esa-sceva/satcom-chunk-collection huggingface dataset:
# Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
## 3 Methodology
### V-R-MATD3 Strategy
#### 3.2.1 Observation and Action Spaces  
In our setup, the observation state \(\mathbf{o}_{i}\) of each robot \(i\) at each time step consists of three components: the range measurements \(\mathbf{r}_{i}\) sampled by the robot's onboard LiDAR, the relative positions \(\mathbf{p}_{i}=\{(d_{ij},\beta_{ij})\}_{j\
eq i}\) to other robots, and the current robot's orientation \(\alpha_{i}\in[-\pi,\pi)\). Specifically, \(\mathbf{r}_{i}\) is a subsampled array of 120 elements, uniformly extracted from the original 720 LiDAR range measurements. This subsampling approach reduces the neural network's input complexity while maintaining sufficient accuracy for lower-layer navigation and mapping tasks. \(\mathbf{p}_{i}=\{(d_{ij},\beta_{ij})\}_{j\
eq i}\) represents the relative distance and relative angle to agent \(j\). It deserves to be mentioned that we use relative positions instead of absolute coordinates to reduce the dependency of the state data on the global map, and each robot only shares its own location and not its own LiDAR information.  
The action space \(\mathbf{A}_{i}\) is a two-element array with continuous values in \((-1,1)\), mapping to selectable target points around the robot. Specifically, \(\mathbf{A}_{i}[0]\) corresponds to an angle in \([-\pi,\pi]\) relative to the robot's forward direction, and \(\mathbf{A}_{i}[1]\in[d_{\text{min}},d_{\text{max}}]\) represents the Euclidean distance that the robot advances in this step. Figure 2 illustrates the refined action spaces for multiple robots--shown as solid red and green arcs--after applying the Voronoi partitioning method, the principle of which is given in Equation (1).
## 4 Analysis of the Deflection Slope Characteristics of Inverted Asphalt Pavement Structures
### TSD Deflection Slope Curve Characteristics for Inverted Pavement Structure  
In this study, due to the symmetric distribution of both the pavement structure and vehicle load, the analysis was simplified to consider only the wheel gap center of the left wheel group. Figure 6 presents a comparison of the deflection slope curves between the inverted asphalt pavement and the semi-rigid base asphalt pavement. The results show that the deflection slope curve trends of both pavement structures are generally similar. Both structures initially rise, then fall, followed by a slow rise and a gradual decrease to zero at a distance of 10 m from the wheel gap center. Additionally, both structures exhibit extreme points at multiple locations, which is consistent with the results in reference [14].  
However, it is noteworthy that at a distance of 0.15 m from the wheel gap center, the extreme point for the inverted pavement structure is significantly higher than that for the semi-rigid base pavement structure. This is attributed to the graded crushed stone layer, which causes a redistribution of stress within the pavement structure.  
Upon further analysis of Figure 6, it can be observed that the deflection slope variation trend for the inverted asphalt pavement remains relatively consistent for different surface layer thicknesses. However, the extreme point at 0.15 m decreases significantly as the surface layer thickness increases. This phenomenon indicates that the modulus of the graded crushed stone layer in the inverted pavement structure is relatively small, while the base layer modulus is large. This combination of material properties leads to increased complexity in the deflection slope curve.  
Although Inverted Structure 1 includes an additional graded crushed stone transition layer, the measurement errors associated with TSD are not significantly different between the two structures. Both rely on a stiff base layer, resulting in a wide deflection basin and non-zero deflection velocity at the reference sensor position (S3500). While the crushed  
Figure 6: Deflection slope characteristics of inverted asphalt pavement structures.  
stone layer slightly alters stress distribution, it does not substantially increase the magnitude of error.
To promote the practical applications of UAV swarms, research on their communication and cooperation networks is needed. Efficient and reliable communication networks between UAVs are essential to complete any cooperative task. Network links among UAVs enable individual UAV nodes to directly communicate for information sharing and cooperative tasks [13]. UAV swarms are characterized by high mobility, frequently changing architectures, limited computing resources, and easy disconnection. Therefore, improving the efficiency of resource utilization is of great significance to ensure communication among UAVs. In particular, channel access and network routing are essential for efficient communication among UAVs [14; 15].  
