esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of ECAP on Erosion-Corrosion at Different Impacting Angles  
The impacting angle plays a major role in E-C behaviors of copper as shown in Figure 8 below. The highest E-C rates were obtained at an impacting angle of 45\({}^{\circ}\) in all ECAP passes. This result is consistent with the findings reported by the other previous studies [26]. It is reported that the maximum E-C rate were obtained with impact angle ranges from 30\({}^{\circ}\) to 50\({}^{\circ}\) for a range of materials such as pure iron, 304 stainless steel and aluminum [26; 27; 28; 29]. The lowest values of weight loss due to E-C were recorded at an impacting angle of 90\({}^{\circ}\). Comparing the E-C rate for annealed copper and four ECAPed passes specimens at impacting angles of 90\({}^{\circ}\) and 45\({}^{\circ}\) the weight losses decreased by 28.2% and 32.7%, respectively. Additionally, the hardness value after E-C tests for different impact angles are shown in Figure 9. A drop of hardness values is reported with impact angles of 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\), whereas at the impact angle of 45\({}^{\circ}\) no changes in the hardness of the copper specimen were observed. A probable reason can be that the ductile materials show maximum erosion rate at 30\({}^{\circ}\)-50\({}^{\circ}\) (45\({}^{\circ}\) for the ECAPed copper). Furthermore, the impact angle plays a major role in material removal whereas the maximum value of erosion rate arises at normal angle 90\({}^{\circ}\) with micro fractures. With a further increase of the impacting angle, the tangential force component will be very low compared with the normal component.  
Figure 8: Variation of weight loss with ECAP passes at different impacting angles (\(v\) = 3.8 m/s, 3.5% NaCl, and sand concentration = 20 wt.%).  
When the impacting energy exceeds the elastic strain energy of the material, the plastic deformation takes place in the indentation form as shown schematically in Figure 10. The cold work generated by the impacting particle would have a stress relaxation effect on the surface of the specimen after ECAP. This effect results in the reduction of hardness. On the other hand, at 45\({}^{\circ}\) the normal and tangential components of the impacting force are equal and the rate of material removal is maximized. In case of pure copper samples, it is found that the impact angle has the major effect on the erosion mechanism of solid particles [26]. Further studies are needed to understand and verify this point.
## 2 Materials and Methods
### Experimental Equipment  
The experimentation was conducted at the Addilan V0.1 manufacturing center, specializing in arc-DED technology. The setup includes an EWM brand generator (Mundersbach, Germany), Titan XQ 400 AC puls DW, and an M drive 4 Rob5 XR RE wire feeder, as depicted in Figure 1. Additionally, the setup is equipped with a laser profilometer (Laser Scanner Q4 Series) and a compact Optris pyrometer, which is placed on the vicinity of the nozzle. The chosen generator mode was the pulsed GMAW mode specified by the manufacturer, enabling the introduction of a pulsed intensity signal into the supplied wire. The MC500 Weld Camera (Redman) offered a visual window into the deposition process from an external perspective. A shielding gas mixture of Ar (97.5%) and CO2 (2.5%) was utilized, with a flow rate of 17 L/min. The nozzle diameter used was 20 mm, accompanied by a 15 mm stick-out, and the wire diameter employed was 1.2 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**C** & **Mn** & **Cr** & **Ni** & **Nb** & **Fe** & **Ti** \\ \hline
0.22\% & 0.43\% & 0.01\% & 35.66\% & 1.38\% & 61.6\% & 0.53\% \\ \hline
**Sample** & \multicolumn{3}{c}{**Average CTE 0–100 \({}^{\circ}\)C [10\(-\)6C\(-\)1]**} & **UTS (MPa)** & **Elongation (\%)** \\ \hline GMAW-WD [29] & & 1.94 & & 505 \(\pm\) 2 & 29 \(\pm\) 1 \\ \hline GMAW-BD [29] & & 1.96 & & & \\ \hline CMT-WD (Horizontal) [8] & & 1.54 & 533 \(\pm\) 9 & 28 \(\pm\) 2.5 \\ \hline CMT-BD (Vertical) [8] & & 1.32 & & & \\ \hline Commercial laminated material & & 2.29 & & 518 & 34 \\ (ASTM B753-07-2018 [32]) & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: The chemical composition of the invar wire used during experimentation, and the thermal and mechanical properties of Invar after arc-DED in the literature.
