esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Heat Treatment on Microstructure and Creep Behavior of Fe-40 Ni-24 Cr Alloy
## 1 Introduction
Specific to creep rupture is the fact that the nucleation and the stable growth are time dependent and occur at a constant stress. Furthermore, typically for creep rupture, it occurs by intergranular cracking only. It seems to be generally accepted that creep rupture occurs by the formation of two alternative types of cracks [4], namely, intergranular cavities and intergranular wedge cracks. Fe-Ni-Cr alloy subjected to long term thermal and pressure conditions would result in precipitates formation and materials creep failure due to constant high temperature and pressure. The high temperature creep test on Fe-Ni-Cr alloys would give a better understanding in creep damage behavior, microstructure changes, failure mechanism after creep test and safety evaluation process of high temperature structural material [3; 4; 5; 26]. While many investigations have been carried out regarding microstructural changes and mechanical properties of Fe-Ni-Cr superalloy, limited study has been conducted regarding microstructure change and creep behavior of Fe-40 Ni-24 Cr alloy development concerning the application of this alloy in gas-turbine industries in the future. Hence, it is vital to comprehensively understand the creep mechanism and behavior of Fe-40 Ni-24 Cr alloy at higher temperatures. Therefore, the main approach of the present study is to evaluate the creep properties and creep microstructure evolution on selected heat-treated (1050 \({}^{\circ}\)C, 1200 \({}^{\circ}\)C, 1225 \({}^{\circ}\)C and 1250 \({}^{\circ}\)C) Fe-40 Ni-24 Cr alloy at temperatures of 800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C under 100 MPa stress.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments
### Quantitative Evaluation
#### 5.2.2 Ablation Study on Initialization  
As mentioned in Section 3.1, we propose a set of new mask techniques to address the texture discontinuity problem. We now give a detailed analysis of image quality of the proposed techniques including the multi-scale scheme, incremental mask and multi-frame mask fusion. To fairly compare these techniques, we follow Section 5.1.1 and divide them into four groups, and we compare the general Image Quality Assessment (IQA) scores of the four groups using the following term proposed in [60]:  
\[Q=\frac{1}{N_{p}}\sum_{i}^{N_{p}}y_{i} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**MPI Sintel Dataset + Style**} \\ \cline{2-7}  & **Scene1** & **Scene2** & **Scene3** & **Scene4** & **Scene5** & **ave** & **Improvement** \\ \hline Baseline [10] & 10.55 & 7.21 & 6.34 & 12.06 & 6.34 & 8.50 & * \\ RGB-Level only & 2.71 & 3.17 & 2.79 & 5.93 & 2.90 & 3.50 & 58.8\% \\ Both Levels & 2.43 & 3.00 & 2.59 & 5.74 & 2.75 & 3.31 & 61.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: **Ablation study on Coherent Losses (\(\times 10^{-2}\)) of five testing videos in MPI Sintel dataset.** Style: Woman with A Hat. Scene1: alley_2, Scene2: bamboo_2, Scene3: bandage_1, Scene4: cave_4, Scene5: market_2. The best result is highlighted with underline. As baseline method doesn’t have improvement of itself, thus we add * in below.  
Figure 17: Ablation study on Sharpness Losses of Alley_2 scene from MPI Sintel dataset [66]. _The higher ARISM score is better_. The outputs with our Sharpness Losses achieve higher ARISM scores than those without pixel loss and Sharpness Losses, which indicates that Perceptual Losses and Pixel Loss in the proposed Sharpness Losses both contribute to reducing blurriness artefacts.  
where \(N_{p}\) denotes the number of patches which are chosen from the given image, and \(y_{i}\) denotes the estimated visual qualities of patch \(i\). We chose this IQA score (refer to DIQaM-NR method in [60]) as our general image quality metric because it is capable of assessing image quality by coping with several distortion types such as luminance and contrast changes, compression, Gaussian noise and the Rayleigh fading channel. Figure 18 shows the detailed ablation study carried out on the proposed mask techniques, and **a lower score indicates better visual image quality**. Note that the multi-scale scheme only (magenta line) reduces untraceable flow errors (see Figure 8) but it causes higher scores than the naive method (blue line). The incremental mask technique (red line) helps to achieve lower average scores than the naive method (blue line) and w/ multi-scale scheme method (magenta line), and multi-frame mask fusion (green line) further improves the image quality by decreasing the average score from 30.69 (dashed blue line) to 29.71 (dashed green line). This observation practically follows the qualitative evaluation in Figure 11.
