esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Thus, the communication rate can be maintained above 1 Gbps all the time. We compare the cross-correlation algorithm in [36] with the method in this paper, as shown in Figure 11.The simulation results show that under the SINR, modulation and coding rate, the cross-correlation algorithm has poor performance in the frequency offset estimation and compensation, so the communication rate is low. Compared with the perfect frequency offset compensation, the simulation results show that under the proposed synchronization scheme, the communication rate will fluctuate slightly, as shown in the enlarged part of the red circle in Figure 11, which is caused by the frequency offset estimation error. The overall communication rate can still be maintained above 1 Gbps. Figure 9: SINR for link between satellite 2 and IoT terminal versus time.
## 3 Methods
### Fractal Dimension  
The mathematician [PERSON] conducted research on objective phenomena by utilizing the concept of fractal dimension. He proposed the theory of fractal geometry to quantify the degree of irregularity and self-similarity observed in intricate porous materials. Prior research has established that reservoir rocks possess fractal characteristics within their intricate pore systems [27; 28]. The fractal dimension of the pore structure ranges from 2 to 3 in a three-dimensional Euclidean space. A higher fractal dimension value, approaching 2, is indicative of enhanced pore homogeneity, a more refined pore-throat surface, and improved reservoir performance of the rock. On the contrary, an augmentation in fractal dimension yields heightened heterogeneity, a coarse pore-throat surface, and degradation of rock physical properties [29; 30]. Based on this characteristic, [PERSON] et al. conducted a comprehensive study on the pore characteristics of various rock samples [31]. They derived an approximate fractal geometry formula for nuclear magnetic resonance \(T_{2}\) spectra and calculated the fractal dimension of rock pore structures using a piecewise regression method. The calculation formula is as follows:  
\[S_{V}=\left(\frac{T_{2 max}}{T_{2}}\right)^{D-3} \tag{1}\]  
After taking logarithms from both sides, we can obtain:  
\[\text{lg}(S_{V})=(3-D)\text{lg}(T_{2})+(D-3)\text{lg}(T_{2 max}) \tag{2}\]where \(S_{V}\) is the percentage of the micro-nanopores' cumulative pore volume corresponding to pores with a relaxation time less than \(T_{2}\) in total pore volume; \(T_{2 max}\) is the maximum relaxation time; and \(D\) is the fractal dimension.  
Equation (2) illustrates a linear relationship if the pores present in the samples demonstrate a self-similar pore structure and exhibit fractal characteristics. The fractal dimension of the rock pore structure can be determined by solving for the coefficient in the regression equation.
# Nanoscale Topographies for Corneal Endothelial Regeneration
## 3 The Impact of Bio-Instructive Nanosubstrates for Culture on Corneal Endothelial Cells  
Intuitively, recreating the native Descemet's membrane structure is one of the most effective strategies to generate nanotopographies that can provide essential biophysical cues for the expansion of endothelial cells and maintenance of their phenotype. Since a direct replication of the native tissue, for instance via polymer casting on ex vivo tissue, could be technically complex to accomplish, [PERSON] and coworkers recreated Descemet's membrane-like topographies by a two-step process [44]. Firstly, rabbit [PERSON]'s membrane topography was obtained by peeling and decellularization using ethylenediaminetetraacetic acid (EDTA) and subsequently scanned using confocal microscopy; this process highlighted the hexagonal structure of the [PERSON]'s membrane with irregularly shaped honeycomb features having maximum depressions of 1 \(\upmu\)m and lateral size of 20 \(\upmu\)m. These topographical data were, thereafter, used to fabricate polydimethylsiloxane (PDMS) substrates with micro- and nanofeatures by two-photon lithography followed by soft-lithography. In this study, the authors demonstrated that human mesenchymal stromal cells (hMSCs), isolated from skin, could be differentiated towards an endothelial phenotype when cultured on such Descemet's membrane-like substrates using Dulbecco Modified Eagle Medium with 4.5 g/L glucose, glutamine without pyruvate (DMEM) supplemented with fetal bovine serum. These cells were able to grow in a confluent monolayer, exhibiting the characteristic polygonal morphology of hCECs, in contrast to the fibrobl morphology typical of MSCs. The cells cultured on [PERSON]'s membrane replicas also expressed key markers of hCECs such as ZO-1, Na+/K+-ATPase, both at protein and mRNA level. The additional use of collagen coating on PDMS Descemet's membrane-like structures (collagen also supports hCECs in the native Descemet's membrane) induced further expression of hCEC-specific markers such as _COL-8A2_ and _PITX2_, therefore showing enhanced control over the cell fate. The possibility to efficiently switch from mesenchymal to endothelial-like cell phenotype using physical factors underlines the correlation of cell phenotype with the physicochemical microenvironment. Such phenotypical transition caused by nanotopographies can be considered remarkable if compared to the opposing undesired endothelial-to-mesenchymal transition generally occurring when attempting to expand primary hCECs on conventional 2D cell culture substrates. In clinical applications, the reversal from a pathological to a physiological hCECs phenotype is also observed in procedures such as [PERSON]'s membrane transfer (DMT) [69], where a replacement of the [PERSON]'s membrane is sufficient to induce the regeneration of host hCECs in vivo,
## 4 Experiments
### Datasets  
We used the following nine datasets as the client dataset and ImageNet [47] as the reference dataset. Table 2 provides an overview of the dataset statistics, detailing the number of samples used for training, validation, and testing, along with the number of classes associated with each dataset.  
