esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
## 4 Conclusions  
A numerical parametric analysis was performed on the cold spray process. The deposition and distribution of injected nickel powder were analyzed for the purposes of creating pin fin geometries for a hydrogen electrolysis process. The parametric study analyzed the effects of substrate SOD, mask SOD, mask type, and nozzle inlet operating conditions. Commercially available masks, as used in previous studies, were employed. Although identifying the optimal conditions was challenging due to the interplay of multiple parameters (such as particle type, size, operating conditions, nozzle geometry, and mask configuration), near-optimal deposition conditions for Ni particles were found. Among the analyzed parameters, the nozzle inlet conditions had the most significant impact on particle deposition. Increasing the inlet pressure to 4 MPa achieved a deposition efficiency (DE) of 99%, consistent with our previous findings [31]. This high-pressure inlet condition enhanced the gas flow velocity, both inside and outside the nozzle, resulting in a higher particle impact velocity. At this high pressure, the mask type and standoff distance did not affect the DE, making 4 MPa the optimal condition. If achieving 4 MPa is not feasible, optimal deposition at lower pressures, such as 2 MPa, requires a longer nozzle-to-substrate SOD (approximately 20 mm) and the mask positioned closer to the substrate. These findings align with the experimental results of [PERSON] et al. [23], who experimentally demonstrated an improved deposition for aluminum and stainless-steel particles under reduced pressures. However, at a medium pressure (2 MPa), the DE ranged from 12 to 21%. It was also found that the DE increased linearly as the mask SOD increased. This relationship is due to the higher average particle velocity that was achieved when the mask SOD was increased. It was found that the average velocity of the particles before entering the mask slightly increased, also linearly, when the mask SOD was increased. This was due to the particles being allowed to accelerate for a longer period. Regarding the particle distribution and deposition, it was shown that the nickel particles deposited primarily in the central opening of the mask (i.e., mask opening in line with nozzle centerline). While this study focused on key process parameters, future work should investigate the influence of particle feedstock nonuniformity, including sphericity, to further refine the understanding of particle dynamics and deposition outcomes.  
Conceptualization, A.D. and M.J.; methodology, N.N. and M.J.; software, N.N.; writing--original draft preparation, N.N.; writing--review and editing, A.D. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Simplified Free Vortex Wake Model of Wind Turbines for Axial Steady Conditions
## 1 Introduction
In FVW methods, the number of discrete elements per vortex filament can be very large, making the tracking process memory-intensive and computationally demanding, although still considerably less demanding than when using CFD methods. The FVSW model has a computation time of about tens of minutes at one steady condition, and the TVW model has a computation time of about several minutes. Although the TVW model requires much less computational time than the FVSW model because it has fewer wake nodes in the far wake, the computational cost makes it somewhat impractical as an iterative optimization design tool. The main objective of this study is to reduce the number  
Figure 1: Schematic of the initial wakes of different FVW models. (**a**) FVSW model. (**b**) TVW model. of wake nodes that need to be computed and achieve lower computation time in the axial steady condition. The velocity in the plane of rotation of the blade induced by the near wake is remarkable, so the near wake is still modeled by the vortex sheets in order to reflect the actual flow in this study. The effect of the far wake on the plane of rotation is relatively smaller due to the increase in distance, so the far wake is simplified into a series of vortex rings through learning from the studies of [PERSON] [13] and [PERSON] et al. [15]. Therefore, the new simplified FVW model is referred to as the vortex sheet and ring wake (VSRW) model. In Section 2, the development of the VSRW model is described. Section 3 describes the analysis of the effect of the length of the near wake. The results are presented in Section 4 and include the rate of convergence of the wake iteration, the wake geometry, the induction factor in the wake, and the low-speed shaft torque (LSST). The conclusions are drawn in Section 5.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### PFCs Removal Efficiency Through Pilot Test
#### 3.4.1 Removal Variation by Coagulation-sedimentation  
As shown in Figure 3, the PFCs in the coagulation-sedimentation test showed a phased concentration decrease. However, overall, the removal did not increase despite the additional injection of the coagulator of more than 40-50 mg/L. When 40-50 mg/L of coagulator was injected, the removal of PFPeA was highest at 12.3%, followed by PFHxS at 11.5%, PFNA at 10.9%, PFHxA at 8.9%, and PFOA at 6.4%.  