As real-world missions become more complex in dynamic and uncertain environments, traditional approaches face challenging problems such as computational complexity, low cooperativeness, and limited intelligence of UAV swarms. However, thanks to recent significant advances in artificial intelligence (AI) algorithms, the application of AI algorithms in UAV swarm control offers some significant improvements. AI enables the use of and learning from massive data to improve UAV control. Compared with conventional optimization techniques, AI can handle the resources on UAVs flexibly [16]. With the help of AI, environmental data from devices connected to UAVs are collected and used effectively [17]. Several methods, including Global Positioning System (GPS) monitoring, computer vision, and machine learning algorithms, can be used to achieve these. Speech recognition, scene recognition, object detection, and image classification are just some of the many areas where AI is penetrating. Especially, deep learning methods for AI are mentioned. In addition to supporting the enhancement of UAV data processing and communication capabilities, AI also greatly supports UAV swarm control. Typically, it enhances some UAV swarm features such as automatic decision making, coordination under interference, flexibility, and scalability of the formation. However, the application of AI algorithms also brings some challenges, such as the requirement of large computational resources, the ability to update training data for AI in real time, and high bandwidth requirements among UAVs when exchanging information.
# Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines
###### Abstract  
The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A.  
corrosion monitoring; FPGA; offshore wind turbines; ultrasound; thickness loss
## 2 Materials and Methods
### Site Profiles
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Profile** & **Depth (m)** & **Soil Type** & **Avg. SPT-N\({}_{60}\) Value** & **Density (g/cc)** \\ \hline S-1 & 0–9.1 & Silty Sand & 8 & 1.73–1.81 \\  & 9.1–10.7 & Gravel with Sand & 42 & 1.83 \\  & 0–4.1 & Silty Sand & 4 & 1.66–1.68 \\  & 4.1–5.6 & Sandy Silt & 36 & 2.16 \\  & 0–7.5 & Poorly Graded Sand & 15 & 1.60–1.74 \\  & 7.5–12 & Gravel with Sand & 43 & 1.95 \\  & 0–11.6 & Silty Sand & 12 & 1.55–1.95 \\  & 11.6–13.7 & Silt with Sand & 20 & 1.89–1.91 \\  & 13.7–18.3 & Lean Clay with Sand & 43 & 1.85–2.20 \\  & 0–6.5 & Silty Sand & 13 & 2.0–2.1 \\  & 6.5–15 & Lean Clay & 2 & 1.9–2.1 \\ S-5 & 15–21 & Silt with Sand & 4 & 2.1 \\  & 21–25.5 & Silty Clay & 5 & 2.0 \\  & 25.5–33 & Silt & 22 & 1.9–2.0 \\  & 0–5 & Silty Sand & 11 & 1.8–2.0 \\  & 5–10.5 & Lean Clay & 2 & 2.0 \\  & 10.5–27.4 & Silty Clay & 4 & 2.0–2.1 \\  & 27.4–36 & Silt & 33 & 1.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Soil type and properties of the site profiles. Figure 3: Map of the study area showing the marked sites. In the absence of directly measured Vs values in the field, the relationship proposed by [50] was utilized, as shown in Equation (1). The rationale for using this correlation was fourfold: first, the equation was developed for coastal regions, representing similar strata; second, its suitability has been well investigated and recommended by [51; 52] for the coastal regions of Turkey and the Philippines; third, the relationship demonstrates a strong goodness of fit (R\({}^{2}\) = 0.83); and fourth, both uncorrected and energy-corrected correlations can predict the Vs with an equal accuracy [53]. The average Vs for the top 30 m of shallow borehole logs was evaluated using the method outlined in [54], and the natural period (TG) of the site profiles was computed using Equation (2). \[\mathrm{V_{s}} = 90.75\times\mathrm{N_{60}}^{0.304} \tag{1}\] \[\mathrm{TG} = 4\sum_{i=1}^{n}\frac{\mathrm{h_{i}}}{\mathrm{V_{si}}} \tag{2}\]  
where \(\mathrm{h_{i}}\) and \(\mathrm{V_{si}}\) are the thickness and Vs of each analyzed layer. The site profiles were classified as Soil Type D (S-1 through S-4), representing stiff soils (Vs: 180-360 m/s), and Soil Type E (S-5 and S-6), representing soft soils (Vs < 180 m/s). This classification effectively captured the stratigraphic variation throughout the investigated region, as outlined in Table 3.  
