esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dataset Description
### Vegetation Measurements  
Additional _in situ_ measurements of vegetation were performed to estimate the following: the Leaf Area Index (LAI), the vegetation water content (VWC), biomass (BIO) and vegetation height (HVE). For each plot, 20-25 hemispherical digital photos were acquired at nadir using a fisheye lens. These photos were then processed using CAN-EYE imaging software to obtain the LAI ([[http://www6.paca.inra.fr/can-eye](http://www6.paca.inra.fr/can-eye)]([http://www6.paca.inra.fr/can-eye](http://www6.paca.inra.fr/can-eye))). Moreover, two grass samples over a 50 cm \(\times\) 50 cm square were collected to determine the fresh grassland biomass (wet weight per unit area). The fresh biomass was then driest to determine the vegetation water content (wet weight-dry weight). Finally, 20 vegetation height measurements were carried out for each training plot. All vegetation measurements within each plot were averaged to provide a mean value for each plot.  
LAI measurements show a high variability due to very quick growth in the irrigated grasslands. For example, the LAI rises rapidly from 0.1 to 2 m\({}^{2}\)/m\({}^{2}\) while the vegetation height increases from 10 to 50 cm (the biomass ranges between 0.3 and 1 kg/m\({}^{2}\)) (Figure 3a). A poor correlation (R\({}^{2}\) = 0.49) is observed between the LAI and the grassland height when it is less than 50 cm. When the grassland height is greater than 50 cm, the LAI derived from optical photography tends to give saturated values (Figure 3a). The grassland biomass increases linearly with the vegetation height (Figure 3b).  
Figure 4 shows the temporal variations in the main vegetation parameters measured at the plot level. The three cuts of grassland are clearly identified on the graph in May (on approximately DOY 120) in June-July (on DOY 185) and in July-August (on DOY 230). Before the first cut, the vegetation parameters VWC, BIO and HVE are greater values in comparison to values measured during the other growth periods [69]. In general, the first yield always has greater hay production and is devoted to horses; the second and the third harvests are lower. The LAI usually reaches similar maximum values (approximately 3-4 m\({}^{2}\)/m\({}^{2}\)) during the first three growth periods (Figure 4). In the fourth period, the LAI  
Figure 3: LAI measurements according to vegetation height (**a**), Biomass measurements according to vegetation height (**b**).  
values are lower (approximately 2-3 m\({}^{2}\)/m\({}^{2}\)). A strong correlation between the different vegetation parameters was observed (Figures 3 and 4). All vegetation parameters increase with time after harvesting, and this increase is very high during the first month of growth.
## 3 Experimental Part
### Research Methodology  
Comparative tests were carried out to determine the effectiveness of metal nanooxides based specimens. The studies used sorbents designated in this article as CDE, PC, EP and hydrophobized nanocrystalline oxide preparation MgO-TiO\({}_{2}\) (designated as FastAct (FA), with bulk density d = 0.47 g/cm\({}^{3}\), grain diameter \(\varphi<\) 10 nm.). The characteristics of the tested sorbents are presented in Table 1. CDE, PC, EP type sorbents are very popular sorbents commonly used by rescue entities in Europe [47; 48].