esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Validation and Simulation Details
### Validation of the Numerical Simulation Method with Experimental Results
#### 3.1.2 Comparison of Experimental Measurements with CFD Simulations  
Cooling conditions for the rudder shaft are calculated for water flow rates of 1 and 0.2 m/s, with results presented in Figure 4 and Table 1. The temperatures measured at the final test moment are compared with simulation results for both inlet and outlet velocities. It is observed that the 1 m/s inlet velocity condition better matches the experimental results. The temperature at the upper flange (top of the rudder shaft) does not exceed 570 \({}^{\circ}\)C, with simulation results slightly higher than the experimental data, showing an error of no more than 3 \({}^{\circ}\)C. At the lower flange measurement point, the simulation result is slightly lower than the experimental data, with a difference of about 2 \({}^{\circ}\)C. The temperature difference at the bottom of the rudder shaft is even smaller, approximately 1 \({}^{\circ}\)C. In comparison, the 0.2 m/s inlet flow rate also provides cooling but results in overall temperatures higher than both the experimental data and the 1 m/s flow condition. Specifically, the temperature at the lower flange is approximately 17 \({}^{\circ}\)C higher, which aligns with theoretical expectations.  
Although the 0.2 m/s inlet condition meets the temperature requirements, the boiling mode also warrants examination. Figure 5 shows that under the 1 m/s conditions, the flow field exhibits mild behavior, with nucleate boiling occurring only in key areas, suggesting good safety but some inefficiency. Under the 0.2 m/s inlet flow rate, intense boiling occurs within the pipe, with some regions approaching film boiling, especially in the water supply pipe corresponding to the lower flange. This intense boiling poses significant risks. Based on the first round of results, it is recommended to slightly increase the flow rate above 0.2 m/s, ensuring that the supply flow rate remains above 0.5 m/s between 500 and 1500 s for safety, with a reduction to around 0.3 m/s in the initial and final 500 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Upper Flange** & **Lower Flange** & **Measurement Point 12 at the Lower** \\  & **Measurement Point 1** & **Measurement Point 9** & **Part of the Rudder Shaft** \\ \hline Experimental result & 566.4 \({}^{\circ}\)C & 256.9 \({}^{\circ}\)C & 47.4 \({}^{\circ}\)C \\ \hline Simulation calculation & 568.8 \({}^{\circ}\)C & 253.2 \({}^{\circ}\)C & 46.1 \({}^{\circ}\)C \\ results at 1.0 m/s & 573.8 \({}^{\circ}\)C & 273.6 \({}^{\circ}\)C & 49.2 \({}^{\circ}\)C \\ \hline Simulation calculation & & & \\ results at 0.2 m/s & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental values and simulation calculation results at different temperature measurement points.  
Figure 4: The temperature distribution on the surface of the rudder shaft and flange under water supply conditions of (**a**) 1 and (**b**) 0.2 m/s.
## 5 Case Studies
### The Life-Support System in a Space Capsule  
The life-support system in a space capsule, as illustrated in Figure 5, comprises four components [41]. The system cost and reliability are given by  
\[C_{s}(\mathbf{R}) =2\sum_{i=1}^{4}K_{i}R_{i}^{\alpha_{i}},\] \[R_{s}(\mathbf{R}) =1-R_{3}(\bar{R}_{1}\bar{R}_{4})^{2}-\bar{R}_{3}[1-R_{2}(1-\bar{R}_ {1}\bar{R}_{4})]^{2},\]  
respectively, where \(\mathbf{R}=(R_{1}\),\(R_{2}\),\(R_{3}\),\(R_{4}\)), \(K_{1}=100\), \(K_{2}=100\), \(K_{3}=100\), \(K_{4}=150\), \(\alpha_{i}=0.6\), and \(\bar{R}_{i}=1-R_{i}\) for all \(i\).  
