esa-sceva/satcom-chunk-collection huggingface dataset:
# Compton Scattering Imaging of Liquid Water in Porous Carbon-Based Materials
## 2 Materials and Methods  
The CSI experiment was performed with the high-energy inelastic scattering beamline, BL08W, of SPring-8. The experimental setup is shown in Figure 1. The energy of the incident X-rays was 115 keV. The incident X-ray beams were focused to 10 \(\upmu\)m in the vertical direction using a Ni compound refractive lens [26; 27] and the horizontal direction was 1 mm in the asymmetric Johan monochromator [28]. Compton scattered X-rays from the GDL material, dry or wet, were detected by the two-dimensional CdTe X-ray detector with a pixel size of 25 \(\upmu\)m and a pixel array of 80 \(\times\) 80 (HEXITEC) at a scattering angle of 90 degrees, through a W pinhole with a diameter of 50 \(\upmu\)m. A cross-sectional image was taken with an in-plane spatial resolution of 75 \(\upmu\)m. The energy spectra of the Compton scattered X-rays were measured at each CdTe pixel. The measurement time was 300 s for each image. From the measured X-ray spectra, the Compton scattering intensity was evaluated by integrating the spectra over the energy range of the Compton scattered X-rays.  
Figure 1: Experimental setup for Compton scattering imaging.  
The intensity of the Compton scattered X-rays is proportional to the effective electron density at the gauge volume. The intensity distribution on the 2D X-ray detector, \(I(x,y)\), is given by the following:  
\[I(x,y)\propto\int_{z^{\prime}-\frac{H_{z^{\prime}}}{2}}^{z^{\prime}+\frac{H_{z^ {\prime}}}{2}}\rho_{e}(x,y,z)dz=\overline{\rho_{e}}(x,y)_{z=z^{\prime}}H_{z^{ \prime}} \tag{1}\]  
where \(\rho_{e}(x,y,z)\) is three-dimensional electron density; \(H_{z^{\prime}}\) the vertical width (\(z\)-direction) of the incident X-ray beams; \(\overline{\rho_{e}}(x,y)_{z=z^{\prime}}\) is the two-dimensional, average electron density at the vertical position \(z^{\prime}\), integrated over the width of \(H_{z^{\prime}}\). In this experiment, \(H_{z^{\prime}}\) was 10 \(\upmu\)m.  
The GDL material used in this study was a porous carbon fiber and carbon composite with a Teflon treatment, TGP-H-030 (TORAY Industries, Inc., Tokyo, Japan). The size was 30 mm \(\times\) 30 mm, and the thickness was 110 \(\upmu\)m. As shown in Figure 2, a copper plate with a thickness of 500 \(\upmu\)m was placed on the GDL material, dry or wet.
## 7 Conclusions  
EC has emerged as a transformative solution for modern power systems, addressing critical challenges associated with latency, bandwidth limitations, and real-time data processing. This comprehensive review has systematically examined the state-of-the-art EC applications in energy distribution systems, highlighting its role in optimizing grid operations and enhancing overall efficiency. The study explored various EC architectures, methodologies, and integration strategies with advanced technologies such as AI, machine learning, and IoT. Additionally, it examined key technological components, including containerization, software platforms, and hardware architectures, which play a crucial role in the deployment and efficiency of EC systems. Key applications of EC, including data transmission, monitoring and control, microgrid systems, smart metering, anomaly detection, energy management, and fault detection, have been analyzed in depth. The findings suggest that EC significantly improves system resilience, reliability, and operational flexibility by enabling local data processing and reducing dependency on centralized cloud infrastructures.  
Despite these advantages, several challenges remain, such as security concerns, network optimization, and the need for scalable deployment strategies. Ensuring data privacy, developing robust security frameworks, and optimizing communication protocols are crucial for the widespread adoption of EC in smart grids. Additionally, integrating EC with emerging technologies offers opportunities to enhance security, efficiency, and automation. Future research should focus on refining EC architectures, improving interoperability between EC and CC, and addressing real-time energy management constraints.  