The overall removal of PFCs was about 10%, demonstrating that they were somewhat removed by coagulation-sedimentation treatment. However, the effect was shown to be negligible and that it would be limited in removing non-degradable PFCs.
# Software Fault Localization Based on SALSA Algorithm
## 5 Results and Analyses  
The software fault localization based on the SALSA algorithm proposed in this paper first constructs a Root Set Control Dependency Graph (RSCDG) that contains information on fault propagation. Then, it employs a random walk strategy to analyze the link structure of the RSCDG and scores each node, identifying important nodes related to fault propagation. Each node ultimately has two scores: a hub score and an authority score, which represent the importance and centrality of the faulty statement in the fault propagation process. This paper prioritizes the use of the hub score for the final ranking.  
To explore the software localization effects of SA-SBFL, this paper conducted experiments on five projects in Defects4J, using six statistical formulas to calculate the suspicion values of statements as the initial weights of nodes in the RSCDG. The experiments were evaluated from multiple metrics, including Top-1, Top-2, Top-5, and EXAM. To comprehensively verify the effectiveness and practicality of the experiment, this paper evaluates three aspects.  
RQ1: How accurate is SA-SBFL in fault localization?  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Program** & **\#Faults** & **LoC** & **\#Tests** \\ \hline Chart & JFreefigure & 25 & 96 k & 2205 \\ Closure & Closure Compile & 133 & 90 k & 7927 \\ Lang & Apache commons-lang & 65 & 22 k & 2245 \\ Math & Apache commons-math & 98 & 81 k & 3546 \\ Time & Joda-Time & 27 & 28 k & 4130 \\ \hline \end{tabular}
\end{table}
Table 4: Defects4j dataset.  
RQ2: Which score is more advantageous in fault localization, the final hub score or the authority score of the node?  
RQ3: What are the differences in fault location accuracy when using suspicious values obtained from different statistical formulas such as node weights?
###### Abstract  
The characterization of communication devices in a certain frequency band can be accelerated if a fast frequency sweep technique is used instead of a discrete frequency sweep. Existing fast frequency sweep techniques are either complex or specific for a certain electromagnetic solver. In this work, a new fast frequency sweep method is proposed that consists in segmenting the device under analysis into simple building blocks. Each building block is characterized with a generalized (multimode) circuital matrix whose elements present a simple and flat frequency response that is interpolated using natural cubic splines with very few points. In this way, the response of each block along the whole frequency band is obtained efficiently and accurately with as many frequency points as desired. Then, the circuital matrices of all the blocks are cascaded and the circuital matrix of the whole device in obtained. The new fast frequency sweep was successfully applied to the analysis of different types of devices (all metallic rectangular waveguide filter, dielectric loaded rectangular waveguide filter, and substrate integrated waveguide filter). The computational times were reduced to 15% or 19%, depending on the device, when compared with a discrete frequency sweep using the same electromagnetic solver.  
electromagnetic analysis; fast frequency sweep; microwave filters; substrate integrated waveguide; rectangular waveguide +
Footnote †: journal: Applied Sciences
# An Insight into the Chemistry of Cement--A Review
## 4 Setting of OPC
### Flash Setting vs. False Setting  
At this point, it is mandatory to clarify the difference existing between flash setting and false setting. \"Flash setting\" consists of the fast loss of plasticity of the pastes once mixed with water, thus reducing the effective time of workability of the cement. Such a rapid transformation is caused by the increment in the early reactions involving aluminate and ferrite chemical species, with the production of plates of AFm all over the material, and the release of a high heat of hydration [65]. The meshwork of AFm plates, responsible for minimizing the possibilities of remixing, is overcome by the more compact coatings of AFt phases formed in presence of gypsum, which is a set regulator. Gypsum dissolves in water, releasing Ca\({}^{2+}\) and SO\({}_{4}\)\({}^{2-}\) ions. As previously discussed, the quantity of hydrated C\({}_{3}\)A and C\({}_{4}\)AF in the pre-induction period is reduced, with the consequent formation of the AFt phase during hydration. This AFt phase precipitates at the surface of the cement grain forming a microcrystalline layer. In this way, the flowability and plasticity of the cement paste is maintained until the formation of the hydrated phases, such as C-S-H that induces the \"normal setting\".  
