esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Since the beginning of the 1990s duplex and superduplex stainless steels have played an essential role in the oil and gas industry, in transportation, in construction applications, and in the process industry [1].  
However, the practical application of superduplex stainless steels using large thickness plates and pipes is strongly dependent on the use of welding for fabrication, and there is still an important lack of knowledge regarding multi-pass welding of these steels. In multi-pass welding, some areas of the weld region are repeatedly heated. It is well known that when heated to, and in particular when repeatedly heated to, the range of approximately 600 to 1000 \({}^{\circ}\)C, superduplex stainless steels and weld metals are prone to intermetallic phase formation (e.g., \(\alpha\)-phase, \(\chi\)-phase, and R-phase) [2; 3]. Reheating can also induce secondary austenite formation and nitride precipitation. The presence of these phases is strongly detrimental to corrosion resistance and toughness.  
Measuring and monitoring temperatures is a key stage in quality control and process productivity for multiple industrial processes, including welding. However, measuring temperatures during welding shows experimental difficulties caused by the welding process itself. Welding fumes, sparks, or light reflections can be important sources of error when using non-contact techniques for temperature measurements. Alternatively, the use of contact techniques such as thermocouples also presents difficulties: the electric arc can interfere with the signal, and the high power beams can burn the thermocouples. It might also happen that some displacement in the location of the thermocouples occurs because of the motion and flow in the weld pool, causing therefore uncertainty in the positioning of the final measurement location. Several previous studies have focused on temperature measurements, i.e., in laser welding of austenitic stainless steels [4; 5], in Gas Tungsten Arc Welding (GTAW) of duplex stainless steels [6; 7], and in laser metal deposition of nickel alloys [8]. The involvement of the current paper's authors in the previously cited research was a helpful background to develop and establish the method used in this research to measure and monitor temperatures during multi-pass welding.  
This work aimed at making multi-pass welding of large thickness superduplex stainless steels more efficient by investigating the influence of the multiple welding cycles on the formation of undesirable phases. Therefore, the recently developed method for monitoring local thermal cycles was applied in GMAW and FCAW multi-pass welding of 20-mm-thick superduplex stainless steel. The thermal cycles were correlated to the local microstructure and to impact toughness and corrosion resistance. The findings in this research have some implications on the current practical welding guidelines, suggesting a review in case of large thickness weldments.
## 2 Materials and Methods
### Feedstock Characterization  
The OFMSW was characterized by the following parameters: total solids (TS), volatile solids (VS), and fixed solids (FS) (method 2540) [23]; total chemical oxygen demand (tCOD) and soluble chemical oxygen demand (sCOD) (method 5220 B) [23]; total Kjeldahl nitrogen (TKN) (method ISO 5663:1984), ammonium nitrogen (N-NH\({}_{4}\)+) (method ISO 5664:1984), and organic nitrogen (o-N) (difference between TKN and N-NH\({}_{4}\)+); total phosphorus (tP) (method ISO 6878:2004). Elemental analysis (EA) comprised the quantification of CHNS, which was performed in a Thermo Finnigan Elemental Analyzer (CE Instruments, model Flash EA 1112 CHNS series). Volatile fatty acids (VFAs) comprised the quantification of the acetic acid (AA), propionic acid (PA), and butyric acid (BA), which were analyzed with an HPLC system (Dionex ICS3000, Sunnyvale, CA, USA) equipped with a Biorad Aminex 87H column, at 30 \({}^{\circ}\)C, and a UV detector at 210 nm. The eluent used was H\({}_{2}\)SO\({}_{4}\) 10 mN, with a flow rate of 0.6 mL.min\({}^{-1}\), at 30 \({}^{\circ}\)C.  
