esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Computed Tomography Automation Architecture
## 2 Computed Tomography Setup and Initial Methods
### CT Setup Positioning  
Linear placement of the sample between the source and detector is a focus of multiple projects. [PERSON] et al. [6] investigated the influence of sample placement on the uncertainty of measurements in industrial CT. They optimized the object placement using its stereolithography (STL) model to find the placement that minimized the attenuation power. This reduced the presence of overlapping metal regions in the scan. With the STL model and object orientation determined, a minimum source-to-object distance was calculated  
Figure 3: Partial architecture based on “CAD-based defect inspection with optimal view angle selection based on polychromatic X-ray projection images” by [PERSON] et al. [4].  
that required the object to be fully inside the extent of the cone beam through a complete rotation about the chosen axis. A minimum photon energy was also determined. The minimum source-to-object distance maximized the distance from the object to the detector and resulted in a magnification of the image. The voxel size of the CT scan was decreased, and they claimed that resolution was increased as well. However, the cost of increasing the geometric unsharpness value when this magnification method is used was not mentioned in this paper.  
[PERSON] et al. [7] experimented with varying the sample's linear placement between the source and detector. They focused on the precise source, object, and detector positioning based on the source energy. The pixel magnification was measured while shifting the sample rotary stage forward and backward. The effective position of the source and detector plane varied depending on the source spectrum, the absorbing characteristic of the object, and the detector material and thickness. This was based on the varying penetration depth in the detector, which is where the incident radiation interacts with the scintillator.  
[PERSON] and [PERSON] [8] present an additional theoretical method to identify an optimal imaging geometry. They include modifications to the source, object, and image plane distances as well as looking at the X-ray focal spot size in order to optimize the magnification of the imaging.
# Numerical Investigations on the Blade Tip Clearance Excitation Forces in an Unshrouded Turbine
## 3 Results and Discussion  
### Monitored Data and Dynamic Coefficients  
Figure 6 presents the monitored rotor motion data of two cycles in the time domain during the transient calculation process and the corresponding transformed results in the frequency domain with the x-direction excitation. According to the results in the frequency domain, the motion amplitude in the x-direction of each frequency was 5 \(\upmu\)m and that in the y-direction was 2.5 \(\upmu\)m, which were exactly the target values in Table 3. This indicates that the rotor movement followed Equation (3) as expected.  
Figure 7 shows the response forces in the time and frequency domains with the x-direction excitation. It is shown that the direct response force (\(G_{x}\)) increased first and decreased afterward with the frequency in a small range, where the frequency of the maximum direct force amplitude was about 100-120 Hz. The cross-response force (\(G_{y}\)) increased with frequency in a relatively larger range, where the maximum amplitude appeared at a frequency of 260 Hz. The variations of the direct and cross-response forces with the whirling frequency indicates that the rotordynamic coefficients were dependent on the whirling frequency.  
Figure 6: Rotor motion in the time and frequency domains with the x-direction excitation: (**a**) time domain and (**b**) frequency domain.  
Figure 7: Response forces in the time and frequency domains with the x-direction excitation: (**a**) time domain and (**b**) frequency domain.  
Using the monitored data above, the rotordynamic coefficients were solved. Figure 8 presents the variation of the rotordynamic coefficients with frequency. It was shown that the rotordynamic coefficients were dependent on frequency. Moreover, the direct stiffness and damping coefficients were the same (\(K_{xx}=K_{yy}\), \(C_{xx}=C_{yy}\)) and the cross-coefficients were equal in magnitude with opposite sign (\(K_{xy}=-K_{yx}\), \(C_{xy}=-C_{yx}\)). The direct stiffness coefficients \(K_{xx}\) and \(K_{yy}\) were positive at all selected frequencies, which indicates the direct force contributions in the tip clearance excitation forces. [PERSON] and [PERSON] did not predict the direct forces in their model; instead, these forces were confirmed in later experiments. It also should be noted that since the direct stiffness coefficients decreased with the frequency, the direct stiffness might be zero at a frequency around 270 Hz. Moreover, the cross-stiffness coefficient \(K_{xy}\) was positive at all frequencies and it increased with the frequency, which indicates that the tip clearance excitation forces could promote forward rotor whirl. Compared to the stiffness coefficients, the damping coefficients were quite small, which indicates that there was no damping effect. Hence, the damping coefficients are not discussed in the following sections.
