esa-sceva/satcom-chunk-collection huggingface dataset:
# Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile
## 2 Materials and Methods
### FEM Simulations with CanDo  
FEM is a numerical method for solving partial differential equations (PDEs) in 2D or 3D space variables. The method subdivides a large system into smaller, simpler parts that are called finite elements to solve the equations. In our case, FEM simulations were performed with CanDo on single base-pair resolution to study 3D equilibrium conformations of the DNA origami tile (without linker staples). The axial rise per base-pair and the helix diameter were set at 0.34 and 2.25 nm. The crossover spacing was 10.5 bp. All the mechanical properties of dsDNA were set as default: axial stiffness at 1100 pN, bending stiffness at 230 pN\(\cdot\)nm\({}^{2}\), torsional stiffness at 460 pN\(\cdot\)nm\({}^{2}\). To simplify the properties of ssDNA, nick stiffness factor was set at 0.01, which accounts for the property ratio between ssDNA and dsDNA. For example, the axial stiffness of ssDNA is 0.01 \(\times\) 1100 pN, which is 11 pN. The simulations yield an inherent global twist in the origami conformation.
## 3 Model-Based System Engineering for Preliminary Design
### Integration of System Requirements within the Capella System Model
#### 3.3.2 Traceability and Validation in Capella  
1. Requirement Integration: * Import and Structuring: Requirements were imported and hierarchically structured within Capella's Requirement Viewpoint, assigning them to relevant logical and physical components to ensure traceability throughout the model.
2. Traceability Mechanisms: * Logical Layer Traceability: Requirements were linked to logical components and functions, ensuring adherence to specified needs. * Physical Layer Traceability: Physical components were traced back to logical functions and requirements, maintaining continuity. * Bidirectional Traceability: Capella facilitated bidirectional traceability, allowing backtracking from physical components to high-level requirements and forward tracking from requirements to detailed physical designs.
3. Documentation and Configuration Management: * Requirement Allocation Matrix: Visual tools within Capella, such as the Requirement Allocation Matrix, illustrated relationships between requirements, logical components, and physical components. * Traceability Diagrams: Diagrams like Requirement Coverage Diagrams visually represented requirement coverage by system elements, highlighting any gaps or overlaps.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics are expressed as the mean and standard deviation for continuous variables with normal distribution verified by the Shapiro-Wilk test or as a median and interquartile range, 25 th and 75 th percentiles, in case of the skewed distribution. Nominal data are presented as counts and percentages. For normally distributed continuous variables for changes over time, the paired Student's \(t\)-test was used. The signed-rank test was used for continuous variables that were not normally distributed. A \(p\)-value \(\leq\) 0.05 was considered statistically significant. Additionally, due to the small sample, the significance of differences in measurements of continuous variables (Outcomes and QoL) for which we did not reject the null hypothesis stating normal distribution (Shapiro-Wilk test \(p\) > 0.05) was verified by non-parametric tests as a sensitivity analysis. The results are presented in the Supplemental Materials. [PERSON]'s or Spearman's correlation coefficients, assuming a linear regression, were calculated to investigate the association between Outcome and QoL and age of patients, body mass index (BMI), and baseline measurement, as appropriate. A comparison of HTR-induced changes in Outcome and QoL between the different groups was performed by \(\text{M}ST\)-test or the Wilcoxon rank-sum test (two-sample test). Multivariate analysis was performed by means of a linear, backward regression model (PROC GLMSE-LECT in SAS) adjusting for predictors of HTR-induced changes in Outcome in QoL that demonstrated a significant effect (at the \(p\) < 0.1 level) on univariate analyses. The statistical analyses were performed with SAS software (v9.4).
