esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Big Data Archives in Time-Domain Astronomy
## 2 Big Data in Astronomical Domain
### The Four Vs of Big Data  
The four Vs of big data (volume, velocity, variety, and value) are the pillars of this concept [16]. They are explained as follows:  
1. **Volume:** The quantity of data is known as the volume. The data are described in terms of terabytes, petabytes, and even exabytes. Consequently, the collection, cleaning, curation, integration, storage, processing, indexing, search, sharing, transfer, mining, analysis, and visualization of large amounts of data are complicated by the quantity of data [10]. There are too many data for current technology to handle them effectively. There is an avalanche of data in astronomy generated by a number of earth- and space-based broad astronomical observations.
2. **Variety:** Variety is an indicator of data complexity. Structured, semi-structured, unstructured, and mixed data are all types of data [17]. Images, spectra, time series, and simulations constitute a large amount of astronomical data. Catalogs and databases contain the vast majority of information. This complicates data integration from different telescopes and projects because the data are stored in different formats. The high dimensionality problem is exacerbated because each piece of data has thousands or more features.
3. **Velocity:** The term \"velocity\" represents the rate of creation, communication, and analysis of data. For ten years, The Rubin Observatory Legacy Survey of Space and Time (LSST) [18] generated one Sloan Digital Sky Survey (SDSS) [15] of data volume one night [19]. It is necessary to analyze data in a bundle, stream, near-instantaneous, or instantaneous setting. The LSST expects to discover a thousand additional supernova explosions every night for the next ten years. Scientists have a significant challenge in determining how to mine, correctly identify, and target supernova prospects in ten years.
4. **Value:** Discovering new and unusual astronomical objects and events is a challenge that has inspired and exhilarated scientists. Therefore, spotting a new pattern or law in the data distribution is valuable. The term \"value\" refers to the enormous value of the data [10].  
Optical time-domain astronomy is nearing a tipping point in terms of the data rate and volume. By 2023, the amount of data is expected to grow by a factor of three. As the number of recognized sources increases, it is necessary to create efficient and well-designed databases. To successfully manage these data, highly efficient machine learning algorithms for categorizing source types are required [20].
## 4 Results and Discussion
### Potentiostatic Tests  
#### 4.2.1 Epoxy Impregnated Carbon (E4)  
No optical changes could be detected on the carbon textile E4 after the potentiostatic polarization at level 1. The epoxy resin impregnation remained even, smooth and shiny. The specimen of level 2 shows isolated areas on which the surface is mat and rough and the structure of the carbon textile is recognizable as well as white deposits indicating the chemical bonds between carbon, calcium and oxygen. These deposits become more significant with increasing polarization, which can be seen at Figure 7 right at level 3 of potentiostatic polarization.  
Figure 8 shows the SEM images of the carbon fibre E4 without polarization and after the polarization. No pores can be seen on the entire reference specimen (Figure 8a) in contrast to previous findings [12]. There were only a few of them, as well as defects. Apart from impurities and a mechanical crack, the surface is smooth due to the sample preparation. In some places the carbon fibres shine through the impregnation. The epoxy resin layer does not appear to be uniformly thick. Epoxy resin was used in various test series to interrupt conductivity [10; 13; 14; 15; 16]. Van Nguyen et al. [17] have found that the embedding of carbon anodes with epoxy resin was not successful due to insufficient conductivity.  
Figure 5: SEM images of unpolarized carbon fibres (**a**) and a polarized carbon fibre (**b**).  
Figure 6: EDX analysis of the deposited film.  
Figure 8: SEM images of E4: unpolarized reference (**a**), after polarization at level 1 (**b**), level 2 (**c**) and level 3 (**d**).  
Figure 7: From left to right: Anodes E4 of levels 1 to 3 after polarization.  
