esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Boundary Conditions  
The metallic alloys used to manufacture the single spool gas turbine are of paramount importance, since they will be subjected to high structural and thermal loads, and, consequently, they will be subjected to several failure modes. To illustrate, a few existing loads can be identified: centrifugal loads generated by rotational speed; tensile load due to thrust; pressure of gases that are compressed or expanded by discs blades or vanes, and, specially, high temperatures, which contribute to a reduction of the metallic alloys yield strengths and the appearance of high thermal stresses. Thus, very few metallic materials can be subjected to these conditions. Based on previous experience and acquired knowledge of other similar aeronautical gas turbine projects, the materials selected for the computational simulations are presented in Figures 3 and 4, whose composition are given in Table 1.  
The values of the main mechanical properties of the metallic alloys used in the computational simulations of this work are presented in Table 2. Additional information about temperature and creep data can be found at MatWeb.com [20]. The boundary conditions were obtained from the mathematical modeling presented in Section 2. Figure 5 shows the compressor and turbine stages with respective working fluid pressure measurement points. Table 3 shows the respective numerical values of working fluid pressure at the compressor and turbine stages. All the heat transfer coefficients used in the computational simulations are presented in Table 4.  
Figure 4: A one-eighth section of the static assembly showing the chosen metallic alloys for each component. Figure 3: A one eighth section of the rotating assembly showing the chosen metallic alloys for each component. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric Alloys** & \multicolumn{3}{c}{**Composition**} \\ \hline  & C (0.37–0.43\%); Cr (0.70–0.90\%); Fe (95.195–96.33\%) as remainder; \\ AISI4340 & Mn (0.60–0.80\%); Mo (0.20–0.30\%); Ni (1.65–2.0\%); P (\(<\)=0.035\%); \\  & Si (0.15–0.30\%); S (\(<\)=0.040\%). \\ \hline  & Al (90.7–94.7\%) as remainder; Cr (\(<\)=0.10\%); Cu (3.8–4.9\%); \\ Al2024-T81 & Mg (1.2–1.8\%); Mn (0.30–0.90\%); Si (\(<\)=0.50\%); Ti (\(<\)=0.15\%); \\  & Zn (\(<\)=0.25\%). \\ \hline  & Al (85.3–89.5\%) as remainder; Cr (0.18–0.28\%); Cu (1.6–2.4\%); \\ Al7178-T6 & Mg (2.4–3.1\%); Mn (\(<\)=0.30\%); Si (\(<\)=0.40\%); Ti (\(<\)=0.20\%); \\  & Zn (6.3–7.3\%). \\ \hline  & Al (6.1\%); B (1.012\%); C (0.12\%); Cr (12.5\%); Ni (77.368\%) as remainder; \\  & Nb/Cb (2.0\%); Ti (0.80\%); Zr (0.10\%). \\ \hline  & Al (5.5–6.75\%); C (\(<\)=0.080\%); H (\(<\)=0.015\%); Fe (\(<\)=0.40\%); \\ Ti-6 Al-4V & N (\(<\)=0.030\%); \\  & O (\(<\)=0.20\%); Ti (87.725–91\%) as remainder; V (3.5–4.5\%). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Compositions of the metallic alloys in terms of elements percentage.
# A Study on the Application of Laser Cleaning on the Weld of the Gas Fuel Supply Pipe for DF Engine
## 2 Materials and Methods
### Analysis Methods  
The roughness and profile of the laser cleaned specimen were observed through a VHX-7000 high-resolution 3D microscope from KEYENCE, and the morphology was taken with a MIRA-3 Scanning Electron Microscope from Tescan (Brno, Czech Republic). The Energy Dispersive Spectrometer System attached to the SEM was used to analyze the component changes on the surface of the specimen before and after laser cleaning. In addition, in order to confirm the cleaning performance of the prototype, compositions were analyzed using SmartLab X-ray Diffractometer of RIGAKU. The X-ray source is a Cu tube and the 2 theta range is 5\({}^{\circ}\) to 80\({}^{\circ}\).
