esa-sceva/satcom-chunk-collection huggingface dataset:
# Space Weather Effects from Observations by Moscow University Cubesat Constellation
Instruments were developed by the Skobel'tsyn Institute of Nuclear Physics of Moscow State University (SINP MSU) to measure the energetic charged particle fluxes operating on Russian weather satellites of the Meteor-M (low orbit) and Electro-L (geo-stationary) series. There are also a number of other satellites employed, including small spacecraft of the CubeSat type that carry out such measurements. However, practically all these spacecraft allow measurements of the energetic charged particle fluxes at very limited regions of space and a limited range of pitch angles. The most advanced, in view of detailed measurements of energetic particle fluxes at different drift shells, the Van Allen Probes project [22,23] does not have the goal of real-time radiation monitoring in near-Earth space. At present, the European Space Agency is developing the DSS (Distributed Space Weather Sensor System) project [24] to create a constellation of small satellites for monitoring the energetic charged particle fluxes in the regions from low to geostationary orbits and auroras. However, this project is currently in the \"zero\" phase; the scientific and technical concept of measurements and segment data handling is not clear. The above considerations and facts determine the relevance of the development of the Moscow State University project for a small satellite constellation, one of the main goals of which is real-time radiation monitoring in near-Earth space.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 4 Results and Discussions
### Comparisons with State-of-the-Art Methods in the SpaceWill Dataset  
We will use the AG and NIQE to evaluate the SR results of all compared models in the SpaceWill dataset. The AG refers to the obvious difference in the grayscale near the shadow or both sides of the border of the image (i.e., the grayscale change rate). Moreover, the size of this change rate can be used to express the sharpness of the image. The larger the AG, the more layers and the clearer the image. NIQE is based on constructing a series of features used to measure image quality, and fitting these features into a multivariate Gaussian model. The larger the values of these parameters, the greater value of the NIQE and the worse of the image quality. As shown in Table 8, our approach is better than other methods. Figure 13 shows the output SR results. Figures 14 and 15 are enlarged partial views of the SR results of each method in the SpaceWill dataset.  
Figure 14: An enlarged partial view of the SR result of each method in the SpaceWill dataset—sample image 1, where the best results are shown in red font.  
Figure 15: An enlarged partial view of the SR result of each method in the SpaceWill dataset—sample image 2, where the best results are shown in red font.
###### Abstract
(2018) synthesized PlanetScope-like SWIR bands from S2 to improve flood segmentation using GANs. Recently, [PERSON] et al. (2018) employed a multispectral generative adversarial network to achieve high performance in harmonizing dense time series of Landsat-8 and Sentinel-2 images. Their approach not only produced higher-quality images but also demonstrated superior crop-type mapping accuracy in comparison to existing methods. In fact, [PERSON] and [PERSON] (2018) applied GANs to enhance L8 spatial resolution to match S2. Therefore, the success of GANs in translating satellite images across domains provides an ideal opportunity for translating L8 to S2, particularly for predicting the additional red-edge bands of S2 from L8. In this paper, we define this task of predicting S2-like images from L8 images as an image translation task where we train a conditional GAN to translate L8 spectral bands to S2-like spectral bands. In addition to predicting the common spectral bands between L8 and S2, we generate S2-like red edge bands from L8 images that are not present in the L8 product. More specifically, we investigate the performance of CGANs in predicting five S2-like spectral bands--green, red edge 1, red edge 2, red edge 3, and narrow near-infrared (NIR) from two spectral bands of L8--green and NIR. In addition to evaluating the benefits of CGANs, we evaluated two approaches for predicting S2-like red edge bands from L8--direct and multistep. The multistep approach divides the task into two parts to reduce per-model complexity. First, we predict the S2 spectral band from the equivalent L8 spectral band before attempting to predict the red edge bands that are not available in L8. In the case of the direct approach, the CGANs are trained to predict the red edge bands directly from the most correlated L8 bands.