# Indentation Measurement in Thin Plates under Bending Using 3D Digital Image Correlation
## 2 Materials and Methods
### Experimental Validation of the Proposed Methodology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Step (mm) 0.** & **0.5** & **1** & **1.5** & **2** & **2.5** & **3** \\ \hline Mean error (mm) & 0.97 \(\times\) 10\({}^{-3}\) & 1.85 \(\times\) 10\({}^{-3}\) & 1.37 \(\times\) 10\({}^{-3}\) & 0.73 \(\times\) 10\({}^{-3}\) & 0.75 \(\times\) 10\({}^{-3}\) & 1.36 \(\times\) 10\({}^{-3}\) \\ and (\%) & (0.19\%) & (0.19\%) & (0.09\%) & (0.04\%) & (0.03\%) & (0.05\%) \\ Standard deviation, s, (mm) and (\%) & 0.69 \(\times\) 10\({}^{-3}\) & 0.61 \(\times\) 10\({}^{-3}\) & 0.51 \(\times\) 10\({}^{-3}\) & 0.32 \(\times\) 10\({}^{-3}\) & 0.32 \(\times\) 10\({}^{-3}\) & 0.29 \(\times\) 10\({}^{-3}\) \\ (0.14\%) & (0.06\%) & (0.03\%) & (0.02\%) & (0.01\%) & (0.01\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the mean and the variance of the measurement differences between 3D DIC and LVDT for each displacement step. be associated to small deviations in the position of the dial gauge or to a slight loss of perpendicularity of the gauge when large deflections are experienced due to bending. As has been presented in Figure 5a,b, the repeatability of indentation measurements has been achieved using two different experimental techniques. 3D-DIC technique provides the indentation measurement and the information related to the contact area between the indenter and the specimen. The maximum indentation point was clearly identified using DIC-3D, while the dial indicator was very sensitive to the gauge head position. Moreover, less scatter has been observed in the results from 3D-DIC when they are compared with those obtained by the dial indicator. During unloading, results were noisier; however, 3D-DIC results again showed less scatter than those obtained with the dial indicator. Figure 5: (**a**) Tests results for the dial indicator setup; (**b**) tests results for 3D-DIC setup; (**c**) results comparison using 3D-DIC and the dial indicator. Only results from four experiments have been presented in Figure 5a,b to have a clear comparison of the results obtained with both techniques. As it is shown, differences were smaller than 6% in all cases. Then, it can be summed up that the adopted setup provides an accurate, repeatable and robust procedure to obtain the material quasi-static contact law. Furthermore, 3D-DIC makes it possible the extrapolation of this methodology to dynamic experiments.
# Dynamic Analysis and Trajectory Tracking Control for a Parallel Manipulator with Joint Friction
## 3 Inverse Kinematics  
### Position Analysis  
The clearance and elastic deformation of the joints and components are ignored in the modeling; therefore, there are no practical differences (from a modeling and analysis standpoint) between a single and multi-platform manipulator. The three PUS modules have the same kinematic structure, and the vector relationship of one closed kinematic chain is shown in Figure 3.  
During the working process, the frames of the moving platform are defined by the roll, pitch, and yaw angle parameters. The orientation of the moving platforms \(m_{j}\) at any time can be described as \(q_{j}=[\alpha_{j},\,\beta_{j},\,\gamma_{j}]^{\mathrm{T}}\). Let \(\mathbf{e}_{i}\) and \(\mathbf{e}_{ji}\) be unit vectors along \(b_{2}b_{1i}\) and \(b_{ji}a_{ji}\). Let \(\mathbf{h}_{j}\), \(\mathbf{r}_{ji}\) and \(r_{ci}\) be the position vector along \(O\alpha_{j}\), \(\alpha_{ji}\) and \(O_{ci}\) in \(O\)-\(XYZ\). From Figure 3, we can express the closed kinematic chain as follows:  
\[\mathbf{h}_{j}+\mathbf{r}_{ji}=r_{ci}+\mathbf{L}_{ji}\mathbf{e}_{i}+l_{ji}\mathbf{e}_{ji},\,\mathbf{r}_{ ji}=\mathbf{R}_{j}\cdot\mathbf{r}_{aji} \tag{1}\]  
Here, \(L_{ji}\) is the distance between slider \(S_{ji}\) and point \(c_{i}\); \(l_{ji}\) is the length of joint lever \(a_{ji}b_{ji}\); and \(l_{ji}=l_{i}\) is a constant.  