Due to the pores, defects and the different thickness of the impregnation, it can be explained why the epoxy resin-impregnated carbon fibres are polarizable at all. The potentiostatic test at level 1 did not cause any change in the surface (Figure 8b). The image of the specimen polarized at level 2 (Figure 8c) shows grooves next to two mechanical cracks and is covered approximately in half with a crystalline deposited film. Calcium, oxygen and carbon could be detected in the coating by EDX analysis. The grooves run along the carbon fibres over which the epoxy resin impregnation is probably thinnest and are due to the destruction of the impregnation. Pores have occasionally formed in the epoxy resin unlike the accumulation of pores in Asgharzadeh et al. [12]. As with the unimpregnated carbon fibres, for the carbon fibres with epoxy resin impregnation destruction in panel area 2 is used, whereas no destruction was observed in panel area 1. Thus, the start of the destruction of the epoxy resin matrix could again be in the transition range between about 1050 and 1150 mV.  
The specimen of the polarization at level 3 (Figure 8d) is almost completely covered with the crystalline deposited film. There are undamaged areas that are not covered with deposited film. This and individual damages between the film indicate that most damages are obstructed by the film. Hence, it can be assumed that the damage is more pronounced than at level 2.
## 5 Comparing Strategies with Protocols  
As illustrated in Figure 3, we find that the proactive strategies AODV and FFA generally record the best performance in latency because the path search is performed offline and is separated from the data transmission phase. The MCT performance is lower because the hierarchical tree is only formed for the nodes to be aware of the sink location. However, the data routing phase is still performed in a contentious manner among neighboring nodes, which initiates more back-off in the process of data routing. The passive strategy (MCF) records the highest latency because much of the flooding nature of the algorithm makes channel access contention a significant factor in addition to the PU activity. The reactive strategies record moderate latency performance, with SCA recording the best, even comparable to the reactive strategies. This performance is achieved because the reinforcement learning module quickly adjusts to the PU environment in an online mode.  
Generally, as shown in Figure 4, the throughput decreases with increasing PU activity. The passive strategy (MCF) exhibits the best performance in this regard due to the number of data duplicates injected into the network. The proactive strategies MCT and AODV display a moderate performance, with FFA having the lowest throughput. In general, the online strategies RTLD and FFT exhibit the best stable time performance. FFA has the worst throughput in this regard because the time required to service the route failures impacts the throughput.  
Figure 3: Latency performance of all protocols _versus_ primary user activity.  
With regard to the quality of the protocol (Figure 5) with medium PU activity, most of the protocols display stable performance of the loss rate, with SCA showing the highest loss rate. However, this loss is drastically reduced at a high PU activity of 40%-60% because of its online reinforcement learning ant strategy. This result is contrary to the general trend of increasing loss rate with increasing PU activity exhibited by all of the protocols. Overall, the proactive AODV protocol shows the lowest loss rate. RTLD contends with increased online activity as PU activity increases, increasing the loss rate.  
In Figure 6, the protocols that achieve general network success in such dynamic networks are those that inject more duplicate data: MCF and FFT. However, the AODV strategy still performs better than others. This approach is comparable to MCF and FFT, with the advantage of reducing duplicate packets in the network.  
The proactive strategies AODV and MCT generally exhibit the best energy efficiency (Figure 7). The poor performance of MCF, FFT and FFA is due to the increased data processing, whereas online decisions and activity are responsible for the poor performance of SCA.  
Figure 4: Throughput performance of all protocols _versus_ primary user activity.  