# Retinal Vessel Segmentation Using Math-Inspired Metaheuristic Algorithms
## 1 Introduction
propose a deep-learning-based network model, called feature selection-UNet (FS-UNet), with the aim of optimizing attention mechanisms and task-specific adaptations when analyzing complex retinal images [48], and proved the efficiency of the model using images from the DRIVE, STARE, and CHASE databases. In [49], a novel deep-learning-based network model, called plenary attention mechanism-UNet (PAM-UNet), is proposed by [PERSON] et al. to enhance the feature extraction capabilities of existing algorithms. The performance of the model was analyzed using retinal images from the DRIVE and CHASE DB1 databases. In [50], [PERSON] and [PERSON] propose a hybrid model which combines metaheuristic and deep learning approaches to detect diabetic retinopathy with high accuracy. Similarly, [PERSON] et al. propose a deep learning architecture based on a metaheuristic algorithm for retinal vessel segmentation [51]. As can be seen from this literature review, math-inspired metaheuristic algorithms have only been utilized for applications within a restricted number of research areas. Retinal image segmentation has become one of the most common application areas in the field of image processing in recent years. In this context, the segmentation of retinal images with high accuracy is extremely important for the detection of eye diseases. The highlights of this work can be expressed as follows:  
* The math-inspired metaheuristic algorithms of CSA, TSA, AOA, GNDO, GOBC-PA, and SCA are implemented for clustering and retinal vessel segmentation. * The segmentation results demonstrate that the considered math-inspired metaheuristic approaches are able to produce similar or better results when compared to evolutionary-based, swarm-intelligence-based, physics-based, and human-inspired algorithms [52; 53]. * The statistical analyses indicate that the considered math-inspired algorithms are able to produce effective results despite their simple algorithmic structures.
## 3 Problem Formulation
### Coverage-Aware Cost Metric  
As mentioned in the Section 1, the primary goal of the proposed ECHR algorithm is to prevent the sensed data from being routed through sparsely deployed areas. Therefore, the nodes in these areas can be less used as data routers but more used as data collectors. This coverage-preservation task requires a coverage-aware cost metric to calculate the overall coverage ratio, the distribution of the remaining energy, and the overlapped sensing area covered by the neighboring sensor nodes.  
We assume that the mission-critical application requires every part of the area to be covered by the sensor nodes in \(V\). Each sensor node performs a sensing task on the points of interest (POI) located within its sensing area. The sensing area of each node is approximated by a circular area around the node with radius \(r_{s}\). Such a model is the simplest and the most common method in determining the sensing coverage of a given WSN. A set of POIs that will be monitored is denoted by \(P\), where \(P=\{p_{j},j=1,\,\ldots,\,m\}\). If the distance between a sensor node \(s_{i}\) and a POI \(p_{j}\) is shorter than, or equal to, \(r_{s}\), the coverage set of the sensor node \(s_{i}\) is then defined by:  
\[C\big{(}s_{i}\big{)}=\Big{\{}p_{j}\mid d\left(s_{i},p_{j}\right)\leq r_{s} \Big{\}}, \tag{1}\]  
