esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
## 1 Introduction  
The fourth industrial revolution, Industry 4.0, brings an integration of digital technologies to create more efficient, flexible, and interactive production processes. Typical features, such as digitalization, data-driven decision making, and transforming producers to smart factories, where monitoring and control is realized in real time, foster production efficiency, quality, flexibility, and responsivity as well as resource optimization.  
Constant progress in hardware technologies offers a higher performance in smaller dimensions, which widens possibilities for their use. The amount of data collected from production increases, which in turn requires the processing of large volumes of data. Therefore, a sufficiently powerful hardware system is needed, which favors the use of cloud technologies. If all requirements are met, a digital production environment can be developed [1, 2].  
Augmented and mixed reality are recently developed technologies that provide functions and services that are extremely useful for industrial applications. This technology also forms a significant part of our project and its inclusion in the proposed platform is one of the main contributions of this work. Therefore, this technology, together with communication between individual smart devices, is at the center of the introductory analysis of the proposed solution. Since our work covers several technologies and fields, the analysis can be divided into two parts [2, 3, 4]:  
* Analysis of communication between smart devices;
* Mixed and augmented reality (AR).
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Ablation Studies  
In order to verify the effectiveness of the proposed module, ablation experiments are designed in this section. Given that the edge-extracted module and edge-guided module in the proposed algorithms support each other and cannot work alone, we eliminate both EEM and EGM in the ablation experiments and keep only the change detection branch as the baseline model, called EGHNet_backbone.  
As shown in Table 3, the ablation experiments on the WHU dataset indicate that the addition of edge guidance significantly improves the performance of EGHNet_backbone by 4.07% in terms of F1 scores. These experimental results validate the effectiveness of introducing edge information into the change detection task and highlight the great potential of this method in improving change detection performance.  
In Equation (12), the loss function of edge information accounts for an important part of the overall loss function, and suitable weights have an impact on the change detection effect of the network.  
Therefore, we conduct the ablation experiment to explore the supervision of the edge map of the change target and the setting of the weight balance coefficient \(\lambda\). In order to comprehensively reflect the influence of the change of \(\lambda\) value, the ablation experiment sets three cases: when \(\lambda\) = 0, it means that no supervision is performed on the edge information; when \(\lambda\) = 1, it means that the edge information is equally valued with the change information; when \(\lambda\) > 1, it means that the percentage of the edge loss in the objective function is elevated. \(\lambda\) = 5 is chosen in this experiment. Table 4 demonstrates the objective evaluation metrics for the proposed algorithm on the dataset WHU when the value of \(\lambda\) is changed. From the data in Table 4, it can be seen that the change detection performance is improved when the edge information is used. Furthermore, the performance is further improved when the weight on the edge information loss as a percentage of the overall loss is increased (as shown in the third row that \(\lambda\) = 5).
# Topology Optimisation in Structural Steel Design for Additive Manufacturing
## 1 Introduction  
### The Origins of Topology Optimisation  
Notwithstanding historical perspectives, as discussed in [1, 2], which root Structural Optimisation and Topology Optimisation (TO) at the very beginning of the classical theory of elasticity, it is usually well accepted that TO had its _de facto_ surgery under the name of Optimal Layout Theory and denoted the ability to optimise not only the structural elements shape and size but also its layout. That dates back to successful attempts made by [PERSON] [3, 4] and [PERSON] [5, 6, 7, 8] from the early 1970s to late 1980s, to generalise the 1904s Michel theory for weight optimisation of thin bars (Figure 0(a)) [9, 10], which is based on the Maxwell theorem for frames [11].  
Almost simultaneously, [PERSON] pioneered the optimal layout design for trusses [12]. In addition, [PERSON] [13] managed to optimise Kirchoff equations solutions for finding the plates optimal thickness, leading to ribbed solutions of good value for the aerospace industry. Further developments were endeavoured by [PERSON] and [PERSON], among others [14, 15, 16].  
After these early developments and proven accomplishments in aerospace structures design, TO experienced rapid growth at the beginning of the 1990s as it became easily distinguishable from Shape or Size Optimisation concepts. The latter already well-disseminated the design practice required near-optimal initial topologies and would yield no better than intuitive final layouts, unlike TO [17, 18].  