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Designation** & **Trade Name** & **Characteristics** \\ \hline \multirow{4}{*}{CDE} & \multirow{4}{*}{Compakt} & CDE is a granulated sorbent based on calcined diatomaceous earth with SiO\({}_{2}\) composition—75\%, Al\({}_{2}\)O\({}_{3}\)—10\%, Fe\({}_{2}\)O\({}_{3}\)—7\%, \\  & & TiO\({}_{2}\)—1\%, MgO—2\%, CaO—1\%, K\({}_{2}\)O + Na\({}_{2}\)O—2\%. \\ \cline{3-3}  & & bulk density d = 0.53 g/cm\({}^{3}\), \\ \cline{3-3}  & & grain diameter 0.5 \(<\varphi<\) 1 mm. \\ \hline \multirow{4}{*}{PC} & \multirow{4}{*}{Unisafe} & PC is a sorbent whose active ingredient is polyacrylamide \\  & & copolymer. \\ \cline{3-3}  & & bulk density d = 0.37 g/cm\({}^{3}\), \\ \cline{3-3}  & & grain diameter 0.05 \(<\varphi<\) 0.1 mm. \\ \hline \multirow{4}{*}{EP} & \multirow{4}{*}{Ekoperl66} & EP is a silicate rock of volcanic approach (expanded perlite) with the composition: SiO2—74.9\%, Al\({}_{2}\)O\({}_{3}\)—12.0\%, \\ \cline{3-3}  & & Fe\({}_{2}\)O\({}_{3}\)—0.8\%, CaO—0.6\%, MgO—0.1\%, Na\({}_{2}\)O—4.6\%, \\ \cline{3-3}  & & K\({}_{2}\)O—4.7\%, TiO\({}_{2}\)—0.4\%. Bulk density d = 0.078 g/cm\({}^{3}\), grain diameter 1 \(<\varphi<\) 3 mm. \\ \hline \multirow{4}{*}{FA} & \multirow{4}{*}{FastAct} & Hydrophobized nanocrystalline MgO-TiO\({}_{2}\), \\ \cline{3-3}  & & bulk density d = 0.47 g/cm\({}^{3}\), \\ \cline{3-3}  & & grain diameter \(\varphi<\) 10 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristics of the tested sorbents.
## 3 Results
### LA-1 Alleviates MCAO Damage Through the PI3K-Akt and Nrf/HO-1 Pathways  
Based on our proteomic data, we examined the levels of proteins associated with PI3K-Akt and Nrf2/HO-1 pathways by Western blot (Figure 8A,B). Compared to sham group, the MCAO group exhibited significantly decreased levels of phosphorylated PI3K (p-PI3K), phosphorylated Akt (p-Akt), Nrf2, and HO-1, indicating that both PI3K-Akt and Nrf2/HO-1 pathways were inhibited during cerebral ischemia-reperfusion injury. In addition, the levels of pro-apoptosis protein Bax were elevated, while the levels of anti-apoptosis protein Bcl-2 were reduced significantly in MCAO group. After treatment with 10 mg/kg of LA-1, the abundance of all the proteins we detected was restored to normal levels. These findings strongly suggest that LA-1 mitigates MCAO-induced damage by activating the PI3K-Akt and Nrf2/HO-1 signaling pathways, and inhibiting the apoptosis pathway.  
Figure 8: WB analysis of brain tissue (n = 3) (**A**) Representative Western blot images displaying the bands corresponding to Bax, Bcl-2, PI3K, p-PI3K, Akt, p-Akt, Nrf2, HO-1, GAPDH. The samples  
corresponded to brain tissue in all cases. (**B**) Quantification of band densities for Bax/GAPDH, Bcl-2/GAPDH, PI3K/GAPDH, p-PI3K/PI3K, AKT/GAPDH, p-AKT/AKT, Nrf/GAPDH, HO-1/GAPDH. Data were presented as the mean \(\pm\) SD, * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.0001, ns represents not significant, n = 3.
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 3 Petroleum-Based Polymers
### Conjugated Macrocyclic Polymers  
Conjugated macro/microcyclic polymer combined the properties and structures of the porous organic network and macrocycles [131; 132]. For instance, [PERSON] et al. reported the preparation of an arene-based conjugated macrocycle (BpP6-OTf-CMP-2) using a Sonogashira-Hagihara palladium-catalyzed cross-coupling reaction [133]. They design the ring to be selective towards CO2 by varying the internal diameter. They observed a CO2-adsorption capacity of 1.83 mmol g\({}^{-1}\) at 273 K at 1 atm.  
[PERSON] and coworkers reported the preparation and characterization of 1,3-diyne-linked conjugated microporous polymer for CO2 capture [134]. Using a Sonogashira cross-coupling reaction, 1,3,6,8-tetraethynylpyrene was reacted in the presence of Pd(PPh3)4, CuI, and \(N\),\(N^{\prime}\)-diisopropylthelamine in DMF to afford microporous-conjugated polymer LKK-CMP-1 (Figure 5). A CO2 adsorption uptake of 2.27 mmol g\({}^{-1}\) at 273 K and 1 bar was measured.  