where \(d(s_{i},p_{j})\) is the Euclidean distance between the node \(s_{i}\) and a POI \(p_{j}\). For example, the set of POIs covered by the sensor node \(s_{1}\) in Figure 1 is \(C(s_{1})\), and \(C(s_{1})=\{p_{1},\,p_{2},\,p_{3},\,p_{5},\,p_{6},\,p_{7}\}\). Usually, multiple sensor nodes in the network cover the same POI. This case is called the coverage redundancy. According to the definition given above, the subset of POIs that are simultaneously covered by multiple sensor nodes can be determined by:  
\[O\big{(}s_{i}\big{)}=C\big{(}s_{i}\big{)}\cap\big{(}C\big{(}s_{1}\big{)}\bigcup C \big{(}s_{2}\big{)}\bigcup...\bigcup C\big{(}s_{i-1}\big{)}\bigcup C\big{(}s_ {i+1}\big{)}\bigcup...\bigcup C\big{(}s_{n}\big{)}\big{)}, \tag{2}\]  
where \(O(s_{i})\) is the intersection of the sets of POI covered by \(s_{i}\) and other sensor nodes. If \(O(s_{i})=C(s_{i})\), the sensor node \(s_{i}\) is identified as a redundant node. For a given WSN, the coverage ratio \(R\) of a given WSN is thereby defined by:  
\[R=\frac{\big\|\bigcup_{i=1}^{n}C\big{(}s_{i}\big{)}\big\|}{\big|P\big{\|}} \times 100\%=\frac{\big\|C\big{(}s_{1}\big{)}\bigcup C\big{(}s_{2}\big{)} \bigcup...\bigcup C\big{(}s_{n-1}\big{)}\bigcup C\big{(}s_{n}\big{)}\big\|} {\big|P\big{\|}}\times 100\%\,, \tag{3}\]  
where \(\|P\|\) is the number of POIs in \(P\). In addition, if a sensor node \(s_{i}\) runs out of its energy, \(C(s_{i})\) in Equation (1) is deflated to an empty set.
# A Prediction Model for Additive Manufacturing of Inconel 718 Superalloy
## 4 Results and Discussion
### Experimental Results
The same result can be observed in the LP-HS diagram when the laser power is over 300 W and the hatch spacing is less than 95 \(\upmu\)m. In addition, the HS-SS diagram demonstrates that by increasing the scanning speed and the hatch spacing values, the surface hardness declines significantly to the range of 260-266 HV. Contrarily, the hardness can rise to over 300 HV when the scanning speed and the hatch spacing are less than 850 mm/s and 95 \(\upmu\)m, respectively. The hardness value, which can be a good representative of mechanical properties, has been reported to have a strong correlation with the energy density level [66, 67]. Based on the results, there was a drop in the hardness value with an increase in hatch spacing. This can be explained by the relationship between the energy density and the adhesion of fabricated layers. Increasing the space between the laser tracks (hatch spacing) reduces the energy density and, thereby, affects the epitaxial solidification. With a lower level of epitaxial bonding, the probability of weak adhesion between the layers is higher. Consequently, this prevents the formation of columnar structures [68]. It has been revealed that there is a strong correlation between the formation of columnar grains and the hardness value [69]. Therefore, a lower hardness value is expected for the sample fabricated with higher hatch spacing. In terms of scanning speed, as an increase in this parameter decreases the energy density, the same logic discussed before can be used. A higher scanning speed value (lower energy density) results in the formation of shallower pools, and shallower pools, in turn, lead to weaker epitaxial solidification. Thus, a weak adhesion between layers leads to a  
Figure 3: The influence of processing parameters on the surface hardness of the SLM-fabricated IN718 samples. testing phases are 0.96246 and 0.95839, respectively. Accordingly, the MLP model indicates a very good fitting with the experimental results for each training and testing phase. The scatter diagrams in Figure 6 demonstrate the trends of sample height (left) and surface hardness (right) related to the testing samples. It is obvious that the correlation between the measured and predicted results is very close, and the statistical reports also proved this previously.