Figure 5: Loss rate of all protocols _versus_ primary user activity.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 3 Results and Discussion  
### Green Biosynthesis of IONPs  
The aqueous extract derived from the leaves of _L. noblis_ was employed in the environmentally friendly synthesis of iron oxide nanoparticles (IONPs), as depicted in Figure 1. The plant extract derived from _L. noblis_ demonstrates the capability to function as a reducing agent for the ferric nitrate solution, leading to the formation of IONPs. Figure 1A illustrates the utilization of _L. noblis_ leaf extract as a reducing agent for the ferric nitrate solution (Figure 1B), resulting in a change from a yellowish-orange color to the formation of dark brown-colored IONPs (Figure 1C). In this particular context, it has been reported that the aqueous extract derived from the leaves of _L. nobilis_ contains a diverse range of bioactive constituents, such as phenolic compounds, alkaloids, saponins, flavonoids, tannins, glucosides, steroids, and proteins [42]. The generation of iron nanoparticles is ascribed to the influence exerted by these phytochemicals [43]. The aqueous leaf extract fulfills a dual role in the process of nanoparticle synthesis. Initially, it serves as a reducing agent for the solution of ferric nitrate, thereby facilitating the formation of IONPs. Additionally, it functions as a stabilizing agent, hindering the aggregation of the synthesized nanoparticles [44]. Various techniques were used to assess the physicochemical characteristics of the biosynthesized IONPs.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 4 Discussion
### Technological Progress Influence on the Growth of Cultivated Land Productivity  
Technological progress plays a crucial role in promoting the steady growth of cultivated land productivity [72]. Continuous innovation in agricultural science and technology, including the application of precision agriculture technology, popularization of efficient water-saving irrigation systems, and promotion of excellent crop varieties, has generated substantial production potential to cultivated lands. These technologies have significantly increased the yield per unit area of crops, enhanced soil fertility management, and reduced the occurrence of diseases and pests, thus ensuring a stable supply and improvement in food and agricultural production quality [73].
## 1 Introduction
As the requirement for high data-transfer rates grows, it is much more critical now to test and validate these interfaces [8]. In particular, this work focuses on testing the application with high-speed serial interfaces. The work is motivated by the experience of developing complex multiprocessor-based embedded system where the most challenging aspect of the implementation remains the testing and debugging of high-speed synchronous serial interfaces with multiple seen or unseen factors affecting the communication. Real-time embedded systems face these types of problems in many domains, such as in radar applications [9], ultrasound systems [10], software-defined radios [11], automotive electronic control unit applications [12], etc., where high-speed processing is required in a real time system. Our novel framework is based on the methodology of creating a knowledge base through simulation. This knowledge base is then used for direct identification or zeroing of bugs that may arise due to issues of timing and synchronization of the interfaces. We applied the framework to SDR for testing. The case study results are presented, showing the testing using a multi-instance simulator. The results show the appropriateness of our approach for finding and repairing bugs that are otherwise quite difficult to identify and correct. The rest of this paper is organized in the following manner. An overview of high-speed interfaces and related work is presented in Section 2. The proposed framework and implementation details are discussed in Section 3. In Section 4, we present the results and explain the validity of the approach. We explain the SDR case study in detail in Section 5. Section 6 presents the discussion. Lastly, Section 7 concludes the paper.
# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 3 Results
### Comparison with Peer Methods  
To evaluate the performance of GraphEPN, we compared it with several mainstream B-cell conformational epitope prediction methods, including SEMA 2.0, BepiPred 3.0, Ellipro, and SEPPA 3.0. These methods are widely used in the field of epitope prediction, each with its strengths, providing valuable benchmarks for comparison. The evaluation metrics included the AUC, AUPRC, F1-score, MCC, and BACC, with the AUC and AUPRC serving as the primary metrics.  
SEMA 2.0 is an advanced web platform for predicting conformational B-cell epitopes via large pre-trained protein language models (PLMs). It integrates sequence-based and structure-based prediction, identifies N-glycosylation sites, and compares antigen structures for enhanced immunogenic analysis [33].  
BepiPred-3.0 is a sequence-based B-cell epitope prediction tool that uses protein language model embeddings (ESM-2) to improve the prediction accuracy for both linear and conformational epitopes. It also incorporates additional input variables and a refined epitope annotation strategy for enhanced performance [34].  
