esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 5 Parametric Study: Finite Element Analysis of a Nailed-Soil Slope with Varying Soil Densities
### Horizontal and Vertical Stresses behind the Nailed-Soil Mass  
The influence of soil density on the horizontal and vertical stresses behind the nailed-soil mass in construction and overburden pressure stages at mid-height points are shown in Figures 18-21. It can be inferred from the graphs that, at construction stages, the horizontal stresses behind the nailed-soil mass rise as the soil relative density decreases. The performance of vertical stresses behind the soil mass with the soil density has an inverse behavior, i.e., the vertical stresses behind the nailed-soil mass increase as the soil relative density increases. Similar behavior has been observed in loading stages. This may be due to increasing density results in the shear strength of nailed-soil mass increase and less movement occurring in the nailed-soil mass.  
Figure 17: Vertical stresses under the soil mass with various soil densities at mid-point.  
Figure 18: Horizontal stresses behind nailed-soil mass with various soil densities. **(a)** Loose sand. **(b)** Medium dense sand. **(c)** Dense sand.  
Figure 19: Horizontal stresses behind nailed-soil mass with various soil densities at mid-height of the slope.  
Figure 20: Vertical stresses behind nailed-soil mass with various soil densities. (**a**) Loose sand. (**b**) Medium dense sand. (**c**) Dense sand.  
Increasing relative density of the sand leads to a rise in the vertical stresses behind the nailed-soil mass with a ratio ranging from 25.0% to 50.0%, with the higher value for the case of loose sand. This may be due to increasing the overburden pressure. Meanwhile, a reduction in horizontal stresses behind the nailed-soil mass occurred, with a ratio ranging from 3.85 to 28.57%, with the lower value for the case of loose sand.
# Electric Drive with an Adaptive Controller and Wireless Communication System
## 7 Conclusions  
In this paper, an IoT-enabled, ARM-based monitoring and control system was developed and applied to an electric drive with finite stiffness. An Elman recurrent neural network and a neural predictor were designed to control a drive with an elastic joint. The comparison of a drive operating with and without the predictor applied was also shown to confirm the superiority of two neural networks cooperating in a single speed control structure. The convenient and effective approach to optimization of the parameters (of the neural network) with the Artificial Bee Colony algorithm was provided. The presented results confirm that the use of a metaheuristic algorithm can facilitate the design process, since there is no given prescription for selection of correct values of coefficients. The developed data server enables online observation of the plant state variables and basic control of the electrical machines. The presented idea of coupling the smart controller utilizing elements of artificial intelligence and online data visualization is coherent with the Industry 5.0 concept. The novelty was not only provided by the original idea of synthesizing the controller structure and data presentation, but also by the implementation of a low-cost mainstream microcontroller. The data flow of the proposed system was described in detail. Thus, the framework for developing similar systems was established.  
The implementation of a low-cost ESP32 chip proves that modern machinery can be monitored with simple devices without complex and expensive specialized HMI systems. Both cost-effective design and online data acquisition are essential to improve electric drive data availability in the industry. The advantage of the proposed HMI is that it was developed as a web page with a customized layout that can be adapted to requirements and can be opened with any device with access to the internet.  
Conceptualization, M.K.; methodology, M.K., R.S., M.M. and M.Z.; software, R.S., M.M. and M.Z.; validation, M.K., R.S., M.M. and M.Z.; formal analysis, M.K., R.S., M.M. and M.Z.; investigation, R.S., M.M. and M.Z.; resources, M.K.; data curation, R.S., M.M. and M.Z.; writing--original draft preparation, M.K., R.S, M.M. and M.Z.; writing--review and editing, M.K., R.S., M.M. and M.Z.; visualization, R.S. and M.Z.; supervision, M.K.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 16: Transients of system signals achieved for an increased load time constant (\(T_{2}=2T_{2n}\)) of the drive.
## 2 Design of Bow-Tie Microstrip Antennas
### Antenna Design by Using RT/Duroid 5880 Material  
In this section, two bow-tie antennas are designed with the same material and different thicknesses. The designed antennas are fabricated and tested. The measured results are compared to the simulation results, while results of both antennas are compared and analyzed. In order to avoid confusion and to easily distinguish between the two antenna results, the capital letter A and B are respectively assigned for the first and second antenna. The antenna A, which is shown in Figure 1 is designed and simulated by CST Microwave Studio. The relative permittivity of the material is 2.2 and the substrate thickness is 1.57 mm. The two microstrip lines are used to excite the antenna with 50 \(\Omega\) input impedance waveguide port. The two triangular shapes are symmetric (exactly the same). Table 1 shows the dimensions of antenna A in millimeters, also Figure 1 shows the antenna B geometry, which is also fed by the same way of antenna A using two microstrip lines connected to the waveguide port. Antenna B has a larger substrate thickness of 3.175 mm. The dimensions of antenna B are presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Antenna Parameters** & **Dimensions (mm)** \\ \hline Patch length PL & 35 \\ Base length BL & 33 \\ Gap & 4 \\ Feed line width Wf & 0.6 \\ Apex length AL & 5.5 \\ Substrate length L & 43.35 \\ Substrate width W & 87 \\ \hline \end{tabular}
\end{table}
Table 1: Antenna A parameters.  
