esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Friction stir welding (FSW) is a solid-state welding process capable of joining a wide range of light metals. However, liquation and solidification may occur during joining of dissimilar metals which leads to eutectic formation. This article aims to discover the influence of tool rotation speed on the formation of eutectic structure during friction stir welding of aluminum to magnesium. To do so, friction stir welding was performed at 600 and 950 rpm to join pure aluminum and ECO-AZ91 magnesium alloy in a lap configuration. In order to investigate the influence of the welding speed, the welding speeds of 23.5 and 37.5 mm/min were also chosen. Scanning electron microscopy (SEM) was used to study the microstructure of the joints. A shear-tensile test was used to evaluate the joints' strengths. The fracture surfaces were also studied by SEM. The results revealed that changing the rotation speed directly affects the eutectic formation, whereas the welding speed had no influence. A lower rotation speed resulted in a thin, continuous intermetallic layer, whereas a higher speed led to the formation of a massive Mg-Al\({}_{12}\)Mg\({}_{17}\) eutectic microstructure. The formation of eutectic, as an indicative of liquation, may affect the material flow during the process due to decreasing the friction coefficient between the tool and material. The macrostructure analyses showed that the phase evolution as well as the mechanism of material flow are highly affected by liquation.  
FSW; aluminum to magnesium joining; intermetallic compounds; eutectic structure; liquation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### System Design
Table 1 provides a comprehensive summary of the FCN layers and their associated parameters utilized for CFO estimation. The architecture of our U-Net-based autoencoder, which has 165,378 trainable parameters, can be seen in Figure 3.  
During the training stage for both networks, a mean square error loss function was utilized. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block** & **Input (\(C_{in},L\))** & **Output (\(C_{out},L\))** & **Parameters** \\ \hline Conv1d & (2, 512) & (32, 512) & Kernel size = 3, Stride = 1, \\  & & & Padding = 1 \\ MaxPool1d & (32, 512) & (32, 256) & Kernel size = 2, Stride = 2, \\  & & & Padding = 0 \\ Conv1d & (32, 256) & (64, 256) & Kernel size = 3, Stride = 1, \\  & & & Padding = 1 \\ MaxPool1d & (64, 256) & (64, 128) & Kernel size = 2, Stride = 2, \\  & & & Padding = 0 \\ Conv1d & (64, 128) & (128, 128) & Kernel size = 3, Stride = 1, \\  & & & Padding = 1 \\ MaxPool1d & (128, 128) & (128, 64) & Kernel size = 2, Stride = 2, \\  & & & Padding = 0 \\ Conv1d & (128, 64) & (1, 64) & Kernel size = 1, Stride = 1, \\  & & & Padding = 0 \\ AdaptiveAvgPool1d & (1, 64) & (1, 1) & \\ \hline \hline \end{tabular} The implementation is conducted using the PyTorch framework. The model has 31,265 trainable parameters.  
\end{table} 
Table 1: Overview of fully convolutional network (FCN) layers and parameters for carrier frequency offset (CFO) estimation. Figure 2: False alarm and missed detection rates as a function of k values.
# Recent Advances of Integrative Bio-Omics Technologies to Improve Type 1 Diabetes (T1D) Care
## 4 Diagnostic Methods  
Traditional diagnostic methods for T1D disregard differences in physiology and may possibly lead to delayed diagnoses. The current diagnostic methodology commonly includes the fasting blood glucose test, oral glucose tolerance test, random blood-glucose test, glycated hemoglobin test (HbA1c).  