ElliPro is a web tool for predicting antibody epitopes based on the geometric properties of protein structures that uses residue clustering and visualization tools [4].  
SEPPA 3.0 is an advanced tool for predicting B-cell epitopes that integrates glycosylation-related features and microenvironmental information to increase the prediction accuracy for glycoprotein antigens [35].  
Figure 2: Performance evaluation of the GraphEPN model. (**a**) ROC curves of 5-fold cross-validation for GraphEPN. (**b**) AUPRC curves of 5-fold cross-validation for GraphEPN. (**c**) Comparison of the ROC curves between the GraphEPN and peer methods. (**d**) Comparison of AUPRC curves between GraphEPN and peer methods.  
In our comparative analysis, we evaluated the performance of each method using the independent Blind_42 test dataset, calculating the AUC and AUPRC scores. The optimal thresholds were selected based on precision-recall curves, and additional metrics such as the F1-score, MCC, and BACC were computed. Figure S3 presents a comprehensive heatmap of all evaluation metrics for each method, visualizing the comparative performance and highlighting GraphEPN's consistent superiority across multiple metrics. As shown in Table 2, GraphEPN outperforms the majority of the peer methods across several key evaluation metrics. Specifically, it demonstrates superior performance in AUC (Figure 2c) and AUPRC (Figure 2d), underscoring its exceptional generalization capacity and robustness.
## 3 Results
### Mechanical Properties of Grinding Wheels  
In order to see the influence of the different additives, tensile tests were carried out. Figure 5 displays the average mechanical properties of the printed samples. As seen in this figure, the pure resin has a strength of around 30 MPa. By adding the abrasive grains (SiC), an insignificant change in strength value was determined. The addition of glass fibers (GFs) with a mass fraction of 1.2 w% to the resin-abrasives composite (TS-2) led to an increase of over 50% in strength. The increase probably comes from the significantly higher tensile strength of the glass fibers with a high aspect ratio by which polymer-fiber entanglement happens, thereby improving the load-bearing ability of the composite. In contrast, when adding carbon fiber (CF), the strength decreased by over 10% (0.6 w% CF) despite its higher tensile strength, which could be attributed to the different utilized fiber lengths. [PERSON] et al. [21] explained the reduction in strength for compounds with shorter fibers: the short fibers detach more easily from the matrix than the long fibers, which leads to failure at low stresses. Adding 5.5 w% of copper led to an increase in strength of roughly 15% compared to TS-1. The main reason for this result is the strengthening effect of particles caused by the existence of copper particles preventing the mobility of the polymeric chains during the tensile test, by which the load needed for sample breakage is enhanced.  
In addition to the tensile strength, the modulus of elasticity was determined from the stress-strain curve. The modulus of elasticity is displayed in Figure 6. The pure resin shows a modulus of elasticity of 1017 MPa. By adding abrasive grains (SiC) 170% increase in the elastic modulus value was observed. This is probably due to the high elastic modulus of SiC. Furthermore, abrasive grains can serve as a barrier to material flow, leading to increased stress concentration and a higher probability of crack initiation between the bond and grain, resulting in reduced tensile strain. The addition of glass fibers with a mass fraction of 1.2 w% led to a reduction in the modulus of elasticity of 15% in comparison with the PF/SiC compound. The addition of carbon fibers with 0.6 w% content led to an increase of 17%. The E-modulus was decreased with the addition of 5.5 w% copper by 5% less than the PF/SiC modulus of elasticity. After adding additives to the resin-abrasives mixture, the modulus of elasticity barely changed. [PERSON] et al. [18] explained this phenomenon as a result of the non-homogenous distribution of particles in the resin matrix.  