## 4 Results and Discussion
### Flow Regime Identification
#### 4.1.3 Probability Density Function  
In the probability theory, PDF is defined as a function whose value at any given point in the sample can be understood as providing a relative probability that the value of the random variable would occur. In simple terms, PDF provides the likelihood of a random variable to occur in sample space. Pressure signal is treated as the random variable in this study. Figure 11 shows the Gaussian (or Normal) probability density function of normalized pressure signals of different flow regimes. The PDF of normalized pressure signals was calculated using a standard algorithm and it shows the relative likelihood of occurrence of dominant pressure. In stratified flow, the turbulence is lower, as expected. This means that the probability of dominant pressure repetition is very high, which leads to a narrow bandwidth of PDF and high PDF value. The pressure value is also low in stratified flow due to lower velocities, which means the PDF is shifted towards the left, in the region of lower pressure values. The maximum PDF is 38, and it occurs at a normalized pressure of 0.26 \(\pm\) 0.02. In slug flow, the turbulence is higher, and the probability of pressure repetition is very low, which means the bandwidth of slug flow is expected to be higher, as shown in Figure 11b. Due to a lower probability of the recurrence of dominant pressure, the resultant PDF value is also lower. In the case of slug flow, the maximum value of PDF occurs at the normalized pressure value of 0.25 \(\pm\) 0.04 with a PDF value of 1.7. In annular flow, the pressure value is higher due to increased velocities, but the fluctuation is lower when compared to slug flow, which means the probability of pressure repetition is higher and PDF has a lower bandwidth and a high PDF value. Due to higher pressure values, PDF also  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Flow Regime**} & **Dominant** & **FFT Amplitude** & **PDF** & **Location of** \\  & **Frequency FFT (Hz)** & **(Pa)** & **Maximum** & **Maximum PDF** \\ \hline
**Stratified** & 0.33 & 70–97 & 24–38 & 0.26 \(\pm\) 0.02 \\
**Slug** & 1 & 100–175 & 1.3–1.7 & 0.25 \(\pm\) 0.04 \\
**Annular** & 12.5–17 & 2.2–3.5 & 19–26 & 0.95 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Flow regime identification criteria based on the PDF and FFT of pressure signals.  
Figure 10: Fast Fourier Transform of the dynamic pressure signals of different flow regimes with normalized FFT magnitude.  
moved towards the right side in the higher normalized pressure region. In annular flow, the maximum value of PDF occurs at 0.95 \(\pm\) 0.02 with a PDF value of 26. The obtained PDF characteristics for different flow regimes are summarized in Table 4. These characteristics are different for each flow regime and are dependent on the interaction of different phases and related turbulence in different flow regimes and hence can be used to identify different flow regimes.
## 3 Calculation Results and Analysis
### The Influence of Functionally Gradient Index on Frequencies  
Figure 4 studies the influence of the functionally gradient index \(k\) on the first three order dimensionless natural frequencies of the nano-annular plates. \(\tau=0.05\), \(\overline{\Omega}\)\(=5\), \(\Delta T=0\) K. It can be seen from Figure 4 that under the two boundary conditions, the first three order dimensionless natural frequencies decrease with the increase in the functionally gradient parameter \(k\), and the rate of change changes from fast to slow and finally converges to a fixed value. The increase in the functionally gradient parameter \(k\) weakens the equivalent stiffness of the annular plates and reduces their natural frequencies. For the nano- annular plates made of Si\({}_{3}\)N\({}_{4}\) and SUS304, the size of the functional gradient parameter \(k\) reflects the ratio of the two material components. When \(k\) increases from 0, the change in \(k\) changes the values of the physical parameters and the position of the physical midplane. This change  
Figure 2: The relationship between dimensionless natural frequencies and the ratio of the inner and outer radius under two different boundary conditions. (**a**) [PERSON] plate; (**b**) [PERSON] plate; (**c**) [PERSON] plate; (**d**) [PERSON] plate; (**e**) [PERSON] plate; (**f**) [PERSON] plate.  
has significant impacts on the stiffness of the annular plates, and as the value of \(k\) gradually increases, the change in the physical parameters and the position of the physical midplane have little effect on the stiffness of the nano-annular plates. It can also be observed from the figure that the data under the three plate theories have little differences and are basically the same.
