a higher tannin content, and at the same time, showed a more intense bitter taste compared to mango seed powders.
## 4 Discussion
In contrast with other assessment results, for IGSO satellites, the difference between the orbit solution from strategy 1 and the GBM orbit is larger than that of the orbit solution from strategy 3. The reason for this is that the GBM orbit adopts the BDS/GPS joint orbit determination method, which is identical to strategy 1, and the difference is small. The reason for the different results of the IGSO satellites stems from the addition of the CNCORS in the orbit determination and the improvement of IGSO orbits. Therefore, the difference between strategy 1 and the GBM orbit is large, and the accuracy of strategy 3 is slightly worse than that of strategy 1, but less different from the GBM orbit. In other words, the GBM orbit is different from the orbit in strategies 3 and 1 for different reasons, which are the addition of the CNCORS and the accuracy of some parameters of BDS/GPS joint orbit determination, respectively. Improvement of the BeiDou system is still required in terms of refining the satellite solar radiation model, the phase center correction of satellite antenna, the evenness of ground station distribution, and other aspects. The recent progress in the field of multi-GNSS precise positioning, including BDS-based precise positioning, inter-system biases, etc., should also be considered [38; 39; 40; 41; 42].
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP
### Phase Biases  
Hardware delays also influence carrier-phase observations. However, their impact varies compared with that of code observations. When ambiguities are treated as float values, phase biases from the same constellation are absorbed into the ambiguity terms. Thus, in float-ambiguity PPP, phase biases typically raise no concerns except for the ISB terms, which must be incorporated into the phase observation in Multi-GNSS scenarios. It is important to note that while the ISB value for phase observation might differ from that of code ISB, any discrepancy is lumped into the float ambiguities.  
In contrast, in ambiguity-fixed PPP, it is crucial to separate the phase biases to retrieve the integer nature of ambiguities. While this article focuses on the standard float PPP, an overview of ambiguity-fixed PPP can be found in Section 3.3.
## 2 Materials and Methods  
The authors propose an innovative method for the maritime sector, inspired by a well-established medical approach--screening--to enable the early identification of potentially suspicious vessels. The process comprises several sequential phases. The conceptual framework of the system is illustrated in Figure 1.  
In the first stage, the area is monitored using radio surveillance systems, including AIS, radar, and LRIT (Long-Range Identification and Tracking). In the future, satellite-based monitoring will also be employed, incorporating AIS signals and image recognition technologies. Vessel movement parameters and trajectories are continuously analyzed. Simultaneously, data is systematically collected from both national and international maritime databases.  
These databases store essential information about a vessel's history and performance. This stage is crucial, as the collected data including vessel parameters, activity records, and certification history significantly impacts the accuracy of the final results.  
In the second stage, the vessels identified in the initial process undergo a multi-criteria evaluation. This type of analysis is an effective method for comparing the characteristics of different objects. It allows for the application of weighted criteria, and the computations enable the simultaneous assessment of various factors. As a result, the evaluated vessels are ranked from the one that best meets the criteria to the one that meets them the least, effectively ordering the ships according to their risk level.  
In the second stage, vessels detected through this process undergo a multi-criteria evaluation. Those identified as potentially threatening are subjected to further scrutiny in the third stage, which includes the prediction of their future trajectories. If the level of  
Figure 1: System concept.  
suspicion increases as a result of this analysis, appropriate naval and law enforcement units are alerted and mobilized for potential intervention.
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 3 Application of the Proposed Approach to Insulator Detection  
### Dataset Preparation for CNN  
The quality of prediction generated by convolutional neural networks depends to a large extent on the use of an appropriate machine learning dataset [8]. The dataset built to solve a specific problem should be characterized by the following features [9; 10]:  
* A large number of samples;
* Balance in terms of individual classes;
* High quality of images;
* Diversity in terms of depicting the analyzed objects (position, rotation, scale, lighting, obscuration);
* Accuracy of annotations (objects marking) in images.  
The quality of digital images is significant. As demonstrated in Reference [11], an increase in the current conditions for large datasets meets many challenges as follows:  
* The diversity of data sources brings abundant data types and complex data structures, which increase the difficulty of data integration;
* Data volume is tremendous, and it is difficult to evaluate data quality within a reasonable amount of time;
* Data change very fast, and the \"timeliness\" of data is very short, which necessitates higher requirements for processing technology;
* No unified and approved data quality standards exist, and the research on the quality of large datasets only just began.  
The main focus during the process of creating a training dataset for convolutional neural networks was on using as few training frames as possible. The aim was to improve the quality of the training dataset and significantly shorten the pre-processing and training time of individual neural network architectures.
## 3 Results and Discussions
### Thermal Deactivation Analysis  
The deactivation constants (\(k_{d}\)) for the first-order thermal deactivation were calculated using Equation (1). The \(k_{d}\) values for _Mut2_ and _Mut3_ at 50 \({}^{\circ}\)C were 8.9 \(\times\) 10\({}^{-3}\) min\({}^{-1}\) and 1.88 \(\times\) 10\({}^{-2}\) min\({}^{-1}\), respectively, compared to 5.5 \(\times\) 10\({}^{-2}\) min\({}^{-1}\) for the WT luciferase  
Figure 4: Thermostability and inactivation curves of WT luciferase and its mutants. (**a**) The thermal stability of the WT and mutant enzymes. (**b**) The thermal inactivation of the enzyme at 50 \({}^{\circ}\)C was determined from 0 to 50 min, with residual activities measured immediately after heat treatment. The activity of the pre-incubated enzyme at 4 \({}^{\circ}\)C was taken as 100%. Data are given as the means \(\pm\) SD, \(n\) = 3.  