## 2 Material and Methods
### Preparation of Gangue Powder
#### 2.3.2 Mineral Compositions of Gangue  
During the ball-milling of broken gangue, mineral compositions in gangue are subjected to the externally applied mechanical force, so the crystal structure and micro-morphology may change. This process is likely to induce the transformation of the mineral compositions in the gangue, thus influencing the properties. An intelligent multi-functional X-ray diffractometer (XRD) (Smart Lab SE, Rigaku, Tokyo, Japan) was employed to measure the mineral compositions of cement and gangue powder attained via ball-milling for 1 h, 3 h, and 5 h. The XRD spectra of cement and gangue powder specimens are shown in Figure 5.  
Analysis of Figure 5 reveals that the cement and gangue powder have different mineral compositions. The cement mainly comprises calcium silicate oxide, larnite, and gypsum, while gangue powder specimens mainly include quartz, kaolinite, calcite, and muscovite. As the ball-milling duration is increased, quartz, the main mineral composition of gangue, shows a slight variation in the diffraction intensity at 21\({}^{\circ}\), 27\({}^{\circ}\), and 50.5\({}^{\circ}\); the diffraction intensities of kaolinite and calcite in gangue gradually attenuate at 12.5\({}^{\circ}\), 25\({}^{\circ}\), and 28\({}^{\circ}\). Since kaolinite and calcite begin to decompose at temperatures above 400 \({}^{\circ}\)C and 897 \({}^{\circ}\)C, respectively, and the ball milling process typically operates below 90 \({}^{\circ}\)C, this indicates that their structural degradation under mechanical force is not thermally induced but rather caused by applied stress.
# Question Difficulty Estimation Based on Attention Model for Question Answering
## 4 Experiments
### Experimental Setting
The _Fuse_ function in Equation (3) is set as the concatenation function. Adam optimizer [38] with default settings is used to train all models, and early stopping over the validation set is executed where 100 is the maximum number of epochs. The proposed model is mainly compared with TACNN [14] which is widely used as a main baseline for question difficulty estimation. TACNN uses CNN [39] to obtain the representations of a questionary sentence and a set of information components. Then, it constructs the relationships between the questionary sentence and each information component using a simple attention model, but does not consider the inter-information among the components. Some pre-trained language models are also adopted as baselines of the proposed model, since the language models achieve top performances in many QA tasks. The baseline language models adopted are BERT [32], RoBERTa [40], and XLNet [41]. They concatenate a questionary sentence and all information components with a special token [SEP] and then convert the concatenated sequence to the standard input format of each language model. After that, the formatted sequence is encoded to embedding vectors by each language model. Finally, the embedding vector for the [CLS] token is used to predict the difficulty level in BERT and RoBERTa, while the embedding vector for the last token is used in XLNet. All the models are evaluated with F1-score and accuracy.
# Evaluation of Machinery Readiness Using Semi-Markov Processes
## 3 Data Handling
### Fit of Distributions  
The next step was to assess the form of distributions of the individual states. Considerations in this respect were presented using the example of state S\({}_{6}\), and for the others the same was done. The goodness of fit to the selected theoretical distributions that were considered most likely was verified based on a Cullen and Frey graph, presented for state S\({}_{6}\) in Figure 2.  
For further analysis, the Weibull and Beta distributions were selected, for which the estimated parameters are presented in Table 2, while the goodness of fit of empirical data to the individual distributions is presented in Figure 3.  
For each of them the _AIC_ (Akaike information criterion) was calculated according to the formula (16) and based on that, the one with the better fit was selected.  
\[AIC=-2\ln L+2k, \tag{16}\]  
where \(k\)--number of parameters in the model, \(L\)--credibility function.  
The same calculations were made for the other states. The proposed distributions are presented in Table 3.  
