esa-sceva/satcom-chunk-collection huggingface dataset:
# Selective Laser-Melted Alloy 625: Optimization of Stress-Relieving and Aging Treatments
## 1 Introduction
A further loss in corrosion resistance can be determined by the simultaneous precipitation of chromium-rich carbides that can be obtained at 870 \({}^{\circ}\)C after short exposure times, as demonstrated by the TTP diagram in Figure 1. The dramatic toughness loss in the presence of the \(\delta\) phase is induced by its elongated and needle-shaped morphology. As reported in the literature [29], SLMed alloy 625 shows a faster kinetic of \(\delta\)-phase precipitation in comparison to the conventional wrought alloy. In fact, in the alloy processed using SLM, at 870 \({}^{\circ}\)C, the formation of the \(\delta\) phase starts after 15 min, while at least 20 h are required for the wrought alloy. Some studies also demonstrate that the presence of residual stresses promotes the precipitation response, but this effect has a lower importance compared to micro-segregation [17]. Consequently, this research work investigates alternative stress-relieving temperatures that are able to relieve residual stresses, avoid the formation of \(\delta\) phase, and maintain acceptable mechanical and corrosion properties. As reported by [PERSON] et al. [25], stress-relieving treatments performed at reduced temperatures in the range from 750 \({}^{\circ}\)C to 800 \({}^{\circ}\)C prevent the formation of the \(\delta\) phase, but only a partial reduction (from 50% to 90%) in the residual stresses is obtained. Therefore, this research work investigates the feasibility of higher stress-relieving temperatures in terms of both mechanical and corrosion properties. Considering that the stabilization heat treatment is performed at 980 \({}^{\circ}\)C in the conventional wrought alloy [12], we decided to investigate the stress-relieving temperature of 980 \({}^{\circ}\)C to also permit a simultaneous improvement in the corrosion resistance, with a clear beneficial industrial effect related to the production of SLM parts with high mechanical resistance and superior corrosion properties. Starting from the results published in prior research papers of the authors, the aging response of the as-built selective laser-melted material upon single- and double-aging treatments was analyzed in different time-temperature conditions among the most promising alloys by comparing the results against those obtained in the conventional alloy. The mechanical properties were measured using hardness and tensile tests, while corrosion resistance was analyzed in selected conditions in terms of susceptibility to intergranular corrosion in a solution of ferric sulfate and sulfuric acid for 120 h, according to the ASTM G28-A standard [38]. After the investigation of the aging treatment, the stress-relieving temperature was studied to improve the mechanical strength, tensile deformability, and corrosion resistance.
# Collision Risk Evaluation and Verification of GNSS-Based Train Integrity Detection
## 1 Introduction  
In railway freight transport, there might be decoupling accidents that would seriously threaten a train's operation safety. As shown in Table 1, according to the analysis of train integrity-related accidents or events caused by train couplers and traction devices in the United States in the past 40 years (1975-2015) [1], coupler breakage, caused by acceleration, and fault of management or maintenance, leads to train vehicle separation. Without protection, the left behind train vehicles will lead to rear-end collision with the approaching train, which would cause severe casualties and economic loss. Train integrity detection means checking and reporting on train completeness in movement. The train integrity monitoring system (TIMS) is a signaling platform to ensure trains consistently remain intact. Once trains split unintentionally, TIMS will send alarms to the relevant personnel to take appropriate measures to avoid a collision.  
A train integrity monitoring system based on on-board equipment is a low-cost solution for freight trains in middle and low-density railway lines. So far, there are train integrity solutions based on brake air pipe pressure, wireless sensor network, and GNSS as presented in reference [2, 3, 4, 5, 6]. In these systems, GNSS and other sensors are employed for self-localization and wireless communication. The risks of using GNSS are due to its inherent features [7], especially for safety relevant TIMS. It is necessary to point out the positioning faults or failures when the GNSS-based position is used in TIMS to ensure a safe and reliable position determination. So, a safety evaluation and verification in the system design phase should be done for the development of GNSS-based TIMS. CPN [8, 9] has been successfully applied for the modeling and verification of safety-relevant systems, including risk analysis, accident modeling, and system verification [10, 11, 12]. Colored PetriNet (CPN) is selected as the simulation of train positioning errors for the newly developed GNSS-based TIMS.  