Such revolutionary abilities justify the TO classification in several engineering disciplines throughout the product design and manufacture. The automotive industry soon followed the aerospace and applications widened to medical devices and personalised medicine, defence, electronics, several kinds of consumer goods and mechanical engineering endeavours, new materials design, and even arts and architecture. Civil and structural engineering may be latecomers after an auspicious start with truss optimisation investigations [12; 19], but also show an accelerating trend in the TO application. Currently, the fourth industrial revolution and its reliance on Additive Manufacturing (AM) processes, on the opposition to standard subtractive processes, face significant challenges, as more advanced, scalable, and user-friendly design methods are required to unleash its incredible potential. TO is undoubtedly an adequate answer for such an enterprise. Its systematic use may overcome the knowledge barriers still moving many practitioners away from AM, as better described by [PERSON] et al. [20].
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Ablation Study on Update Strategies  
To evaluate the effectiveness of each core component in ADDAEIL, an ablation study was conducted involving three simplified variants: a static iForest model with no update, and two partial update strategies that use either path length-based detector ranking (Performance-only) or statistical test-based replacement ratio estimation (Distribution-only). These are compared against the full ADDAEIL framework. Results are presented in Table 3.  
Figure 4: Average AUC, F1-score, precision, and recall across datasets.  
As expected, the No Update baseline performs substantially worse than the other three variants across all datasets. Especially in non-stationary environments such as Yahoo and NAB, it achieves very low F1-scores of 0.31 and 0.47. This clearly demonstrates that in the presence of significant concept drift, static models become increasingly misaligned with the evolving data distribution. The lack of any adaptation mechanism leads to the accumulation of stale decision boundaries, particularly under highly non-stationary data streams where concept drift is pronounced. As the structural mismatch grows over time, detection accuracy deteriorates accordingly. In contrast, the other three variants all benefit from different model update strategies, highlighting the critical role of adaptive mechanisms in maintaining long-term model reliability under distributional shift.  
Both the Performance-only and Distribution-only variants clearly outperform the static baseline. These results show that incremental updates are crucial in non-stationary settings. Both distribution- and performance-based concept drift detection mechanism can guide effective adaptation. On the Yahoo dataset, which exhibits gradual concept drift, the Performance-only variant achieves a higher F1-score of 0.74 than Distribution-only of 0.71. This suggests that structure-based performance evaluation is particularly effective under incremental drift. In such cases, subtle distributional changes may not trigger statistical tests but can still cause misalignment in the internal structure of base detectors.  
Neither the performance-based nor the distribution-based variant achieves the best performance across all datasets or evaluation metrics. In contrast, ADDAEIL combines both types of drift detection. It uses statistical tests to determine update intensity and structure-based performance evaluation to rank which detectors should be replaced. This hybrid design yields consistently better results across all evaluation metrics. ADDAEIL achieves the highest average F1-score of 0.76, precision of 0.75, and AUC of 0.90, while maintaining a top-level recall of 0.95. These results demonstrate that both modules are complementary and essential. Together, they enable ADDAEIL to deliver robust, targeted, and interpretable adaptation under various types of concept drift.
## 4 Results  
### Input Data Correlations  
Figure 4 shows the correlation coefficients of the input variables used for estimating the hourly SSI and the ground-based SSI measurements from the KMA ASOS stations in different datasets. Except for the SZA, all input variables (19 input variables) showed a positive correlation coefficient with hourly SSI from the KMA ASOS stations. For intense cloud conditions, even if the ESR was high, the SSI was observed to be low; however, for clear sky conditions, the SSI increased as the ESR increased. Furthermore, the SSI was consistently observed to be 0 at nighttime, with an ESR of 0. The ESR changes depending on environmental conditions such as the Earth-Sun distance, the solar elevation, and the solar activity. The higher the solar activity, the closer the Earth-Sun distance, and the higher the solar elevation, the higher the ESR value. Hence, the ESR showed the highest correlation coefficient (0.74). Because the 3.8 \(\upmu\)m channel is a useful channel for detecting low clouds and fog, a high brightness temperature indicates no fog and low clouds, and a high SSI is measured under clear sky conditions. Hence, among the input variables related to the channel, IR3.8 showed the highest correlation coefficient (0.61). Conversely, only the SZA showed a negative correlation coefficient (\(-\)0.74), because the SZA was highly inversely correlated with an ESR of \(-\)0.98. As the SZA decreased, the solar altitude increased; hence, the ESR increased, which increased the SSI. In addition, with an SZA above 90 degrees at nighttime, the SSI was consistently observed to be 0. The 1.6 \(\upmu\)m channel and 1.3 \(\upmu\)m channel are solar channels that show only at daytime and show a high reflectance for cloud area at daytime, like the visible channels. Furthermore, because the 1.6 \(\upmu\)m channel had an ability to distinguish water-based clouds from a snow-covered surface and depict the land surface, it showed a higher correlation coefficient (0.57) than the visible channels. However, although the 1.3 \(\upmu\)m channel had the ability to detect cirrus clouds, it could not depict the land surface, so it showed the correlation coefficient closest to 0, with a value of 0.06, compared with the other input variables.