Conceptualization, K.C.B., E.T., Y.Y. and F.Y.; methodology, F.Y. and Y.Y.; investigation, F.Y.; resources, F.Y.; writing--original draft preparation, F.Y. and Y.Y.; writing--review and editing, F.Y., Y.Y. and K.C.B.; visualization, F.Y.; supervision, K.C.B., E.T. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
[PERSON] was employed by the Inavitas Energy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Modified Rockfall Hazard Rating System for Pakistan (RHRSP)  
#### 2.2.1 An Overview of Rockfall Activity through Animals  
Rockfall is the most studied and frequent mountain hazard worldwide, caused by various sources [35; 42]. The most common triggering agents are earthquakes, rainfall, weathering, erosion, and freezes-thaws, but animal activity at higher elevations is classified as a less common triggering agent. Although the triggering of rockfalls through animals has not been placed in the first category, its impact is observed worldwide. Its historic evidence extends back to an art book named \"Modern painters\", where the author mentions the throwing of stones from a goat that caused an injury [43]. Although this simile does not belong to the geohazard category, it proves that animals causing rockfalls were not even neglectable in historic times. In the last few decades, various researchers have included animals as the source of rockfall triggering. One thousand three hundred miles of California highways were affected by continuous rockfall events over the course of a year. For a thorough assessment of this issue, rockfall data were collected from 92 stations, and it was concluded that animal activity was responsible for initiating 0.3% of the total rockfall events [44]. Similarly, [PERSON] [45] presented a review of rockfall mechanics. Among the other rockfall causes, he included animal activity as a minor cause but an effective triggering agent. [PERSON] et al. [46] applied an integrated seismic and image data processing technique for rockfall monitoring and early warning. He had mentioned the goats' herd as a major source of seismic noise and rockfall triggering. Similarly, quantifying weather conditions for rockfall hazards, [PERSON], et al. [47] considered the animal movement on a slope as the cause of the loosening of material, hence increasing the potential of rockfall hazard.  
A study undertaken for rockfall hazard assessment along the cut slope of the Indian state highway in Maharashtra concluded that rockfalls are initiated through biological activities, including animals. This research rated animal activity and animal dens as the major cause of rockfalls [48]. The triggering of rockfalls at higher altitudes and at steep slopes is categorized as secondary rockfall triggering caused by animals and anthropogenic activities followed by earthquakes, rainfall, and erosion triggering [49]. A dataset of fatal global landslides compiled from 2004 to 2016 also concluded that animal activity is one of the triggering agents of landslides [50].  
Apart from the published literature, which mentioned rockfall events through animal activities, a bulk of evidence comes through news reports, social media, and electronic media, where personnel such as mountainers, bikers, hikers, climbers, and visitors mention their tragic interactions with rockfalls. Mountainers have continuously reported these events specifically along goat tracks at higher altitudes, which have caused injuries and fatalities.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 3 Simulation Analysis of Factors Affecting Detection Effect
### Thermodynamic Properties of Materials  
When using laser scanning thermography to detect cracks, the difference in material thermodynamic parameters will affect the detection effect, so it is necessary to explore the influence of thermodynamic parameters. Aluminum, titanium, nickel-based and iron-based  
Figure 5: Surface temperature distribution at different frames when a laser scans through a crack.  
Figure 6: Crack detection at different crack widths: (**a**) superposition results of the \(\Delta T\); (**b**) the superposition results of \(T_{G}\).  
alloys are used as model materials for the simulation analyses, and the width of crack is set to be 40 \(\upmu\)m. The laser effective power is 1200 mW, the laser scanning speed is 10 mm/s, and the thermal image acquisition frequency is 50 Hz.  
Figure 7a shows the superposition results of temperature change images of four materials under laser excitation. Figure 7b shows the superposition results of temperature gradient field of temperature change images, from which the geometric characteristics of cracks can be clearly observed. \(T_{G}\) represents the gradient value of the temperature change field. Due to the scanning excitation of the circular Gaussian heat source, \(T_{G}\) at the crack also satisfies the Gaussian distribution. For several materials with different thermodynamic parameters, the distribution and amplitude of \(T_{G}\) are obviously different.  