(Figure 5). The half-lives of _Mut3_ and WT luciferase were determined using Equation (2). _Mut2_ exhibited a half-life of 77.9 min, significantly longer than that of the WT luciferase (12.6 min). Compared to the WT, the half-lives of mutants _Mut2_ and _Mut3_ were increased by 6.18 and 2.92 times, respectively. The introduction of artificial disulfide bonds thus yielded two thermally stable mutant strains.
## 4 Data Analysis  
The measurements collected were statistically analysed while taking into consideration the mean and standard deviation of the measured data and the statistical power of the sample size.  
The statistical software of IBM SPSS Statistics (version 26.0, USA) was used to analyse the data. The Kolmogorov-Smirnov test was used to assess the distribution normality of continuous variables and the reliability by calculating the Cronbach's coefficient (\(\alpha\) < 0.05). To compare the ISQ (lower, upper, and distal) within times and between the presence or absence of laser treatment, repeated measurement analysis of variance (ANOVA) was used for within-subject comparisons.  
This test was followed by univariate analysis and the Bonferroni multiple comparison test.  
The level of significance was established at 95%. The value \(p\) < 0.05 was accepted as the significance level of the tests.  
The sample size was calculated by G*Power Statistical Power Analyses Software version 3.1.9.7 [21]. The power analysis showed that to have a power of 80% with an effect size of (d = 0.8) and \(\alpha\) error of 0.05, a sample size of 40 mini-screws for a within-subject study design was sufficient.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 5 Experiment and Results
### General Results  
The results presented in the tables demonstrate the effectiveness of the proposed blockchain-based IIoT system in addressing the key challenges, including data integrity, security, and efficiency. The system achieved 100% data integrity across 1 million transactions, ensuring tamper-proof and reliable data storage. It also demonstrated rapid threat response capabilities, with a Mean Time to Detect (MTTD) of 12 s and a Mean Time to Mitigate (MTTM) of 28 s during a simulated DDoS attack. Additionally, the integration of edge computing reduced energy consumption by 62% and improved latency by 40%, highlighting the system's scalability and sustainability.  
The findings underscore the transformative potential of blockchain in the Industrial Internet of Things (IIoT), particularly in enhancing cybersecurity, enabling predictive maintenance, and supporting real-time decision making. By leveraging decentralized security mechanisms, smart contracts, and edge computing, the proposed framework ensures the robust protection of sensitive data while maintaining operational efficiency. Table 9 highlights the practical benefits of the blockchain-integrated Industrial Internet of Things (IIoT) system for various stakeholders.  
Future research will address emerging challenges, including threats from quantum computing and scalability issues associated with larger deployments. These efforts will further solidify blockchain's role as a cornerstone technology in the evolution of Industrial Internet of Things (IIoT) ecosystems.
# Random Fiber Array Generation Considering Actual Noncircular Fibers with a Particle-Shape Library
## 2 Random RVE Generation Using Actual Noncircular Fibers from the Particle-Shape Library  
The Particle-Shape Library Construction of Actual Noncircular Fibers Using a Microscopic Image of the M55J/M18 Composite  
According to our previous work, a FE model was generated from a microscopic image of the M55J/M18 composite [18] by the level set method and the trimming mesh method [22]. First, we binarized the microscopic image (see Figure 1a,b) by a black-white filter and proper noise removal, and then used it to generate a voxel mesh after resizing the image whenever necessary (see Figure 1c). The minimum signed distance function (SDF) of the image was obtained using the level set method (see Figure 1d), and the trimmed mesh technique was applied to smooth the voxel mesh (see Figure 1e). Finally, remeshing was conducted to reconstruct the four-noded quadrilateral elements (see Figure 1f).  
From the final FE model, a set of the outer nodes of the intact fibers was stored in the particle-shape library of fibers as shown in Figure 2. In this work, we stored 482 fiber samples in the particle-shape library. Furthermore, it can be seen that the actual fibers of the M55J/M18 composite are concave and convex.  
Figure 1: Procedure of generating FE model from a microscopic image of M55J/M18 composite: (**a**) original image, (**b**) binary image, (**c**) voxel mesh with image rescaling, (**d**) minimum SDF, (**e**) trimmed mesh, and (**f**) remeshing to quadrilateral elements.  
Figure 2: Schematic illustration of the particle-shape library consisting of actual noncircular fibers.
## 2 Results and Discussion  
### Mercury Removal  
The mercury solution was firstly equilibrated in the dark for 15 min during which a negligible adsorption on the catalysts was observed. Then, mercury removal was evaluated using a sun simulator with a cut-off filter at 340 nm in the presence of the prepared photocatalysts (Figure 1).  
All the synthesized materials showed a removal efficiency higher than the benchmark TiO\({}_{2}\)-P25, which under these experimental conditions was not able to lead to the metal deletion. By contrast, the ZnO-based photocatalysts managed to achieve almost the complete mercury abatement from the solution in less than 1 h. Indeed, using ZnO and ZC, 80% of the Hg(II) was removed after only 15 min.  
Mercury removal was also tested at different values of pH (4, natural pH, which was equal to 6, and 8) and catalyst loads (0.2, 0.5 and 1 g/L). The best performance was obtained at natural pH in the presence of the highest quantity of the catalyst.  
Experiments were repeated without nitrogen stripping, but it was observed that the presence of oxygen severely reduced the photocatalytic activity compared to anoxic conditions.