Not all the distributions could be fitted to the parametric ones. Moreover, none of the distributions belong to the family of exponential distributions, which is a condition for using the Markov process [25]. The form of distributions makes the parameters estimation possible based on the semi-Markov model only. In order to compare whether meeting the condition of the distribution form affects the obtained results, the subsequent part of the article compares the limit values of the probabilities of the object's dwelling time according to two different models.  
Figure 3: The assessment of goodness of fit of the empirical data of \(S_{6}\) state to the Weibull and Beta distributions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Distribution** & **Test Statistic KS** & \(p\)**-Value** \\ \hline S\({}_{1}\) & non-parametric & & \\ S\({}_{2}\) & Beta & D = 0.07 & 0.793 \\ S\({}_{3}\) & non-parametric & & \\ S\({}_{4}\) & Gamma & D = 0.03 & 0.726 \\ S\({}_{5}\) & non-parametric & & \\ S\({}_{6}\) & Beta & D = 0.07 & 0.385 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Goodness of fit results for states S\({}_{1}\)–S\({}_{6}\).
## 5 Conclusions  
When the SMOTE algorithm and the improved CSH-SVM algorithm were applied at the same time, the classification effect was further improved: of the 6000 samples that are not uniformly extracted from the tower fault dataset, only 63 samples had errors, and the accuracy rate was 98.95%. This shows that the method proposed in this study is well-suited for the specific application scenario of tower fault classification. Figure 14 shows the SMOTE and CSH-SVM improved algorithm classification results.  
In this paper, a tower mechanical fault classification method based on the SMOTE algorithm and an improved CSH-SVM algorithm is proposed for monitoring the tower operation status and the stability of the mechanical structure. ANSYS Workbench was used to model the tower mechanical structure and analyze the sensor placement position and accuracy requirements. The tower operation data was processed using the SMOTE algorithm to make its data features suitable for machine learning and to obtain better classification results. The improved CSH-SVM algorithm was applied to classify and predict tower faults; it improved the potential problems of one-vs-all classification methods for the classification of samples in the transition area and the accuracy of classification and prediction of samples in the transition interval, and it thus provides a guarantee of safety in the transmission process of towers. Compared with the method in the literature [15], the  
Figure 14: SMOTE and CSH-SVM improved algorithm classification results.  
Figure 13: Classification results of improved CSH-SVM algorithm.  
improved CSH-SVM algorithm does not need to calculate the distance from the sample point to the hyperplane frequently, which reduces the amount of calculation in the prediction process. Moreover, the application of the SMOTE algorithm has a good balance for the tower fault dataset targeted by this study. Compared with the existing traditional monitoring methods, the prediction results were more accurate and were calculated faster, especially in the classification process of unbalanced datasets. However, this study also had certain limitations. When the dataset itself is highly balanced, the improvement effect was smaller than that of the unbalanced dataset. Therefore, when applying this method, it should be inclined to classify and predict the unbalanced dataset to achieve the best classification effect.
# A New Smoke Segmentation Method Based on Improved Adaptive Density Peak Clustering
## 3 Procedure of TSDPC
### Automatic Selection of Clustering Centers  
One of the disadvantages of the DPC algorithm is that it requires the manual selection of clustering centers. Before selecting the clustering center, it is necessary to calculate the decision value \(\gamma\) of each sample point, and then artificially select the sample point with a large value as the clustering center. The decision value \(\gamma\) is calculated by Equation (8).  
\[\gamma=\rho*\delta \tag{8}\]  
Usually, the decision value of the clustering center is much larger than the decision value of other sample points. According to this feature, this paper uses the sample point with the largest slope difference between the two straight lines as the number of clustering centers.  
The decision values are normalized and processed in descending order. Figure 3 is a schematic diagram of the decision diagram, \(n\) represents the total number of sample points, and \(\gamma\) represents the decision value of the sample point, in which there are 5 sample points;the decision value of sample point 1 is 1, and the decision value of sample point 5 is 0. The number of clustering centers is usually greater than one but less than the total number of sample points. We take these two points as fixed points and connect the intermediate sample points to obtain two straight lines. Then, we calculate the tangent value.  