* Cars [48]: The Stanford cars dataset is designed for fine-grained vehicle classification. It comprises a diverse collection of car images that capture subtle differences between car makes and models.
* DTD [49]: The describing textures in the wild (DTD) dataset focuses on texture recognition. It contains images representing a wide variety of textures found in natural and artificial environments.
* EuroSAT [50]: EuroSAT is a remote sensing dataset derived from Sentinel-2 satellite imagery. It provides geo-referenced images covering a broad range of land use and cover types.
* GTSRB [51]: The German Traffic Sign Recognition Benchmark (GTSRB) dataset contains images of traffic signs collected from real-world driving environments.
* KITTI [52]: The KITTI dataset is extensively used in autonomous driving research. It includes diverse driving scenes captured in urban and suburban settings and provides data from multiple sensors such as cameras and light detection and ranging. The dataset supports multiple objectives such as object detection, object tracking, and stereo vision tasks.
* MNIST [53]: The MNIST dataset is a well-known benchmark for handwritten digit recognition consisting of grayscale images of handwritten numbers (0-9).
* RESISC45 [54]: RESISC45 is a remote sensing imagery dataset designed for scene recognition tasks. It encompasses many geographic scenes, providing a solid foundation for developing and assessing algorithms focused on scene understanding and classification in remote sensing imagery.  
* SUN397 [55]: The SUN397 dataset contains an extensive set of scene imagery covering a wide range of indoor and outdoor settings. It is commonly applied to scene recognition, providing diverse scene categories to benchmark the effectiveness of models in scene interpretation tasks.
* SVHN [56]: The street view house numbers (SVHN) dataset contains digit samples collected from real-world street view images. It is primarily used for digit recognition tasks in natural contexts with challenges such as varying backgrounds, illumination, and perspectives.
* ImageNet [47]: ImageNet is a widely used visual recognition dataset, providing millions of labeled images spanning numerous object classes.  
Each dataset offers unique challenges and is suited to different applications, such as computer vision, remote sensing, and autonomous driving.
## 4 Results and Discussion
### Bulk Density and Foamability  
The calculated values of bulk density and foamability were plotted against BA content at various CTs, as shown in Figure 7. It can be seen from Figure 7a,b that there is a general trend of increasing bulk density and decreasing foamability with an increase in BA content at each CT. Figure 7a,b shows that the minimum bulk density and maximum foamability are presented by TGPFs cured at 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, as can be seen from 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C curves.. There is an exception at 600 \({}^{\circ}\)C, where the bulk density decreases when we increase BA content from 30% to 40%. The higher density in samples treated at 600 \({}^{\circ}\)C is attributed to the viscous sintering which takes place above glass transition temperature of sodium silicate.  
In Figure 8a,b, the bulk density and foamability values of TGPFs have been plotted against CT for all the compositions. Figure 8 shows that as we increase CT from 200 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, bulk density decreases and foamability increases. Then with the increase in CT from 400 \({}^{\circ}\)C to 600 \({}^{\circ}\)C, there is an increase in bulk density and decrease in foamability in general. Figure 8a,b shows that 10% and 20%  
Figure 6: Degree of geopolymerization dependence on bottom ash content.  
