esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation on Base Metal Block Shear Strength of Ferritic Stainless Steel Welded Connection
## 1 Introduction
Studies on buckling behaviors and web crippling design of ferritic stainless steel structural members have been carried out by [PERSON] et al. [18], [PERSON] et al. [19] and [PERSON] et al. [20]. [PERSON] [21] performed an experimental study on the ferritic stainless steel (STS430) welded connection with base metal block shear fracture and evaluated the ultimate strength depending on the length of the weld line according to loading direction. It was found that the current design equations for carbon steel welded connections did not consider the shear lag effect, stress triaxiality effect, and stress distribution factor in the critical fracture section for ferritic stainless steel welded connection. This study examines the stress and strain distribution of welded connections based on the previous experimental results of ferritic stainless steel (STS430) fillet-welded connections with base metal block shear fracture and develops a finite element analysis model to predict the ultimate strength of welded connection with the base metal block shear fracture. Additional analysis on the weld length on the welding direction without further testing to compare and review the fracture mode and strength according to the variables was also performed. In addition, this study considered the applicability of the design formula by comparing the ultimate block shear strength of the analysis results with the predicted strength according to the current design equations and the equations proposed by [PERSON] [14], [PERSON] [15], and [PERSON] et al. [13]. The current design equations such as KBC/AISC and EC3 and the equations proposed by [PERSON] and [PERSON] were conservative to predict the base metal block shear strength of ferritic stainless steel. Although proposed equations by [PERSON] and [PERSON] et al. for carbon steel and austenitic stainless steel provided more accurate ultimate strengths, it is required that tensile stress and shear stress factors in the block shear path to reflect the characteristics of ferritic stainless steel material properties. Finally, a modified block shear strength equation considering the effects of the stress triaxiality on ultimate strength, stress distribution at the critical block shear section of ultimate state and weld length of the ferritic stainless steel welded connection was proposed.
## 2 Brief Historical Context of Chilean Construction Regulations  
For the Chilean territory, from colonial times, urban planning regulations, such as the Discovery Ordinances, New Populations and Pacifications of 1573, were issued to regulate the conformation, growth or reforms of cities, mainly motivated by demographic growth and for reasons of health [8].  
In Chile as a republic, the first approach to regulations related to urban subjects was decreed in 1854, which assigned to city halls the responsibility for defining urban development guidelines about construction or urbanism. Between 1874 and 1912, urbanism laws were enacted by different city halls in Chile; these laws were collected during the reform of the city halls law in 1915 [8]. Although Chile has historically recorded more than 10 earthquakes with great magnitude since achieving independence in 1810 (today, earthquakes are catalogued with magnitudes higher than Mw 8.0 [9]), laws or regulations issued prior to 1927 did not include any type of seismic consideration for construction. As an international context for this decade, the first regulation of seismic design in the world was decreed in 1924 in Japan.  
In 1928, the Mw 7.6 earthquake in Talca greatly damaged structures, which emphasized the urgent need to regulate building construction while considering the seismic demands of the country. From the previous background, in 1929, a law project was decreed that authorized developing a general ordinance to establish regulations to which building construction must conform in the different districts of Chile. These regulations would consider criteria to prevent collapse, for the first time indicating that construction must \"_avoid, as much as possible, the risks coming from an earthquake_\". Thus, between 1930 and 1931, the First Law and Ordinance of Construction and Urbanization (OGCU), began seismic resistance regulation for construction in Chile [10], joining countries with normative design criteria, such as Japan, 1924 [11], the USA, 1927 [12], and New Zealand, 1935 [13]. Note that criteria and definitions with respect to seismic design are contained mainly in the version of the OGCU established in 1936, OGCU-1936 [14].  
Between 1936 and 1971, the main regulation with seismic design criteria was the OGCU, which underwent different changes motivated by the large seismic events that occurred in the country. In 1972, the first Chilean seismic design code, referred to as NCh 433, was promulgated, the most recent version of which corresponds to the proposal evaluated between 2018 and 2020, which is in the process of being approved.  
3 Seismic Site Classification Methodologies According to Chilean Design Regulations or Seismic Codes
## 3 Results and Discussion
### Morphological Aspects of the Solids by SEM-EDS  
SEM-EDS analyses are performed to examine the morphological and compositional aspects of the solids (Figure 3).  
