esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.  
defect segmentation; data augmentation; generative adversarial networks; industrial manufacturing: quality inspection; photometric stereo +
Footnote †: journal: Applied Sciences  
Article
# Effects of Diet on Mercury Bioaccumulation in Farmed Gilhead Seabream (_Sparus aurata_)
## 3 Results  
With the exception of muscle tissue, significant differences in mercury concentrations were observed between the exposed and unexposed fishes across all the analyzed tissues (KW \(p\)-value \(<\) 0.05) (Table 2). In particular, mercury levels in fish fed with D1 and exposed to contaminant significantly increase after 20 days of exposure in the gut (up to 0.72 \(\pm\) 0.12 \(\mu\)g g\({}^{-1}\)), liver (up to 1.2 \(\pm\) 0.37 \(\mu\)g g\({}^{-1}\)), gill (up to 2.69 \(\pm\) 1.15 \(\mu\)g g\({}^{-1}\)) and kidney (up to 3.96 \(\pm\) 1.84 \(\mu\)g g\({}^{-1}\)) (Table 2; Figure 2). Similarly, the levels of mercury in fish fed with D2 and exposed to mercury significantly increase in the gut (up to 0.46 \(\pm\) 0.13 \(\mu\)g g\({}^{-1}\)), liver (up to 0.85 \(\pm\) 0.12 \(\mu\)g g\({}^{-1}\)), gill (up to 1.67 \(\pm\) 0.18 \(\mu\)g g\({}^{-1}\)) and kidney (up to 2.40 \(\pm\) 0.3 \(\mu\)g g\({}^{-1}\)) after 20 days of exposure (Table 2; Figure 2). The pattern of mercury concentration in the gills indicates a rapid accumulation during the first 10 days of exposure. After this period, the curve tends to flatten, showing a slowing down of the accumulation rate. The slope of the accumulation curve for the fish fed with D1 is significantly greater than that fed with the CD, indicating higher mercury uptake over the same period of exposure. Conversely, the D2 does not alter mercury bioaccumulation compared to the CD (Figure 2a). In the gut, mercury accumulation follows an exponential trend. After an initial phase with small changes in mercury concentration, the curve demonstrates a progressive acceleration of the accumulation. While D1 and the CD led to similar exponential accumulation trends, the mercury concentration of fishes fed with D2 increases less rapidly, with significantly lower concentrations after 20 days of exposure (Figure 2b). In both the liver and kidney, mercury accumulation follows a linear trend over time. No difference is observed between fish fed with D1 and CD, while the concentrations in fishes fed with the D2 are significantly lower after just 10 days of exposure (Figure 2c,d). No significant differences among the three diets were observed in the muscle (Table 2). Finally, it is interesting to note that, for both diets, after the exposure interruption (T30), the levels of mercury decrease suddenly in the gills, gut and liver, while levels remain more or less constant in the kidney and muscles (Table 2; Figure 3). Figure 2: Comparison among Hg bioaccumulation pattern in fish fed with commercial (CD) and experimental diets (D1 and D2) in gill (**a**), gut (**b**), kidney (**c**) and liver (**d**). Hg concentrations are expressed in \(\upmu\)g/g.
## 3 Results and Discussion
### Discussions  
Traditional thermal methods, such as boiling, steaming, or freezing, can effectively eliminate fish-borne parasites, although they frequently compromise product quality. [PERSON] et al. [8] eliminated _Optishorchis viverrini_ by exposing fillets to 70 \({}^{\circ}\)C for 5 min or freezing at -20 \({}^{\circ}\)C for 2 h, but observed the consequent toughened meat and heightened energy expenditure. The study [9] achieved the instantaneous elimination of _Cryptocity_ lingua by heating to 100 \({}^{\circ}\)C; however, overcooking rendered the fish unfit for consumption.  