Figure 7: Effect of bottom ash content on (**a**) bulk density and (**b**) foamability of TGPFs.  
BA compositions demonstrated the minimum density and maximum foamability at all of the curing temperatures. The maximum bulk density can be seen for the 40% BA curve, as shown in Figure 8a, and the maximum foamability can be observed for the 10% BA composition, as shown in Figure 8b. The maximum bulk density of 0.858 g/ cm\({}^{3}\)was observed for BA30-600, and the maximum foamability of 92.34% was observed for BA10-400.
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Feedstock
#### 3.1.2 Inoculum  
The choice of right inoculum type in combination with the right temperature is the key to set a desired anaerobic start-up condition [51; 52]. Generally, biogas production and the substrate utilization rate in an anaerobic digester show an inverse trend with increasing input of substrate to inoculum (S:I) [53; 54]. The usage of too little inoculum can result in incomplete feedstock degradation and process problems associated with VFA accumulation, inhibition and slower methane production rate. For example, depending on the type of inoculum, operating temperature, and the volatile solids content of the substrate, a four-fold increase in S:I ratio could contribute to decreasing as much as 40% of biogas production from AD of cattle manure [55]. Additionally, a higher S:I ratio can lead to longer HRT, resulting in larger reactor working volumes [56]. However, the use of a high amount of inoculum or a low S:I ratio might induce process instability as well as low biogas yield [57]. Thus, a careful compromise is necessary. The effect of the type and amount of inoculum on biogas production from various substrates was studied previously; for example, by [PERSON] et al. [58].
## 3 Results
### Cyberattacks in Smart Grids: Challenges and Implications of Cybersecurity
#### 3.1.5 Examining the Real-World Impacts: Case Studies of Cyberattacks on Operational Smart Grids  
Real-world case studies examine the actual effects of cyberattacks on operational smart grids:  
* **Ukraine Power Grid Cyberattack (2015)**: analyzes a sophisticated attack on Ukraine's power grid, leading to widespread outages. The lessons learned encompass the methods used, impacts, and incident response [192].
* **BlackEnergy and CrashOverride Attacks (2016)**: focuses on malware-driven attacks in Ukraine causing power grid disruptions. Explores technical aspects, implications, and mitigation strategies [193, 194].
* **NotPetya Ransomware Attack (2017)**: explores how the NotPetya attack impacted smart grids globally, disrupting grid management, communication, and billing systems [195].
* **Dragonfly/Energetic Bear Attacks (2011-2014)**: investigates a series of cyberattacks on energy companies, emphasizing tactics, motives, and consequences for smart grids [196].
* **Israel Electric Corporation Cyberattacks (2016-2018)**: studies cyberattacks on Israel Electric Corporation's grid infrastructure, highlighting disruptions and cybersecurity measures [197].  
These case studies showcase the tangible impacts of cyberattacks on operational smart grids, emphasizing the significance of cybersecurity measures, incident response planning, and collaboration among utilities to safeguard critical infrastructure.
## 4 Results and Discussion
### Possible Measures for the Management of Suspended Sediment in the River Drava  
Sedimentation in the reservoirs of the Slovenian River Drava is an important issue for hydropower plant operators as it affects the efficiency of the plants. It is estimated that between 30,000 and 40,000 m\({}^{3}\) of sediment accumulate in the reservoirs annually [27]. It was determined from the observations that the volume of reservoirs is decreasing. It was noted that the total volume of the reservoirs decreased by 32% from the commissioning of the hydropower plants to 2014. In addition, the usable volume of the reservoirs has reduced by about 33,000 m\({}^{3}\) annually [13].  
Historically, river sediments have been treated as waste and managed in a variety of ways, including landfilling or reintegration into rivers and aquatic systems [13; 28; 46]. However, some alternative solutions for the treatment and reuse of river sediments from the River Drava have been developed [27; 28]. An investigation into whether the sediments from the River Drava are appropriate as a filling material in the construction of levees and its further potential was given in reference [27].  
In reference [13], a solution to the problem of sedimentation in the reservoirs of the River Drava in Slovenia was proposed. Sedimentation in reservoirs due to changed hydraulic conditions strongly affects the efficiency of hydropower plants. Based on the particle size distribution of the suspended sediment, most of the material is fine-grained, so flooding the sediment into the core of the stream at higher flow velocities and further flushing would be the most appropriate action. However, the proposed solution does not comprehensively solve the sedimentation problem, as the problem is only shifted downstream.  