Endocrine clinics might also request to test C-peptide antibodies, insulin antibodies (IAA), insulin-associated-2 antibodis (IA-2A), ring-transporter 8 (ZnT8 Ab), islet cell cytoplasmic autoantibodies (ICA), and Glutamic Acid Decarboxylase Autountibodies (GADA or Anti-GAD). There are two main issues associated with the current methodology of diagnosis. First, diagnosis is often delayed until symptoms develop, which is already too late in the disease to preserve beta-cell function using pharmacologic treatment, such as [42]. Second, the tests insufficiently take into account physiological differences across gender and race. Stratified population studies demonstrated that the use of genomic information and other biomarkers may augment traditional methods for a more comprehensive, accurate, and more rapid diagnosis [26; 43]. The identification of specific biomarkers for certain demographic groups may aid diagnosis methods for T1D, including age, gender, and race. A study on 711 children, newly diagnosed with T1D, highlighted how the islet autoantibody type is strongly associated with demographics [44]. Additionally, [PERSON] et al. assessed the quantity of specific biomarkers related to each demographic group through proteomic analysis [7]. The most common traditional biomarkers are islet autoantibodies including IA-2A, GAD, ZnT8 Ab autoantibodies [45]. The later-age onset of T1D is related to characteristic levels of GADA, thyroid peroxidase (TPOA), and gastric parietal cell antibodies (PCA) in the individual. Females with T1D often have autoantibodies for GAD, TPO, PC, and transglutaminase (TGA). GADA are found more commonly in African Americans while TGA are more frequently found in Native Americans with T1D [42]. These gender and race-associated differences should be leveraged to improve diagnostic methods for T1D. [PERSON] et al. also analyzed metabolomic data of T1D individuals for potentially characteristic expression profiles [7]. Advanced glycation end-products (AGE) were found to be deposited in the skin of individuals with T1D [46]. AGE can be measured using skin autofluorescence, which might serve as a non-invasive method to diagnose T1D [47]. Lastly, autoimmune diseases are often associated with other immune alterations, such as hypothyroidism and/or celiac disease. Therefore, \"-omics\" technology can offer an opportunity to identify commonalities in genetic variability that can be targeted with specialized therapeutics [48]. Further investigation is necessary to better define endotypes and identify novel biomarkers that can be used by care providers to improve detection methods for T1D.
## 2 Materials and Methods
### Characterizing FIREFLY with Integrating Sphere  
A Helios D Series uniform source integrating sphere configured with 150 W tungsten lamp and variable attenuator (Labsphere, North Sutton, NH) was used to observe FIREFLY response over a series of radiance levels from 0 to \(\sim\)1300 mW. Sphere irradiance was also recorded with an ASD spectrometer to provide calibration measurements for FIREFLY response. To ensure an accurate dark current correction for the observations, dark current was measured in every band over five repeats and then averaged.
## 3 Results
### Spatial Changes in ESVs  
Most of the areas along the Grand Canal showed high ESVs, areas with medium ESVs were scattered, and areas with low EVs were few and concentrated (Figure 3). In 1991, low-value centers formed in Beijing and Tianjin, while high-value areas were contiguous in the middle and southern sections of the Grand Canal. In 2006, high-value areas were mainly concentrated in water bodies such as Hongze Lake, Gaoyou Lake, and Taihu Lake, medium-value areas were mainly distributed across cultivated land, woodland, and grassland areas, and low-value areas were mainly concentrated in the Beijing-Tianjin region. In 2021, medium-value areas were mainly distributed across the cultivated land, woodland, and grassland areas in the middle section of the Grand Canal, and high-value areas extended beyond water bodies such as Hongze Lake, Gaoyou Lake, and Taihu Lake to include the initial and southern sections of the Grand Canal, where the ESVs shifted from medium to high. From 1991 to 2021, the ESVs in the Grand Canal areas showed a trend of first decreasing and then increasing, and medium-value areas were the most widely distributed and relatively stable. During the same period, low-value areas gradually expanded, with Beijing and Tianjin as centers, and the expansion changed significantly from 2006 to 2021.  
Comparison of ESVs of the areas along the Grand Canal from 1991 to 2006, 2006 to 2021, and 1991 to 2021 showed similar trends in changes in ESVs (Figure 4). This was due to the relatively fast urbanization as a result of economic development along the Grand Canal, leading to the conversion of grassland and cultivated land into construction land. Except for the northern and southern sections of the Grand Canal as well as water bodies such as Hongze Lake, Gaoyou Lake, and Taihu Lake, where ESVs increased, most areas experienced a decrease in their ESVs.  
Figure 2: Changes in individual ESVs.