The results show that the thermal conductivity \(\lambda\) is the main thermodynamic parameter that affects the thermal response [34]. Among the four materials, aluminum alloy has the largest thermal conductivity, so it has the fastest thermal diffusion under heat excitation, and the smallest surface temperature and temperature gradient amplitude. Although this is not conducive to highlighting the edge features of cracks, it is conducive to expanding the detection range of cracks. Titanium alloy material has the minimum thermal conductivity \(\lambda\), the diffusion rate of thermal wave is slow, and the amplitude of surface temperature rise and thermal gradient caused by heat source excitation is the largest, but the heat affected zone is small, so it is necessary to reduce the scan line spacing to ensure the integrity of defect detection. Due to the similar thermal diffusivity of nickel-based and iron-based alloys [35], the characteristics of temperature change field of the two materials are similar under circular Gaussian heat source excitation, and the difference in crack detection is small [36].
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### AI-Enabled Interference Mitigation
#### 4.4.5 Standardization Challenges and Open Issues  
While AI-based frameworks hold significant promise for dynamic and intelligent IM, their deployment in real-world B5G networks faces a number of standardization and technical challenges. One of the most prominent issues is the integration of FL into the RAN. Although FL offers privacy-preserving benefits by avoiding centralized data aggregation, it struggles with non-IID data across edge devices, which can hinder model convergence and stability [159]. Furthermore, frequent model updates in FL incur communication overhead and require secure aggregation protocols, which are still under investigation in 3 GPP Release 18 study items [204].  
Figure 8: Main issues related to IM and their promising solutions according to 3 GPP.  
Another major limitation lies in the lack of explainability and interpretability of AI models, particularly deep neural networks. These models often behave like black boxes, making it difficult for network operators to verify decisions related to IPR, RA, or POC. This undermines trust and raises regulatory compliance concerns in mission-critical applications [162].  
Moreover, deploying AI at the network edge introduces latency and hardware limitations. Many IM decisions such as POC or dynamic scheduling require sub-millisecond response times, which can be difficult to achieve with existing AI inference frameworks on mobile devices or edge servers. Efforts to compress and optimize AI models, such as pruning and quantization, are being explored but are not yet standardized within 3 GPP [158].  
Lastly, cross-vendor interoperability remains an open issue. AI modules deployed across multi-vendor networks must be interoperable and compatible with diverse hardware and software implementations. 3 GPP and the open RAN alliance are currently working toward standardized interfaces and common ML frameworks, but trust frameworks, model verification, and lifecycle management remain active areas of research and standardization [185].  
Addressing these challenges is essential to ensure that AI-based IM techniques can transition from controlled simulations to scalable, trustworthy, and production-grade deployment in B5G and future 6G networks.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis
### Comparison with Other Existing Models
Figure 10b shows that the benchmark models have a large MAE from 12 a.m. to 3 p.m., indicating that they struggle to accurately forecast the minimum electricity load. In contrast, Prophet-CEEMDAN-ARBiLSTM demonstrates lower MAE values during this period. The reason is that the Prophet and CEEMDAM method effectively decomposes the components that capture the load variation. Additionally, the ARBiLSTM can learn temporal features affecting changes in load data. Figure 10c shows that Prophet-LSTM and Prophet-GRU exhibit significant MAE from 11 a.m. to 5 p.m. The reason is that the features extracted by Prophet cannot accurately learn the change trend of the electricity load. Compared with these models, the proposed model and Prophet-EEMD-LSTM can produce more accurate forecast results. Figure 10d demonstrates that Prophet-GRU produces a large forecasting error at 3 p.m., indicating the challenge in forecasting the maximum load. In summary, Prophet-CEEMDAN-ARBiLSTM achieves a low MAE. It indicates that Prophet-CEEMDAN-ARBiLSTM can effectively forecast the electricity load data of different seasons. Figure 11 shows that the proposed method effectively fits actual electricity load data on the weekdays and weekends. These experiments show that Prophet-CEEMDAN-ARBiLSTM outperforms other models during various time periods, highlighting its high generalization capability. Figure 10: Forecast error comparison of the methods in different seasons. (**a**) Spring. (**b**) Summer. (**c**) Autumn. (**d**) Winter.