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 2 Related Work
### Spectral Information Divergence  
In this section, we introduce SID that is used in Section 3. SID is a critical parameter for measuring spectral correlation. It quantifies the spectral similarity of the target to other pixels using Kullback-Leibler (KL) divergence. Supposed \(\mathbf{x}=(x_{1},x_{2},...,x_{L})\) is target pixel, \(\mathbf{y}=(y_{1},y_{2},...,y_{L})\) represent the other pixel, and \(L\) is the number of bands. The probability vector of pixel \(\mathbf{x}\) is given by \(\mathbf{p}=(p_{1},p_{2},...,p_{L})\), and the probability vector \(\mathbf{y}\) of pixel is given by \(\mathbf{q}=(q_{1},q_{2},...,q_{L})\), where \(p_{i}=\frac{x_{i}}{\sum_{i=1}^{L}x_{i}}\), \(q_{i}=\frac{y_{i}}{\sum_{i=1}^{L}y_{i}}\). According to the definition of KL divergence, the relative entropy of the target pixel \(\mathbf{x}\) with respect to pixel \(\mathbf{y}\) is expressed as:  
\[S(\mathbf{x}\|\mathbf{y})=\sum_{i=1}^{L}q_{i}\log\frac{q_{i}}{p_{i}} \tag{1}\]  
where \(S(\cdot)\) represents KL divergence.The SID is composed of two directions of relative entropy, given by:  
\[SID(\mathbf{x}\|\mathbf{y})=S(\mathbf{x}\|\mathbf{y})+S(\mathbf{y}\|\mathbf{ x}) \tag{2}\]  
Using this method, SID accounts for the bi-directional spectral differences between two pixels, effectively capturing the spectral information between them.
## 3 Results
### Statistical Tests of Comparisons of SD Recorders  
To compare the means of two samples, the Two-Sample t test can be used. It determines whether there is sufficient (statistical) evidence for a difference in the means. However, in order to apply the Two-Sample T tests, the assumption of normality must be met. As it is indicated from Figure 3, the SD data are not normally distributed. Similar graphs were obtained for the other locations and time periods. Therefore, the Mann-Whitney nonparametric alternative to the Two-Sample T test was applied. In this case, the medians are checked. Significant differences were only detected in the case of the pair of CS and Pr at Athalassa (2012-2015) using both tests. In the rest of the cases the differences are not statistically significant at the 5% significant level.
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 1 Introduction
### Literature Review
[35; 36] further enhanced VO applicability by incorporating maneuverability constraints via trajectory prediction and VO boundary analysis. However, conventional VO algorithms and their enhanced variants demonstrate predominant applicability in open waters or linear channels, while remaining constrained in curved inland waterways due to complex hydrodynamic interactions and restricted maneuverability space. Advancements in artificial intelligence have accelerated the intelligentization of the maritime industry. [PERSON] [37] designed a neural evolution-based collision avoidance system for bionic underwater robots, while [PERSON] [38] fused artificial neural networks (ANNs) with sparse polynomial chaos expansion to process high-dimensional sensor data. Subsequently, [PERSON] [39] advanced genetic algorithm encoding by incorporating environmental parameters (wind/waves/currents) and rule-based fitness functions. Subsequent developments include [PERSON]'s [40] ECDIS-integrated multi-objective algorithm for dynamic obstacle navigation, [PERSON]'s [41] ship dynamics-constrained Tennyson-Whisker search enabling safety-cost tradeoffs, and [PERSON]'s [42; 43] co-evolutionary multi-swarm genetic algorithms for autonomous optimal path generation. However, despite their potential, intelligent algorithms face critical limitations: their design complexity often offsets computational efficiency gains, suboptimal solutions are prone to emerge during optimization, and the reliability and stability of results remain challenging to guarantee. These shortcomings are particularly unacceptable in safety-critical navigational decision-making systems. In summary, while substantial scholarly attention has been devoted to autonomous navigation decision-making systems for maritime ships, the domain of inland tributary waterways navigation has remained relatively under-explored. This research gap stems from the unique challenges posed by the distinct navigational characteristics and environmental complexities of these waterways. The operational environment in tributary systems presents three primary challenges: unpredictable target ship maneuverability, intricate navigational conditions, and significant constraints on ship mobility. Therefore, an autonomous navigation decision-making method for ships in tributary waterways accounting for the constraints of the inland river environment is proposed, which fully integrates the \"Rules of the People's Republic of China for Inland River Collision Avoidance\" (RPRCIRCA), good seamanship, and ship maneuverability.
## 2 Method
### File Processing Required Prior to the Training Phase  
After manually extracting parts of the UML diagrams using the built-in macOS screenshot tool, we proceeded to rename all gathered images systematically. Each screenshot, initially saved to the required directory, was renamed using the format <UML_DIAGRAM_NAME> _<NUMBER>. We started by identifying the UML diagram name for consistency. Then, we renamed files in bulk, appending a sequential number to maintain order. For instance, if the UML diagram name was \"class\", the files were renamed as \"class_1\", \"class_2\", and so on. For UML and non-UML features, we named them for example \"uml_1\", \"uml_2\" and \"non_uml_1\", \"non_uml_2\" respectively. This organized approach ensured that the images were easily identifiable and systematically ordered, facilitating efficient retrieval and reference during image processing and training phases.  