Figure 1: The design geometry of antenna A and B with same RT/Duroid 5880 materials substrate and different substrate thickness.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 4 Proposed Regenerator Placement Heuristics  
The regenerator placement heuristics proposed in this paper are based on: (1) defining either the possible or feasible regenerator sets for each source-destination node pair, as described in Sections 3.2 and 3.3, respectively; (2) estimating the load offered to each node by considering only the load of the routes which demand regeneration, and (3) Given the estimated load per node and the number of regenerators installed per node, estimate the overall network blocking probability mitigation when a new regenerator is installed in a specific node.  
In this section, we propose two strategies to solve RPP. The proposed strategies coincide in steps (1) and (3) and differ in the form of how the offered load to each network node is estimated. We describe in Section 4.1 how the offered load to a node is estimated in the proposed strategies. It is important to mention that the load and blocking probabilities estimates presented in Sections 4.1 and 4.2 are not intended to give an exact evaluation of such values. Instead, they are intended to be used as an approximate, although precisely enough, estimate so that an efficient heuristic approach to solve RPP may be built.  
Although both proposals consider the network traffic pattern forecast and intensity as input information to solve RPP; they can provide efficient regenerator placement even when the actual network traffic is moderately different from that one forecast, as discussed in the results section.
## 4 Conclusions  
As already mentioned, this is the first study characterizing the composition of volatile compounds in four different bee products (beeswax, beebread, bee pollen, and honey) collected in the same batch. The conducted study showed that each bee product possessed its own unique profile of volatiles. However, the volatiles present in bee pollen played an important role in creating the volatile compound profile of beebread. The bee pollen was characterized by the highest number of volatiles, when compared to the other bee products. Alkanes were the main compounds detected in the bee products (except for honey). Three volatile compounds (nonanal, benzaldehyde, and furfural) were present in all samples of beeswax, beebread, bee pollen, and honey. Moreover, the major volatiles in beeswax were 2,4-dimethyl-heptane, octanal, nonanal, and 4-methyl-octane; in beebread: Decane, 1-tridecane, and furfural; in bee pollen: 2,4-dimethyl-heptane, 4-methyl-octane, decane, acetic acid, and 5-methyl-decane, whereas in honey: Benzaldehyde, furfural, and linalool. Thereby, the content of sugars, color, and odor descriptors such as honey-like, sweet, and pungent, were positively correlated only in honey. Beeswax was related to a waxy odor, bee pollen to a plant-based aroma, and beebread to an acid aroma. Therefore, it can be concluded that the volatile profile as well as sugar content, and furthermore sensory analysis depend on the stage of their processing by bees. Moreover, the characteristic of the volatile and sensory profiles and sugar content in the bee products can help the broader use of these products in the food industry to develop new functional foods.  
Conceptualization, M.S. and T.S.; methodology, M.S., P.H., and T.S.; formal analysis, M.S., P.H., G.L., and T.S.; investigation, M.S., P.H., G.L., and T.S.; writing--original draft preparation, M.S. and T.S.; writing--review and editing, M.S. and T.S.; visualization, M.S. and T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Analysis and Discussion  
The latitude and longitude of each CR are converted to theoretical ranks using Equations (1)-(3) and RPC parameters. We compared the actual ranks of the CRs extracted from the Level 1A-SLC images with the theoretical ranks to obtain the range and azimuth pixel localization accuracies of the images, and converted the pixel position errors to distance errors based on the ranging and azimuthal pixel spacing. The results shown in Table 3 are obtained after summarizing and analyzing the image localization accuracy for different dates.  
According to Table 3, the geometric positioning accuracy of CRs within the same image exhibits clustering. Except for the high positioning errors of the October 16 C-SAR/02 FSI image and the November 22 C-SAR/01 FSII image, the range-direction positioning accuracy of most CRs is better than 60 m, and the azimuth-direction positioning accuracy is better than 80 m.  