The paper is organized as follows. Firstly, system structure and train integrity detection logic of GNSS-based TIMS are introduced. Secondly, the safety of train integrity detection is evaluated based on the positioning errors and protection level using an indicator of Failure per Hour (PFH) to quantify the criteria of SIL, and an experiment-based Monte Carlo simulation verification procedure is proposed. Finally, simulations are carried out by CPN and MATLAB using the statistics from field tests.
## 5 Conclusions  
An IoT-based IICS was designed and built in this study. This system can monitor the soil moisture levels of different profile layers in real time and perform calculations and analyses to realize automatic irrigation. The IICS can be controlled remotely and can involve other professionals to help users make better decisions. The system also enables various controlled irrigation treatments. In this study, we successfully constructed different regulated deficit irrigations through the IICS utilizing the IoT. The soil moisture of each plot was accurately controlled within the set range. The influential characteristics of SIF, the NDVI, NDRE and OSAVI of winter wheat on different regulated deficit irrigations were analyzed. The effects of different drought intensities on SIF, the NDVI, NDRE and OSAVI were different. Compared to well-watered, mild, moderate and severe drought, all had significant effects on SIF. In contrast, the NDVI, NDRE and OSAVI did not change significantly under mild drought and decreased significantly only under moderate and severe droughts. Moreover, the response of SIF was more drastic than the NDVI, NDRE and OSAVI when irrigation was performed. The results indicate that SIF is more sensitive to drought than the NDVI, NDRE and OSAVI and more suitable for drought monitoring.  
Conceptualization, W.Z.; methodology, W.Z.; software, W.Z.; validation, W.Z. and Q.S.; formal analysis, W.Z.; investigation, W.Z., J.Y., X.H. and Q.S.; resources, J.W.; data curation, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, J.W.; project administration, J.W.; and funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42071401.  
The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge [PERSON] for providing valuable suggestions on the modification of the pictures.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
## 4 Discussion
In the simplest case, the bending due to temperature is described by  
\[R=\frac{\alpha\Delta tH^{2}}{2D}, \tag{23}\]  
where \(\alpha\) is a linear extension coefficient of material (\(\alpha\) = 12.1 \(\times\) 10\({}^{-6}\) 1/\({}^{\circ}\)C for structures made of steel, \(\alpha\) = 10.8 \(\times\) 10\({}^{-6}\) 1/\({}^{\circ}\)C for structures made of concrete), \(\Delta t\) is the temperature difference for different sides of the structure, \(H\) is a height, and \(D\) is a mean structure size in the plane. For the structure with \(D\) = 100 m and \(H\) = 420 m, we obtained the values given in Table 9.  
Figure 31: VOS installation scheme for curvilinear structure. The VOS measurement range for displacements was taken from Figure 13. Regardless of the material, the VOS measurement range covers the possible deformation by almost three times. So, in this case, there are no special requirements or restrictions on the VOS application.
## 4 Diversity Performance
### Channel Capacity Loss (CCL)  
The CCL is another important parameter of diversity performance. The CCL shows the maximum data rate at which the transmission may persist with negligible loss. The CCL can be determined using the S-parameter of the antenna. The formula for CCL is given below:  
\[\text{CCL}=\ln\left([AB]-[CD]\right) \tag{9}\]  
where  
\(A=1-|S_{ii}|^{2}+|S_{ij}|^{2}\)  
\(B=1-|S_{ji}|^{2}+|S_{ji}|^{2}\)  
\(C=S_{ji}^{s}S_{ji}+S_{ji}^{s}S_{ii}\)  
\(D=S_{ii}^{s}S_{ij}+S_{ji}^{s}S_{jj}\)  
The maximum acceptable value of CCL in a MIMO system is below 0.4 bits/s/Hz. Figure 12 shows the CCL performance of the system. The graph shows that the CCL value is significantly below the threshold value, which means that the proposed MIMO system is practically applicable. Finally, the prototype of the proposed antenna is shown in Figure 13.  
Figure 12: Channel coefficient loss (CCL) of proposed MIMO antenna.