On the other hand, the term \"false setting\" refers to the formation of secondary gypsum (i.e., calcium sulphate dehydrated) crystals, in the presence of either a very low amount or an absence of C\({}_{3}\)A. In this case, this \"false setting\" is caused by the interlocking of gypsum crystals, and, simply, intensive mixing can restore the plasticity of the mixture. \"False setting\" is typical for cements containing high concentrations of either K\({}_{2}\)O (i.e., precipitation of K\({}_{2}\)Ca(SO\({}_{4}\))\({}_{2}\)), or C\({}_{3}\)A (i.e., excessive formation of the AFt phase).
## 2 Materials and Methods
### Quarry's Study Case with Elevation Profile from DEM Generation
#### 2.3.2 Processing  
Phase one to begin, for a successful DEM generation, are the selection of two appropriate images [36]. These two images allow advantageous conditions as short temporal and convenient perpendicular baselines and suitable atmospheric conditions. The main software provided by ESA development, making use of their satellite constellations for the scientific exploitation of Earth Observation, is the free open-source Sentinel Application Platform (SNAP) software [37]. After downloading, from Copernicus Open Access Hub, the two products use the SNAPHU unwarping algorithm for phase unpacking. As a first step, it needs to import the two Sentinel-1 zip file products and apply the coregistration with S1 TOPS Split. The S1 TOPS Split is used to choose only those bursts required for the analysis, to reduce the area of interest by selecting the sub-swath and VV polarisation. As a second step, it was used the Apply Orbit File determination to improve the precise orbit determination and the geometric accuracy for both split products. Next, the application of the S-1 Back Geocoding operator, a coregistration step to generate a stack of co-registered data, has been applied. To raise the quality of the coregistration shall apply the Enhanced spectral diversity operator. Apply the Interferogram formation and the Flat Earth phase to remove the phase linked to the Earth's curvature is calculated using the orbital metadata information and subtracted from the complex interferogram. The S1 TOPS Deburst allows fusion bursts by removing the overlap and creating a single continuous image. For the image corrections, the quality of the fringes existing in the interferogram can be implemented by applying specialised phase filters such as Golden Phase Filter, a phase noise-sensitive filter. To prevent unwrapping errors is used to apply the Subset function to remove the grainy parts of the image. To remove any errors in the estimation of the phase difference related to the topography of the acquired area, Topo Phase Removal has been estimated. Phase Unwrapping is capable to bind the interferometric phase to the topographic height in three actions export, unwrapping, and import. Next, it has been applied Phase to elevation to transform the radian units into absolute heights and Terrain Correction to generate a map-projected product. For running the DEM, the best option is Shuttle Radar Topography Mission (SRTM) 1 sec HGT. Finally, the latter procedure before saving the product, the ground correction geocoding lets to create a projected map as UTM/WGS84.  
Many techniques make it possible to obtain an altitude profile from a DEM. Within the Quantum GIS software 3.28.6 _'Firetze'_, we could consider: the plugin Profile tool [38], the plugin qProf [39], Whitebox Tools for Processing [40], and SAGA Terrain analysis profiles [41]. The plugin Profile tool turns out to be the most expeditious altitude profile calculator while the SAGA method allows building customizable profiles.
## 7 Simulation Results  
We compare SLP and SLP-E with the typical source location protection protocols including phantom single-path [4] and PRLA [9] by OPNET. The network deployment is the same as [4, 9]. Specifically, 10,000 sensors are distributed evenly over an area of \(6000\times 6000\) m\({}^{2}\). In order to achieve random and even distribution of the sensors, we divide the monitored area into grids. Each sensor is located at the center of a grid at first. In order to generate a more reality sensor topology, we add a random and small perturbation \(\varepsilon\) to the location of each sensor, where \(\varepsilon\) is drawn from a normal distribution, _i.e._, \(\varepsilon\sim N(\mu,\sigma^{2})\)The attacker always starts his tracing from the base station. The radius of the visible area of the attacker \(r\) is 6. The sensors that have no more than three neighbors take a percentage of 1% in all the deployed sensors. The base station is static and the source appears randomly. In Figures 4 and 6, we set 60 to \(H\) and repeat the simulation for 50 times with different \(h\) to obtain the average result. Similarly, \(h\) is set to 15 in Figures 5 and 7 and the simulation is repeated for 50 times with different \(H\) to obtain the average result. We adopt the safety period used in [4,7,9] to evaluate the security performance of the three protocols, which is defined as the number of hops before an attacker reaches the source [4,9].