## 2 Materials and Methods
### VRFB Performance Tests  
The evaluation of the charging-discharging performance of the VRFB was performed using a lab-made RFB unit cell. A 2.0 M V\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)/2.0 M H\({}_{2}\)SO\({}_{4}\) aqueous solution was used as the cathode electrolyte, and a 2.0 M VOSO\({}_{4}\)/2.0 M H\({}_{2}\)SO\({}_{4}\) aqueous solution was employed as the anode electrolyte. Carbon felt (GF20-3, Nippon Graphite) was used as the electrode, and the effective area of the electrode and membrane was 12.5 cm\({}^{2}\). Using an automatic battery cycler (WBCS 3000, Wonatech), it was charged to 1.9 V at a current density of 20 mA/cm\({}^{2}\) and then discharged to 0.9 V. Coulombic efficiency (CE), VE, and energy efficiency (EE) for charging-discharging performance evaluation were calculated through the following Equations (9)-(11), respectively.  
\[\mathrm{CE}=\frac{\mathrm{Discharge\ capacity\ (Ah)}}{\mathrm{ Charge\ capacity\ (Ah)}}\times 100\ [\%] \tag{9}\]  
\[\mathrm{VE}=\frac{\mathrm{Average\ discharge\ voltage\ (V)}}{\mathrm{ Average\ charge\ voltage\ (V)}}\times 100\ [\%] \tag{10}\]  
\[\mathrm{EE}=\mathrm{CE}\times\mathrm{VE}\ [\%] \tag{11}\]
# Microplastic Pollution on the Beaches of the Black Sea in Romania and Bulgaria
###### Abstract  
Microplastic pollution has gained attention in recent years due to its adverse impact on the environment. As a major threat to marine ecosystems and biota, the accumulation of microplastics along coastlines has become a growing concern. This study focused on quantifying and characterizing the presence, distribution, and composition of microplastics along the beaches of Romania and Bulgaria. Microplastics were extracted from beach sand samples using a saturated NaCl solution. The particles were then analyzed through FT-IR and DSC spectral analyses to identify their chemical composition. Sampling was conducted across several resorts along the Romanian and Bulgarian coastlines. The findings revealed varying concentrations of microplastics across different beaches, with Romanian beaches showing concentrations of between 40 and 213 particles per sample (470-2500 microplastics/kg), which were notably higher in areas like Mamaia and Costinesti. On Bulgarian beaches, the average concentrations reached up to 137 particles per sample (1612 microplastics/kg), particularly in areas like Sunny Beach and Nessessbar. Polyethylene (PE) was identified as the most prevalent polymer (55%), followed by polyamide (PA), polypropylene (PP), polyethylene terephthalate (PET), and polyurethane (PU). These polymers were linked to common sources such as packaging, textiles, and industrial products. Microscopic examination, combined with FT-IR and DSC spectral analysis, confirmed the plastic nature of the particles, revealing distinct chemical structures characteristic of each material type. This study underscores the widespread contamination of Romanian and Bulgarian beaches with microplastics, emphasizing the environmental risks to coastal ecosystems. The presence of synthetic polymers highlights the urgent need for policies targeting plastic waste management to mitigate the growing pollution in marine environments.  
microplastic; pollution; sand; Black Sea; Romania; Bulgaria; statistics +
Footnote †: journal: Applied sciences
# Asymmetry Propagation in a Pipe Flow Downstream of a 90\({}^{\circ}\) Sharp Sarp Elbow Bend
## 3 Results
### Asymmetry in Mean Velocity Profiles  
While WSS effectively indicates flow asymmetry in the near-wall region, it offers no information on the flow behaviour in the core (bulk) flow region of the pipe. Thus, we examined the flow asymmetry of different time-averaged flow fields in radial profiles, which crossed the centre of the pipe in two perpendicular directions: the y-profile (perpendicular to the elbow plane) and z-profile (parallel to the elbow plane). For fully developed flow in long straight pipes, both of these profiles should be symmetric, and the asymmetry parameter should approach zero with the increased duration of time-averaging. Thus, it is important to show that the observed asymmetry is indeed the effect of the pipe bending and not an artefact of short time-averaging or any other reason.  