Dielectric-based techniques have been explored to decrease treatment times while preserving texture. Reference [28] indicated that an industrial microwave system raised  
Figure 10: Infrared thermal images and post-treatment visual assessment of fish scales and muscle, showing spatial heat distribution and surface effects at various distances from the horn antenna.  
muscle temperature to 50 \({}^{\circ}\)C after 10 min, facilitating roughly 90% parasite elimination; yet, uneven energy distribution led to overcooking in specific regions. Refs. [21; 22] proposed the amalgamation of microwave and RF sources with real-time sensors to achieve uniform, adaptive heating; however, the associated complexity and expense hinder extensive application in aquaculture. These studies collectively reveal three persistent shortcomings: inadequate tissue-specific heating, limited energy efficiency, and operational complexity.  
This study addresses these deficiencies by employing a horn-antenna dielectric heating system tuned for the dielectric properties of _Cirrhinus microlepis_. Positioning the antenna 9 cm from the sample enabled parasite-infested scales to reach 70 \({}^{\circ}\)C in approximately 75 s, although the underlying muscle stabilized at roughly 50 \({}^{\circ}\)C--a notable reduction from the 4-5 min boiling benchmark cited by [8]. The exact energy absorption decreased heat damage to muscle, preserving its unrefined texture and appearance. The approach is more energy efficient as it eliminates the need to heat a significant volume of water, aligning with the efficiency enhancements shown in [24; 25]. In conclusion, our advanced dielectric-heating system offers rapid, tissue-targeted parasite inactivation with diminished energy usage and simplified hardware relative to earlier dielectric methods, thereby providing a scalable and sustainable solution for liver-fluke control in aquaculture.
## 2 Existing Technology and Challenges
### Momentum Exchange Tethers
Tether usage here occurs early in the mission lifetime, with a rotation and release of multiple payloads into desired destination orbits. This research work aims to keep the design of any applied tether systems within immediate feasibility by utilizing conventional materials and significantly shorter tether lengths on the order of meters instead of kilometers, and also put theory into practice by demonstrating this technology in the space environment. Currently, the TRL of a MET-based constellation deployment system can be estimated to be near a level 3 or 4 at the time of this article's writing (late 2023/early 2024), but the overall research effort aims to raise this to the level 6-7 range. This TRL-raising work will culminate into a technology demonstration mission called ADRASTEA that will demonstrate symmetric and asymmetric tether release operations in the space environment following a launch partnered with NASA scheduled for August 2024. Post-flight results from this mission, when they are available, will be discussed in future publications.
# Deep Learning-Based Sound Source Localization: A Review
###### Abstract
UAV monitoring systems integrate lightweight models with spatio-temporal Transformers to attain sub-meter accuracy at \(-10\) dB SNR while addressing edge computing constraints. Nevertheless, three critical challenges persist: the insufficient integration of environmental physics limiting model generalization [10]; the prohibitive costs of data annotation hindering unsupervised/few-shot learning [11]; unresolved trade-offs between real-time processing and accuracy in edge deployments [12]. The German Aerospace Center (DLR) exemplifies cutting-edge applications through two initiatives:  
**Marine Systems**: The DLR Institute for the Protection of Maritime Infrastructures developed Autonomous Underwater Vehicles (AUVs) equipped with dual-frequency side-scan sonars for seabed mapping and multibeam echosounders for bathymetry. Integrated with terrestrial and aerial surveillance networks, these systems enable the near-real-time detection of mobile underwater targets. **Aerial Systems**: At the National Experimental Test Center for Unmanned Aircraft Systems in Cochstedt, the DLR tests UAV platforms for medical supply delivery to remote areas, disaster relief, agricultural monitoring, and industrial logistics. These applications underscore the urgent need for robust UAV detection and localization technologies (see Figure 1). This comprehensive review structures its analysis as follows:  
**Section 2** categorizes technical challenges and deep learning solutions across three domains: marine [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] (e.g., phase-sensitive networks), indoor [28; 29; 30; 31; 32; 33; 34; 35] (e.g., attention-based fusion), and UAV [36; 37; 38; 39; 40; 41; 42; 43; 44] (e.g., spatiotemporal Transformers). **Section 3** elucidates fundamental SSL principles, detailing feature extraction (time-frequency transforms, phase-difference fusion) and model architectures (CNN, LSTM, GFGRU, U-Net, GAN) with mathematical formulations and schematic diagrams. **Section 4** critically evaluates method trade-offs in computational efficiency, generalization capacity, and data dependency through comparative analysis. **Section 5** outlines future directions, proposing physics-informed modeling, multimodal edge intelligence, and ethical compliance frameworks to advance SSL toward high-precision, robust, and energy-efficient intelligent systems.