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 3 Research Method
### Exclusion Criteria
#### 3.2.2 Identification of Citing Studies
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **ML Methods** & **Computing Paradigm** & **Employed IoT Devices** \\ \hline
[80] & DRL & EdgeML & Light sensor, ultrasonic sensor, and infrared sensor. \\
[83] & PSO & Cloud-based & Ultrasonic sensor, smart lighting device, lux sensor. \\  & Supervised Learning & & \\  & methods (SVM, LR, & \\  & and KNN), Ensemble & Cloud-based & Light sensor. \\  & Learning (RF), and a DL & & \\  & method (LSTM) & & \\
[84] & PSO & Cloud-based & Light sensor, \\
[86] & DL & Cloud-based & None \\
[74] & KNN, NearCount-PoS & EdgeML & Motion sensor, PIR sensor \\  & & & Human presence sensors, \\
[78] & DL, DNN, RL & Cloud-based & environmental sensors, \\
[79] & NN Classifier. & EdgeML & Microphone. \\
[76] & RF classifier. & Cloud-based & Light sensor, occupancy \\
[87] & SR & Cloud-based & None. \\  & & & Light sensor, MCU, Monitor \\
[85] & ANNs & Cloud-based & (Node-RED), Arduino MCU \\  & & & (MKR WiFi 1010) \\
[81] & RF classifier. & Cloud-based & Smart camera, climate \\  & & & sensmitter, smart lighting \\  & & & sensor, smart phone. \\
[88] & ANN, LSTM, CNN, & & \\  & and Naive Bayes & & \\
[90] & KNN, SVM, DT, NB, & Cloud-based & PIR \\  & and Ensemble Voting. & & \\
[91] & DNN, KNN & EdgeML & PIR \\  & CNN, RF, DT, polynomial & & \\
[92] & regression, SVR, Ridge, & & \\  & Lasso, Elastic net, KNN, & & \\  & and BPNN. & & \\
[93] & KNN & EdgeML & PIR \\
[94] & DT, RNNs, and LSTM. & Cloud-based & PIR \\
[95] & CNN & EdgeML & Raspicam \\
[96] & CNN & Cloud-based & Raspicam \\ \hline \hline \end{tabular}
\end{table} 
Table 30: Data synthesis from primary studies (3). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Contribution** & \\ \hline  & An IoT-based smartphone energy assistant (isEA) tool. iSEA aims at promoting smart \\
[70] & energy-aware behaviours among the occupants of commercial buildings. iSEA methodology and its IoT architecture are detailed. \\  & Classification of highway digitalization into five components: smart highway LS is one of \\
[82] & them. An architecture for smart highway lighting, smart traffic, and emergency \\  & management is also proposed. \\  & Design and implementation of a smart LS, which dynamically controls the classroom \\
[71] & lighting in accordance with the learning context. The primary aim is to improve students’ performance. \\  & Description of a neural-network learning control system composed of light sensors, \\
[72] & dimmable LED light fixtures, cameras, and a firmware devoted to crop monitoring and performance evaluation. \\  & A solution to HAR through passive sensing is proposed and evaluated. Leveraging \\
[73] & ensemble ML algorithms, the data extracted from the ESP32 microcontroller is used for classifying different human activities. \\ \hline \hline \end{tabular}
\end{table} 
Table 30: Data synthesis from primary studies (3).
# Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
## 4 Prediction and Discuss
### Experiment 2  
To explore the influence of the proposed network input length on the prediction, the sample input length was set as 3, 5, 7, 9, 11, and 13 to conduct six comparative tests. The network structure parameters of Attention-LSTM are shown in Table 4.  