## 6 Simulation of Meshing
### Simulation of Spur Gears Meshing  
The meshing simulation of a crowned involute spur gear pair is accomplished by solving the new type of fundamental three-dimensional tooth surface contact equations with two unknown parameters (\(\overrightarrow{u}_{i}\) and \(\overrightarrow{v}_{i}\)) presented in Section 2. Modelling was carried out for two types of angular misalignment--yaw (in-plane) and pitch (out-of-plane)--with different degrees of misalignment. The level of misalignment selected was based on the literature [1,50,51]. For each degree of misalignment, all three types of modification were applied in order to observe how each crowning would affect the TE function and the path of contact. Moreover, for every modification, a range of parameters that met the established criteria was determined, i.e., the continuous function of the TE and the contact path closest to the tooth surface center. To evaluate the results of each modification, the maximum amplitude \(\Delta\varphi_{max}\) and area \(S_{c,p,l}\) of the TE functions were determined. Table 1 shows the pinion and gear parameters that were used for the simulation. During the simulation, the rotating axes of the pinion and gear are parallel \(\left(\overrightarrow{w}_{1}\middle|\right|\)\(\overrightarrow{w}_{2}\)) and directed along the z-axis. In addition, it should be noted that during the simulation, crowning was applied only for the involute pinion, while the involute gear remained unmodified.
## 3 Results and Discussion
### Effect of Electrode Design on Radial Overcut at Hole Entrance (ROC_en)  
Another performance characteristic of EDM considered in this study was the radial overcut at the hole entrance. Overcut is the universal fact of EDM, which indicates that the dimension of the machined impression along the horizontal plane is larger than the dimension of the tool. For example, by creating a circular impression through circular tool the size of the machined imprints comes out to be larger than the tool diameter. Since the progression of the hole is the result of such imprints, therefore an overcut is observed at the hole entrance. The half of the difference in the diameters of the tool and the hole entrance is termed as the radial overcut as described in the materials and methods section by Equation (3). Comparing the overcuts produced by all the tool designs, it was observed that the tool having conventional cylindrical design (D\({}_{\mathrm{C}}\)) imparted the largest radial overcut, i.e., 249 \(\upmu\)m. It indicates that the actual diameter of the produced hole, at the entrance, was 249 \(\upmu\)m larger than the diameter of the tool. The overcuts resulted by the remaining tools (modified through relief angles and relief with land) were significantly less than the overcut caused by the conventional design as can be observed in Figure 8. There was no precise pattern of radial overcuts with the increase in relief angles, however the overall range of overcuts values was substantially smaller than overcut in the case of the conventional tool. The range of the overcut caused by the relief angle design type (D\({}_{\mathrm{R}}\)) was from 115.5 \(\upmu\)m to 152.75 \(\upmu\)m. On the other end, for the design type of the relief angle with constant land thickness (D\({}_{\mathrm{RL}}\)) the overcut was within the range of 176.25-203 \(\upmu\)m. This range was somehow larger than the range obtained by the design type of D\({}_{\mathrm{R}}\). Since, in the case of the conventional design, the interacting surface area of the tool with the inner walls of the hole was the largest following by the surface area in the case of design type D\({}_{\mathrm{RL}}\) and D\({}_{\mathrm{R}}\). The larger was the interacting surface area, the greater was the overcut at the hole entrance. That is why the overcuts produced by the design type D\({}_{\mathrm{C}}\) (conventional design), D\({}_{\mathrm{RL}}\) (relief angle with land), and D\({}_{\mathrm{R}}\) (relief angle) were 249 \(\upmu\)m, 176.25-203 \(\upmu\)m, and 115.5-152.75 \(\upmu\)m, respectively.
## 4 Results
### Pore Types  
As proposed by [PERSON] et al. (2012) [8], there are generally four types of pores developed in shale reservoirs, including organic matter pores, interparticle pores (inter-P),  
Figure 5: Characteristics of the three major lithofacies identified in the Wüfeng–Longmaxi shales. (**A**) Core photograph of siliceous shale showing abundant graptolites, well DB, depth = 3812.4 m; (**B**) Thin section image of siliceous shale showing clay and silt-sized quartz, well DB, depth = 3812.4 m; (**C**) Core photograph of siliceous–argillaceous mixed shale showing a moderate amount of graptolites, well DA, depth = 2247.09 m; (**D**) Core photograph of siliceous–argillaceous mixed shale with less amount of graptolites, well DB, depth = 3792.44 m; (**E**) Core photograph of argillaceous shale showing a few graptolites, well DA, depth = 2220.08 m; (**F**) Thin section of argillaceous shale showing a large amount of silt-sized detrital quartz, well DB, depth = 3765.57 m.  
intraparticle pores (intra-P), and micro-fractures. The latter three types are collectively referred to as inorganic pores.  