The average characterization of the OFMSW is reported in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Values** \\ \hline pH & Sorensen & 4.63 \(\pm\) 0.04 \\ TS & g.L\({}^{-1}\) & 94.3 \(\pm\) 13 \\ VS & g.L\({}^{-1}\) & 82.7 \(\pm\) 13.4 \\ FS & g.L\({}^{-1}\) & 11.4 \(\pm\) 1.7 \\ tCOD & g O\({}_{2}\).L\({}^{-1}\) & 113 \(\pm\) 7 \\ sCOD & g O\({}_{2}\).L\({}^{-1}\) & 67 \(\pm\) 9 \\ tP & g N.L\({}^{-1}\) & 2.9 \(\pm\) 0.2 \\ TKN & g N.L\({}^{-1}\) & 1.8 \(\pm\) 0.1 \\ H-NH\({}_{4}\)+ & g N.L\({}^{-1}\) & 1.1 \(\pm\) 0.3 \\ o-N & g N.L\({}^{-1}\) & 525 \(\pm\) 92 \\ C & (\% \(w/v\)) & 10.3 \(\pm\) 0.2 \\ N & (\% \(w/v\)) & 7.89 \(\pm\) 0.05 \\ H & (\% \(w/v\)) & 0.69 \(\pm\) 0.07 \\ S & (\% \(w/v\)) & \textless{}0.01 \\ O (\({}^{1}\)) & (\% \(w/v\)) & 79.9 \\ C/N & - & 15.0 \\ C/P & - & 159.1 \\ \hline \hline \end{tabular} \({}^{(i)}\) Calculated by the difference: O = 100 –(C + H + N + S + FS).  
\end{table}
Table 1: Characterization of the OFMSW (average \(\pm\) standard deviation; \(n\) = 10 samples).
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility
### Procedure and Experimental Methodology
#### 2.3.7 Model Estimation  
Model estimation is a critical step in evaluating the performance of a classification model, especially when working with imbalanced data. In this study, we utilized standard measures to estimate the performance of our stroke prediction model, which were computed via entries of a confusion matrix.  
The entries of the confusion matrix were denoted as true positive (TP), false positive (FP), true negative (TN), and false negative (FN). Additionally, we used a Python command to display the computed values of measures such as precision, recall, F1-score, and accuracy. However, we recognize that in large imbalanced datasets, high-accuracy measures may neglect the performance of the minority class.  
Therefore, we provided precision and recall measures for each class to address this issue. By examining the precision and recall measures separately for each class, we were able to gain a better understanding of the model's performance for both the majority and minority classes.  
Our results showed that our model achieved high accuracy, precision, and recall measures for both classes, indicating that it was effective in predicting the occurrence of stroke. This was further supported by the fact that our model outperformed previous research efforts in stroke prediction.  
Moreover, our study highlights the importance of using appropriate measures for model evaluation when working with imbalanced data. By providing precision and recall measures for each class, we were able to gain a more accurate understanding of our model's performance and avoid neglecting the minority class.  
In summary, our study demonstrates the value of using appropriate measures for model estimation when working with imbalanced data, especially in the field of stroke prediction. We believe that our findings can be applied to other domains where imbalanced data are a common challenge, leading to the development of more accurate and reliable prediction models.  
In Table 2, the general structure of a confusion matrix is presented. The first class, denoted by 0, is a negative one. This structure is used by [PERSON] et al. [17]Their definitions are standard and they are used in many papers subject to machine learning analysis. In our research, the positive observations are assigned with Class 2 (denoted by 1) and the negative ones with Class 1 (denoted by 0), as described in Table 2. These values are used to compute the accuracy, precision, and recall metrics displayed in the confusion report [17]. The formulas are the following and cited in [18]:  
\[Acc(X_{test})\coloneqq\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN};\]  
Precision (Class1) = \(\frac{TN}{TN+FN}\);  
Precision (Class2) = \(\frac{TP}{TP+FP}\);  
Recall (Class1) = \(\frac{TN}{TN+FP}\);  
Recall (Class2) = \(\frac{TP}{TP+FN}\).
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.3 Selsnam Project--Magallanes Region  
The Selsnam on-shore Wind Farm is a facility that will be located in the north of Punta Arenas, Magallanes region. It will have 192 wind turbines, the owner of which will be Albatros--Alfanar and ENAP. Furthermore, the height of the towers from ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 1150 MW, with an estimated capacity factor of 0.60. Figure 12 shows a panoramic image of the landscape of the project site, and Table 5 provides some key technical data for this GH2 production facility.  