The predicted results of the Attention-LSTM network are shown in Figure 14. It can be seen that the degree of fitting between the Attention-LSTM prediction results and the actual values was different under different input lengths. With the increasing sample length, the fitting degree of the network first increased and then decreased. When the sample input length reached 7, the predicted value was closest to the measured value when compared to the rest of the experiments. However, as the input length continued to increase, the prediction accuracy of the network began to decline. This is because the input step was too long; consequently, the network could not focus on the important value. In addition, the input step length was too long to prevent LSTM from integrating the current point with the contextual information in time. Taken together, these factors may cause prediction accuracy to decline after reaching its peak.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Name** & **Input Length** & **Input Matrix** & **Number of Parameters** \\ \hline \multirow{8}{*}{Attention-LSTM} & 3 & 3 \(\times\) 6 & 1623 \\  & 5 & 5 \(\times\) 6 & 1641 \\  & 7 & 7 \(\times\) 6 & 1667 \\ \cline{1-1}  & 9 & 9 \(\times\) 6 & 1701 \\ \cline{1-1}  & 11 & 11 \(\times\) 6 & 1743 \\ \cline{1-1}  & 13 & 13 \(\times\) 6 & 1793 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Six test structure parameters of Attention-LSTM.  
Figure 14: Results of six model tests based on Attention-LSTM.  
In addition, the coefficient of determination \(\mathrm{R}^{2}\) and mean absolute error MAE were calculated to measure the predictive effect in all trials (Figure 15). As can be seen from the figure, MAE showed a trend of first decreasing and then increasing with the increase in the sample length, while \(\mathrm{R}^{2}\) showed the opposite trend in the whole experiment (first decreasing and then increasing). When the input length of the independent variable reached seven, the \(\mathrm{R}^{2}\) of the expected value and the actual value was the highest value, 0.942. At the same time, the MAE of the predicted value and the actual value was the lowest value, 51.85, which indicates that the whole network had the best prediction performance. In general, the prediction accuracy of the Attention-LSTM network was affected by the input sample length. When the input sample length reached seven, the prediction effect was optimized.
## 3 Description of Case Study and Ground Motion Inputs  
The seismic response of a 9-storey steel building with and without DFP bearings is investigated in this study. As mentioned before, a linear elastic model for the building can be assumed in this case since the aim of the isolation system is to limit the building behaviour in the elastic range. Moreover, the emphasis of the paper is on the behaviour of the bearing rather than the building. It has also been seen that the stiffness of the floors is considerably higher than the overall stiffness of the storey due to the columns. Thus, each floor diaphragm constituted by slabs and beams can be assumed to be a rigid diaphragm. As a result, a shear building model is presented, and the torsion effect is ignored in this study.  
Figure 3 shows spring-mass models of the building with and without DFP bearings. In these models, the soil-structure interaction (SSI) effect is neglected. However, many studies have indicated that a system of the steel building-foundation should be taken into account, especially ones subjected to near-source earthquakes [18]. An input, the values of mass and stiffness of each floor are assumed to be the same, given as: \(m_{i}\) = 0.0714 kNs\({}^{2}\)/mm and \(k_{i}\) = 100 kN/mm. These values are approximately selected according to the full-scale steel building model from the experimental test by [PERSON] et al. [15]. The damping ratio (\(\xi\)) is chosen as 2.5%, resulting in the fundamental period of the structure \(T_{1}\) = 1.016 s. Concerning the bearing characteristics, the following values are selected:  
* effective radii \(R_{eff1}\) = \(R_{eff2}\) = 1968 mm,
* displacement limits \(d_{1}\) = \(d_{2}\) = 250 mm,
* friction coefficients \(\mu_{1}\) = 0.02-0.06, \(\mu_{2}\) = 0.06-0.1, \(\alpha\) = 0.02,
* and constants in Equation (4) are selected based on the work of [PERSON] [6], i.e., \(A=1\), \(Y\) = 0.25, \(\beta\) = 0.1, \(\gamma\) = 0.9, and \(\eta\) = 2.  
The input ground motions of the Northridge-01 event are obtained from the PEER NGA-West2 database [19]. These records are selected from two stations Rinaldi Receiving and LA-Obregon Park with epicentral distances of 6.5 and 37.36 km, respectively. The selection aims to distinguish two different cases of the ground motion characteristic, i.e., near-source and far-field. The site to fault distance for the near-source ground motion is commonly defined to be less than 10 km [20]. The detail of the selected ground motions is