## 2 Related Work
### Dedicated Equipment  
In order to perform specific testing and monitor systems in real environments, dedicated equipment is required. In these cases, direct current measurements are preferred and provide better accuracy for SEL detection and classification. The equipment should be accurate, automated, and provide a fast response. In [11], the authors propose dedicated equipment and procedures to perform precise SEL characterization. The proposed setup is robust and designed to be used in challenging testing environments. It is composed of analog front-end components for the measurement and conditioning of the DUT current, power switches for handling the supply line, and a flash-based FPGA for controlling signals and logging. In [20], in-flight data is compared with ground estimations based on experiments in particle accelerators. For in-flight monitoring, different approaches can be used, but dedicated equipment is implemented to be integrated with the system and be resilient to the space environment, as demonstrated in [21].
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Dynamic Data Filtering
#### 2.7.3 2D-Gaussian Kernel Data-Density  
The second method to determine the data density is based on the calculation of a two-dimensional kernel. We assume that \(u_{r}\) and \(\alpha\) are subjected to random error processes; thus, their variability can be represented with a bi-variate Gaussian distribution [24], even when the overall behavior may be non-Gaussian. The validity \(\
u(u_{r},\alpha)\) for each measurement point with \(\alpha_{k}\) and \(u_{rk}\) in the time interval \(T\), with \(k\in\ \left\{1,...,n^{T}\right\}\), can then be assigned by the normalised data-density kernel in the \(u_{r^{\prime}}\)-\(\alpha^{\prime}\) reference system:  
\[\
u^{T}(u_{r},\alpha)=\frac{K^{T}(u_{r},\alpha)}{\max(K^{T}(u_{r},\alpha))} \tag{11}\]  
with  
\[K^{T}(u_{r},\alpha)=\ \frac{1}{n^{T}}\sum_{k=1}^{n^{T}}\frac{1}{\sqrt{2\pi( \sigma_{u_{r},\alpha})^{2}}}e^{-\frac{(\
u^{T}_{k})^{2}+(\alpha^{\prime}_{k}) ^{2}}{2(\sigma_{u_{r},\alpha})^{2}}} \tag{12}\]  
As the one-dimensional case from Section 2.7.1, the selection of \(\sigma_{u_{r},\alpha}\) is based on a Botev-estimator [21].  
The distinction between valid and invalid data is now made by the calculation of the validity for each measurement point using Equation (11). The following classification is based on a threshold, \(\
u_{th}\), which refers to the validity. Measurement point with a validity  
\[\
u(u_{r},\alpha)\geq\
u_{th} \tag{13}\]  
may be seen as valid. The influence of \(\
u_{th}\) to the resulting error is shown in the Appendix A.
## 5 Discussion
### Limitations and Future Work
#### 5.3.3 Support Strategies from the Psychological Literature Were Not Considered  
The present article focused on information modelling technologies and largely ignored the question of how information should be presented in the human-machine interface. This focus was chosen because much of the Human Factors' literature is concerned with interface design, while information modelling has not received much attention to date. Concerning the question of how to design interfaces that support contextualization, promising insights can be drawn from the work on ecological interface design [6; 8; 196], visualizations of causal relations [197; 198], and visualizations that provide continuity and context across interfaces with different levels of abstraction [199]. Besides interface design, the psychological literature provides an arsenal of methods to support contextual and causal reasoning via instruction and training. For instance, overconfidence can be reduced by asking people to consider the unknowns [43], the understanding of causal relations can be enhanced by explication and structural alignment [200], and the learning and transfer of complex system principles can be supported by simulations [201]. Future work should extract support strategies from different fields of psychology and integrate them with the technological possibilities of digital transformation.