SEM image of Fe sample shows large particles with non-distinguished shaped and soft surface morphologies (Figure 3a). The magnified image of an individual particle (Figure 3a) depicts numerous aggregates of small particles.  
Moreover, the elemental analysis by surface EDS indicates the composition of Fe and O at 73.1 and 23.9% respectively (Table 2). The impurities from the preparation of the solids such as W and S elements are also observed in a minor amount.  
In addition, SEM images for FeC exhibits similar morphologies as those of Fe sample, however in this case all particles tended to agglomerate, as shown in Figure 3b. From EDS as shown in left of Figure 3b, a composition of Fe, O and C being 63.9%, 29.1% and 6.7%, respectively is found, as presented in Table 2. Both Fe species and carbon are present on the surface of the composite, which is consistent with FTIR, XRD and Raman measurements.  
For Fe/FeC sample, the particles possess irregular morphology shapes as depicted in Figure 4 Additionally, the agglomerated composite is quite larger in comparison with other two samples (Figure 4, left side of the panel).  
Figure 4: SEM-EDS images of the Fe/FeC solid.  
Figure 3: SEM-EDS images of the (**a**) Fe and (**b**) FeC samples.  
From EDS, the surface of the solids consists of Fe and C elements indicating that iron is successfully incorporated to the structure, in which Fe\({}^{3+}\)/Fe\({}^{2+}\) ions form Fe\({}_{3}\)O\({}_{4}\) and \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\), besides amorphous carbon as determined by Raman measurements.
## 5 Conclusions  
1. The results of the three risk evaluation methods concluded that the soil's heavy metal contamination level in the study area is high, and the combined potential ecological risk is high, with As, Hg and Cd as the main contaminating elements. Zn has the highest proportion of directly available biological state, which is a medium pollution risk, but the toxicity coefficient of Zn is low, which makes it difficult to cause ecological risk. It mainly originates from the waste rock and tailings left behind by the mines, Hg mainly originates from the early mining areas and from the chaotic and unorganized mining and gold extraction methods, and Cd mainly originates from mining activities and agricultural activities. In the process of mining and smelting, the direct discharge of waste residue, wastewater and waste gas will cause serious pollution, destroy the surrounding ecological environment and pose a potential threat to human health. Therefore, the international advanced mining and sorting process should be adopted, and the tailings should be treated and utilized in a timely manner to progress in the direction of non-waste mining. At the same time, the generated blue gas is sprayed and purified to reduce the risk of dry and wet settlement.
2. The predominant components of Cr, Ni, Cu, Zn, As, Cd, Pb and Hg in the soil within the study area are residues, accounting for 94.20%, 95.01%, 84.00%, 84.35%, 83.85%, 91.80%, 81.99% and 96.63%, respectively. The average proportion of the speciation of heavy metals in the soil is not much different, and the fraction of Cd and Zn mild acid-soluble is relatively high in individual sampling points, which needs to be paid attention to. In general, the soil's heavy metal forms in the study area are relatively stable, and the change of soil pH should be monitored and the ecological risk situation of individual sampling sites should be paid attention to. It is recommended that further testing and research on citrus, maize and other crops grown in the study area should be carried out as a next step.  