Figure 4: Typical structures of polymeric ionic liquids used for CO2 sorption [129].  
Figure 5: Structures of conjugated macrocyclic polymers for CO2 capture [134; 135; 136].  
An interesting report from [PERSON] et al. showed the preparation of a metal-organic conjugated microporous polymer incorporating a bipyridine moeity [135]. In short, the CMP-(bpy)20 was prepared by a Sonogashira cross-coupling reaction of 1,3,5- triethynylbenzene with 5,5'-dibromo-2,2'-bipyridine and 1,4-dibromobenzene (Figure 5). The obtained polymer was purified and then refluxed in diethyl ether for 24 h with [Mn(CO)\({}_{5}\)Br] to afford the targeted CMP-(bpy)20-Mn. A CO\({}_{2}\) uptake of 1.06 mmol g\({}^{-1}\) at 298 K and 1 bar was observed.  
Another example of a conjugated polymer network was reported by [PERSON] et al. [136]. They prepared a \(N\)-\(N^{\prime}\)-bicarbazole homopolymer by Yamamoto cross-coupling reaction using 3,3',6,6'-tetrbromo-\(N\),\(N^{\prime}\)-bicarbazole as the starting material and ([Ni(cod)\({}_{2}\)] as the catalyst. The obtained polymer CMP-YA showed a CO\({}_{2}\) uptake of 1.27 mmol g\({}^{-1}\) at 298 K and 1 bar (Figure 5).
# Prediction of Compressive Strength Loss of Normal Concrete after Exposure to High Temperature
## 4 Results and Discussion
### Vim  
In this paper, a VIM was implemented using the RF model to rank the importance of the inputting parameters on compressive strength loss of normal concrete after high temperature, and the result is illustrated in Figure 12. The importance ranking is in the sequence of T \(>\) W/B \(>\) V \(>\) MD \(>\) RD \(>\) C. Heating temperature had a crucial influence,  
Figure 8: Correlation of RF modeling. Figure 9: Relationship between temperature and loss of compressive strength of concrete after high temperature. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**BP**} & \multicolumn{2}{c}{**PSO-BP**} & \multicolumn{2}{c}{**RF**} \\ \cline{2-7}  & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline RMSE (\%) & 4.17 & 3.14 & 2.60 & 2.12 & 2.56 & 2.23 \\ MAE (\%) & 3.12 & 2.78 & 3.17 & 2.63 & 2.57 & 1.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error evaluation. accounting for 67.3%. Influences contributed by W/B and heating velocity were quite similar, accounting for 11.6% and 10.3%, respectively, followed by the duration at target temperature and duration after cooling, accounting for 5.6% and 4.2%, respectively; the influence of the cooling method was insignificant, accounting for only 1.0%. It is clear that, for normal concrete, the decomposition of hydration products, cracking, loosening of matrix structure and expansion and/or decomposition of aggregates caused the loss of compressive strength with the increase in temperature [3]. Consequently, the temperature should be the most important factor influencing the compressive strength loss of concrete. As discussed in Section 4.2, it is usually considered that water cooling will result in thermal stress in concrete to dramatically reduce its strength. At the same time, it is also found that shrinkage of cement matrix with temperature would compensate for such stress to lower the influence on the strength loss. Therefore, the importance of the cooling method is less significant. Furthermore, a smaller amount of literature has discussed the influence of the cooling method on the compressive strength of normal concrete; therefore, a smaller data size would also have an influence on the ranking of the cooling method.
## 2 Modeling of Novel Electrical Load Resources in UDNs  
The UDNs incorporating VPPs considered in this paper are shown in Figure 1. The VPP controls the operating power of various adjustable load devices within the UDN through intelligent terminals, participating in external demand response as an integrated VPP entity.  