## 4 Conclusions  
* The setting time of the BJPA-GGBFS composite geopolymer increased with a rise in the L/S and BJPA-substitution ratios. An L/S of 0.5 and BJPA-substitution ratio of 100% resulted in the longest initial (126 min) and final (183 min) setting times;
* The slump flow value dramatically decreased at BJPA-substitution ratios of 60%, 80% and 100%, whereas the slump value dropped with a rise in the L/S. The optimal slump value was 11.7 cm and the slump flow value was 21.6 cm;
* At an L/S of 0.3 and a BJPA-substitution ratio of 0%, the maximum compressive strength of a 3-day geopolymer was 33.4 MPa; the maximum compressive strength of a 56-day geopolymer was 81.1 MPa and the strength decreased by 8.5-21.5% with the increasing BJPA-substitution ratio;
* In terms of stress-strain behavior, an L/S of 0.3 and BJPA-substitution ratio of 0% obtained a maximum strain of 0.41% and a maximum modulus of elasticity of 53.1 GPa. BJPA-substitution ratios of 0%, 20% and 40% all resulted in higher moduli of elasticity than that of OPC;
* The growth trend of ultrasonic pulse velocity was consistent with that of compressive strength. The pores within the specimen reduced compressive strength and ultrasonic pulse velocity, with the highest and lowest velocity being 4274 m/s and 1695 m/s, respectively. In terms of the thermal conductivity coefficient, although the pores within the specimen reduced the compressive strength, they were effective in thermal insulation: a BJPA-substitution ratio of 100% achieved a minimum thermal conductivity coefficient of 0.8614 W/mK;
* The best ratio result of this study is that when L/S = 0.3, the BJPA substitution amount is 10%;
* The SEM revealed that crack size decreased with an increase in the BJPA-substitution ratio, with the maximum and minimum sizes being 5.80 \(\upmu\)m and 176.8 nm, respectively. Because the BJPA was unable to undergo full polymerization, the unreacted and incompletely reacted BJPA indirectly filled the cracks formed by the GGBFS during polymerization.  
Conceptualization, W.-T.K. and Z.-R.C.; Data curation, C.-U.J. and Z.-R.C.; Formal analysis, W.-T.K. and C.-U.J.; Investigation, W.-T.K.; Methodology, W.-T.K. and Z.-R.C.; Project administration, W.-T.K.; Resources, W.-T.K. and Z.-R.C.; Supervision, W.-T.K.; Validation, W.-T.K. and C.-U.J.; Visualization, Z.-R.C.; Writing - review & editing, W.-T.K. and C.-U.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the Ministry of Science and Technology of Taiwan for their financial support of this research under contract No. MOST 104-2221-E-151-040.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Design and Manufacturing
#### 3.1.4 Dashboard--Cloud Computing  
Tests were carried out to store some strain readings in a Google spreadsheet. Data were saved in a comma-separated-values (CSV) file, and the gathered data from the sensor were stored and displayed in the spreadsheet. The script read the last saved row that contained information, and then received the information package and provided the needed format to post counter, timestamp, measurement ID, and the raw value in a separate column. At the end of this action, the script increased its counter by 1 so that the correct order was maintained in the spreadsheet.  
The IoT approach has information distributed among platforms in order to have offline and online data-analysis capabilities. For this purpose, the dashboard was implemented using the Clic Data website to upload the data log from the Google spreadsheet to the cloud and display it on an online dashboard.  
The indicator-panel programming included graphs and numerical variables for the application and a chart. A line chart was selected to present the measured strain as a function of time. Moreover, the dashboard also included columns Strain Value for the section Value, Timestamp for the section Dates, and ID for the section Series. The Click Data website provided all these options within the graph settings, including an update of the displayed data every minute.  
To present the results on the dashboard, configuration was carried out within the webpage. The users had to schedule the updating periods or updating cycles for each desired information panel. The shortest time lapse between updates that Clic Data managed was 1 min. The Google spreadsheet needed to be updated along with the dashboard, so the same schedule was set. After displayingthe link in the browser, it needed to be refreshed by the user to visualize changes on the dashboard, or changes were communicated via email. The server was configured to send an email to the user with the dashboard attached after every update. The summarized data processing from the CPU to the cloud computing and the dashboard followed the sequence in Figure 9.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 3 Results and Discussion
### Ensemble Results  
After clustering horizontal GPS velocities with the methods mentioned above, it is aimed to determine the most accurate clusters by ensemble clustering algorithms. Figure 6a-c gives the ensemble clustering results from HGBF, MCLA, and NMF, respectively. As seen in Figure 6a,c, HGBF and NMF results are not satisfactory. In both methods, the Eurasian plate does not consist of a group of GPS sites in one shape and color, but two mixed clusters. Furthermore, in the NMF method, there is no Aegean cluster, even though it exists in all clustering method results. Moreover, in the HGBF method, the Aegean cluster is mixed with Anatolian clusters. In Figure 7, one can see the clusters for HGBF, MCLA, and NMF in velocity space, respectively. This figure provides a more discriminating assessment of ensemble results. In Figure 7b, the clusters are in groups and not mixed, but in Figure 7a,c, the situation is the opposite, the objects are involved in different clusters, not split correctly. For example, HGBF performs a clustering process such that each cluster has approximately equal numbers of data. This is because it is a more suitable algorithm for balanced clusters [9]. In balanced clustering, the HGBF results are not consistent because it performs a special clustering process where cluster sizes are constrained. Moreover, in the NMF method, the success of the results depends on the diversity and distribution of the compounds in the data set. Since it neglects local relationships between data points, it fails to reveal the geometric structure of data distributions, which is one of its main drawbacks [59]. On the contrary, MCLA considers a different approach even though it has  
Figure 5: Turkey GPS-derived velocity clustering results for \(k=5\) clusters with: (**a**) BIRCH; (**b**) HAC; (**c**) \(k\)-means; (**d**) mini batch \(k\)-means; (**e**) spectral clustering algorithms. Black solid lines are the block model at [5]. Gray thin lines show active faults [23].  