###### Abstract  
Background: Retention is a critical aspect of orthodontic treatment with aligners as it counteracts vertical displacing forces and ensures greater predictability of tooth movement. The aim of this study is to evaluate and compare the retention effectiveness of 3D-printed aligners and thermoformed aligners, analyzing how margin design and thickness gradients affect retention under different occlusal conditions. Methods: Tensile tests were conducted using a Sauter TVO-A01 machine, recording the force required to displace each aligner from the models. Quantitative data on the retention force of each aligner were collected based on malocclusion type and design specifics. Results: Scalloped thermoformed aligners demonstrated significantly lower retention values (\(p=0.029\)) compared to 3D-printed aligners with high margins with horizontal and vertical gradients. Scalloped margin aligners exhibited significantly lower retention values (\(p=0.008\)) compared to straight margin aligners. Additionally, 3D-printed aligners with uniform thickness gradients had significantly lower retention values (\(p=0.040\)) compared to thermoformed aligners. Conclusions: The 3D printing production process enables customizable designs tailored to the unique characteristics of each patient. High-margin 3d-printed aligners with horizontal and vertical gradients provide superior retention, particularly in complex clinical situations such as dental crowding.  
Keywords:aligns: 3D-printed aligners; thermoformed aligners _Article_
## 2 Data and Methods  
### Study Sites and Flux Measurements  
The flux observed net ecosystem exchange (NEE), ecosystem respiration (Re), ET and meteorological data in the period of 2003-2005 were acquired from ChinaFLUX website [20]. The daily data were produced by summing the filled half-hourly values [20, 21]. More information about original observed half-hour values is available in [PERSON] _et al._[20] and [PERSON] _et al._[12]. Gross ecosystem exchange (GEE) is then derived by subtracting Re from NEE as: GEE = NEE - Re. Then, the negative GEE is equal to GPP (GPP = -GEE), and flux derived GPP values are presented in the unit of gC/m\({}^{2}\)/d. In this study, eight sites include five different land cover types, such as croplands (Yucheng croplands site (YC) in Shandong province), grasslands (Xilinguole grasslands site (XLGL) in Inner Mongolia autonomous region, Haibei grasslands site (HB) in Qinghai province and Dangxiong grasslands site (DX) in Tibet autonomous region), mixed forests (Changbaishan forests site (CBS) in Jilin province), evergreen needleleaf forests (Qianyanzhou forests site (QYZ) in Jiangxi province) and evergreen broadleaf forests (Dinghushan forests site (DHS) in Guangdong province and Xishuangbanna forests site (XSBN) in Yunnan province). A brief description of these sites is presented in Table 1 and Figure 1. For consistency with the time interval of eight-day composite of MODIS-GPP and -ET, we also aggregated the eight-day GPP and ET from the site measurements based on daily values.
## 2 Aggregates and Test Design
### Test Design
The value of \(V\)-\(V_{p}{}^{b}\) can be used to quantify the packing volume contribution of \(a_{s}\) to the combination. It can be concluded that the major function of \(a_{s}\) is to build the skeleton in the packed combination when the value of \(V\)-\(V_{p}{}^{b}\) is larger than that of \(V_{p}{}^{s}\). If the value of \(V\)-\(V_{p}{}^{b}\) is smaller than that of \(V_{p}{}^{s}\), the air voids filling function will be more than the skeleton building function for \(a_{s}\). The relationship between \(V\)-\(V_{p}{}^{b}\) and \(V_{p}{}^{s}\) can be defined as the packing function index (\(I_{pf}\)) of the smaller particles (Equation (9)), which is convenient for investigating the packing function of smaller particles in the packed aggregate combination. When \(I_{pf}\) equals 1, the smaller particles have the same skeleton building function as the bigger particles. When \(I_{pf}\) tends to 0, the smaller particles have an air voids filling function. \[I_{pf}=\frac{V-V_{p}^{b}}{V_{p}^{s}} \tag{9}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Bigger Particle**} & \multicolumn{2}{c}{**Smaller Particle**} & \multirow{2}{*}{**Combination Name**} & \multirow{2}{*}{**Size Ratio**} \\ \hline
**Size (mm)** & **Size Range (mm)** & **Size (mm)** & **Size Range (mm)** & & \\ \hline
16 & 16–19 & 4.75 & 4.75–9.5 & 16 VS 4.75 & 0.297–0.5 \\
16 & 16–19 & 2.36 & 2.36–4.75 & 16 VS 2.36 & 0.147–0.25 \\
16 & 16–19 & 1.18 & 1.18–2.36 & 16 VS 1.18 & 0.074–0.124 \\
4.75 & 4.75–9.5 & 2.36 & 2.36–4.75 & 4.75 VS 2.36 & 0.5 \\
4.75 & 4.75–9.5 & 1.18 & 1.18–2.36 & 4.75 VS 1.18 & 0.25 \\
2.36 & 2.36–4.75 & 1.18 & 1.18–2.36 & 2.36 VS 1.18 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Combinations of the two-size packing. All SSP and TSP tests were performed in accordance with the methods of T0309 and T0311 in the \"Test Methods of Aggregate for Highway Engineering (JTG E42-2005)\" of China [44], where two-parallel experiments were required and the average values were taken.