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 2 Related Work  
Energy efficiency is a critical issue for a WSN. To make a WSN effective in hazardous environments is a big challenge. To date, the bulk of research concerning the lifetime performance of WSN has been done. [PERSON] et al. explored the mobility concept of nodes in search of energy [PERSON] et al. (2019). Mobile nodes can transfer power to the immobile nodes to elongate the lifetime of the WSN.  
[PERSON] et al. upgraded an innovation for charging numerous devices concurrently by using appropriate coupled magnetic resonators [PERSON] et al. (2019). [PERSON] et al. considered the issue of scalability for nodes in rechargeable wireless networks [PERSON] et al. (2019). [PERSON] et al. described the optimization of multi-hop dynamic data routing and scheduling for the wireless charging vehicle by using the wireless power transfer technique [PERSON] et al. (2019). Motivated by their research in wireless power transfer, we have explored both single-hop routing and multi-hop routing in this study by using the proposed weighted grid clustering algorithm. Additionally, the WPCD travels periodically along the proposed route and charges the nodes that come along its way. The total distance from the first node to the last node, that the WPCD navigates in the network is called the traveling cycle, whereas the entire time which the WPCD spends at a rest station (RS) to recharge its battery for the next cycle is called vacation time.  
[PERSON] et al. introduced the concept of energy synchronization charging (ESync) between nodes based on a set of nested traveling salesman problem (TSP) tours to enhance the charging travel distance as well as the charging delay of sensor nodes [PERSON] et al. (2019). [PERSON] et al. developed an approach, which used wireless power transfer for electrical vehicles in their work [PERSON] et al. (2019). [PERSON] et al. proposed an energy efficient GR-Protocol to minimize energy consumption and time complexity through the calculation of traveling (computation) time for WCV [PERSON] et al. (2019). Of late, they have also developed a framework for simulating a WSN environment for wireless power transfer using mobile vehicles [PERSON] et al. (2019).  
Most of the existing work on rechargeable wireless sensor networks investigates the optimized path for the WPCD using an approach that increases the vacation time. Previous researchers consider mathematical optimization to optimize an objective function based on the traveling path of the WPCD and energy model. However, these optimization algorithms are not economically feasible for the sensor nodes and need experts to implement them. Assuming all these considerations, we proposed a unique traveling path for the WPCD based on the shortest path to increase its vacation time, which is relatively economical to compute.
## 4 Results
Interestingly, the probability distribution for female agent matches exhibited a longer tail, with top female agents receiving 2-3 times the number of matches of median males in M1 and 3-5 times the median male agents in M2. This implies a greater clustering of matches for female agents than for male agents. However, the median number of matches for male agents in M1 was lower but much more probable than either its female agent analogue with male agent median value in M1 representing 17% of sample compared with 12% for female agents and 11% and 8% for M2 agents, respectively. Messaging distribution (Figure 5C) analysis was relatively straightforward to consider since agents in M1 simulations did not possess the ability to send messages. M2 agents received a median of 2 messages for male agents and 3 messages for female agents and the messaging probability distribution carried a right skew with some agents receiving more than 10 message per simulation run. Of central interest is the mean number of matches (\(\mu\)) generated by M1 when compared to M2--almost precisely double. What is of additional significance is the variance reduction and shorter tail of M2. Specifically, the coefficient of variation for M1 is 0.42 while for M2 we calculate it as 0.53. This suggests a more even and equal distribution of matches among nodes--or in real-world terms, agents who were previously unsuccessful in receiving many matches in M1 receive more matches in M2, hinting towards the emergence of specialized strategies. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Statistic** & **N** & \(\mu\) & \(\sigma\) & **0 th** & **25 th** & **75 th** & **100 th** \\ \hline ’Like’ Count & 1000 & 33.030 & 8.504 & 10 & 27 & 39 & 58 \\ ’Dislike’ Count & 1000 & 146.600 & 60.624 & 48 & 105 & 207.2 & 264 \\ ’Match’ Count & 1000 & 11.694 & 5.029 & 0 & 8 & 15 & 31 \\ ’Message’ Count & 1000 & 0.000 & 0.000 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: M1 Model Run Summary Statistics including type of statistic, mean, standard deviation, and quartiles. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relation Type** & \(\Delta b_{M}\) & \(t\) & \(p\) & **95\% CI** \\ \hline ’Like’ & 0.001 & 0.002 & 1.00 & [\(-\)0.81, 0.8] \\ \hline ’Dislike’ & 3.901 & 1.53 & 0.12 & [\(-\)1.07, 8.87] \\ \hline ’Match’ & 5.85 & 31.35 & \(p\) \textless{} 0.001 & [5.48, 6.21] \\ \hline ’Like Message’ & \(-\)2.874 & \(-\)43.57 & \(p\) \textless{} 0.001 & [\(-\)3.00, \(-\)2.7] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical comparison of M1 and M2 models using a paired \(t\)-test. Shown are the parameter difference \(\Delta b\), the \(t\)-statistic, \(p\)-value and confidence interval of the parameter estimate. This table shows no mean difference in the number of likes yet a statistically significant mean difference in the number of matches between M1 and M2.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 4 Discussion of SFS Variability
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SFS Type** & **Effect** & \
\begin{tabular}{c} **Effectiveness as a Supplementary** \\ **Cementitious Material** \\ \end{tabular} &
\begin{tabular}{c} **Effectiveness as a Soil Stabilizer** \\ \end{tabular} \\ \hline \multirow{3}{*}{EAF Slag} & Increase & [95; 152; 195] & [196; 197; 198; 199; 200; 201] \\ \cline{2-4}  & Decrease & [95; 133] & \\ \cline{2-4}  & No change & [202] & \\ \hline \multirow{3}{*}{BOF and LD Slag} & Increase & [33; 115; 203; 204; 205; 206; 207] & [208; 209; 201; 202; 203; 204; 205; 206; 207] \\ \cline{2-4}  & Decrease & [38; 214; 215] & \\ \cline{2-4}  & No change & [128; 216; 217] & \\ \hline \multirow{3}{*}{LMF Slag} & Increase & [134] & [218; 221; 222] \\ \cline{2-4}  & Decrease & [26; 27; 238; 223; 224; 225] & \\ \cline{2-4}  & No change & & \\ \hline \multirow{3}{*}{AOD and Stainless Steel Slag} & Increase & [226] & \\ \cline{2-4}  & Decrease & [26] & \\ \cline{2-4}  & No change & & \\ \hline \multirow{3}{*}{Other Steel Slags or Unspecified} & Increase & [227; 228; 229; 230] & [155; 231; 232; 233; 234; 235; 236; 237; 238; 239; 240; 241] \\ \cline{2-4}  & Decrease & [167; 242; 243; 244; 245; 246] & \\ \cline{1-1} \cline{2-4}  & No change & [243; 247; 248] & [249; 250] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of SFS as a supplementary cementitious material or a soil stabilizer. possibly chemical reactions occurring in the cement paste and the SFS particle, such as for BOF slag [113], EAF slag [9], or other or unspecified SFS [151; 154] aggregates. Lastly, the variability in concrete behavior with SFS aggregates could be attributable to the aggregate absorption capacity and absorption. Particularly for aggregates with high absorption capacities, improperly accounting for the moisture content can drastically affect concrete properties and microstructure development (e.g., [13; 14]). The absorption capacity of SFS aggregates is typically \(\leq\)4% [258; 259; 260], with coarse and fine SFS aggregates typically ranging 1-2% and 2-4%, respectively [260]. As these values are comparable to conventional virgin aggregates, variability in concrete properties may not be significant, provided that the moisture content is accommodated during the mix design and batching processes.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 4 Discussion and Analysis