We calculate the absolute value of the tangent of the angle between the two straight lines and the horizontal line. In order to avoid the problem of too many clustering centers, it needs to be restricted, which is expressed as:  
\[\varphi_{i}=\frac{1}{(i-1)^{2}}||\tan\alpha|-|\tan\beta||(1<i<n) \tag{9}\]  
where \(n\) is the total number of sample points, \(i\) is the number of sample points, and the number of sample points corresponding to the maximum value of \(\varphi\) is that of clustering centers. Algorithm 2 shows the adaptive selection of clustering centers. We normalize the decision values and process them in descending order, obtain a descending sequence \(\{\gamma_{i}\}_{i=1}^{n}\), presenting the decision values in a decision diagram with two auxiliary lines for each sample point in 2 nd to \(n-1\)st, respectively. Calculate the tangent value of the sample points and substitute it into Equation (9), then we obtain the sequence \(\{\varphi_{i}\}_{i=2}^{n-1}\). Find the number of sample points corresponding to the largest value of \(\varphi\) in the sequence; \(\{\varphi_{i}\}_{i=2}^{n-1}\) is the number of adaptively selected clustering centers.  
```
1:Input: Decision value \(\gamma\);
2:Normalization: get a descending sequence \(\{\gamma_{i}\}_{i=1}^{n}\);
3: Do the auxiliary line to get the tangent value;
4:For\(i=2\) to \(n-1\),do
5: Calculate the \(\varphi\) value and get the sequence \(\{\varphi_{i}\}_{i=2}^{n-1}\);
6:endfor
7: Find the sequence number \(C\) corresponding to the maximum value in the sequence \(\{\varphi_{i}\}_{i=2}^{n-1}\);
8:Output: cluster center \(C\)
```  
**Algorithm 2**Automatic selection of clustering centers
## 4 Results and Analysis
### Factors Affecting the Surface Velocity of Glaciers in Svalhard
#### 4.2.1 Influence of Climate on Glacier Velocity  
An important feature of glaciers in Svalbard is the seasonal variation in glacier migration, with the lowest flow rates in autumn and winter, accelerating in spring, peaking in summer, and with high surface flow rates gradually extending into the upper part of the glacier over time. The local ERA5 is collected to reanalyze the meteorological data, the daily average temperature data of each image pair time interval, and the cumulative precipitation data of each image pair time interval, and we superimposed them on the glacier flow velocity corresponding to the P1 location (Figure 6). From Figure 6a, it can be seen that the change in glacier surface flow velocity and temperature maintains a good consistency in time. In June and July, when the temperature is the highest, the glacier also reaches a peak in flow velocity. On the one hand, when the temperature rises, the viscosity of the ice decreases rapidly, which leads to the increase in plasticity and the increase in the glacier flow speed. On the other hand, temperature also affects the hydraulic characteristics of glaciers and controls the phenomenon of glacier meltwater. Water flows into the subglacial water network along the cracks, breaking the stress balance of the glacier drainage system, increasing the glacier sliding and moraines deformation, and further increases the glacier flow velocity.  
Figure 5: The cumulative displacement of the Monacobreen glacier under different time baselines in 2021.  
In addition, the impact of precipitation on glacier movement is similar to the above-mentioned temperature change caused by glacier meltwater, and further affects the hydraulic system of the glacier, aggravating the wetting of the ice bottom, speeding up the surface flow, and replenishing the glacier material. After data analysis, the cumulative precipitation of the Monacobreen glacier area in 2021 was only 279.89 mm. Even though the precipitation is not large in magnitude, its recharge effect on the glacier can still be observed on the graph. From May to August, the flow velocity increased first and then decreased with the change in accumulated precipitation. In mid-September, the precipitation experienced a small increase, and the surface velocity also showed an increase, which was considered to be a small bump in microwave activity.