Given the project used parts of UML diagrams, representing features to train the model, each image was saved at 150 \(\times\) 150 px. The images were not directly saved in 150 \(\times\) 150 px size, and we used Python's library pillow or PIL [[https://pypi.org/project/pillow/](https://pypi.org/project/pillow/)]([https://pypi.org/project/pillow/](https://pypi.org/project/pillow/)) (accessed on 10 May 2024). Pillow is a widely used Python library for image processing that offers an easy-to-use interface for tasks such as loading, manipulating, and saving images. It supports various image processing functions including resizing, cropping, filtering, and format conversion, making it an indispensable tool for handling and comprehending images.  
We created the resize_and_pad function, a robust image processing method designed to resize an input image to fit within specified dimensions while maintaining its aspect ratio. Additionally, it adds padding to ensure the final image precisely matches the desired size. By default, the padding color is white, but it can be customized as needed.  
The function involves the following key steps:  
1. Image resizing: The function first resizes the input image. This step ensures that the image fits within the specified dimensions while preserving its original aspect ratio.
2. Padding addition: Next, padding is added to the resized image, and this extends the canvas to the target size, with the padding area filled in the specified color.
3. Saving the result: Finally, the processed image is saved to the designated output.  
Adjusting the size parameter allows for control over the image dimensions, and the background color can be set to suit specific requirements. In our case, white was the best since images contain such a background.  
All images were in either.png or.jpeg format, with most of them being black and white, and only some being colored as well. For each category, we saved 750 images. The total size of the data for this project was 82.42 MB, with an average of 16.48 MB per category. The split between.png and.jpeg files in each category was not balanced, nor the same across every directory.
## 4 Results
### Pumping Test  
During the initial pumping, the groundwater level in the well initially declined slowly and steadily. However, after approximately seven minutes, a marked, sudden drop occurred, with the groundwater level decreasing by more than 2 m (Figure 3). This sharp decline, followed by rapid stabilization after approximately 17 min (total drawdown of about 2.6 m), was likely due to the reactivation of the well. The well was drilled in an extremely challenging environment and remained inactive for a considerable period of time after drilling. Therefore, with the start of pumping, we reactivated and flushed the well, effectively cleaning the filter sections. However, the groundwater level continued to drop, eventually reaching the intake basket of the submersible pump, which led to the pump shutting off.  
The response during the second pumping phase initially mirrored the first prolonged pumping phase. After a gradual and steady initial response, the groundwater level began to drop more intensively about 30 min into pumping and continued to decline steadily over the following two hours until the end of the pumping phase. Following the cessation of pumping, the groundwater level showed a rapid initial rise, which then continued gradually over the course of the following day. Analysis of the drawdown using the Theis method indicated a gravel permeability of \(K=2.24\times 10^{-3}\) m/s.  
During the pumping test, a total of 45 m\({}^{3}\) of water was pumped, resulting in a drop in the groundwater level in borehole PP-12A by 0.42 m. Assuming an effective porosity of 20% for the aquifer, we estimate the aquifer area to be around 550 m\({}^{2}\). Taking other data into account [39], we estimate the aquifer volume in the immediate recharge area of the Urbas spring (Ui-1) at 2026 m\({}^{3}\), with a groundwater volume of 405 m\({}^{3}\).  