### NMGs' Configuration
DRL methods offer alternative approaches which do not rely on explicit models and technical parameters. However, they are not without their own set of challenges. DRL methods encounter complexities related to coordination, management, and the accuracy of data. Single-agent DRL approaches may face limitations in communication, while multi-agent systems introduce complexity and time-consuming processes. Moreover, DRL methods rely on learning from trial and error, and under extreme events, the data may be scarce and less accurate, potentially impacting the training processes negatively. The trade-off between the benefits of model-free methods like DRL and the challenges they present underscores the need for careful consideration and evaluation in the context of NMGs' formation. The configuration problem of NMGs, given the inclusion of hybrid AC/DC MGs, demands more than conventional PFA techniques. The amalgamation of AC and DC introduces unique challenges, particularly non-smooth characteristics resulting from DGs and converters. Traditional power flow algorithms prove inadequate in addressing such challenges, leading to distributed power flow calculation results that significantly deviate from the actual model constraints. DNMGs are envisioned as the future of NMGs, behaving akin to smart grids with capabilities for dynamic forecasting, responsive actions, and adaptive behavior. They not only react to changes in demand but also possess the capability to alter their configuration in diverse conditions, showcasing efficiency in response and adaptation. While many acknowledge the potential of DNMGs, the lack of real-world testing is noticeable in most works. Furthermore, to attain optimal control and flexibility in DNMGs, it is imperative to deploy remotely controlled automatic switches and control agents at each node and line. However, this requirement introduces complexities in the formation of DNMGs and may present economic challenges, potentially leading to a more delayed restoration process.
## 4 The Role of the Extracellular Polymeric Substance in Biofilm Formation and Function  
EPS constitutes the structural basis of biofilms, significantly contributing to their unique three-dimensional architecture. Research indicates a dual role of EPS in biofilm formation. EPS promotes bacterial migration and plays a role in biofilm dispersion, thereby influencing the mobility of surface biofilms [99]. Secondly, EPS improves bacterial adhesion to surfaces, a critical process in the early stages of biofilm formation and its ongoing stability [100]. EPS functions as a biological adhesive, facilitating the adhesion of bacterial cells, providing mechanical support, and enhancing resistance to shear forces and physical disruptions [1]. Interactions between EPS and eDNA are essential for the spatial organization of biofilms, especially during their later developmental stages [101].  
EPS safeguards biofilm cells against environmental stressors. The matrix is a barrier to ultraviolet (UV) radiation by reducing light penetration, thereby protecting bacterial DNA from UV-induced damage [102; 103]. Additionally, EPS inhibits dehydration by sustaining elevated hydration levels, fostering a moist internal environment essential for microbial survival in low moisture or nutrient scarcity [104; 105]. The hydration property is essential for maintaining the internal stability of the biofilm, particularly in adverse environmental conditions.  
The EPS matrix enhances the biofilm's resilience to extreme environmental fluctuations, such as temperature variations and changes in pH. EPS's adaptability enables it to shield microbes from immune responses and restrict antibiotic penetration, thereby contributing to extended inflammation and resistance to standard antibiotic treatments. This resilience notably complicates the management of infections [2; 3; 106].  
The conservation and distribution of nutrients within biofilms are significantly affected by EPS. The matrix captures and stores essential nutrients and growth factors, ensuring bacterial cells possess sufficient resources for survival and growth [107]. EPS contributes to nutrient cycling in peripubric biofilms by promoting the accumulation of nitrogen and phosphorus and enhancing organic carbon content [108]. These functionalities are particularly crucial in practical scenarios, such as enhancing biofilm resistance on medical implants, which may lead to chronic infections and complicate therapy [109; 110].  