[PERSON] and [PERSON] [22] combined multiple objective evolutionary algorithms with interval arithmetic (referred to as \"MOEA-IA\" hereafter) to optimize both the volume and cost of the box-shaped solution space simultaneously while ensuring that it meets pre-defined reliability requirements. The multi-objective optimization problem considered in this paper is identical to that in [22]. It can be formulated as follows:  
\[\max_{\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}}} \quad v(\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}}),\] \[\min_{\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}}} \quad f_{1}^{*}(\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}}),\] subject to \[\quad f_{2}^{*}(\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}})\geq f_{2}^ {l},\] \[\quad R^{l}\leq\mathbf{R}^{\text{low}}\leq\mathbf{R}^{\text{up}}\leq\mathbf{R }^{\text{u}},\]  
where \(\mathbf{R}^{l}=(0.80,0.80,0.80,0.80)\), \(\mathbf{R}^{u}=(1.00,1.00,1.00,1.00)\), \(v(\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}})=\prod_{i=1}^{4}(x_{i}^{\text{up}}-x_ {i}^{\text{low}})\), \(f_{1}^{*}(\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}})=\max_{\mathbf{R}\in[\mathbf{R}^{ \text{low}},\mathbf{R}^{\text{up}}]}C_{s}(\mathbf{R})\), \(f_{2}^{*}(\mathbf{R}^{\text{low}},\mathbf{R}^{\text{up}})=\min_{\mathbf{R}\in[\mathbf{R}^{ \text{low}},\mathbf{R}^{\text{up}}]}R_{s}(\mathbf{R})\), and \(f_{2}^{l}=0.99\). The computation is conducted using the following initial parameters: population size \(\text{pop}_{\text{size}}=200\), maximum generation \(\max_{\text{gen}}=50\), crossover probability \(p_{c}=0.95\), and mutation probability \(p_{m}=0.1\).  
In the last generation, the proposed DIRECT-NSGA-II yields 200 non-dominated solution spaces. Figure 6 presents our results alongside those obtained using the MOEA-IA method in [22]. It is evident that the cost of the box-shaped solution space obtained by our method is lower than that obtained by the MOEA-IA method when the volume of the solution space is the same. This suggests that the Pareto-optimal front obtained by the proposed DIRECT-NSGA-II method completely dominates that reported in [22]. These results demonstrate the efficiency and effectiveness of the DIRECT-NSGA-II method in identifying Pareto-optimal solution spaces.  
Figure 5: The life-support system in a space capsule.
## 1 Introduction
This process considers various factors, such as task characteristics, resource availability, performance requirements, and uncertainties associated with the number of tasks and computational resource availability. There are two main technical challenges in solving the risk-aware two-stage DRO problem. Firstly, it is difficult to solve the DRO problem due to the fuzzy sets. Secondly, the first-stage offloading decision involves zero-one integer programming. The second stage involves a resource allocation procedure for latency-insensitive and computation-intensive tasks. However, in this paper, we have to solve the risk-aware two-stage DRO problem, which brings new challenges. If we can solve the risk-aware two-stage DRO problem, we can achieve improved revenue while considering uncertainties and ensuring QoS in the SAGIN network architecture. Our contributions are summarized as follows:* We investigate the task offloading decision model in the SAGIN environment. The task offloading model consists of two stages: the first stage involves the task offloading decision, while the second stage focuses on edge-cloud collaboration and cloud resource allocation. * A fuzzy set of computational resources for edge computing nodes was constructed, considering CVaR. Then, based on the theory of Lagrange duality, the model is transformed into a semidefinite programming form, and the RaDROO algorithm is proposed to solve the task offloading problem with distributional robustness under risk awareness. * We conducted simulation experiments from two aspects. On one hand, we adjusted certain parameters of the proposed model to obtain the optimal values for those parameters. On the other hand, we fixed the parameters and compared them with the state-of-the-art algorithms in different computation and network environments. The experimental results demonstrate that our proposed model and algorithm have better results than the state-of-the-art methods in terms of usability, robustness, and risk. Table 1 shows the superiority of our algorithm compared with relevant literatures. The remainder of this paper is organized as follows. Section 2 provides a brief introduction to SAGIN and computational task offloading. Section 3 describes the network architecture and proposes a computation task offloading model under uncertain computation environments. Section 4 presents the RaDROO algorithm for obtaining optimal task offloading decisions and resource allocation strategies. Section 5 provides the performance evaluation and analysis. Finally, Section 6 concludes the work and presents future directions for research.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 5 E-Health Sustainability Model
### E-health Sustainability Model
#### 5.2.3 Resources  
The resources dimension is concerned with the aspects needed for the continuity of e-health's operation. These aspects are human resources, financial resources, availability of technical support and training, and the readiness of the ICT infrastructure within the healthcare organization. Human resources refers to the availability of enough users to use and support the e-health solution to provide the healthcare service or complete its tasks. Even with the existence of needs and demands for e-health, a shortage in human resources will affect the work process and cause the e-health solution to be suspend or stopped. The financial resources aspect refers to the availability of financing that supports the operation of the e-health solution, such as covering the expenses of purchasing a new system feature or upgrading the software license to cover even more features, such as technical support and maintenance. The technical support and training aspect involves the technical support and maintenance for the e-health solution's components and users, and providing them with the necessary knowledge to use the e-health solution. Maintaining the soundness of the e-health solution and technical skills among users will contribute to using the system, hence, a higher sustainability of the e-health solution is expected. The readiness of the ICT infrastructure is reflected in the e-health performance and therefore has an impact on its sustainability, because even with an efficiently designed e-health solution, its capabilities will not be optimally utilized if the ICT infrastructure within the organization is not ready to operate the e-health (e.g., no steady networking system, no sufficient servers' cablists, or outdated equipment.), hence affecting its performance.