Based on Equation (1), according to the assembly scheme of the parallel manipulator, the displacement of \(S_{ji}\) can be represented as follows:  
\[L_{ji}=\mathbf{h}_{j}+r_{jiz}-\sqrt{l_{ji}^{2}-\left(r_{jix}-r_{cix}\right)^{2}- \left(r_{jiy}-r_{ciy}\right)^{2}} \tag{2}\]  
where \(r_{jix}\), \(r_{jiy}\), \(r_{jiz}\) are the direction vectors for the decomposition of \(\mathbf{r}_{ji}\), and \(r_{cix}\), \(r_{ciy}\), \(r_{ciz}\) are the direction vectors for the decomposition of \(r_{ci}\).
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 4 Approaches and Domains in UAV Routing in Existing Literature

Table 3: Overview of (vehicle routing problem) VRP approaches used in UAV routing. There is a trend to use UAV routing in WSN domain for surveillance, exploring, and monitoring large regions [101, 102, 103, 104, 105, 106]. The recent papers related to UAV routing in various wireless networks are presented in Figure 6. These systems can integrate information from ground with WSN, in atmosphere with the sensors of UAVs and Global Positioning System (GPS) [109, 110, 111, 112, 113, 117, 118, 119, 120]. These collaborative systems have been used for large-scale monitoring, increasing mobility, accessibility, and reaction time in case of emergencies [101, 110, 111, 112, 113, 114, 115, 116]. WSNs and ad-hoc networks have been widely used for data collection using UAVs in both outdoor and indoor settings [108, 109, 110, 111, 112, 113] and in numerous application domains, and WSNs have been enhanced with data processing abilities in the direction of on-board intelligence [104, 105, 106, 107, 108, 109, 110, 111]. Physical constraints of energy consumption, communication range and autonomy, data transmission, and processing capabilities have been analyzed literature in the domain of WSN design considerations of UAV routing [117, 118, 119, 120, 121]. UAV routing in scheduling problems mainly focuses on assigning tasks to UAVs in an approach that efficiently utilizes the fleet of UAVs [122, 123, 124, 125]. UAV routing should be done to have a seamless flight during the operations and the required navigation control is derived from a command, which is provided by a scheduler [123, 124, 125, 126]. Generally, these problems aim at assigning various tasks, and actions such as recharge, hover, and wait-on-ground are assigned to the UAVs at different execution times [126, 127, 128, 129, 4, 129]. There is limited literature on UAV routing in scheduling domain [122, 123, 124, 125, 126, 127, 128, 129] and a summary of related studies that focuses on UAV scheduling is presented in Figure 7.  
From the overviews of UAV routing literature, the graphical representation of the UAV routing taxonomy is presented in Figure 7. This leads this domain of research to conclude that chief among them are the extensions of VRP and TSP, while next major contributions are identified in wireless networks as UAV routing is linked with various types of wireless network communication methods.
# A Review on Wind Turbine Deterministic Power Curve Models +
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Number of Parameters** & **Ease to Use** & **WT Parameters** & **Accuracy** \\ \hline LM1 & 4 & Very high & Yes & Very low \\ LM2 & 8 & Medium & No 2 & Medium \\ QM1 & 4 & Very high & Yes & Low \\ QM2 & 5 & High & Yes & Medium \\ QM3 & 8 & Medium & No 2 & Medium \\ CM1 & 4 & Very high & Yes & Low \\ CM2 & 3 & Very high & Yes & Very low \\ SPI & 200 1 & Very low & No & Very high \\ SP2 & 80 1 & Very low & No & Very high \\ NM1 & 7 & Low & No & Medium \\ NM2 & 10 & Low & No & Medium \\ NM3 & 4 & Very high & Yes & Very low \\
6 PLE & 6 & Low & No & Very high \\
5 PLE & 5 & Low & No & Very high \\
4 PLEE & 4 & Very high & Yes & High \\
4 PLE & 4 & Low & No & High \\
3 PLE & 3 & Very high & Yes 3 & High \\
6 PL & 6 & Low & No & High \\
5 PL & 5 & Low & No & High \\
4 PL & 4 & Low & No & Low \\
3 PL & 3 & Low & No & Low \\ \hline \hline \end{tabular} The following observations, given as notes in Table 7, can be mentioned: 1 The number of parameters is around that value, 2 Some parameters can be expressed as a function of the WT parameters, but not all, 3 There are two possible relationships.  