## 2 Improved WITI Model Considering Gaussian Mixture Clustering
### Improved WITI Model  
The WITI has been used to measure the impact of weather on the traffic flow of the national airspace system of the US, which has demonstrated that it is suitable for large-scale and long-term weather impact analyses [19].  
In the original WITI model, the sector airspace within the research scope is divided into several very small areas using a grid, where each area is a computing unit. It is assumed that the sector airspace network has m rows and N columns, and the grid of the \(i\)-th row and \(j\)-th column is denoted as grid \(ij\). This approach means that the continuous sector airspace system is discretized into grid points.  
The weather information and traffic flow information in the grid are calculated and superimposed on the research sector airspace system. The weather information on grid \(ij\) is recorded as \(W_{ij}\), and the traffic flow information is recorded as \(T_{ij}\). Initially, weather information refers to severe convective weather, which can be identified and gridded on the weather radar. Traffic flow information refers to the number of flights passing through the grid during the \(t\)-th time period. Based on the WITI model, the value of WITI (\(k\)) during the \(k\)-th time period is  
\[\text{WITI}(t)=\sum_{i=1}^{m}\sum_{j=1}^{n}T_{ij}(t)W_{ij}(t) \tag{7}\]  
Traffic flow information \(T_{ij}\) in the model refers to the number of aircraft flying through grid \(ij\) during the \(t\)-th time period of the reference weather, while weather information \(W_{ij}(t)\) is a Boolean variable:  
\[W_{ij}(t)=\left\{\begin{array}{ll}0,\text{ {grid}}\text{ {ij}}\text{ has { no bad weather during time period }}t\\ 1,\text{ {grid}}\text{ {ij}}\text{ has {bad weather during time period }}t\\ \end{array}\right.\]  
The classical WITI model can only be used to calculate the effect of thunderstorm weather on the sector capacity, in terms of the number of flights. However, in the actual situation, there are many types of bad weather conditions that affect flight operation; that is, this is not limited to thunderstorm weather. This problem is addressed by using a Gaussian mixture clustering model to optimize the situation. According to the clustering results, the bad weather is classified according to the echo intensity, and bad weather of type \(a\) is obtained. By calculating the weather-influence coefficient \(W_{ij,a}(t)\) under different bad weather conditions, the optimized WITI model is obtained:  
\[\text{WITI}^{\prime}(t)=\sum_{i=1}^{m}\sum_{j=1}^{n}T_{ij}(t)W_{ij,a}(t) \tag{8}\]  
\[W_{ij,a}(t)=\frac{F_{ij}^{\prime}(t)}{F_{p,ij}(t)} \tag{9}\]  
where \(F_{ij}^{\prime}(t)\) is the number of affected aircraft and \(F_{p,ij}(t)\) is the number of scheduled flights during the \(t\)-th time of grid \(ij\).