## 4 Case Studies
### IEEE 123-Node System  
The IEEE 123-Node System [65; 66] (shown in Figure 5) was used to test the grid-following and grid-forming inverter constraints. The implementation of the system is based on the IEEE 123-Node Short-Circuit Case provided with the OpenDSS source code [63]. This was modified by adding distributed generation and removing both voltage regulators and switches. OpenDSS models these devices as transformers and lines respectively, but with very low impedances.  
Five different versions of the IEEE 123-Node System were considered: a base system with (1) distributed generation connected to the bulk transmission grid, (2) distributed synchronous generation for both grid-connected as islanded modes, (3) a single grid-forming inverter in islanded mode, (4) multiple grid-forming inverters in islanded mode, and (5) multiple grid-forming and grid-following inverters in islanded mode. The multiple synchronous generators and inverters were installed at the same nodes and with the same power ratings. These nine locations are indicated with red dots in Figure 5, based in part on the modified case study system with distributed generation presented by [73]. Their respective rated powers in MVA are shown in bold fonts. For synchronous generators, the subtransient reactance was assumed to be 0.15 p.u. The centralized inverter is indicated with the bulls-eye symbol, with its power in MVA shown in bold font. Table 2 summarizes the considered network configurations.  
In the case of grid-following inverters, OpenDSS contains a PV inverter model with current limiting capability. This was used to validate the introduced grid-following inverter model (Section 2.3). Table 3 provides a comparison of fault currents between the OpenDSS results and the results produced by PMsP for a single inverter configuration.  
In the case of grid-forming inverters, three-phase faults resulted in a system segmented into up to four sections, depending on the exact fault location. To keep the system angles synchronized and help optimization, the inverters at buses 8, 48, 95, and 108 were used as reference buses.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Configuration** & **Generation Type** & **Generation Placement** & **Islanding State** \\ \hline Base-GC & none & – & grid-connected \\ SG-M-GC & synchronous & multiple & grid-connected \\ SG-M-I & synchronous & multiple & islanded \\ GflI-M-GC & grid-following & multiple & grid-connected \\ GfrI-S-I & grid-forming & single & islanded \\ GfrI-M-I & grid-forming & multiple & islanded \\ GflI-GfrI-M-I & grid-following andgrid-forming & multiple & islanded \\ \hline \end{tabular}
\end{table}
Table 2: Summary of case study configurations.
## 3 Results
### FTIR-Solid \({}^{13}\)C-Nmr
#### 3.1.2 Solid \({}^{13}\)C-NMR  
To additionally verify the modifications of the rGO monoliths with TMOS-TMON-TMON-TMON-TMON, we have used solid \({}^{13}\)C-NMR.  
Figure 3 shows solid \({}^{13}\)C-NMR spectra of monolithic samples modified with TMOS, TMON and TMOSN prepared at 60 \({}^{\circ}\)C and with 0.5 analogy of AsA. As expected, for monolithic reduced graphene oxide materials [9], many GO groups are eliminated during the reduction process (temperature and AsA). The main peak is attributed to sp2 of the graphene lattice, around 120-130 ppm. Some bands attributed to the rGO are displayed in Figure 3[9; 38]. These bands are found at around 190 ppm (C=O), 164 ppm (O=C-O), 95 ppm (O-C-O), and 69/58 ppm (C-OH/C-O-C epoxy). After the modification reactions, the bands that show actual modification and are common to all samples are found 10-50 ppm attributed to -CH\({}_{3}\) and -CH\({}_{2}\). Especially samples with TMOSN that have more of these groups showed a more intense band (Figure 3). Moreover, TMON modified sample displays another band attributed to -N-C(O)-NH\({}_{2}\);163 ppm. Similarly, TMOSN modified monolith presents two more bands; one at 117 ppm (-CN) [39] and one at 223 ppm (-SC(S)-S) [40]. In the case of TMOSN, a band characteristic of the -SC(S)-S is clearly shown, and in the case of -CN, a broader band in this area is attributed not only to sp2 but also to the -CN, is observed.