For the current study, the bedrock was modeled as an elastic half-space with a damping ratio of 2% and a unit weight as 25 kN/m\({}^{3}\). The bedrock Vs was defined as 762 m/s, consistent with the Vs of the bedrock found in the region. Figure 4 illustrates the site profiles, detailing the soil layers and Vs variations.
# Possible Blockchain Solutions According to a Smart City Digitalization Strategy
## 3 Digitalization Strategy of City Governance
### Healthcare Application  
The healthcare system consists of hospitals that are under a central authority and are, therefore, subject to a single point of failure [46]. Hospitals do not have enough resources, and citizens demand better service, and here we come to a disparity between the requirements and the available resources. As a solution, decentralized blockchain technology is offered, which enables the improvement of services by offering smart hospitals, vehicles, and urgent responses to customer requests. Decentralized Application (DApp) for transparent, secure, and anonymous transactions in a healthcare system is proposed in [47] and proved that blockchain is the right solution for a decentralized healthcare network. The technology allows doctors to access patient data at remote locations to treat patients on the spot [48]. Below are the steps that need to be taken to introduce blockchain technology into the health system [49] as shown in Figure 8:  
* IoT sensors collect patients' health information like sugar, heart rate, respiratory information, blood pressure, body temperature, etc.
* The collected data are used to create a patient's report.
* The doctor analyses the report and recommends the treatment.
* The treatment reports can be shared using a distributed database with other doctors for further analysis.
* The reports should be shared in an encrypted format.
* Patients can easily request their treatment records from the Cloud Service Provider (CSP).
* After successful validation, the encrypted file is sent to the patient.
* The patient decrypts the received file with his private key.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### Gpu
#### 3.1.4 Renewable Energy Integration  
Renewable energy integration is the next application where GPU architecture is widely used refs. [1; 178; 179; 180; 177; 178; 179; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190]. GPU has been used for solar potential estimation using light detection and ranging data [179] and for solving the OPF problem, including uncertainties from renewable generators [1].
_J. Sens. Actuator Netw._**2012**, \(1\), 3-35; doi:10.3390/jsan1010003  
_Journal of Sensor and Actuator Networks_  
ISSN 2224-2708  
www.mdpi.com/journal/jsan/  
_Article_  
A Discrete Wavelet Transform (DWT)-Based Energy-Efficient Selective Retransmission Mechanism for Wireless Image Sensor Networks  
**Daniel G. Costa 1,2,* and Luiz Affonso Guedes 1**  
* DCA-CT-UFRN, Campus Universitario, Lagoa Nova, Federal University of Rio Grande do Norte, 59072-970 Natal RN, Brazil; E-Mail: affonso@dca.ufrn.br
* DTEC-UEFS, Av Transnordestina, S/N, Novo Horizonte, State University of Feira de Santana, 44036-900 Feira de Santana BA, Brazil
* Author to whom correspondence should be addressed; E-Mail: danielgcosta@uefs.br; Tel.: +55-84-8827-1120.  