## 4 Reasoner Development  
Using the seven-step process presented by [22], ML models are used for the reasoner development. The process starts with data gathering, preparation and model selection. It continues with model training and evaluation and finishes with parameter tuning and prediction. The performance of a classifier is often measured by its accuracy, but the reasoner performance can also be measured by precision rate, recall rate and F1 score, and their formulas can be found in Equations (1)-(4):  
\[\text{Accuracy}=\frac{\text{Number of Correct Predictions}}{\text{Total No. of Predictions}} \tag{1}\]  
\[\text{Precision}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Positive}} \tag{2}\]  
\[\text{Recall}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Negative}} \tag{3}\]  
\[\text{F1}-\text{Score}=2\,\frac{\text{Precision }\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{4}\]  
After evaluation, the parameters related to the model are fine-tuned. Necessary changes are made to them, which could potentially increase the model performance. In real-world applications, supervised learning is commonly employed for fault detection and diagnosis, where the input and expected output are available. The process of fault isolation and diagnosis is fundamentally a pattern recognition problem. When signatures of a fault are used to map it to an exact fault case, the process becomes a classification problem [23].  
The performance of a classifier is often represented in the form of a confusion matrix. It is often referred to as an error matrix or confusion chart and represents the actual labels of instances in the dataset versus the label assigned to it. Figure 5 shows the format used for the EBS reasoner development. The table shows the number of true positives, true negatives, false positives and false negatives generated by the classifier. The diagonal elements in the confusion matrix indicate the correct predictions made by the classifier. The entire process of reasoner development is illustrated in Appendix A.
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 2 Materials and Methods
### Kabsch Algorithm  
The relative transformation between the two systems is calculated using the Kabsch algorithm [35] once the two sets of points in the 3D space are obtained (one expressed in the camera reference system and the other in the LiDAR reference system).  
The Kabsch algorithm calculates the optimal rotation and translation between two systems by minimizing the mean square error deviation, returning three rotation angles \((\alpha,\beta,\gamma)\) and three translation values \((x,y,z)\).
###### Abstract  
This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it describes the space of chemical characteristics in which the model can make predictions with a given reliability. This work studies the AD definition of a ML model throughout its development procedure: during data preprocessing, model construction and model deployment. Three AD definition methods, commonly used for outlier detection in high-dimensional problems, are compared: isolation forest (_FForest_), random forest prediction confidence (_RF confidence_) and k-nearest neighbors in the 2D projection of descriptor space obtained via t-distributed stochastic neighbor embedding (_tSNE2D/RNN_). These methods compute an anomaly score that can be used instead of the distance metrics of classical low-dimension AD definition methods, the latter being generally unsuitable for high-dimensional problems. Typically, in low- (high-) dimensional problems, a molecule is considered to lie within the AD if its distance from the training domain (anomaly score) is below a given threshold. During data preprocessing, the three AD definition methods are used to identify outlier molecules and the effect of their removal is investigated. A more significant improvement of model performance is observed when outliers identified with _RF confidence_ are removed (e.g., for a removal of 30% of outliers, the _MAE_ (Mean Absolute Error) of the test dataset is divided by 2.5, 1.6 and 1.1 for _RF confidence_, _Forest_ and _tSNE2D/kNN_, respectively). While these three methods identify X-outliers, the effect of other types of outliers, namely Model-outliers and y-outliers, is also investigated. In particular, the elimination of X-outliers followed by that of Model-outliers enables us to divide _MAE_ and _RMSE_ (Root Mean Square Error) by 2 and 3, respectively, while reducing overfitting. The elimination of y-outliers does not display a significant effect on the model performance. During model construction and deployment, the AD serves to verify the position of the test data and of different categories of molecules with respect to the training data and associate this position with their prediction accuracy. For the data that are found to be close to the training data, according to _RF confidence_, and display high prediction errors, tSNE 2D representations are deployed to identify the possible sources of these errors (e.g., representation of the chemical information in the training data).  