## 4 Results
### Comparison between Datasets  
The Compression ratio (CR) was calculated using the following Equation (1), where US is the uncompressed size and CS is the compressed size. All results were rounded to the second decimal place. Average CRs were used to statistically explain the overall compression performance of the solution, and standard deviation (SD) CRs were used to explain the performance stability of the solution. Note that the lower the SD value, the higher the stability.  
\[\text{CR}=\frac{\text{US}}{\text{CS}} \tag{1}\]  
Six DNA FASTQ samples were used for insulin, leptin, Epstein-Barr virus, cystic fibrosis, and lung cancer samples. In summary, a total of 25 FASTQ DNA samples from 5 different disease classifications were used. The results of the proposed algorithm's performance (average and standard deviation) for the 5 database groups are shown in Table 2.  
BAQALC CR performance results for the selected 25 samples showed an average and standard deviation of 11.17 \(\pm\) 0.11, 10.61 \(\pm\) 0.56, 13.65 \(\pm\) 0.7, 10.09 \(\pm\) 0.15, and 17.01 \(\pm\) 0.06 for insulin, leptin, Epstein-Barr virus, cystic fibrosis, and lung cancer, respectively. The total average and SD of BAQALC  
\begin{table}
\begin{tabular}{c c c c} \hline
**Database** & **CR** & **Used Data** & **CR Average \(\pm\) SD** \\ \hline \multirow{6}{*}{_Insulin_} & 10.98 & SRX2528043 & \\  & 11.24 & SRX2528042 & \\  & 11.22 & SRX2528041 & \\  & 11.25 & SRX2528040 & \\  & 11.18 & SRX2528039 & \\ \hline \multirow{6}{*}{_Epstein-Barr_} & 14.7 & SRX2776811 & \\  & 13.79 & SRX2776810 & \\  & 13.63 & SRX2776809 & \\  & 13.36 & SRX2776808 & \\  & 12.78 & SRX2776807 & \\ \hline \multirow{6}{*}{_Lung_} & 17.01 & SRX2388195 & \\  & 16.98 & SRX2388194 & \\  & 17.08 & SRX2388193 & \\  & 17.06 & SRX2388192 & \\  & 16.94 & SRX2388191 & \\ \hline \multirow{6}{*}{_Leptin_} & 10 & ERX1600597 & \\  & 10.25 & ERX1600596 & \\ \cline{1-1}  & 10.44 & ERX1600595 & \\ \cline{1-1}  & 11 & ERX1600594 & \\ \cline{1-1}  & 11.38 & ERX1600593 & \\ \hline \multirow{6}{*}{_Cystic Fibrosis_} & 10.06 & SRX2935589 & \\ \cline{1-1}  & 10.13 & SRX2935588 & \\ \cline{1-1}  & 9.85 & SRX2935587 & \\ \cline{1-1}  & 10.17 & SRX2935586 & \\ \cline{1-1}  & 10.26 & SRX2935585 & \\ \hline \multicolumn{2}{c}{_Total CR_} & All Data & 12.51 \(\pm\) 2.64 \\ \hline \end{tabular}
\end{table}
Table 2: CR Results of BAQALC for Five Disease Datasets.  
was 12.51 \(\pm\) 2.64. BAQALC CR was the highest for lung cancer (average 17.01). BAQALC performance also showed the highest stability for lung cancer (SD 0.06).
## 4 Fault Current Limiting Devices
### Superconducting FCL
#### 4.2.7 Resistive Type FCL: Resistive Type SFCL in the Rotor Side  
In addition to placing the resistive type SFCL in the terminal of the DFIG, different configurations have been located in the rotor side. In [42; 43], the proposed resistive type SFCL in the rotor side operates effectively to limit the rotor transient over-current. Meanwhile, due to active power consumption in the rotor side, the DC-link over-voltage is avoided. However, the RSC controllability gets worse in practice, as mentioned before. Although, the voltage dip in the PCC has been improved by the SFCL in the rotor side, the SFCL in the stator side, either inductive or resistive, has a better performance for voltage sag compensation compared to the rotor side.  
In [44], a resistive solid state FCL has been studied in the rotor side. The FCL is composed of antiparallel semiconductor switches, which are employed to bypass or insert the resistance. The FCL operates as a passive compensator to aid the converters to act as an active compensator, as previously discussed in [40].  