An effective sediment management measure in streams is sediment fences [47], which are temporary sediment barriers made of permeable fabric that trap and retain small amounts of sediment generated by construction activities or unsecured areas. These fences cause the water to slow down, allowing sediment to settle in a sediment basin in front of the fence. However, they are unsuitable for high waters and require regular maintenance, inspection, and sediment removal.  
Sediment traps [48; 49] are used to retain sediment from construction sites, but they are unsuitable for functioning water courses.  
To minimize the sediment transport of fine-grained material, sediment curtains of impermeable materials [50; 51; 52] are an efficient alternative. They can extend to the bottom and prevent sediment passage over the entire height of the curtain or only to a certain depth to prevent the transport of suspended sediment in the upper part of the water column.
## 5 Results
### Sensitivity Analysis  
As the results were obtained based on experts' evaluations, it is very important to examine their stability. This implies carrying out a sensitivity analysis, that is, an analysis of changes in the final ranking of alternatives due to changes in the input parameters of the model. In this study, for the sensitivity analysis, seventeen scenarios (Sc. 1-17) were created, in the first fifteen of which the weight was gradually reduced (by 20%, 40%, 60%, 80%, and 100%) for the three most significant criteria (criteria with the highest weights). In the penultimate scenario, all three of the most important criteria were eliminated, and in the last one, the weights of all criteria were equalized. The results of the performed sensitivity analysis are shown in Table 6 and Figure 2. It can be seen that in the mentioned scenarios, there are slight changes in the ranking of alternatives. In seven scenarios, the ranking is identical to the base zero scenario (Sc.0). In six scenarios, minor perturbations occur between alternatives \(P_{1}\), \(P_{2}\), and \(P_{3}\), which are ranked between second and fourth place. In the remaining four scenarios, there are somewhat larger changes in the ranking of alternatives, but even in those scenarios, the three best-ranked alternatives from the basic scenario dominate; that is, they occupy one of the top three places. Following the analysis, it can be concluded that the results obtained in the initial ranking are stable enough and can thus be adopted as the final results. The most favorable practice is \(P_{8}\) (supply chain policies), followed by practices \(P_{2}\) (biodiversity goal setting, monitoring, reporting, and transparency) and \(P_{3}\) (education and awareness raising).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Biodiversity**} & \multicolumn{3}{c}{**Biodiversity**} & \multicolumn{3}{c}{**Education**} & \multicolumn{3}{c}{**Sakeholder**} & \multicolumn{3}{c}{**Application**} & \multicolumn{3}{c}{**Application**} & \multicolumn{3}{c}{**Sustainable**} & \multicolumn{3}{c}{**Supply**} & \multicolumn{1}{c}{**Compliance**} \\  & **Impact** & **Monitoring** & **Awerness** & **Collabora-** & **Collabora-** & **Technolo-** & **Technolo-** & **Technolo-** & **Resources** & **Chain** & **with** \\  & **ment** & **and Transparency** & & & & & & & & & \\ \hline Sc. 0 & 4 & 2 & 3 & 5 & 7 & 6 & 8 & 1 & 9 \\ Sc. 1 & 4 & 2 & 3 & 5 & 7 & 6 & 8 & 1 & 9 \\ Sc. 2 & 4 & 2 & 3 & 5 & 7 & 6 & 8 & 1 & 9 \\ Sc. 3 & 4 & 2 & 3 & 5 & 7 & 6 & 8 & 1 & 9 \\ Sc. 4 & 3 & 2 & 4 & 5 & 7 & 6 & 8 & 1 & 9 \\ Sc. 5 & 3 & 2 & 4 & 5 & 8 & 6 & 7 & 1 & 9 \\ Sc. 6 & 4 & 2 & 3 & 5 & 7 & 6 & 8 & 1 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensitivity analysis results.  
Figure 2: Sensitivity analysis.