Figure 5: Influence of the additives on the tensile strength.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Anticipated Cooperative Collision Avoidance (ACCA)
#### 3.3.2 Telco Requirements  
The telecommunications operator infrastructure is required to provide MEC functionalities, making possible the execution of standardized ITS geo-positioning and signaling services with direct connection to the base station. In addition, the infrastructure must provide service guarantees to a cloud-based ITS system, provided by a third party such as a road or infrastructure operator. Such interfacing is required to coordinate and distribute detected hazards across different MEC located geoservers. Functionalities such as slicing should be used to guarantee the interconnection between the cloud infrastructure at the ITS provider, so that the delay and reliability requirements are met. These slices should consider cross-border infrastructures through internet exchange points.  
The MEC capability should support virtualization in order to enable extension and coexistence of different geoservice solutions without implying any change to the operator infrastructure.  
In a cross-border situation, where partial information is handled by the geoservices running at the different MECs hosted by different MNOs, it is crucial to enable efficient information distribution and exchange. In this case, the 5G network infrastructure plays a fundamental role in order to handle interconnection between MNOs.
## 5 Discussion  
This study investigates the efficacy of an SHM scheme in identifying the failure mechanisms of U-shaped jacketing and detecting damages in retrofitted RC beams. The assessment entails the implementation of advanced structural technologies based on the EMI method facilitated by PZT sensors. Additionally, the statistical metric RMSD was utilized to analyze the collected data comprehensively, quantify the extent of damage, and assess the efficacy of the SHM method.  
Figure 27: (**a**) Cracking at failure and “X” PZTs’ notation and position for “B200b-J”, and (**b**) RMSD index values for “X” of “B200b-J”.  
Furthermore, the study seeks to evaluate the effectiveness of the PZT-enabled SHM system in detecting variations in the structural integrity of an existing Reinforced Concrete (RC) element. This was achieved by strategically installing PZT transducers in various configurations across the examined RC elements. By analyzing a large set of measurements collected from the multiple deployed PZTs, the study aims to ensure the reliability and accuracy of the proposed monitoring method. The diversity in sensor placements and configurations provides a robust dataset, thereby enhancing the system's capacity to identify and assess any structural alterations. To the authors' best knowledge, there is no similar experimental project investigating the effectiveness of retrofitting schemes by implementing advanced structural monitoring technologies.
###### Abstract  
Increasing reported works identify that drones could and should be sufficiently utilized to work as aerial base stations in the upcoming 6G aerial vehicular networks, for providing emergency communication and flexible coverage. Objectively, light-emitting diode (LED) based lighting devices are ubiquitously integrated into these commercially available drone platforms for the general purposes of illumination and indication. Impresively, for further enhancing and diversifying the wireless air interface capability of the above 6G aerial vehicular networks, the solid-state light emitter, especially LED-based visible light communication (VLC) technologies, is increasingly introduced and explored in the rapidly developing drone communications. However, the emerging investigation dimension of spatial light beam is still waiting for essential research attention for the LED-based drone VLC. Up to now, to the best of our knowledge, almost all LED-based drone VLC schemes are still limited to conventional Lambertian LED beam configuration and objectively reject these technical possibilities and potential value of drone VLC schemes with distinct non-Lambertian LED beam configurations. The core contribution of the study is overcoming the existing limitation of the current rigid Lambertian beam use, and comparatively investigating the performance of drone VLC with non-Lambertian LED beam configurations for future 6G aerial vehicular networks. Objectively, this work opens a novel research dimension and provides a series of valuable research opportunities for the community of drone VLC. Numerical results demonstrate that, for a typical drone VLC scenario, compared with about 6.40 Bits/J/Hz energy efficiency of drone VLC based on the baseline Lambertian LED beam configuration with the same emitted power, up to about 15.64 Bits/J/Hz energy efficiency could be provided by the studied drone VLC with a distinct non-Lambertian LED beam configuration. These results show that the spatial LED beam dimension should be further elaborately explored and utilized to derive more performance improvement of the 6G aerial vehicular networks oriented drone VLC.  
d 2025 15, 5835 +
Footnote †: journal: Applied Sciences  
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