The imaging satellites, imaging modes, imaging time, orbit direction, and other information of the 11 images were combined and comprehensively analyzed to obtain the results shown in Figures 17-19. The localization error of the point target in each image varied slightly with the satellite, imaging mode, and orbit direction.  
With respect to the three satellites, the positioning error of the GF-3 image was the smallest, with the range-direction error being less than 50 m and the azimuth-direction error being less than 15 m. This can be attributed to GF-3\({}^{\prime}\)s extended operational history and regular geometric calibration, ensuring its high positioning accuracy. The overall positioning errors of the C-SAR/01 and C-SAR/02 satellites were larger than those of GF-3.  
The errors of the FSII mode of the C-SAR/01 satellite in both the range and azimuth directions were greater than 100 m. On October 16, the FSI mode of the C-SAR/02 satellite had an error greater than 100 m in both the range and azimuth directions, but the FSI mode of the C-SAR/02 satellite on October 18 had an error of less than 20 m in the range direction and 50 m in the azimuth direction.  
In addition, all the experimental images were divided into ascending and descending orbit (all right-looking). An analysis of their positioning accuracy errors revealed that those of ascending orbit images were positive in the azimuthal direction and were mostly less than 80 m and those of descending images were all negative in the azimuthal direction and were mostly less than 100 m.  
Figure 19: Positioning accuracy of different orbital directions of the C\(-\)SAR series satellites.  
Figure 18: Positioning accuracy of different imaging modes of the C\(-\)SAR series satellites.
## 1 Introduction  
Humankind faces an unprecedented challenge with regard to achieving climate objectives. Without carbon capture and storage (CCS), this challenge becomes infinitely greater [1]. CCS is a technology capable of capturing CO\({}_{2}\) from large-scale CO\({}_{2}\) emission sources and storing it in the ground. Post-combustion capture processes can be classified into three types: absorption, adsorption, and membrane technologies. The most commercially available technique is absorption, which typically uses various amine solvents [2; 3; 4]. The types of amines used in the solvents are divided into primary, secondary, and tertiary amines [5]. In general, CO\({}_{2}\) absorption is achieved with primary and secondary amines through a carbamate reaction and with tertiary amines through a bicarbonate reaction [6]. Primary and secondary amines can also form bicarbonate but only a small amount after the carbamate reaction is completed. In the past, a single amine such as monoethanolamine (MEA) and water were mixed and used commercially as a CO\({}_{2}\) capture solvent, but researchers have investigated the blending of various amines to improve the performance of the solvent. A typical example is the BASF's aMDEA(r), a blend of methyl diethanolamine (MDEA) and piperazine (PZ), and research on the optimal mixing ratio of two amines is also being conducted through various methods [7].  
In this study, we attempted to develop a solvent with low reboiler heat duty by blending several types of amines. For high CO\({}_{2}\) loading, we decided through a literature review and by reviewing existingexperiments to use polyamine 3,3'-iminobis (N, N-dimethylpropylamine) (IBDMPA), a polyamine, as the main amine. Hereafter, we use IBD to denote IBDMPA for simplicity of presentation. As a triamine, one mole of IBD can react with up to two moles of CO2 which is four times that of one mole of MEA reacting with 0.5 mole of CO2 [8; 9]. Like aMDEA(r), PZ was selected as the reaction rate enhancer. Research on increasing the reaction rate by blending PZ with IBD was conducted by [PERSON] et al. [10] and [PERSON] et al. [11]. The use of a high concentration of IBD leads to high viscosity of the solvent. Therefore, supplementing IBD and PZ with a non-high viscosity auxiliary amine was considered. MEA, 2-(butylamino)ethanol (BAE), and 2-amino-2-methyl-1-propanol (AMP) were chosen as auxiliary amine candidates.  
Comparing CO2 solubility, absorption rate, and viscosity measurements in the laboratory, we finally chose a blend, IBD-AMP-PZ of 40:6:6 mass%, as a new recipe, and test operations were conducted in a pilot-scale carbon capture process (PCCP) that can normally handle 150 Nm3/h of flue gas. These tests sought to investigate the stability of operation, the reboiler heat duty, the CO2 loading, and the temperature profile in the absorber column at a constant flue gas flow rate. To compare the performance of the proposed solvent, a baseline evaluation was conducted first using MEA 30 mass% solvent.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 2 Related Work
First, researchers again consider the linear network model and prove that it cannot deliver information in a timely fashion and suffers from an imbalance of workload, because the relays closer to the substations have to handle a lot more traffic that those which are located farther away. Therefore, they formulate the general solution for minimizing the delay of data delivery. The main idea is to divide all the nodes into different groups, where each group contains the nodes that send information to the same base station. Finally, using introduced formulas for optimal placement of direct links, researchers provide numerical results and analyze how the number of groups influences time delay and energy consumption. Improvement in minimizing time delays achieved by the proposed method is significant. However, focusing on optimal transmission time, scientists do not consider the security of the transmission. As recent studies show [2; 3; 13], the idea of using WSNs in power grids is an important issue. However, none of the works examined above, pays attention to the security of the data traversing through wireless network. Since radio waves in a wireless communication spread in the air, one common risk is that wireless channels are more insecure and susceptible to numerous attacks than wired networks. This is why the security of the electrical transmission power grid using wireless sensor networks is one of the most essential factors in this type of deployments. In this paper, we try to face this problem, considering not only the minimization of time delays, energy consumption and the financial cost, but due to the data sensitivity, also take into account the security of the transmission.