## 3 Results and Discussion
### Inhibition of Nitric Oxide (NO) and Prostaglandin E2 (PGE2) Production by the Extracts
Approximately 826.5 pg/mL PGE2 was produced when the UE was administered at a maximum concentration of 0.1 mg/mL. In general, the amount of PGE2 produced decreased according to concentration in the same pattern as that of NO production. The ultrasonic extract had a more dramatic reduction compared to the extract obtained by the general extraction process, reconfirming that ultrasound extracts have stronger anti-inflammatory effects. When compared to _Coridalyis bungeana_[51], _Coptials rhizome_[52], and Royal jelly [53], the _Spirulina_ extracts from this UE showed anti-inflammatory effects selectively on brain nerve cells similar to or higher than those of the other substances. The extract containing large amounts of the useful bioactive substance in intact forms without the destruction of activity obtained through ultrasonic extraction has better anti-inflammatory properties, therefore confirming the excellence of ultrasonic low-temperature extraction once again. In particular, NO is known to be excessively produced by macrophages in response to stimulation with substances, such as LPS and amyloid-beta which are toxic intracellular substances to cause cytotoxicity and inflammation [54]. PEG2 is synthesized by COX-2 to mediate the pain and fever  
Figure 2: Secretion of nitric oxide from BV-2 cells by the treatment of various concentrations of the \(S\). _maxima_ extracts. EE, 70% ethanol extraction at 80 \({}^{\circ}\)C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 \({}^{\circ}\)C for 4 h. Values are presented as means \(\pm\)SD; \({}^{*}\)\(p\)\(<\) 0.05 and \({}^{**}\)\(p\)\(<\) 0.01 compared with the LPS group. on damaged tissues or cell regions and is known to be involved in the induction of Parkinson's and Alzheimer's disease at high levels [55]. Therefore, effective inhibition of these substances may reduce factors that cause inflammation in brain nerve cells, thereby improving cognitive functions through anti-inflammatory effects. This information can be understood in the same context as previously reported findings, indicating that _Spirulina_ can protect brain nerve cells and improve cognitive function and memory [31; 32; 35].
## 2 Proposed Method
### Fundamentals of Meta-Learning  
Meta-learning, or learning to learn, is a significant paradigm in machine learning aimed at enabling models to generalize from limited experiences to perform well on new tasks [18]. This approach leverages accumulated knowledge to quickly adapt to novel learning challenges with minimal data. Meta-learning operates at two levels: the meta-level, focusing on the learning strategy across tasks, and the base-level, where task-specific learning occurs.  
The core objective in meta-learning is to fine-tune the model parameters, \(\theta\), to optimize performance not just on a single task's training data but across a diverse set of tasks represented by the distribution \(p(\mathcal{T})\). This goal is pursued through episodic training, which involves sampling tasks \(T\sim p(\mathcal{T})\) and dividing each into a support set \(S\) for learning and a query set \(Q\) for evaluation. The model's parameters are updated based on the performance on \(Q\), facilitating the model's ability to assimilate information from \(S\).  
Formally, the meta-learning objective for a given task \(T\), with support set \(S\) and query set \(Q\), is to minimize the expected loss \(\mathcal{L}_{Q}\) on \(Q\) after learning from \(S\), as defined by:  
\[\min_{\theta}\mathbb{E}_{T\sim p(\mathcal{T})}[\mathcal{L}_{Q}(f_{\theta^{ \prime};S}(x_{Q}),y_{Q})] \tag{1}\]  
where \(\theta^{\prime}\) represents parameters updated from learning on \(S\), and \((x_{Q},y_{Q})\) denote the samples and labels in \(Q\). The ultimate aim is identifying \(\theta\) that facilitates rapid adaptation to new tasks from \(p(\mathcal{T})\).  
Various meta-learning models exist, including model-based, optimization-based and metric-based approaches. Each type aims to enhance the model's ability to learn efficiently from minimal data, thereby broadening the scope of tasks it can handle--from few-shot learning challenges to rapid adaptation in reinforcement learning contexts.  
Model-Agnostic Meta-Learning (MAML) [36; 37] is a notable strategy that exemplifies meta-learning's essence by preparing the model to significantly improve performance on new tasks with only a few adjustments. MAML seeks an initial parameter set \(\theta\) that is optimal for quick learning across tasks. After initial adaptation through gradient updates on \(S\), resulting in task-specific parameters \(\theta^{\prime}_{T}\), the model's performance is evaluated on \(Q\):  
\[\theta^{\prime}_{T}=\theta-\alpha\
abla_{\theta}\mathcal{L}_{T}(f_{\theta^{ \prime}};S) \tag{2}\]This approach's generality allows its application across various models and tasks, highlighting the versatility and potential of meta-learning.  