Organic matter (OM) pores are a significant component of pore systems in overmature shales [7; 36]. The OM pores, the predominant pore type in the WF-LMX shale reservoirs, are commonly found and widely developed (Figure 6). Both sponge-like and bubble-like OM pores were observed, and these pores are sub-rounded to rounded in shape with diameters ranging from approximately 5 nm to 300 nm. In addition, a large number of OM pores were observed between the framboidal pyrite crystals (Figure 6F). Generally, siliceous shale has abundant OM pores due to high TOC content.  
Compared to organic pores, the amount of inorganic pores is much lesser in the WF-LMX shales. Inorganic pores are usually distributed dispersedly and differ significantly from organic pores (Figure 7). Intergranular pores refer to pore space formed between different mineral particles and crystals, such as clay minerals, quartz, calcite, and other rigid minerals. Intergranular pores mostly appear as polygons with flat edges and have large pore sizes mainly ranging from tens of nanometers to several microns (Figure 7A-C).  
Intraparticle pores exhibit elliptical and irregular shape, and generally occur as dissolved pores within feldspar, calcite, and dolomite grains (Figure 7D-F). Micro-fractures that are associated with clay minerals and organic matter were frequently observed (Figure 7G-I). The micro-fractures are characterized by a slit shape and several microns in length (Figure 7H). The micro-fractures developed between organic matter and mineral grains are probably due to the shrinkage of organic matter (Figure 7I).
# Correlations between Anthropometric Measurements and Sports Discipline Aptitude
## 2 Materials and Methods  
The data used in this study were obtained by reviewing the content of various PubMed-gathered publications on sport-related anthropometric measurements. Of many, only a few provided comparable means and standard deviations for stature, sitting height, leg length, arm length, forearm length, waist circumference, chest circumference, and neck circumference [6; 13; 21] measured at the same anatomical points.  
The model-building procedure consisted of two steps. Step 1: Generation of a random, normally distributed reference sample of N = 500 subjects using the data reported in Table 1. Step 2: Outlier rejection was accomplished by removing values that fell beyond the 95% confidence interval (CI) for a given sample.  
The parameters, such as the ratio of height/sitting height (H/SH), height/ length (H/LEL), height/arm length (H/AL), height/waist circumference (H/WC), height/chest circumference (H/CC), sitting height/leg length (SH/LEL), sitting height/arm length (SH/AL), sitting height/waist circumference (SH/WC), sitting height/chest circumference (SH/CC), arm/leg length (AL/LEL), and arm length/forearm length (AL/EWL), were computed and are gathered in Table 2.  
A supervised machine learning algorithm, a decision tree classifier [21], verified using a classification tree [22], was used to elucidate the correlation between a sports discipline and specific anthropometric parameters. The parameters used for the DTs for each model were the following: in pairwise comparison, the studied sports disciplines, for example, wrestling and badminton, were hot-encoded as 0 and 1, the maximum search depth was equal to 5, and the loss function that compares the class distribution before and after the split was the Gini impurity [23] for the measurement of impurities [24; 25]. A graphical representation of the study scheme is shown in Figure 1. All computations and graphs were completed using the Python programming language.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Code**} & \multicolumn{2}{c}{**Triple Jump [21]**} & \multicolumn{2}{c}{**Wrestling [6]**} & \multicolumn{2}{c}{**Tennis [13]**} & \multicolumn{2}{c}{**Badminton [13]**} \\  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline H & 180 & 2.7 & 171 & 8.7 & 185 & 4.8 & 182 & 4.6 \\ SH & 91 & 1.3 & 92 & 4.2 & 96 & 3.2 & 96 & 2.5 \\ WC & 78 & 1.6 & 84 & 10.5 & 80 & 4 & 82 & 3.8 \\ CC & 94 & 1.8 & 105 & 10.7 & 96 & 5.2 & 96 & 4.3 \\ LEL & 102 & 1.5 & 102 & 7 & 104 & 4.2 & 102 & 3.5 \\ AL & 80 & 1.2 & 75 & 4.4 & 82 & 5.4 & 80 & 4.4 \\ EWL & 26 & 0.4 & 26 & 1.8 & 26 & 6 & 27 & 4.1 \\ \hline \end{tabular}
\end{table}
Table 1: Literature derived the body part length and circumference ratio for wresters, triple jumpers, badminton players, and tennis players. Abbreviations: SH—sitting height, H—body stature (height), AL—arm length, LEL—leg length, EWL—forearm length, WC—waist circumference, CC—chest circumference, MNC—neck circumference. All original measurements are in centimeters.