Considering Figure 12, it is evident that the Selsnam on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
Figure 12: Panoramic view of the landscape in the Selsnam project.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & East of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & Albatros—Alfanar and ENAP & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 192 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 1150 & MW \\ \hline Capacity Factor & 0.6 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 1,121,250 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 863 & MW \\ \hline GH2 Production & 99,188 & Tons/year \\ \hline NH\({}_{3}\) Production & 556,313 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 991,875 & Tons/year \\ \hline \hline \end{tabular}
\end{table}
Table 5: Selsnam GH2 plant main specifications.  
According to Table 5, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 1,121,250 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 863 MW of installed capacity. In this sense, the GH2 production plant will generate 99,188 tons/year of GH2, and 556,313 tons/year of NH\({}_{3}\). Finally, this facility will save 991,875 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
## 3 The Proposed OAM&WDM-PON Architecture Design with the P2 MP Topology
### Description of the Development Environment  
Considering the fact that there are no suitable present simulation programs, we decided to use the Matlab development environment for realizing a simulation model and for acquiring numerical results. Matlab is an interactive system that represents a tool for highly productive research, development, and analyses in the industry wherein basic data elements are data fields in matrixes and a wide variety of specific application solutions called toolboxes can be utilized [22]. In this programming environment, we created and realized the simulation model related to the conventional OAM&WDM-PON architecture with the P2 MP topology. In this model, novel algorithms for allocation methods can be applied. Afterward, resource efficiencies of wavelengths and OAM channels can be analyzed. In the simulation model, a maximum transmission capacity of one wavelength in [Gbit/s] can be determined based on the real GPON (Gigabit-capable Passive Optical Network) of one Slovak telecom operator. Therefore, it is supposed that all utilized wavelengths have the same 10 Gbit/s maximum transmission capacity for realized simulation trials. A value generation of ONT bandwidth requests is characterized by setting probability curves for data requests related to ONT environments. The best option is represented by real traffic loading values related to the Slovak telecom operator's GPON under consideration. Simulations are designed for network services intended for combined residence and office customers and ONT terminals are determined for provisioning three basic customer services. These are IPTV (Internet Protocol Television), HSI (High-Speed Internet), and VoBB (Voice over Broad Band) services. The HSI service operates at the maximum 1 Gbit/s data rate, the IPTV service utilizes the 600 Mbit/s transmission capacity of wavelengths, and the VoBB service can utilize 120 Mbit/s or 60 Mbit/s data rate alternatively. This data is used in the simulation as input parameters for requests of the bandwidth for particular ONUs. Examples of the real GPON traffic loading of Slovak telecom operators are displayed in Figure 2[20]. Based on the OLT control software application (OptiXaccess EA5801), there is a presentation of the traffic loading of one GPON port all day long.  
Figure 1: The simplified scheme of the proposed P2 MP OAM&WDM-PON architecture.
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 3 Proposed Method
### Loss Function  
In situations with data imbalance, using cross-entropy [29] alone in typical classification models has its limitations. We found this issue in object-detection models, which aim to locate and mark objects of interest in images with bounding boxes. The object-detection process involves identifying candidates in the image and determining whether they are objects of interest. Most areas are background, and the objects of interest are a minority, posing a significant problem. In order to address this, one common approach in object-detection models is the use of focal loss. Focal loss modifies the cross-entropy loss function by reducing the weight for well-learned classes and increasing it for classes that are not learning effectively. This feature allows more focused learning on minority classes, enhancing disease classification accuracy in scenarios with scarce data. This aligns with the goals of our research and significantly contributes to improving the classification of challenging data, such as those for glaucoma, compared to the classification of normal data.  
Focal loss [30] is particularly meaningful in fields such as medical data, where the healthy data vastly outnumber disease data. Utilizing this loss function aligns well with the goals of our study, emphasizing the importance of higher classification accuracy for disease data over normal data, which is crucial for ensuring effective disease detection and management.  