**Author Contributions:** Writing--original draft preparation, Y.M. and M.W.; Writing--review and editing, M.W. and P.L.; Collecting the samples, Y.M., Y.J. and M.W.; Software and data processing, Y.M., Y.J. and X.Z.; Funding acquisition, M.W. and P.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Guangxi (2022 GXNSFBA035548) and the National Natural Science Foundation of China (42163004 and 42203067).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results
Later, in [16], deep learning models like VGG-16 and Inception were incorporated via transfer learning, showing improved performance, particularly for the high-risk BI-EIM5 class, with F1-scores above 85% when combined with 5-NN. In contrast, the proposed approach focuses on selecting only the relevant information from all the layers and performing a binary classification task (low suspicion vs. high suspicion). This early fusion generates variability maps that enhance the representation of diagnostically relevant information. Among the methods tested, Wavelet-PCA fusion achieved the best results, with an accuracy of 89.5% and an F1-score of 81.5% when used with XGBoost. Additionally, strong performances were achieved with Random Forest and Logistic Regression, utilizing both Wavelet-PCA and the CV, with F1-scores exceeding 80%. While previous studies focused on analyzing the statistical conductivity values or classifying individual EIM slices, our proposed method directly employs complete EIM images to build integrated representations. This approach demonstrates that it is possible to identify patterns associated with breast lesions by fusing EIM layers rather than analyzing each one separately. Moreover, the results suggest that such an integration allows for the preservation of relevant diagnostic details across the depths while simplifying both visual interpretation and the automatic classification process. Overall, the results suggest that integration using the CV and fusion through Wavelet-PCA are the most effective strategies for enhancing the discriminative power of EIM images. When comparing the two types of preprocessing, Wavelet-PCA ranked as the most effective global approach, consistently outperforming integration techniques in terms of its accuracy and F1-score. However, the CV remains a highly competitive option, especially when paired with simpler, computationally efficient classifiers. These findings confirm that the success of EIM-based classification is closely linked not only to the model architecture but also, more importantly, to the quality of the image representation used as the input. The current methodology is restricted to binary classification based on the grade. While this approach is practical, it requires a more extensive and balanced dataset. Therefore, it is essential to increase the number of samples, which would also enable testing with advanced machine learning techniques, such as convolutional neural networks (CNNs) or deep learning (DL) models.
## 3 Proposed Algorithm
### Skeleton Algorithm  
In this paper, the OpenPose algorithm is used as a skeleton algorithm in the proposed algorithm. The OpenPose algorithm was first presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) and various studies using the OpenPose algorithm have been published [27; 28; 29]. The OpenPose algorithm is an algorithm that estimates the skeleton of a person in an image in real time, allowing for the estimation of the poses of multiple people. The algorithm can detect 25 key points of the human body, including the head, neck, shoulders, elbows, wrists, hips, knees, and ankles. It has high accuracy even in various poses, clothing, and lighting conditions, making it useful in a wide range of fields, including human action recognition, pose estimation, and emotion analysis.  
\(S=(S_{1},S_{2},\ldots,S_{l})\) has \(I\) confidence maps, one per part, where \(S_{i}=R^{w\times h},i\in{1,\ldots,I}\). \(L=(L_{1},L_{2},\ldots,L_{C})\) has \(C\) confidence maps, one per part, where \(L_{c}=R^{w\times h\times 2},c\in{1,\ldots,C}\), and each image location in \(L_{c}\) encodes a 2D vector. The S represents the confidence maps, which are Gaussian models centered on the key point. In addition, the L is the Part Affinity Fields (PAFs) that contain connection information, which is a tensor with direction and location information to determine the correlation between joints. In other words, Part Affinity Fields (PAFs) contain directional and positional information to determine the correlation between joints in order to predict whether the joints match the next joint. We generate individual confidence maps \(S_{i,n}^{*}\) for each person \(n\). The value at location can be expressed as follows:  
\[S_{i,n}^{*}(p)=exp\left(-\frac{\|p-x_{i,n}\|_{2}^{2}}{\sigma^{2}}\right), \tag{1}\]  
where \(p\in R^{2}\), \(x_{i,n}\in R^{2}\) is the ground truth position of body part \(i\) for person \(n\) in the image. The OpenPose algorithm automatically detects and extracts human body parts from RGB images. To do this, it first takes an image as input and uses VGG-19, which is based on the Convolutional Neural Network (CNN), to extract the positions of each body part. Afterthat, joints are detected from the Part Confidence Maps (PCM), denoted as \(S_{i,n}^{*}\), and the connection between joints is found using PAFs. Then, the Part Confidence Map (PCM) is used to refine the positions of each body part. PCM is a network that locates joints to track human posture and predicts the areas where there may be joints for each part, displaying them as a heat map.