## 4 Discussion  
Plant survival can be threatened by environmental perturbations such as climate changes and improper agronomical practices. The latter leads to loss of arable lands and other devastating consequences on crop yield. Salinization is one of the common causes of soil degradation, and since about half of the world's agricultural land is affected by salinity, the problem urgently needs a solution. According to the FAO, the level of salinization, usually reported according to the EC of the soil, can be classified into five classes, i.e., non-saline soil (EC = 0-2 dS/m), slightly saline soil (EC = 2-4 dS/m), moderately saline soil (EC = 4-8 dS/m), strongly saline soil (EC = 8-16 dS/m) and very strongly saline soil (EC > 16 dS/m). While plant salt tolerance can range from sensitive (glycoplytic) to tolerant (halophytic) species. The area dedicated to canola cultivation worldwide is growing because of its economic importance. Previous studies highlighted that, in presence of moderate saline stress (EC = 4-8 dS/m), _B. napus_ growth is reduced [14; 20]. To relieve the inhibitory effect of soil salinity on canola growth, different strategies have been developed and tested, including plant acclimation [10], seed priming [14], and exogenous application of methyl jasmonate [47]. In addition, the inoculation of plants with PGPB can significantly improve plant tolerance to stressful conditions [14; 16]. For this study, we selected five bacterial strains based on their plant growth-promoting activities [24; 29; 30; 31; 32; 33]. The results obtained clearly demonstrate the improvement of plant growth following inoculation with PGPB in both experimental conditions. According to the literature [48], auxin-producing bacteria can utilize this hormone to decrease salinity-mediated osmotic stress. In this study, all of the strains tested are IAA producers [31; 33; 49], and this may in part explain the elevated halotolerance detected. As suggested by some authors [50], plant inoculation with halotolerant bacteria strains can represent a good tool to improve plant fitness of salt-sensitive cultivars [51]. In addition, proper plant physiological responses to stress involve eliciting different stress response mechanisms. Under this perspective, bacterial metabolic activity may play a key role [16; 18; 33; 50], leading to better plant responses to stress. Confirming these observations, the data reported in this study showed an increase in the growth of inoculated plants grown in saline soils [20; 52]. Certainly, the reduced water loss recorded in inoculated plants is one of the most important traits of the bacterial treatment of plants (Figure 2). In
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 3 Precoder and Equalizer Design
### Hybrid Equalizer
#### 3.2.1 Design of Iterative Digital Equalizer
```
Input:\(\mathbf{W}_{a}\)
```
1:\(\mathbf{\Psi}^{(0)}=\mathbf{0}_{U}\)
2:\(\overline{\mathbf{R}}_{k}^{(0)}=\mathbf{H}_{k}(\mathbf{I}_{U}-|\mathbf{\Psi}^ {(0)}|^{2})\mathbf{H}_{k}^{H}+\sigma_{n}^{2}\sigma_{u}^{-2}\mathbf{I}_{N_{n}}\)
3:for\(i=1,\ldots,i_{\text{max}}\) do
4:\(\mathbf{W}_{d,k}^{(i)}=\mathbf{\Omega}_{d}\mathbf{H}_{k}^{H}\mathbf{W}_{a} \Big{(}(\mathbf{W}_{a})^{H}\overline{\mathbf{R}}_{k}^{(i-1)}\mathbf{W}_{a} \Big{)}^{-1}\)
5:\(\mathbf{B}_{d,k}^{(i)}=\Big{(}\mathbf{W}_{d,k}^{(i)}(\mathbf{W}_{a})^{H} \mathbf{H}_{k}-\mathbf{I}_{U}\Big{)}\Big{(}\mathbf{\Psi}^{(i-1)}\Big{)}^{H}\)
6: Compute\(\mathbf{\Psi}^{(i)}\)
7:\(\overline{\mathbf{R}}_{k}^{(i)}=\mathbf{H}_{k}(\mathbf{I}_{U}-|\mathbf{\Psi}^ {(i)}|^{2})\mathbf{H}_{k}^{H}+\sigma_{n}^{2}\sigma_{u}^{-2}\mathbf{I}_{N_{n}}\)
8:endfor
9:return\(\mathbf{W}_{d,k^{\prime}}^{(i)}\)\(\mathbf{B}_{d,k}^{(i)}\) ```
**Algorithm 1** Digital part of the equalizer
# A Polarization-Based Method for Maritime Image Dehazing
## 3 Experiments and Proposed Method
### Experimental Environment  
In this research, a polarimetric camera was utilized to capture images at a coastal site under foggy weather conditions to gather the necessary experimental data and evaluate the dehazing effects of the proposed method. The weather during the shooting is cloudy, with high humidity. The camera, with a resolution of 2448 \(\times\) 2048 pixels, employs a 2 \(\times\) 2 pattern on its sensor to capture polarized image information for the directions of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\). Through