## 2 Pantograph Slide Positioning Detection Method Based on Convolutional Neural Network
### YOLO v8 Network Structure
The network structure of YOLO v8 is shown in Figure 1.  
Since the catenary scene does not need to support multiple categories of classification information, the model does not need to use complex deep networks for feature extraction, and can be implemented using simpler and lighter networks [17]. C2 FDarkNet-53 is an efficient and fast feature extraction network. It optimizes the network structure according  
Figure 1: Network structure diagram of YOLO v8. to the design guidelines of efficient networks. Compared with ShuffleNetv2, its operating efficiency is greatly improved. The comparison of the two network models is shown in Table 1.  
As can be seen from the table, the network parameter size of C2 FDarkNet-53 is reduced by nearly 100 times compared to ShuffleNetv2, and the running time is also reduced from 14 ms to 3 ms. It can be seen that C2 FDarkNet-53 has a faster calculation speed and is more suitable for feature extraction tasks on large-scale data sets. Since the activation function needs to be embedded and calculated after each basic operation, the time cost and computational overhead of using the Mish function are relatively large, and the accuracy rate is not significantly improved. The pantograph intelligent recognition model needs to have both accuracy and running speed, so this article replaces the activation function and uses the LeakyRelu activation function, which is simple to calculate and has basically no decrease in effect, to replace the Mish function. The calculation formula of the LeakyRelu activation function is shown in Equation (1). In the formula, \(x\) is the characteristic value calculated by network convolution; the value range of \(a\) is (0, 1). \[\text{LeakyRelu}= \left\{ \begin{array}{l}x,x\geq 0\\ ax,x<0\end{array} \right. \tag{1}\]  
Since the recognition task of the pantograph skateboard condition-monitoring device has a lot of background interference and is difficult to detect, a more powerful feature extraction network is needed in the feature fusion part [18]. NAS-FPN uses NAS (Neural Architecture Search) technology to optimize the architecture of FPN (Feature Pyramid Networks). This method uses a search method instead of manually planning the feature fusion method between various scales, so it is better in terms of feature fusion effect, and the calculation speed is not seriously affected.
# Analytical Methods for Exploring Nutraceuticalticals Based on Phenolic Acids and Polypphenols
## 4 Analytical Methods for the Determination of Active Compounds in Nutraceutical Products
### Spectroscopic Methods
#### 4.1.2 Flavonoid Assays  
Apart from the indexes that provide information about the total AC, some assays focus on families of flavonoids, such as the aluminum complexation assays, or the 4-dimethylaminocinnamaldehyde (DMAC) spectrophotometric method.  
For instance, flavonoids and flavones form yellow complexes with Al(III) at neutral pH, and the absorbance of the solution is measured in the 400-430 nm range. Furthermore, Al(III) forms red complexes with some flavonoids (e.g., rutin, luteolin, and catechins) in  
Figure 2: Comparison of antioxidant indexes for the series of hydroxybenzoic acids. Data correspond to the normalized slopes of the calibration curves of each analyte and each method. Compound assignation: 3-HBA, 3-hydroxybenzoic acid; 4-HBA, 4-hydroxybenzoic acid; 2,4-DHBA, 2,4-dihydroxybenzoic acid; 3,5-DHBA, 3,5-dihydroxybenzoic acid; 2,5-DHBA, 2,5-dihydroxybenzoic acid; 2,3-DHBA, 2,3-dihydroxybenzoic acid; 3,4-DHBA, 3,4-dihydroxybenzoic acid; 2,4,6-THBA, 2,4,6-trihydroxybenzoic acid; 2,3,4-THBA, 2,3,4-trihydroxybenzoic acid; gallic acid, 3,4,5-trihydroxybenzoic acid. Method assignation: FC, Folin–Cicolateau; FRAP, ferric reducing antioxidant power; DPPH, 2,-diphenyl-1-picrylhydraz; ABTS, 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid)(adapted from reference [48]).  
the presence of sodium nitrite in an alkaline medium, and the absorbance of the solution is measured at 510 nm [49].  
Currently, DMAC is the most popular reagent for the quantification of the overall PAC content in nutraceuticals and functional foods. DMAC is an aromatic aldehyde that, under strongly acidic conditions, reacts with flavanols via the formation of a reactive electrophilic carbocation. The DMAC reaction is specific to flavanols and their gallates. DMAC reacts with the C8 position of the terminal unit to form a green chromophore with maximum absorbance at ca. 640 nm. Hence, interferences from the colored anthocyanins can be avoided [50; 51; 52; 53]. PACs with a high degree of polymerization (DP) have lower responses per unit weight than monomers and dimers [28; 50; 54].