## 2 Principles of Improved Harmonic Detection Methods
### Comparison of Bartlett-Hann Windows and Common Windows
\tag{8}\]  
where, \(\delta\) is a relative error, \(\Delta\) is an absolute error, \(\alpha\) is the measured value and \(\beta\) is a theoretical value. The data are shown in Table 2.  
As can be seen from Table 2, the accuracy of the ordinary DFT algorithm is weaker than the Bartlett-Hann window function interpolation DFT algorithm. The relative error of the frequency detection of the ordinary DFT algorithm is less than 2.34%, while the relative error of the amplitude detection can reach 254.05%, which does not meet the national power quality standards. However, the interpolation DFT algorithm based on Bartlett-Hann window function has a relative error of less than 1.86% in frequency detection, and a relative error of less than 4.3% in amplitude detection. Therefore, the Bartlett-Hann window interpolation DFT algorithm is relatively accurate for detecting harmonics.
## 3 Computational Methodology
### Reaction Force Analysis  
For the elementary steps of interest, the intrinsic reaction coordinate (IRC) algorithm [23] was used at the same level of theory. The total electronic energy (E) over the normalized reaction coordinate (\(\xi\)) was determined from the forward and reverse IRC profile, starting from the rate-controlling transition state optimized structure. After that, the reaction force (\(dE/d\xi\ =-F\)) was calculated from the numerical derivative of the energy with respect to the reaction, which was used to build the reaction force profile corresponding to the rate-controlling process of the selected mechanism [16; 24; 25; 26; 27].  
In the reaction force profile, the reaction coordinates' points correspond to a cut across the horizontal axis (F = 0), and their maximum or minimum leads to the determination of regions (_j_-th) where the energy of the process can be analyzed in separated-way using the associated works (_wj_). Values for works were calculated by numerical integration, as the area under each region (from critical point \"a\" to critical point \"b\": \(w_{j}=-\int_{k}^{b}Fd\xi\)). Those works were then used to determine how the energy of the process decompose into geometrical or electronic rearrangements, which relates to each region of the F profile.  
Additionally, from IRC calculation outputs (that contain the information of all atomic coordinates in each point of the IRC profile), geometrical parameters in this case, some selected bond lengths between atoms of interest were followed through the reaction coordinate leading to additional analysis.
## 3 Materials and Methods
### Intrusion Detection Using Extreme Learning Machine  
Following the dimensionality reduction via the Autoencoder, the Extreme Learning Machine (ELM) classifier is employed to identify network intrusions by classifying traffic as either benign or malicious. ELM is a fast and effective neural network model, making it ideal for real-time applications that require high speed and efficiency. The dimensionality reduced feature set, obtained from the Autoencoder, is fed into the input layer of the ELM, which contains 18 nodes, ensuring efficient processing of the data. The hidden layer, consisting of 500 nodes, uses ReLU activation functions to capture complex patterns within the data. The output layer uses a sigmoid activation function, enabling binary classification to differentiate between benign and malicious traffic. A significant advantage of ELM is its distinctive training method, which initializes the hidden layer weights randomly and calculates the output weights using a pseudo-inverse closed-form solution. This significantly shortens the training time in comparison to conventional neural networks, while still ensuring strong generalization capabilities. Hyperparameter tuning is performed to optimize performance, with a focus on adjusting the number of hidden nodes and the threshold value. A grid search technique is used to optimize the number of hidden nodes, beginning with 100, while an initial threshold of 0.5 is set to improve classification accuracy. Traffic is categorized based on the output of ELM, where values that meet or exceed the threshold are classified as malicious, and those below the threshold are identified as benign. This approach merges the dimensionality reduction capabilities of Autoencoders with the fast classification power of ELM, creating an efficient, scalable, and precise framework for protecting complex network environments from evolving cyber threats.