image analysis, these polarized images are separated into four distinct images (I(0), I(45), I(90), I(135)), each with a resolution of 1224 \(\times\) 1024. The camera was mounted on a stable platform and secured using a tripod and gimbal to ensure high-quality image acquisition. The experimental setup is illustrated in Figure 1, where Figure 1a shows a model OR-250 CNC-P polarimetric camera; Figure 1b shows the lens, which has a diameter of 50 mm and a relative aperture of 1:2.8; Figure 1c depicts the combination of the camera and the lens; and Figure 1d illustrates the imaging page of the camera. The arrangement of pixels in the polarized detector is shown in Figure 2. Through image analysis, four polarimetric images can be decomposed, as shown in Figure 3.  
Figure 1: Experimental setup. (**a**) Polarimetric camera; (**b**) the lens; (**c**) camera and lens combination; (**d**) camera imaging page.  
Figure 2: Distribution of the polarization detector’s pixels.
# Microscopic Analysis of Structure and Wear for Metallic Materials Using SEM
## 2 Materials and Methods
### Effect of Corrosion on Wear  
All purposely produced materials are under slower or faster erosion when they are in contact with the external environment, and it stands for the process of physico-chemical destruction of materials due to the influence of the environment, which is the fundamental aspect and definition of corrosion [12; 13].  
Pipes are commonly a very critical part of individual industrial applications, including in the construction industry, whether they are for fire and potable water distribution, technical water, or heating. When designing pipeline systems, it is necessary to pay attention to the increased requirements for safety and the selection of suitable materials. The material for pipe systems should meet a number of requirements, involving required strength, technological aspects, as well as resistance to corrosion in the given environment.  
The susceptibility of pipelines to corrosion depends on many indicators and also on the type of construction of the pipeline system [8; 12; 13].  
The degradation corrosion process in a liquid medium can be described as an electrochemical process that is superimposed by other agents, such as impurities, microorganisms, chemical compounds (salts, hydroxides, acids, organic substances), and many others. Corrosion in water is influenced by dissolved oxygen, pH, and water temperature [8; 14; 15].  
If corrosion occurs in the pipes, corrosion fumes are produced and abrasive particles occur, leading to the acceleration of the entire corrosion process. Corrosion products can destroy protective layers and increase friction, thus accelerating the entire wear process. The combination of corrosion and wear can lead to the formation of cracks and rupture of the material, resulting in the overall deterioration of the material properties [10; 11; 16; 17].  
The interaction of metal materials with the external environment is a critical aspect affecting their service and lifetime. The factors, such as temperature, humidity, environmental chemistry, and pressure, can affect the rate of corrosion and wear. The long-term impact of these deteriorative factors can lead to a gradual weakening of material properties that affect the ability to withstand loads and preserve integrity [11; 14; 16; 17].
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 2 Materials and Methods
### Defining the Input Variables and Fuzzyfication of the Values  
In this paper, the KAQI model is designed on the basis of concentration levels of pollutants based on the Environmental Protection Agency air quality index guidelines.  
As shown in Figure 2, AQI ranges from 0 to 500 and is divided into five levels: good, 0-50; moderate, 51-100; unhealthy for sensitive groups, 101-150; unhealthy, 151-200; very unhealthy, 201-300; and hazardous, 301-500.  
The higher the AQI value, the poorer the quality of air; the lower the AQI value, the better the quality of air. The three input crisp parameters used to define air quality in this paper include the following: nitrogen dioxide (NO\({}_{2}\)), sulphur dioxide (SO\({}_{2}\)) and particulate matter (PM\({}_{2.5}\)). The output of the system is taken as KAQI. The inputs are taken in the form of linguistic variables as well as the output.  
Figure 1: Main components of fuzzy model designed.