This section focuses on modeling novel electrical load resources in UDNs incorporating VPPs, and the permissible range of bus voltages in the UDN is 0.95-1.05 p.u. The aim is to thoroughly investigate and analyze the electrical characteristics of new types of loads, such as 5G base stations, IDCs, electric vehicle (EV) charging stations, and energy storage devices, and their impacts on UDNs. By modeling these novel loads, theoretical support is provided for effectively integrating VPPs and developing efficient proactive voltage control strategies for UDNs.
## 3 Flight Control System Health Assessment under Uncertainty
### Uncertainty Propagation Based on MCS  
The MCS algorithm is a random sampling method [29] which can truly simulate the system's actual dynamic process, and its calculation result has very little error compared with the actual result, so it has become a standard uncertainty calculation method. The MCS method is used to assess flight control systems' health status. The basic steps are as follows:  
1. For uncertain input parameters, large enough random numbers, \(x_{1},x_{2},...,x_{n}\), with the same distribution are generated according to its probability density function.
2. Substitute the generated parameters into the health assessment process to obtain multiple health assessment values, \(y_{1},y_{2},...,y_{n}\).
3. Compute the probability characteristics of the health assessment values. The probability feature solving process is described in detail in Section 3.4 below.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### Alarming Approach
#### 2.2.1 Ground Monitoring and Alarming
The introduction of DIF is mainly to take into account the differences in the sensitivity and tolerance of aviation and non-aviation users to satellite ranging errors. We hope that while ensuring integrity, the use of DIF will help improve the continuity and availability for non-aviation users.
## 5 Experiment and Analysis
### Computation and Results Analysis
#### 5.2.3 Comparative Analysis of Results  
The results of the different algorithms evaluated in this paper are listed in Table 9. In summary, through the training and testing of the BTR dataset, the CNN model achieved a diagnostic accuracy of 100% (it could accurately identify and predict all tail rope statuses), which was higher than the 94.04% of KNN and the 96.44% of ANN-BP. The time consumption of each iteration was 32 s, with each step being 10 ms, which meets the requirements of system accuracy and real-time functioning. Additionally, the \(L_{2}\) regularization process of the fully connected layer F1 could prevent overfitting, which allowed the network to achieve a good generalization performance. Although ANN-BP had less time consumption, its accuracy and stability were worse than those of CNN. At the same time, KNN was worse than CNN in terms of accuracy and time consumption. Therefore, we can clearly conclude that the performance of CNN was better than that of KNN and ANN-BP for the health monitoring of tail ropes. Therefore, the CNN model is more suitable for the actual health monitoring of hoisting systems.
## 5 Conclusions  
Numerical modeling of localized heating of a tumor embedded within an anatomically accurate female breast model using an eight-dipole antennas array, with the given optimization procedure, offered satisfactory results, regardless of the tumor size. Due to the dimension limitations around a female breast, we selected the 1-GHz shortened-length dipole antennas for the EM excitations, whose phases and powers were changed while maintaining an unchanged power of 8 W for the entire dipole matrix. The FDTD-based in silico simulation provides a reliable numerical solution of described electro-thermally problem and produces satisfactory thermal tumor profiles at the level of 40-41 \({}^{\circ}\)C for analyzed tumors of different sizes. Importantly, the considered model includes the non-linear temperature dependence of the tumor perfusion, which allows for a more realistic estimation of tumor temperature. Increasing the tumor temperature above the physiological temperature of 37 \({}^{\circ}\)C was found to significantly reduce perfusion in the tumor, which, in turn, caused even greater heat accumulation in the tumor when conducting cancer-localized MW hyperthermia. This investigation demonstrates that optimal thermal tumor profiles depend on the radius of the modelled tumor. The numerical calculation findings indicate that the increased tumor diameters caused higher temperature increments within the breast tumor tissue. Therefore, the applied algorithm deals better with large tumors that concentrate EM energy, but also offers a satisfactory solution in the case of smaller tumors. The performed simulations demonstrated that the breast tumor could not be heated evenly across its entire volume. The heterogeneity in tumor temperature distribution is mainly due to the different perfusion rates of tissues surrounding the tumor and, thus, different levels of cooling around the peripheral tumor layer. This reiterates the fact that each breast cancer clinical case should be considered independently, and various factors including tumor size, shape, locations, and physiological aspects, as well as the physico-thermal properties of the tumor and the healthy surrounding tissue, should be taken into account, as these factors influence the focused heating of the tumor tissue. The multi-objective optimization procedure can be considered as a critical tool for prospective regional hyperthermia treatment planning, i.e., to stipulate the crucial parameters for the dipole antennas for the expected therapeutic effects inside female breast tumors before actual targeted MW hyperthermia treatment is initiated in clinics.  