# Research on Fleet Size of Demand Response Shuttle Bus Based on Minimum Cost Method
## 4 Numerical Simulation
### Analysis of Key Influencing Factors  
In order to further study the variables affecting the optimal fleet size, this Section simulates the fleet size, demand density and DRC total system cost for different vehicle types, and seeks the relationship between fleet size and total system cost under different vehicle types and different demand densities, and it is not difficult to find out that there exists a minimum total system cost under different demand densities, and the fleet size corresponding to the minimum cost is the optimal fleet size [45]. The fleet size corresponding to the minimum cost is the optimal fleet size, and the optimal fleet size changes with the change in demand density and vehicle capacity. The change trend is shown in Figure 4.  
Based on the above research, in order to further analyze the relationship between the key influencing factors in the DRC optimal fleet size model, obtain the optimal fleet size and the minimum cost value of the system under different vehicle capacities and different demand densities, and analyze the relationship between the vehicle capacities, demand densities, fleet sizes, and the total system costs. The specific simulation results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**B**} & \multicolumn{5}{c}{**Q**} \\ \cline{2-6}  & **10** & **20** & **30** & **40** & **50** \\ \hline
1 & 0.80 & 1.02 & 1.24 & 1.46 & 1.68 \\
2 & 0.69 & 0.80 & 0.91 & 1.02 & 1.13 \\
3 & 0.66 & 0.73 & 0.80 & 0.88 & 0.95 \\
4 & 0.64 & 0.69 & 0.75 & 0.80 & 0.86 \\
5 & 0.63 & 0.67 & 0.71 & 0.76 & 0.80 \\
6 & 0.62 & 0.66 & 0.69 & 0.73 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Numerical simulation results of average waiting time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**B**} & \multicolumn{5}{c}{**Q**} \\ \cline{2-6}  & **10** & **20** & **30** & **40** & **50** \\ \hline
1 & 0.53 & 0.68 & 0.83 & 0.97 & 1.12 \\
2 & 0.46 & 0.53 & 0.61 & 0.68 & 0.75 \\
3 & 0.44 & 0.49 & 0.53 & 0.58 & 0.63 \\
4 & 0.43 & 0.46 & 0.50 & 0.53 & 0.57 \\
5 & 0.42 & 0.45 & 0.48 & 0.51 & 0.53 \\
6 & 0.41 & 0.44 & 0.46 & 0.49 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Numerical simulation results of average in-train time.  
The results show that there is a certain linear relationship between the demand density and the optimal departure size under different vehicle capacities. When k = 10 seats/vehicle, the optimal fleet size is 0.096 times the density demand; when k = 20 seats/vehicle, the optimal fleet size is 0.089 times the density demand; when k = 30 seats/vehicle, the optimal fleet size is 0.084 times the density demand; when k = 40 seats/vehicle, the optimal fleet size is 0.080 times the density demand.
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
###### Abstract  
Acid mine drainage (AMD) is a severe form of environmental pollution that has the potential to contaminate surface and ground waters by introducing heavy metals and lowering the pH. The feasibility of using nanofiltration (NF) as a potentially attractive and cost-effective remediation method to treat acid mine drainage was investigated in this study. The performance of an acid-stable NF membrane focusing on the effects of the water pH and membrane charge on ion rejection was systematically studied. A single salt solution experiment showed that Mg, Cu, and Mn containing species were highly rejected at above 97%. Below the membrane iso-electric point (IEP), Mn showed an increased rejection of 99%, while Mg and Mn rejections were relatively constant within the investigated pH range of pH 2 to 7. Rejection of monovalent Cl\({}^{-}\) decreased with increasing concentration of an accompanying divalent SO\({}_{4}\)\({}^{2-}\), showing that Donnan related effects are more prominent at higher ionic concentrations. The sulfate rejection decreased drastically below pH 3 due to the formation of HSO\({}_{4}\)\({}^{-}\), which permeated through the membrane, which can be utilized as a way of separation of the metals from the accompanying sulfur-containing compounds. For mixed salt solutions, rejection of silicate dropped from 52% to 38% when magnesium sulfate was added, owing to shielding of the membrane surface charge by Mg\({}^{2+}\) ions. The NF process performance with a simulated AMD solution was found to be similar to that with model salt solution experiments, both in terms of ion rejection values and general pH-dependent rejection trends. The results obtained can be used as a fast preliminary tool for evaluating the feasibility of using NF for treating AMD with a given ionic composition and pH.  
acid mine drainage; nanofiltration; ion rejection; Donnan effect +
Footnote †: journal: Applied Soft. 2020, 10, 400; doi:10.3390/app10010400