Figure 3: Pumping test results in PP-12A.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Poultry
#### 4.2.2 State and Potential of Industry 4.0 Benefits
This still requires the specialist skills of employees but can compensate for the shortage of workers in the areas of manure removal and biosecurity (e.g., manure belts and disinfection robots) as well as daily work such as egg collection (egg belts) [42; 43; 44]. Automated de-stalling, as is now partly used in broiler chicken and turkey stocks (e.g., Chicken Cat) and has also been tried and tested as an animal-friendly process, shows a reduced use of labor or is less physically demanding [45; 46]. Correct and precise sensor technology can significantly contribute to needs-based husbandry by recognizing the smallest deviations and thus also promotes the protection of resources (reduction in \"surplus\"). The future increased use of organic material to fulfill species-specific behavior and to promote well-being must be seen in the sense of improving animal welfare and induces a reduction in the risk of behavioral disorders and the promotion of animal health. **Consistent process orientation**: Poultry farming with the production of egg and meat products is extremely process-oriented. However, vertical integration in particular enables networking from the hatchery to the slaughterhouse in terms of risk-oriented inventory management and production. This also includes the veterinary care of the stock, the production of feed, and the production and use of additional materials such as bedding and employment materials. In the future, this will play a significantly greater role, also in terms of process assurance, and can be promoted by smart technology and AI [44]. **Automated knowledge and learning**: With the use of imaging systems such as camera-based systems for detecting plumage damage [47; 48], behavioral disorders should be recognized early in the future and corresponding risk factors should be derived [49; 50; 51]. New technology such as augmented reality gives the opportunity to compare actual behavior and predicted behavior in order to improve the understanding of single events and to adapt management decisions [52]. So far, these systems are being tested at the research level and offer great potential for ensuring an animal-friendly housing environment. **Collaboration competence and productivity optimization**: In the field of sensor-based technology, there are already tried-and-tested systems such as drinking height adapted to animal size. This reduces the amount of water entering the litter and significantly improves the climate in the barn and the quality of the litter. Potential can be seen in the linking of audiovisual processes because this allows objective herd observation at times when the animal owner is not in the barn (24/7).
# Feature Point Identification in Filtel Weld Joints Using an Improved CPDA Method
## 1 Introduction
The geometry of a welding seam can be effectively extracted by the developed image processing and feature extraction algorithms, even when the captured image quality is low [23]. With a developed line-shaped structured-light active sensing system, the weld seam can be recognized and located accurately and robustly [24]. By implementing an adaptive median filtering and feature point extraction algorithm based on automatic threshold processing for butt joints, a narrow weld seam of 0.1\(\sim\)0.5 mm can be accurately identified [25]. However, there are relatively few methods developed for feature point identification in fillet joint welding, which is more complex due to an indirect visual angle. The Hessian-matrix-based method [26] was found to have high center line extraction accuracy in fillet joint welding with an inclined laser stripe. However, it was computationally demanding. With an extracted center line, feature detectors were needed to detect the corner points of the fillet welding seams. Grayscale-intensity-based [27; 28] and contour-based [29; 30] methods have been proposed in the literature. However, the intensity-based methods were found to have low robustness in welding laser line images with insufficient grayscale variance, while the computation efficiency of the contour-based method needed to be further improved. In this paper, a weld seam recognition and positioning method based on primary component analysis (PCA) and chord-to-point distance accumulation (CPDA) technologies is proposed for robotic fillet joint welding. Firstly, the obtained image with a structured laser stripe of a fillet weld joint is pre-processed. Then, the center line of the laser stripe is extracted using a proposed Gaussian-weighted, PCA-based method, after which the feature points of the fillet weld are extracted using the improved CPDA method with polygon approximation. Finally, for the robot welding application, the feature corner points that indicate the 3D position of the fillet weld seam can be identified and reconstructed.
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
###### Abstract  
Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Ghajn Tuffieha, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel 2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.  
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D 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Ghajn Tuffieha, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel 2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.  
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# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 2 Background
### Global Navigation Satellite System  
The Global Navigation Satellite System (GNSS) makes use of satellites in space around the Earth. The idea began in the 1970s among the U.S. military as Americans sought a way to overcome the difficulties of previous navigation systems in use. GNSS began to be extensively used in the 1990s [18].  
The principle of the GNSS is sending the location of the transmitter to the receiver in space (satellite) and next retrieving information from the receiver to the transmitter on Earth [19]. GNSS transmitters require access to open skies; otherwise, interference or loss of GNSS signals can occur. This type of communication experiences failures in areas where construction is very high or in areas of forests or mountains. GNSS tracking is applied in a variety of fields, including animals, car travel, hiking, and even sports [20; 21; 22].  
The GNSS device is essential to autonomous vehicles in need of high availability and accuracy. A vehicle can plot a pre-known or pre-programmed route autonomously without any human control [23]. Therefore, it is very important that an autonomous vehicle is able to receive and send its location promptly to the mobile network and/or satellite communication. A major advantage of using GNSS is that the data do not depend on previously received information and therefore localization errors do not accumulate over time.  
One of the GNSS's essential attributes is its accuracy, which depends on the environment in which the GNSS transmitter is located and the number of satellites it reads at any given time, alongside the location of the transmitter and the environment in which it is located: for example, in an urban environment, underground parking, a forest, or an open space.