a similar structure (graph-based) to HGBF. MCLA first creates meta-clusters for expected clusters and places data points into these meta-clusters through a voting process.  
The results show that HGBF and NMF ensemble algorithms are not suitable for GPS velocity field clustering voting for Turkey. However, the MCLA method (Figure 6b) appears to be usable for the GPS velocity field for this region.  
As seen in Figures 6b and 7b, MCLA results are coherent with five clustering methods, and also with block borders given in [5]. Details will be discussed in the comparison of clustering results with the ensemble clustering part.
## 3 Parametric Studies  
### Design Procedure  
To achieve the desired goal, the prime design was selected from the trial designs to yield optimal performance. An iterative method was applied to assess the response of the unit cell. The length, width and distance between the SRRs together with the sizes of the slits were changed on a trial-and-error basis. Figure 2 depicts some of the trial designs. Figure 2a contains two square rings with a uniform width of 0.50 mm. The dimension of the outer and inner square ring is 9.2 \(\times\) 9.2 \(\times\) 0.035 mm\({}^{3}\). Following the simulation, 2 major response peaks at 5.92 GHz and 10.67 GHz were observed. Next, a split gap of 0.20 mm in each square ring was introduced into the design in Figure 2a to produce SRR. The design in Figure 2b yielded 3 resonance frequencies at 2.38 GHz, 4.72 GHz and 9.55 GHz for S\({}_{21}\). Figure 2c illustrates the introduction of two epsilon-shaped metal strips into design 1. The outer and inner radii of each epsilon were 2.5 mm and 1.9 mm, respectively, with a width of 0.60 mm. This altered design exhibited two resonances at 5.82 and 9.60 GHz. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Dimension (mm)** & **Parameter** & **Dimension (mm)** \\ \hline a & 10 & G & 0.20 \\ L & 10 & r\({}_{1}\) & 2.5 \\ b & 0.50 & r\({}_{2}\) & 1.9 \\ c & 0.60 & H & 4.6 \\ d & 0.50 & h & 3.5 \\ e & 1.50 & t & 1.524 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parametric description of the unit cell.  
(1.524 mm) as the substrate (Rogers RT6002) were designed by keeping other components unaltered. The numerical results are demonstrated in Figure 4. It is evident from Figure 4 that the sizes of 11 mm and 12 mm exhibited 3 resonances at nearly the same frequency with a lower bandwidth, which is quite low compared to the 10 mm unit cell. On the other hand, the 10 mm unit cell exhibited 3 major resonances at 2.38 GHz, 4.55 GHz and 9.42 GHz. Therefore, finally the 10 \(\times\) 10 \(\times\) 1.524 mm\({}^{3}\) dimension was selected for the projected metamaterial unit cell.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 4 UTM System Architecture
### Emergency Management
In general, the system should favor safety over efficiency, so higher values for \(\beta\) than for \(\alpha\) are expected. Finally, it is important to remark that all the actions sent by the EM to the UAS are just recommendations. According to the current regulation of the U-space ecosystem, the UTM can only suggest automatically possible correction actions, but those must be accepted or rejected by each UAS operator eventually. Nonetheless, our approach would be able to accommodate a UTM system where the whole process is executed autonomously without the need for human intervention, which is the final objective in the U-space framework.