The equations below represent cross-entropy (1) and focal loss (2). \(y_{i}\) denotes the probability distribution of the actual labels, and \(y_{i}\) denotes the probability distribution  
Figure 4: The process of color mapping using color palettes.  
of the predictions. \(\alpha\) and \(\gamma\) are hyper-parameters used to address imbalanced samples. In order to mitigate data imbalance, we used \(\alpha\) = 0.25 and \(\gamma\) = 2. While cross-entropy uniformly learns from all classes, focal loss focuses on the classes that are misclassified, thereby improving the classification accuracy of those classes.  
\[\text{Cross Entropy(y, }\hat{y})=-\sum_{i=1}^{N}[y_{i}\log(\hat{y}_{i})+(1-y_{i}) \log(1-\hat{y}_{i})] \tag{1}\] \[\text{Focal Loss(y,}\hat{y})\ =-\sum_{i=1}^{N}\alpha(1-\hat{y}_{i})^{ \gamma}y_{i}\log(\hat{y}_{i}) \tag{2}\]
## 4 Conclusions  
MPNTS was successfully synthesized by hydrosilylation of 1,1,1,3,5,5-heptamethyltrisiloxane and methylpropenyl polyether. The introduction of polyether segments is grafted onto the organosilicon heptamethyltrisiloxane main chain to produce the silicone surfactant MPNTS with hydrolysis stability. MPNTS exhibits both low surface tension (18.68 mN/m) and a critical micelle concentration (CMC) of 78 mg/L, indicating its potency even at small dosages. MPNTS, due to its compatible performance, will have good hydrolysis resistance, and it cannot be hydrolyzed after standing for 60 days under neutral and alkaline conditions. Compatibility performance analysis showed that MPNTS had a good application prospect in daily chemical products. Heptamethyltrisiloxane exhibits good hydrophilicity by introducing hydrophilic groups, which fully demonstrated that the modification of the main chain of siloxane can provide silicone with different properties. This has a lot to do with the introduction of different groups. Future research will focus on introducing different types and quantities of groups to modify silicone and apply MPNTS research to the development of daily chemical products.  
Conceptualization, H.J.; methodology, L.L.; data curation, Y.C.; writing--original draft, Y.C.; writing--review and editing, H.J.; supervision, W.T.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by the National Natural Science Foundation of China (51972348, 21905313, and 21425627), the National Natural Science Foundation of China-SINOPEC Joint Fund (U1663220), the Guangdong Provincial Key R&D Program (2019B110206002), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017 BT01C102) and Guangdong Provincial Key Laboratory of Plant Resources Biorefinery (2021 GDKLPRB10).  
Figure 6: Surface tension changes of different types of surfactants mixed with MPNTS in different mass ratios: (**a**) compatibility of MPNTS with cationic surfactants and (**b**) compatibility of MPNTS with anionic surfactants and nonionic surfactants.  
Data Availability Statement: The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 6 Discussion
Efficient diagnostic and analytical techniques are needed to be used in complex situations; also to reduce the number of misclassifications and false positives [64]. It is sometimes difficult to reflect the abstract features used by machine learning systems based on a large set of diverse data collected, and the complex nature of sensor time series requires precise planning of real-time scenarios [65]. Non-linear methods are being explored for feature engineering and wear condition modeling using supervised learning (including support vector machines (SVMs)), k-nearest neighbors (k-NNs)) with maximumaccuracy 74.8% [66]. It is more difficult with small data sets, i.e., when we use machine learning for a small amount of data. This applies when we have limited resources (number of sensors, budget, time, etc.). A hybrid model that combines machine learning with analysis using data sets from physical models may be useful, then [67]. The use of SCADA in the textile production process uses data mining based on databases of pre-processed data to generate predictive suggestions (i.e., optimal settings of textile machines) [68]. Another problem is the rare occurrence of data corresponding to faulty conditions (i.e., everyone is trying to prevent a failure), especially in the case of environmental and safety losses caused by damaged machines [11]. Despite the progress of research and implementation, the main challenges in the industry are still to ensure high accuracy of anomaly detection, the adaptability of the model (to adjust to changing conditions) and, above all, to diagnose new failures that have not occurred before. Multi-stage analyzes are used here, starting from multi-model comparisons, through optimization of clustering and projection algorithms, to detect incorrect signals and transfer the results into alerts throughout the process [69; 70]. Sometimes, however, decision trees are sufficient for the analysis [69; 70]. Most studies include cases from simulation data or assumed abnormal conditions, often depending on the previous life-to-failure. Then, the construction of the PdM system includes data acquisition, data pre-processing, constructing indicators and predicting the remaining useful life [29]. It is also difficult to compare data: the diversity of products and suppliers, the description of knowledge with attributes and label insights make comparative PdM research very complex in a fast-growing market with a wide variety of products [10; 71; 72].