The compact structure of the EPS matrix facilitates close proximity among bacterial cells, thereby enhancing opportunities for genetic exchange. This proximity is essential for horizontal gene transfer, facilitating the dissemination of antimicrobial resistance genes and thereby increasing the adaptability and resilience of biofilm communities [111; 112]. Different functions of EPS in biofilm structure, function, and interaction are presented in Figure 4.
## 2 Fundamentals of the New Algorithms  
The basic scheme of the acquisition method proposed in this paper is illustrated in Figure 1. The left part of the figure indicates the traditional GNSS acquisition process from which a two-dimensional search grid (marked in green color) is generally obtained, while the right side shows the presence of a new additional block able to refine the Doppler frequency estimate in a simple way. The innovation proposed in this paper refers to the algorithms used by this additional block to refine the frequency estimate.
# Self-Sovereign Identity-Based E-Portfolio Ecosystem
## 4 Implementation
### EApp-Chain
#### 4.2.2 Login with Digital Signature  
Once the \(ACin\) is successfully created, the user gains an identity that can be utilized within the e-portfolio ecosystem. The login process encompasses two distinct instances: the initial login and general login.  
Figure 12 illustrates the login flow when the user first accesses another organization. In the e-portfolio ecosystem, this workflow takes place when a student participates in an activity.  
The login process involves the following steps:  
1. The user uses their primary identity to sign a required nonce and generate a digital signature, which is sent to the \(AO\);
2. The \(AO\) verifies the signature and queries the database to determine whether the user is logging in for the first time;  
Figure 11: Creating access control instance flow.  
If the user's EApp-chain \(P_{u}k\) does not exist in the database, the \(AO\) creates another nonce and asks the user to sign it with their EApp-chain \(P_{r}k\);
4. The \(AO\) uses the EApp-chain \(P_{u}k\) to verify the signature and find the \(ACin\) in the app-chain;
5. The \(AO\) records the relationship between the primary identity and the EApp-chain \(P_{u}k\) in the database to record that the user has finished the initial login.  
It is crucial to understand that until the initial login is completed, the \(AO\) does not know the user's identity within the EApp-chain.  
This login flow ensures the security of the e-portfolio ecosystem by verifying the user's identity and recording their access to the EApp-chain.  
Figure 13 shows the general login flow for the e-portfolio ecosystem. Once the user has completed the initial login process and the organization has recorded their identity within the EApp-chain, they can use their primary identity to generate a digital signature for general login.  
The general login flow involves the following steps:  
1. The user creates a digital signature with their primary identity and forwards it to the \(AO\);
2. The \(AO\) verifies the signature and uses the EApp-chain \(P_{u}k\) to find the \(ACin\) in the EApp-chain;  
Figure 12: Initial login flow.  
Figure 13: General login flow.  
The \(ACin\) determines if the user possesses the required access rights to access the requested resources.  
If the user is authorized, the \(AO\) grants them access to the requested resources.  
By using a digital signature for login, the e-portfolio ecosystem ensures the security of user data while providing a convenient and efficient login experience.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 4 Experimental Pipeline and Sensor Installation  
### Experimental Pipeline Introduction  
The pipeline leakage experiment was conducted on part of the fire-fighting pipeline with a total length of 600 m (shown in Figures 7 and 8). The outer diameter and the wall thickness of the pipeline are 219 mm and 6 mm, respectively. The pipeline leakage was simulated by opening the existing valves S1-S11, whose positions are displayed in Figure 7. The actual valve is presented in Figure 8b. A fire service pump was used to load pressure into the pipeline. Fourteen FBG loop strain sensors were mounted on the pipeline model with the same interval of 20 m, forming a sensor array (illustrated in Figure 7). It should be noted that the pipeline between sensors L1 and L14 is the experiment section with the length of 240 m. The effect of temperature on the FBG strain hoop sensor is negligible since pipeline leakage occurs within a few seconds. The four FBG temperature sensors, mounted close to the sensors L1, L4, L8 and L14, were utilized to achieve temperature compensation of the four FBG strain hoop sensors in long-term monitoring.  
Figure 5: Relationship between the pressure and hoop strain.  
Figure 6: Calibration test results.