Figure 11: (**a**) The superconducting magnetic energy storage (SMES) together the SFCL [40]; and (**b**) the superconducting fault current limiter with superconducting magnetic energy storage, with common superconductor [41].
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Parametric Analysis and Optimization of the Results of the 60-GHz Metasurface
#### 3.2.3 Inner Radius, 'b'  
The parametric study of the inner radius 'b' of the circular loop has shown a considerable variation on the transmission coefficient profile, by shifting the resonant frequency. The plots of Figure 7a-c shows that as 'b' increases from 0.89 mm to 1.09 mm, the design frequency decreases to this point. However, a critical change is observed at 'b' = 1.14 mm where the resonant frequency increases. This shows, there is a cut-off point for the inner radius to negate the trend of increasing size that results in lengthening the surface current. At 'b' = 0.99 mm, the resonant frequency is 60 GHz with a reflection phase of 0 deg, therefore, b = 0.99 mm is selected as the optimized value for the proposed unit-cell of the metasurface.
# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Abstract Concept Modeling with RAN
#### 3.2.1 Step 1a: Convex Concept Identification (CCI)  
CCI is a method to determine convex groups in a given dataset. In RAN, each data instance is considered a point in n-dimensional geometric feature space, inspired by the theory of conceptual spaces [64]. In this method, we also determine the cluster centers that are used in the inter-Layer learning operation (see Section 3.2.4). Step 1a in Figure 3 shows input Layer 0; here, nodes S\({}_{1}\) and S\({}_{2}\) correspond to the dimensions of the input Toy-data. To identify the five convex groups, the K-mean [65] clustering algorithm is chosen as the concept identifier (CI), and the value of \(K\) is set to 5 to determine five clusters. Five cluster centers are also identified in this process, as shown in Figure 3, as cluster representative data points (CRPD). Any clustering algorithm can act as a concept identifier provided that it enables the identification of convex regions along with their respective cluster centers.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 8 Behavioral Anomaly and Change Detection
### Point Anomaly Detection  
Typical anomaly detection methods can be grouped into three main categories: rule-based methods, statistic models, and machine learning algorithms. Unlike other approaches, data-driven machine learning methods do not assume any pre-defined rules or statistic data distributions, and could thus be used to learn user-specific, adaptive models. DBSCAN [53], and Isolation Forest [51] are often used to detect abnormal data points, based on density and isolation. However, when applied to time-series data, it is challenging for such algorithms to encode the temporal dependency between time steps. Deep neural networks are also used for anomaly detection in time-series data, where convolutional/recurrent networks are learned to predict future elements of a sequence. Prediction errors are then used to recognize anomalous behaviors. Temporal Convolutional Network (TCN) [54] have shown to be able to efficiently encode long-term time dependency.
# Wireless May Benefit Blockchain +
## 2 System Model
### Spatial Distribution of Wireless Nodes  
A two-dimensional space \(\mathbb{R}^{2}\) is defined as a 1 km by 1 km square, as illustrated in Figure 1. The nodes are assumed to be located at fixed positions. We model spatial distribution of the stationary nodes as a Poisson point process (PPP) in \(\mathbb{R}^{2}\) with density \(\lambda\). Figure 1 visualizes an example distribution of nodes with \(\lambda=400\). Notice that the \(\lambda\) is the parameter that will be varied according to the type of consensus mechanism. For instance,PoS has a smaller \(\lambda\) compared to PoW since its consensus is operated only among the validators who are with more than the required amount of stake.  
As shall be elaborated in Section 2.3, node failures occur in a \"clustered\" manner; the magenta-colored boxes indicate the areas where the failures transpire. It is assumed that the node failures take place within the clusters only; the example presented in Figure 1 assumes the probability of each node failure to be \(\text{p}_{\text{fail}}=0.5\). We will vary this \(\text{p}_{\text{fail}}\) as a method to evaluate the performance of the consensus mechanisms, viz., PoW, PoS, and PoC, which will be presented in Section 5.  
The selection of PPP is justified as follows. Every realization of a finite PPP is a binomial point process (BPP) with the number of realized points [35]. PPP is used to model or abstract a network composed of a possibly infinite number of nodes randomly and independently coexisting in a finite or infinite service area (e.g., large-scale wireless networks such as cellular networks [36]). In contrast, BPP is a more adequate model when the total number of nodes is known, and the service area is finite. Therefore, our choice of PPP is in our pursuit of the generalization of the model. As such, owing to its general applicability in scenarios where nodes appear independently and are randomly distributed over a given area, this paper's selection of PPP aligns well with the characteristics of decentralized blockchain networks, where nodes are typically deployed without a specific pattern or clustering.