## 3 Results
### Regression Models with All Parameters
#### 3.3.1 UAV Regression Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Model** & **R\({}^{2}\)** & **RMSE (g/m\({}^{2}\))** & _(n)_ \\ \hline \multirow{2}{*}{5 May} & RF & 0.91 & 0.51 & 22 \\  & SVR & 0.78 & 0.70 & 22 \\ \hline \multirow{2}{*}{12 May} & RF & 0.92 & 0.31 & 33 \\  & SVR & 0.80 & 0.46 & 33 \\ \hline \multirow{2}{*}{20 May} & RF & 0.85 & 1.12 & 33 \\  & SVR & 0.58 & 1.89 & 33 \\ \hline \multirow{2}{*}{27 May} & RF & 0.95 & 1.63 & 22 \\  & SVR & 0.84 & 2.43 & 22 \\ \hline \multirow{2}{*}{4 June} & RF & 0.91 & 2.36 & 33 \\  & SVR & 0.81 & 3.00 & 33 \\ \hline \multirow{2}{*}{5 and 12 May} & RF & 0.95 & 0.55 & 56 \\  & SVR & 0.77 & 0.84 & 56 \\ \hline \multirow{2}{*}{12 and 20 May} & RF & 0.94 & 0.88 & 67 \\  & SVR & 0.80 & 1.53 & 67 \\ \hline \multirow{2}{*}{20 and 27 May} & RF & 0.95 & 1.37 & 56 \\  & SVR & 0.79 & 2.87 & 56 \\ \hline \multirow{2}{*}{27 May, 4 June} & RF & 0.95 & 2.28 & 56 \\  & SVR & 0.76 & 3.86 & 56 \\ \hline \multirow{2}{*}{5, 12, and 20 May} & RF & 0.92 & 0.87 & 89 \\  & SVR & 0.79 & 1.40 & 89 \\ \hline \multirow{2}{*}{12, 20, and 27 May} & RF & 0.96 & 1.07 & 89 \\  & SVR & 0.87 & 2.07 & 89 \\ \hline \multirow{2}{*}{20 and 27 May, 4 June} & RF & 0.97 & 1.76 & 89 \\  & SVR & 0.85 & 3.79 & 89 \\ \hline \hline \end{tabular} \({}^{\dagger}\) All models are significant at _p-value_ < 0.001.  
\end{table} 
Table 5: Statistics for the calibration of the UAV canopy nitrogen models with 28 variables as a function of the date and modelling approach (RF or SVR) \({}^{1}\).
# Antenna Design for Microwave and Millimeter Wave Applications: Latest Advances and Prospects
###### Abstract
The performance of a wireless link under the line-of-sight and NLOS channels in an indoor environment was measured using the proposed quasi-isotropic antenna as a receiving antenna after validating its radiation and impedance properties experimentally (the measured gain deviation: 5.2 dB, the measured radiation efficiency: 79.2%). This study demonstrated that better properties were achieved using the quasi-isotropic antenna. The quasi-isotropic antenna showed an improved packet delivery ratio (PDR) and received signal strength indicator (RSSI) compared to the results using omni-directional antennas as a transmitting and receiving pair in the NLOS channels.
## 4 Experiments and Analysis
### Dice Loss and Focal Loss Functions  
The use of Dice Loss which is similarity-based and Focal Loss functions is quite common in image-segmentation applications on image data containing imbalance class distributions. Dice Loss focuses on the problem of data imbalance between the foreground and background, while Focal Loss handles the problem of imbalance between positive and negative examples [34]. Balancing the data during training allows focusing on the regions that are more difficult to segment among all samples and increases the sensitivity of the model to complex samples rather than simple scenarios for semantic segmentation. In this study, the total loss function for SUIM semantic segmentation was generated by evaluating both dice loss and focal loss functions together.