_Received: 8 December 2011; in revised form: 16 January 2012 / Accepted: 29 January 2012 / Published: 1 February 2012_  
**Abstract:** Source nodes in wireless image sensor networks transmit much more information than traditional scalar sensor networks, thereby demanding more energy of intermediate relaying nodes and putting energy efficiency as a key design issue. Intermediate nodes are usually interconnected by error-prone links where bit-errors are common, potentially degrading the application monitoring quality. When reliability is assured by retransmission mechanisms, higher packet error rates do not affect the application quality but result in additional energy consumption due to packet retransmission, even though many monitoring applications can tolerate some loss in the quality of the received image. DWT coding can decompose an image in data subbands, each one with different relevancies for the reconstruction of the original image at the receiver side. We propose an energy-efficient selective hop-by-hop retransmission mechanism where the reliability level of each packet is a function of the relevance of the payload data, according to the resulting subbands and the number of times a 2D DWT is applied over the images captured by the sensors' cameras. In so doing, some lost packets are not retransmitted, saving energy of intermediate nodes with low impact to the quality of the reconstructed images. In order to estimate the benefits of this tradeoff between energy consumption and image quality, we designed a comprehensive energy consumption model and applied it in extensive mathematic simulations, providing substantial information about the mean performance of the proposed approach when compared with a fully-reliable transmission mechanism.  
keywords: wireless image sensor networks; DWT; hop-by-hop retransmission; energy-efficiency; wireless sensor networks +
Footnote †: journal: Journal of the Acoustical Society of America
# Current Uncertainties and Future Challenges Regarding NAD+ Boosting Strategies
## 15 Which Age-Associated Diseases Could Be Treated with NAD+ Boosters?  
The therapeutic potential of dietary NAD+ boosters could improve the impaired NAD+ availability that may promote the development of age-related, cardiovascular, sarcopenia, menopausal, metabolic, inflammatory, renal, neurodegenerative, muscle-damaging, and mitochondrial diseases, cancers, and COVID-19 [68]. Animal studies revealed that NAD+ booster supplementation enhanced life span [17] and improved cardiovascular function [33] enhanced muscle regeneration [17], improved mitochondrial and stem-cell function [179], and altered glucose metabolism [23]. Detailed human studies should investigate which age-related degenerative diseases can be treated, delayed, or prevented with NAD+ boosters, and which booster, which treatment duration, and which dosing regimen is the best. Since most human studies have been conducted on \"healthy\" individuals, patients with certain diseases should also be included in clinical studies in the future.
## 2 Materials and Methods
### ReViMS Version 2.0  
_ReViMS_ is an open-source, user-friendly software for estimating the volume of cell _aggregates_ with irregular shape and _organoids_ (i.e., multicellular _aggregates_ that self-organise into ex vivo mini-organs [2])  
Figure 1: Screenshot of the Graphical User Interface (GUI) of the tools considered in this work. (**a**) _ReViSP_ version 2.1; (**b**) _ReViMS_ version 2.0; (**c**) _MITK_ version 2018.04; (**d**) _OpenSegSPIM_ version 1.1.  
by automatically segmenting single slices of \(z\)-stacks of fluorescence images [12]. _ReViMS_ is written in _MATLAB_ and has been designed for biologists: it allows reconstruction of the external surface of a cell _aggregate_ and estimation of its volume without any programming skills. Source code and standalone versions (for _Windows_ and _Linux_) are publicly available for download at: [[http://sourceforge.net/p/revims](http://sourceforge.net/p/revims)]([http://sourceforge.net/p/revims](http://sourceforge.net/p/revims)). In the first software release (i.e., _ReViMS_ version 1.0), for each slice, the border of the _aggregate_ was defined with a standard histogram-based segmentation method (e.g., _Otsu segmentation_[14], _Triangle segmentation_[15], or a combination of them). In order to improve the software applicability, we implemented two new Active Contour segmentation methods that permit handling even blurry fluorescent objects.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Finance
#### 6.2.3 Example 3: Bolstering Fraud Detection  
XAI models have also demonstrated their effectiveness in enhancing fraud detection within the finance sector by introducing heightened transparency into the decision-making process. [PERSON] [77] explores the domain of credit card fraud detection, where the deployment of explainable AI techniques, specifically SHAPs and LIMEs, emerges as a strategic avenue. This approach effectively unravels the contributing factors behind calculated fraud scores, providing clear explanations that empower financial institutions to proactively thwart fraudulent activities. By pinpointing potential fraudulent transactions and offering comprehensible rationales for their determinations, this research resonates as a proactive measure for fraud prevention.  