Despite meta-learning's and MAML's advancements, challenges such as high computational demands, assumptions of task homogeneity and sensitivity to hyperparameters remain areas for future research. Addressing these limitations is crucial for advancing meta-learning towards more practical and scalable applications.
###### Abstract  
Bending deformation gives rise to interwire slippage for spiral strands subjected to multi-axial loads, and further induces wear or fatigue phenomena in practice. The interwire friction would resist bending deformation and lead to uneven tension distribution of individual constituent wires but little research has quantified these effects. To figure out this issue, a beam finite element (FE) is established, into which a penalty stiffness algorithm and a Coulomb friction model are incorporated. A series of free bending simulations are developed for parametric study on deflection near the terminations and tension distribution of individual wire for strands with different levels of length and friction coefficient as well as external loads. Based on the simulation results, it is found that strand length has little influence on bending deformation and tension distribution if the strand length exceeds six times the pitch length. A deflection formula extended from the classical Euler beam model well predicts the sag deflections and the relative error with respect to experimental measurements is less than 10%. Furthermore, additional axial tension induced by the friction is clearly characterized and an approximate expression is proposed to estimate tension distribution for outer layer wires. Its predictions are encouraging for longer strands.  
single-layered strand; beam finite element model; bending deflection; tension estimation +
Footnote †: journal: Applied sciences
## 5 Experiments and Results
### Drone Path Planning for PFBs Inspection  
To facilitate autonomous drone navigation and inspection, real-time object detection is deployed using Haar-cascade classifiers. The following sections discuss the benchmarks of this approach along with the necessary components for blade inspection including distance estimation, travel vector creation, and flight path optimization. The performance of each optimization and algorithm is investigated along with a culmination of these parts in the creation of a dataset, for fault classification, fully autonomously.
## 4 Discussion
### Global Implications for Arid and Semi-Arid Regions  
This study's findings have broad applications for managing arid and semi-arid regions globally, which cover over 40% of the Earth's land area and support 38% of the global population. These regions are highly vulnerable to climate change, making sustainable vegetation and water management critical.  
1. Regional adaptive management of resistance and resilience: The spatial heterogeneity of vegetation resistance and resilience offers insights for other regions. For example, in Africa's Sahel, drought-tolerant species and ecological water projects could mitigate land degradation [57]. Similarly, in Australia's Murray-Darling Basin, this study's methods could inform vegetation monitoring and restoration strategies [58].
2. Global applicability of seasonal drought impacts: The seasonal variability of drought impacts provides a framework for managing droughs elsewhere. In the Mediterranean, optimizing irrigation and crop structures could alleviate summer drought effects [59]. In the U.S. Southwest, winter drought management strategies (e.g., soil moisture conservation) could support spring regrowth.
3. Adaptive ecosystem management under drought trends: The long-term and cumulative effects of drought highlight the need for adaptive management. For example, in Central Asia, restoring natural vegetation and optimizing water resources could enhance ecosystem resilience [60]. In South America's Pampas, this study's framework could guide ecosystem monitoring and management [61].
4. International collaboration and knowledge sharing: This study's findings can support global initiatives like the UNCCD, fostering knowledge exchange and technical cooperation. Additionally, the methods and results can improve global ecosystem models (e.g., DGVMs), enhancing predictions of drought impacts on vegetation.  
In summary, this study provides valuable insights for managing the West Liao River Basin and other arid and semi-arid regions globally. By promoting resistance and resilience assessments, optimizing seasonal drought management, and fostering international collaboration, these regions can enhance ecosystem sustainability and contribute to achieving global SDGs.