## 3 Application in Interventional Surgical Robot Training
### Motion Analysis  
Another demonstration of the piezoresistive sensor was investigated by analyzing the displacement and velocity motion variables of the operator's hand while carrying out an interventional procedure. As basic operations are, in the broadest sense, translation and rotation navigations, the displacement values were calculated as the linear and angular distance traveled for each hand movement, while the velocity was obtained as the first derivative of the displacement with time. Motion analysis was carried out with respect to the relative resistance change during push and rotate operations (Figure 7), with displacement and velocity obtained at the robot's control knob plotted for visualization.  
Figure 7: Robotic response to the application of 3D porous tactile sensor (**a**,**b**) showing the position and velocity changes in the pushing process; (**c**,**d**) the rotation and angular velocity changes in the process of the rotation.  
The position and velocity of the robot's master control for the axial and radial operations are given in Figure 7a-d, respectively. Both the position and the velocity signals were recorded symmetrically with the relative resistance change during the push and rotate hand motions. Some inferences can be made. For instance, integration of the sensing components with the robot did not cause motion damping or an unsmooth trajectory when handling the robot. Thus, it can be concluded that sensor placements did not interfere with the procedure while operating the interventional robot for a range of different hand motions utilized for tool manipulation during intravascular catheterization procedures.
# Phenotypic, Genetic, and Metabolite Variability among Genotypes of _Vicia sativa_ L.
## 2 Materials and Methods
### V. sativa Seed Metabolomics Analysis  
The GC/EI/MS metabolomics analysis was performed using six _V. sativa_ populations, as previously described, with minor modifications [27]. Batches of seeds of each population were pulverized into a fine powder using a blender, and samples (100 mg) were weighed in Eppendorf tubes for metabolite extraction. In total, six biological replications and one quality control (QC) sample were used per population. The previously described protocols were applied with minor modifications [38; 39]. The selected solvent system was a methanol-ethyl acetate (1:1, _v/v_) mixture, 1 mL of which was added to the samples, together with 10 \(\upmu\)L of a ribitol solution in methanol (0.2 mg-mL\({}^{-1}\), Sigma-Aldrich Ltd., Steinheim, Germany). Extraction was assisted by placing the samples into an ultrasonic bath (Branson 1210, Danbury, CT, USA) for 25 min, followed by agitation for 1 h. The resulting extracts were filtered through 0.2 \(\upmu\)m in pore diameter filters (Macherey-Nagel, Duren, Germany) and evaporated to dryness with a vacuum concentrator (Concentrator Plus, Eppendorf, Hamburg, Germany). The derivatization of the dried extracts was performed in two steps as previously mentioned [39], using a methoxylamine hydrochloride solution in pyridine and N-methyl-N-(trimethyl-silyl) trifluoroacetamide (MSTFA, Macherey-Nagel, Duren, Germany). The analysis of the seeds' metabolomics was performed employing an Agilent 6890 MS (Agilent Technologies Inc., Santa Clara, CA, USA) platform equipped with a 5973 inert mass selective detector (MSD). The deconvolution of the obtained GC/EI/MS total ion chromatograms (TICs) was performed with the software AMDIS v.2.66 and the metabolite libraries of the National Institute of Standards and Technology library, NIST '2020 (NIST, Gaithersburg, MD, USA). Data preprocessing was performed using the MS DIAL v.4.80 software [40] and the discovery of trends and biomarkers of the analyzed populations was achieved using the bioinformatics software Simca v.17.0 (Sartorius AG, Goettingen, Germany) according to a previously presented pipeline [39; 41].
# Satellite Edge Computing for the Internet of Things in Aerospace
## 3 The Architecture and Application Scenarios of Satellite Edge Computing
### iSat System  
The space edge computing system consists of several iSats. iSat, as an edge computing node, uses a powerful standardized hardware platform and a fault-tolerant and expandable satellite operating system. It can load different apps according to the task requirements, providing customized services. The capabilities of iSat include and are not limited to the following:  
1. Provide a consistent operating paradigm across multiple satellite infrastructures.
2. Support large-scale, distributed space network environments.
3. Support application integration, orchestration, and migration.
4. Meet hardware resource limits and cost constraints.
5. Capable of running on confined and unstable networks.
6. Meet the needs of ultra-low latency applications.
7. On-board resources can be flexibly shared with multiple users or applications.  
The difference between iSat and traditional satellites will be illustrated from both hardware and software.