###### Abstract  
The transglycosylation behavior and capacity of two clan GH-D \(\alpha\)-galactosidaseases, _Bo_Gal36A from the gut bacterium _Bacteroides ontus_ and Aga27A from the guar plant, was investigated and compared. The enzymes were screened for the ability to use _para_-nitrophenyl-\(\alpha\)-galactosidase (pNP-Gal), raffinose and locust bean gum (LBG) galactomannan as glycosyl donors with the glycosyl acceptors methanol, propanol, allyl alcohol, propargyl alcohol and glycerol using mass spectrometry. Aga27A was, in general, more stable in the presence of the acceptors. HPLC analysis was developed and used as a second screening method for reactions using raffinose or LBG as a donor substrate with methanol, propanol and glycerol as acceptors. Time-resolved reactions were set up with raffinose and methanol as the donor and acceptor, respectively, in order to develop an insight into the basic transglycosylation properties, including the ratio between the rate of transglycosylation (methyl galactoside synthesis) and rate of hydrolysis. _Bo_Gal36A had a somewhat higher ratio (0.99 compared to 0.71 for Aga27A) at early time points but was indicated to be more prone to secondary (product) hydrolysis in prolonged incubations. The methyl galactoside yield was higher when using raffinose (48% for BoGal36A and 38% for Aga27A) compared to LBG (27% for BoGal36A and 30% for Aga27A).  
transglycosylation; \(\alpha\)-galactosidase; enzymatic synthesis +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Delay  
Delay is a critical performance metric for 5G VANETs, particularly when real-time responsiveness is essential for mission-critical applications. In our analysis, we measured average delay per-packet at both the SDN gateway and the gNB across all three test phases to assess how efficiently the framework processed and offloaded critical vehicular data to edge application servers.  
As illustrated in Figure 10, the SDN gateway exhibited significant improvements in delay performance over time. Camera sensor delay dropped from 430 milliseconds (ms) in Test 1 to 150 ms in Test 3, a reduction of approximately 65%. LiDAR delay dropped from 720 ms to 250 ms, while RADAR dropped from 1570 ms to 500 ms. These results reflect the growing efficiency of the SDN Controller in scheduling and pre-processing data streams as traffic volume increased over time.  
Despite these improvements, the delay at the SDN gateway remained higher than that at the gNB, mainly due to the overhead involved in local pre-processing and slice selection. As illustrated in Figure 11, the gNB consistently recorded lower delay values during the offloading phase. In Test 3, where the overall best results were observed, average delays at the gNB were 60 ms for Camera, 90 ms for LiDAR, and 170 ms for RADAR. These delay values are significantly lower than those at the SDN gateway, indicating better data handling once traffic is classified and transmitted upstream.  
Figure 9: Average flow rate at gNB.  
When considering the total end-to-end delay, including in-vehicle pre-processing and gNB offloading, the system achieved a combined delay of 210 ms for Camera, 340 ms for LiDAR, and 670 ms for RADAR during the third test. These figures demonstrate improved performance compared to successive tests, although end-to-end delay needs further optimisation.
## Results and Discussion
### Resistance of Arthrobacter sp. Strain SRS-W-1-2016  
When the genome of strain SRS-W-1-2016 was mined for the presence of antibiotic resistance genes (the resistome), gene determinants for the following drug resistance were noted (percent identity with matching region are shown in the parenthesis): fluoroquinolone (36%), isoniazid (46%), fosfomycin (43%), aminocoumarin (66%), muiprocin (54%), and rifamycin (33%), respectively. Several other genes that potentially confer antibiotic resistance were also identified, including the antibiotic target protection protein (36%), gene involved in self-resistance to antibiotic (66%), efflux pump complex or subunit conferring antibiotic resistances [(39% with _ImrB_ and 40% with _ImrD_--both confer resistance to imcosamides) and 55% with _novaA_ (resistance to novobiocin)], antibiotic resistant gene variants or mutants, and the antibiotic inactivation enzyme (Supplementary Figure S2). Because a lower degree of homology, ranging between 36% to 66%, was observed between the resistome of strain SRS-W-1-2016 with the previously characterized antibiotic resistance genes, there is a strong possibility of a novel resistance that has developed in this strain, likely due to the selective pressure of heavy metals, U, and organic compounds.  
Moreover, when the genome of strain SRS-W-1-2016 was evaluated for the presence of biosynthetic gene clusters (BCs) responsible for the synthesis of secondary metabolites (SMs), seven BCs with 150 associated genes were identified (Supplementary Figure S3). The predominant BC identified from strain SRS-W-1-2016 affiliated with the well-known nonribosomal peptide synthetase (NRPS)-type III polyktetide synthase (T3 PKS), with a total of 69 pfams; other BCs were also identified for bacteriocin (27 pfams) and butylrolactone (11 pfams). Interestingly, many pfams that were found to be overrepresented across the seven BCs identified in strain SRS-W-1-2016 have been shown to play a role in metals-microhe interactions (appear as red boxes in the heat map; Supplementary Figure S3).  