## 4 Conclusions  
Area solar radiation (\(ASR\)) and canopy height (CH) exhibited a significant positive correlation, and canopy height also exhibited positive correlation to total vegetation density (TVD). Statistically significant relationships were evident between the canopy structure and biodiversity data as well. While the available data did not indicate a relationship of significant magnitude between modeled radiant flux and observed biodiversity, there was a significant relationship between CH and diversity, depending on how diversity was measured.  
The _MDI_ linear model (Lm3) comprising the canopy relief ratio (CRR), the texture of heights (TH) and total vegetation density (TVD) was the most significant of the four models analyzed, given the available data. This set of predictors consists of metrics that would influence the variability of irradiance and the extinction of PAR in the lower strata. Unlike canopy height (CH), there was no significant positive correlation between \(ASR\) and vegetation density (TVD). The fact that TVD would have some relationship to MDI stands to reason, as does the relationship canopy variability represented by CRR and TH.  
The _MDI_ linear regression model had a \(p\)-value of 0.01874 and an adjusted \(r^{2}\) of 0.9687. As a predictor variable, CRR had a \(p\)-value of 0.00853 (a 0.01 level of significance), TH a \(p\)-value of 0.01412 (a 0.05 level of significance), and TVD a value of 0.00630 (a 0.01 level of significance). This model was determined to be useful for predictive spatial modeling by providing over 60 data points after adjusting the prediction model for the range of canopy metrics surveyed.  
Spatial statistics at the global level indicated the predicted range of MDI values with respect to proximity was not random overall, and the local hotspot map produced from this predicted data indicated several areas of both high and low biodiversity concentrations based on the predicted values. Within the constraints of the available data, this was an outcome validated by a field survey of the study area.
## 5 The Agent-Based Model
### Configuration Phase  
The virtual environment initial conditions are defined during the configuration stage; moreover, the simulated world was populated with the agents, placing the buildings and assigning them a roof size according to the actual distribution observed in the Emilia-Romagna region (data made publicly available by the region itself). First, the world-area was filled with buildings and their roofs (fixed geographical coordinates). Secondly, the families-households were created (as many as specified by an input parameter) and each one was placed inside a building (buildings are not shared); households with higher income and the more numerous ones get the buildings with larger roofs.  
The positions of the buildings in Emilia-Romagna were obtained by parsing the Ersi shape-files publicly available ([[http://dati.emilia-romagna.it](http://dati.emilia-romagna.it)]([http://dati.emilia-romagna.it](http://dati.emilia-romagna.it))), which are the results of territorial surveys conducted by the region; in these files each building was represented by a polygon encapsulating multiple information about the building. The agent-based model proposed here requires only position (spatial coordinates) and roof size, hence only these relevant information were extracted. QGIS [44] (an open source Geographic Information Systems, GIS) was the tool employed to parse the Ersi files and collect the needed information (position and roof size).  
An important aspect that had a strong effect on the adoption of PV panels (and innovation in general) is the presence of incentive mechanism, aimed at fostering the diffusion of new (or less known) technology. From 2006 to 2014 the Italian government offered national incentives to private citizens willing to install PV panels, namely feed-in tariffs referred to as _Conto Energia_. There have been a few different tariff schemes during the incentives years ([45; 46; 47; 48]), differing for the price guaranteed for the produced electricity. The national incentives are available to all house owners and the tariffs are those actually offered during the considered period. On top of the Italian feed-in tariffs, regional policy makers in Emilia-Romagna devised a number of different additional regional incentive mechanisms, such as investment grants, fiscal incentives, loans, interest funds, etc (see [42] for more details). Historically, the national incentives outweighed the regional ones by at least one order of magnitude therefore their influence has been much stronger [49; 50], thus regional incentives have little or no impact on the agents behaviour.