machine learning; QSPR/QSAR; high-dimensional data; descriptors; thermodynamic properties; applicability domain; outlier detection +
Footnote †: journal: _Algorithms_
## 5 Discussion
### Geomorphic Indices and Relative Tectonic Activity Index  
In the present study, we examined processed data on geomorphic indices that have been used in several previous studies to evaluate the basin landscape in relation to indications of tectonic activity. Previous research evaluating relative active tectonics using geomorphic indices has tended to concentrate on a single mountain front or a limited area [2; 11; 14; 17; 20]. The authors of this work applied an effective technique for determining an averaged index over the study basin that yields a relative tectonic activity (Rta) index. The different indexes were classified arbitrarily into three different tectonic levels: low, moderate, and high tectonic activity (Table 2). Many geomorphic studies have been performed along several regions, but limited studies have attempted to examine the relative tectonic activity along the eastern flank of the Red Sea coast. In this study, we developed an areal index (Rta). This index is assigned by the average of the geomorphic index classes (S/N) and is classified into three different classes. Class 1 assigns high-activity signatures with values of S/N < 1.5; class 2 assigns moderate-activity signatures (S/N between 1.5 and 2); and class 3 assigns low-tectonic-activity signatures (S/N > 2). Table 2 presents the averaged geomorphic indexes of the relative activity S/N and Rta values for 52 sub-basins in the study basin. The lower class of sub-basin values (high tectonic activity) is distributed in the northern, eastern, and southern parts of the study basin, mainly in Al Farrah, Heelan, and Al henakiyah cities, respectively. The high class of sub-basin values (low tectonic activity) is represented by six sub-basins (3, 4, 5, 12, 17, and 22) that are found mainly in the middle part of the study basin around Al mangor city. The rest of the basin is coverd by moderate tectonic signatures (Figure 15).  
Within the study basin, about 61.68% of the area (64,509 km\({}^{2}\)) represents class 2 of tectonic activity as assigned by the Rta index; 31,23% presents spaces of high tectonic activity (32,666 km\({}^{2}\)) (class 1); and finally, 7.09% (7411 km\({}^{2}\)) of the total area of the study basin shows low-tectonic-activity signatures (class 3) based on Rta index values. Consequently, nearly one-third of the study basin is assigned into class 1, whereas the other two-thirds are assigned into the other two classes (class 2 and class 3) (Figure 15). In other tectonic regions and environments, the geomorphic indexes could vary, as could their  
Figure 15: Distribution of the Rta index of relative tectonic activity in the basin between Al Farrah and Heelan in Saudi Arabia.  
value ranges [3]. The Rta index could also display different values, as would the classes of tectonic activity boundaries.
## 2 Materials and Methods  
### Experimental Setup  
The experimental setup involved a mobile test stand (a trailed over-row sprayer equipped with an SAB; Figure 1). The prototype of the tested sprayer was developed by AGROLA [PERSON] (Platkownica, Poland) as part of a research project entitled \"A family of field sprayers with an air sleeve\". The over-row sprayer was equipped with a 1 m\({}^{3}\) sprayer tank and a hydraulic folding boom with a working width of 8 m. Ten diffusers manufactured by Marseplast Sp. z o.o. (Niepolomice, Poland) were installed on the boom (Figure 2). The diffusers were symmetrically positioned relative to the sprayer's axis of symmetry on both sides. The basic geometric dimensions of the tested diffusers are presented in Figure 3. The diffuser's sides were set at an angle of 30\({}^{\circ}\), and the supplied air expanded as it exited the device. The diffusers were supplied with air by a radial fan connected to ten air ducts via a central plenum. Each air duct was connected to one diffuser. The fan was powered by the tractor's power transmission system via a two-speed transmission with gear ratios of 0.22 (I) and 0.28 (II).  
In the prototype over-row sprayer, each diffuser could be configured to work with one or two spray nozzles (Figure 3). To connect the diffuser to one nozzle, the nozzle was aligned with the diffuser's axis of symmetry. To connect the diffuser to two nozzles, the nozzles were positioned at a distance of 9 cm from the diffuser's axis of symmetry. The nozzle axis was shifted 50 mm forward relative to the diffuser axis and the spray cones were parallel to each other.  
Figure 1: Over-row sprayer used in the analysis of horizontal spray distribution.