Notably, a total of 46 secondary metabolite clusters were identified using the antiSMASH pipeline with genes homologous to the biosynthesis of (similarity shown in parenthesis): Amphotericin (23%); Streptomycin (15%); Arsenopolyketides (12%); Echosides (11%); Guadinomine (11%); Thiolutin (8%); Kanamycin (7%); Chlorizidine (7%); Pristinamycin (5%); Meilingmycin (2%); Lobosamide (4%); Enduracidin (4%); and Herboxidiene (2%), respectively. Overall, this resistance analysis depicts the broad metabolic and biosynthetic potential of _Arthrobacter_ sp. strain SRS-W-1-2016.
## 3 Results and Discussion  
This section discusses the results obtained from modelling the catalyst to assess the reactivity in the presence of H\({}_{2}\) by applying the methodology detailed in Section 2. First, the methodology used and the results obtained to carry out the calibration of the OC model are presented. The analysis of the results obtained after the model calibration was focused on the study of the reactivity of the CO and the HC in the OC as a function of the H\({}_{2}\) molar fraction under warm sea-level and cold high-altitude conditions. From the OC overall results, the evolution of the selectivity of CO and HC to O\({}_{2}\) and -OH, HC adsorption, and the conversion efficiency along the cross-section of the monolith were analyzed.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Reaction** & **Reaction Expression Rate** \\ \hline \multicolumn{3}{c}{Zeolite layer} \\ \hline \(C_{n}H_{m}+Zeol\leftrightarrow C_{n}H_{m}-Zeol\) & \(R_{1}=k_{1}c_{HC}(1-\theta_{HC})\) \\  & \(R_{2}=k_{2}\theta_{HC}\) \\ \hline \multicolumn{3}{c}{Platinum layer} \\ \hline \(CO+\frac{1}{2}O_{2}\to CO_{2}\) & \(R_{3}=\frac{k_{1}}{c_{ox}}c_{CO}c_{O_{2}}\) \\ \(C_{n}H_{m}+\left(n+\frac{m}{4}\right)O_{2}\to nCO_{2}+(\frac{m}{2})H_{2}O\) & \(R_{4}=\frac{k_{4}}{c_{ox}}c_{HC}c_{O_{2}}\) \\  & \(R_{5}=k_{SO_{2}}c_{NO}\) \\ \(NO+\frac{1}{2}O_{2}\leftrightarrow NO_{2}\) & \(R_{6}=\frac{k_{5}}{c_{NO_{2}}}c_{NO_{2}}\) \\ \(H_{2}+\frac{1}{2}O_{2}\to H_{2}O\) & \(R_{7}=k_{7}c_{H_{2}O_{2}}\) \\ \(H_{2}+O_{2}+2 Pt\leftrightarrow 2\cdot OH-Pt\) & \(R_{8}=k_{9}\Theta_{OH}(1-\theta_{OH})\) \\ \(H_{2}+2\cdot OH-Pt\to 2H_{2}O+2 Pt\) & \(R_{10}=k_{10}c_{H_{2}}\theta_{OH}\) \\ \(CO+2\cdot OH-Pt\to H_{2}O+CO_{2}+2 Pt\) & \(R_{11}=k_{11}c_{CO}\theta_{OH}\) \\ \(C_{n}H_{m}+\cdot OH-Pt+(\frac{4n+m}{4}\!-\!\!\frac{1}{4})O_{2}\to nCO_{2}+ \left(\frac{m+1}{2}\right)H_{2}O+Pt\) & \(R_{12}=k_{12}c_{HC}\theta_{OH}\) \\ \(CO+H_{2}O\leftrightarrow CO_{2}+H_{2}\) & \(R_{13}=k_{13}c_{CO}H_{hO}\) \\ \(C_{n}H_{m}+nH_{2}O\to nCO+\left(n+\frac{m}{2}\right)H_{2}\) & \(R_{14}=\frac{k_{13}}{c_{Na_{NN}}}c_{CO_{2}cH_{2}}\) \\ \(C_{n}H_{m}+nH_{2}O\to nCO+\left(n+\frac{m}{2}\right)H_{2}\) & \(R_{15}=k_{15}c_{HC}c_{H_{2}O}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: H\({}_{2}\)-OC reaction mechanism.