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Analysis of the MMPB Product  
The majority of the studies concerning the MMPB method use mass spectrometry (MS) as a detector coupled with either liquid or gas chromatography as the method of analysis. Regarding the conditions under which the chromatography is performed, whether liquid or gas, different chromatographic columns are used in the studies that are listed in Table 1. Nevertheless, the parameters used during the MS analysis can be listed. The positive ionization mode of MS is used in many studies [28; 29; 77]. The most commonly searched fragments in this ionization mode, after electron ionization in GC MS, is m/z 176 [60; 71; 75]. In some studies, the fragment analyzed by GC-MS is not MMPB but methyl-MMPB [76]. In this case, the MMPB is methyl-esterified before the analysis in order to volatilize the MMPB for the analysis. This step is performed by using, for example, 1 mL of hydrochloric MeOH (5%) and silver carbonate followed by a heating step at 70 \({}^{\circ}\)C for 1 h [61; 70] or by using 12% trifluoroborate in MeOH [84]. In LC-ESI/MS, the parent ion [M + H] + \(m/z\) 209 is monitored along with its three specific fragments at \(m/z\) 91; 131 and 191 [28; 49]. However, several studies report better results using the negative ionization mode for MS/MS (207 \(\rightarrow\) 131 transitions) [59; 63; 64; 72; 85; 86; 87]. Some alternatives to the classical LC-MS/MS methods exist, namely laser diode thermal desorption-atmospheric pressure chemical ionization coupled to tandem mass spectrometry (LDTD-APCI-MS/MS) [66; 79] and condensed phase membrane introduction mass spectrometry (CP/MIMS) [88]; both methods were applied to aqueous samples with a detection limit of 10 ug/L and 1 mg/L, respectively. Less commonly, other detection systems can be coupled to HPLC for the analysis of the MMPB product generated during Lemieux oxidation, HPLC with photodiode array detection (PDA) [89], HPLC with ultra-violet detection (UV) [63; 67; 69; 90], or HPLC with fluorescence detection (FLD) [68]. Inversely, the use of the Adda ELISA kit on oxidized drinking water samples did not enable the detection of the MMPB fragment following the MMPB method [63].
## 1 Introduction  
With the continuous development of wireless communication and information processing technologies, many intelligent devices are connected to the internet to process real-time data and dynamically give feedback on the information processing results to achieve industrial intelligence [1]. The Industrial Internet of Things (IIoT) collects data through heterogeneous intelligent devices from different regions. It is utilized by Deep Learning, Natural language processing, Computer vision [2], and Robot Process automation [3] to construct systematic and intelligent industrial systems [4]. With the continuous development of the IIoT system, the number of smart devices is growing rapidly. In addition, more and more businesses need to exchange data with other organizations in the network and perform local work based on data interactions. Smart Manufacturing (SM) can use advanced information technology and manufacturing technology to optimize the production and transaction mode. It promotes the production efficiency, quality, and service level of the whole industrial production chain [5]. Intelligent devices of IIoT realize real-time collection and sharing of manufacturing resource data and achieve operational visibility and traceability of materials based on radio frequency identification (RFID) and wireless communication technology [6]. Production resources are delivered to the network through many smart devices, collaborating across different manufacturing to complete a large IIoT project. For example, heterogeneous devices have different computing and storage capabilities and security mechanisms [7], so some devices are more vulnerable than others and, in most cases, become the target of network attacks. As a mirror world very similar to C.P.S., the digital twin system can simulate all possible operations in the physical world in this virtual world and evaluate the consequences, and further tune actual products according to the simulation results of the mirror [8]. In other words, DT allows engineers and designers to work on the details of a product before setting up the actual physical environment but also allows for cost reduction and process optimization while increasing the coordination of multi-party joint work. This is undoubtedly of great significance for SM [9].