Conceptualization, P.G, A.M.; Investigation, P.G., A.M.; Project administration, P.G.; Software, P.G., A.M.; Visualization, P.G., A.M.; Writing--original draft preparation, P.G.; Writing - review & editing, A.M., M.S., Funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
## 3 Results
### Labyrinth Simulations Using Different Turbulence Models
LES provides a more reliable and accurate prediction of wall shear stress and associated flow parameters. Most RANS models, particularly \(k\)-epsilon, may be overestimating frictional forces and energy dissipation within the labyrinth. The observed discrepancies between the computational models highlight the need for further experimental validation to improve model accuracy. Consequently, additional RANS simulations were performed for the entire geometry and compared with experimental data. Although the LES model is known for its higher accuracy and reliability in predicting flow behavior and associated stresses, the computational cost for such simulations would be prohibitively high for this application.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 3 Different Underwater Applications  
There are many applications of underwater images, out of which three of the most common applications have been chosen for evaluation of the proposed underwater image restoration method. The three applications are edge detection, Speeded Up Robust Feature (SURF), and image classification using machine learning (ML). One of the main aims of a restoration method is to reduce blurriness from underwater images; among other things, to facilitate edge detection, which may be performed with the use of Sobel edge-detecting operator. Edge detection is mainly used for obstacle detection by unmanned underwater vehicles. Textured details of underwater images may also be improved by using a restoration method, and the effectiveness of the restoration method towards this objective may be evaluated by considering the number of feature points detected by SURF. For underwater images with coral reefs and fish with a variety of shapes and sizes, SURF is used to detect features of objects, which may be performed with the help of the SURF function in MATLAB software. Finally, image classification is performed to prove the effectiveness of a restoration method in detecting targets. This application is specifically used for underwater pipeline corrosion, marine sea salt detection, subsea terrain classification and mineral exploration.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 6 Conclusions and Future Work  
Thus, in this paper, we considered image recognition employing a complex-valued convolutional neural network with multi-valued neurons (CNNMVN) as a spatial-domain convolutional neural network and a multilayer neural network with multi-valued neurons (MLMVN) as a frequency-domain convolutional neural network.  
We derived the error-backpropagation rule for CNNMVN. We considered in detail how a frequency-domain convolution can be utilized using MLMVN and suggested using frequency-domain downsampling as an analogue of pooling in the spatial domain.  
It was shown that both neural networks can be used for image recognition. Their capabilities were tested using two classical image datasets--MNIST and Fashion MNIST. Our intention was to test both networks using images as they are, without any kind of preprocessing. This allowed us to examine the actual recognition capability of both presented approaches.  
Future work will focus on further investigation of the custom normalization for CNNMVN and the investigation of networks with multiple convolutional layers. Additionally, both approaches should be tested on other popular datasets such as CIFAR-10 and CIFAR-100. It would also be interesting to test how preprocessing data techniques may improve the network's generalization capabilities and recognition rate.  
Conceptualization, I.A.; methodology, I.A. and A.V.; software, A.V.; validation, I.A. and A.V.; formal analysis, I.A. and A.V.; investigation, I.A. and A.V.; data curation, I.A. and A.V.; writing--original draft preparation, A.V.; writing--review and editing, I.A. and A.V.; visualization, A.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data and software used in the simulation experiments should be shared through the links provided in the final version of the paper submitted for publication.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 5 3 GPP Release 18: Dawn of 5G-Advanced  
3 GPP Release 18, marking the beginning of 5G-Advanced, represented a significant leap forward in both enhancing existing technologies and addressing emerging verticals and deployment scenarios.