In essence, the multifaceted applications of XAI within the finance sector, spanning from risk assessment and trading to fraud detection, embody the transformative potential of transparent decision-making. As XAI's prominence continues to grow, its role in fostering regulatory compliance, bolstering investor confidence, and refining financial decision landscapes stands resolute, promising a future where transparency and accountability converge to shape the financial industry.
# Quantifying Fermentable Sugars in Beer: Development and Validation of a Reliable HPLC-ELSD Method
## 3 Results and Discussion
### Application of the Method  
#### 3.3.1 Analysis of Brewing Matrices  
The sugar profiles of key matrices--wort, end of fermentation, and finished beer--were analyzed for four different types of beer (Beer A, Beer B, Beer C, and Beer D). As depicted in Figure 4, significant differences in sugar concentrations were observed between word and finished beer across beer types. Beer C showed lower maltose levels (57.5 g/L), a result of its production with 100% barley malt. The absence of adjuncts like corn grist, which provide additional starch, reduces the enzymatic production of maltose and maltotriose during mashing. By contrast, the Beer D word exhibited a markedly higher sucrose concentration (22.0 g/L), attributed to the intentional addition of brown sugar during mashing to enhance the beer's unique flavor profile.  
Figure 4: Average sugar concentration values for word and finished product across the four different beer types studied (A, B, C, and D).  
In finished beers, maltose (0.80-1.50 g/L) and maltotriose (1.10-2.50 g/L) concentrations remained consistently high, as these disaccharides are metabolized at slower rates due to yeast preference for simpler sugars such as glucose and fructose. This aligns with fermentation dynamics reported in similar studies [19], which have emphasized the sequential consumption of sugars based on yeast enzymatic activity.
## 1 Introduction
For example, GrabCut combined with DeepLabV3+ has improved plant disease detection, while integrating GrabCut with Canny Edge Detection and the Watershed algorithm has enhanced grain classification accuracy [26,27]. At the same time, CNNs can automatically learn morphological and textural features from raw images, making it possible to achieve sufficient classification performance without extensive preprocessing [28,29]. Using raw images reduces preprocessing costs and increases feasibility for real-time applications, making it essential to evaluate whether GrabCut-Watershed preprocessing significantly improves model performance compared to using raw images alone [30,31]. This study aims to explore how plant growth classification models can be effectively developed by evaluating the influence of image processing strategies--specifically, the comparison between raw images and GrabCut-Watershed preprocessed images--on learning behavior and deployment feasibility in industrial plant factories. The primary research objectives are as follows:  
1. To assess the feasibility of CNN-based growth stage classification using raw, unstructured images of butterhead lettuce. 2. To compare classification performance between models trained on raw images and those trained on images processed through GrabCut-Watershed segmentation. 3. To evaluate cross-classification performance between raw and preprocessed domains in order to examine generalization capacity and inform future system design choices. Ultimately, this research serves as a design-oriented investigation that aims to identify which modeling direction--raw image-based or segmentation-preprocessed--is more effective and practical for downstream applications. The findings offer guidance for the development of lightweight, real-time AI models in smart farming systems. This study provides several key contributions:  
* A data-centric analysis of preprocessing strategies in CNN-based growth stage classification models, identifying scenarios where preprocessing helps or hinders model effectiveness. * An empirical understanding of the trade-offs between segmentation-based and raw image-based training approaches, particularly under data-limited conditions. * A foundation for future research into lightweight architectures and efficient data pipelines that support scalable deployment in industrial plant factories. By addressing how data characteristics and preprocessing strategies impact CNN model performance, this research offers practical insights into how to best utilize agricultural image datasets for reliable and scalable AI-based crop monitoring.