Figure 10 shows the calculated asymmetry parameter \(\alpha_{U}\) of the time-averaged streamwise velocity component as a function of axial location \(x\) downstream of the elbow for three different \(Re\). The blue and green curves are calculated for y- and z-profiles, respectively, while the red curve is calculated in the velocity recycling part of the inlet section, which corresponds to an \"infinite\" straight pipe and serves as a threshold indicator when the asymmetry parameter reaches the value typical for an axisymmetric flow. The recycling part section measures only 5 D in length, leading to the results being periodically extended in the plots of Figure 10. They serve merely as an indicator of the anticipated outcome under symmetric flow conditions, given the specified duration of time-averaging. It should also be noted that while this value (red curve) should be zero in the laminar case, interpolation and rounding errors result in a very low nonzero value. The results clearly showed that the asymmetry propagates longer in the flow with lower \(Re\). In fact, for the transitional case, the 100 D long pipe was too short for the flow recovery into a symmetric flow. For the two turbulent cases, the recovery length is about 20-40 D at \(Re=5600\) and 10-15 D at \(Re=10,000\). The exact recovery length depends also on profile direction, i.e., it is systematically shorter for the y-profiles than for the z-profiles. The reason for that is the symmetry of the entire computational domain. Namely, the inlet pipe points in z-direction, which means that the entire system exhibits reflection symmetry in y-direction but not in the z-direction.
## 2 Materials and Methods
### Validation Work  
A rectangular prismatic-shaped tank that creates horizontal oscillating movement was chosen from a study examined in the literature to validate. The validation study was conducted by comparing this study's results with the model we built. The theoretical results of [8] and the experimental results of [43] used by [8] were examined to validate the sloshing event using the CFD method. The reason for choosing the mentioned study is that the horizontal oscillating motion was given as the excitation in a prismatic tank in the study. This working condition was similar to our study. Additionally, the results of previous experimental studies were also examined comparatively.  
Figure 1 shows the schematic representation of the rectangular prism tank, dimensions, and pressure measurement points. It had a thin baffle in the middle, and the liquid level was 70% of the tank height. As the input value, sinusoidal position change with amplitude of 0.02 m and an angular frequency of 5.81 rad/s was defined. For verification, solutions were derived for cases without a baffle, and the ratio of the baffle height to the liquid level was 0.4. The results for the pressure points PT1 and PT4 were evaluated. The curves named \"CFD analysis results, theoretical\" show the results of the analyses made within our validation study's scope. The comparative results for the PT1 and PT4 pressure points for the unbaffled state of [8] are shown in Figure 2a,b, respectively. In Figure 2c, the comparison results of [8] are shown for the PT1 point with other experimental studies. As seen in Figure 2a, the pressure values obtained in our analysis remained low according to the results of [8]. However, as shown in Figure 2c, the analysis was approximate compared to the experimental results of [43]. In Figure 3, the pressure-time graphs of the comparative study are presented for the points PT2, PT3, and PT4 for the system with a baffle placed in the middle of the tank, for the case where the ratio of baffle height/liquid level is 0.4.  
Since the liquid did not reach PT1 at this baffle height, there was no pressure change at the relevant point. The analysis with the baffled structure also gave very close results, as seen from the theoretical pressure-time curves.  
Figure 1: Schematic representation of the tank system used in the study of [8].  
Figure 2: Comparative results for pressure–time curves of the study of the unbaffled state in [8]. (a) PT1, (b) PT4, (c) pressure–time graphs of the experimental studies referenced in [8].