\end{table} 
Table 7: Assessment of the WT power curves models. complex problems, i.e., probabilistic load flow or economic load dispatch, it is more recommendable to choose a model with a very low number of parameters and, if possible, with relationships with the parameters of the WT (4 PLEE or 3 PLE). A different scenario appears when the accuracy is a priority, i.e., when assessing the exact power provided by the model is critical. In that case, the use of one of the SPs (SP1 or SP2) is strongly recommended. More options can be, for example, if the user's intention is to deal with a model that is similar to the WTPC and is related with the WT parameters, i.e., for learning objectives. In that case, models as LM1, QM1, and CM1 can be of interest.
## 3 Results
### Post-Treatment Comparison Between the ASO and NASO Groups (Table 4)  
#### 3.2.1 Post-Treatment Skeletal Differences  
Following treatment, significant skeletal differences were observed between the ASO and NASO groups in measurements such as A to N-perpendicular, B to FH, and pogonion (Pog) to N-perpendicular (\(p<0.05\)). Specifically, point A in the ASO group was positioned 3.13 mm further back than that in the NASO group after treatment (A to N-perpendicular). Similarly, point B in the ASO group was positioned 6.06 mm posterior to that in the NASO group after treatment (B to N-perpendicular). Additionally, Pog to N-perpendicular in the ASO group was positioned 2.97 mm further back than that in the NASO group after treatment. However, significant differences were not evident in the SNA, sella-nasion-point B (SNB), and ANB post-treatment. The respective ANB values of 3.22  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ASO Group**} & \multicolumn{2}{c}{**Non-ASO Group**} & \\ \hline
**Measurements** & **Mean** & **SD** & **Mean** & **SD** & **Difference** & \(p\) \\ \hline SNA & 82.17 & 4.16 & 79.85 & 3.07 & 2.31 & 0.01 ** \\ SNB & 76.69 & 4.01 & 76.08 & 3.12 & 0.60 & 0.49 \\ ANB & 5.48 & 1.13 & 3.77 & 1.41 & 1.71 & 0.00 ** \\ FMA & 29.51 & 6.03 & 29.22 & 4.46 & 0.29 & 0.82 \\ SN-GoMe & 38.13 & 6.47 & 39.47 & 4.84 & \(-\)1.34 & 0.35 \\ A to N-perp & 0.87 & 3.13 & 0.11 & 2.91 & 0.76 & 0.31 \\ A to FH & 35.77 & 3.35 & 34.65 & 4.22 & 1.11 & 0.24 \\ B to N-perp & \(-\)9.35 & 5.78 & \(-\)6.67 & 4.89 & \(-\)2.68 & 0.04 * \\ B to FH & 72.85 & 6.58 & 73.16 & 6.47 & 9.69 & 0.00 \\ Pog to N-perp & \(-\)10.41 & 6.80 & \(-\)8.84 & 5.94 & \(-\)1.56 & 0.32 \\ FABA & 77.79 & 3.64 & 80.13 & 3.80 & \(-\)2.33 & 0.01 * \\ \hline U1 to FH (\({}^{\circ}\)) & 114.81 & 6.50 & 116.51 & 6.58 & \(-\)1.70 & 0.29 \\ U1 to FH (mm) & 63.50 & 4.17 & 57.50 & 5.87 & 6.00 & 0.00 ** \\ L1 to FH (\({}^{\circ}\)) & 45.68 & 4.84 & 51.83 & 7.23 & \(-\)6.15 & 0.00 ** \\ L1 to FH (mm) & 61.31 & 4.11 & 55.89 & 5.36 & 5.42 & 0.00 ** \\ U1 C to N-perp & 8.52 & 4.95 & 6.24 & 4.24 & 2.28 & 0.046 * \\ U1 R to N-perp & \(-\)2.99 & 3.99 & \(-\)4.19 & 2.93 & 1.20 & 0.16 \\ L1 C to N-perp & 5.16 & 4.89 & 2.87 & 4.17 & 2.29 & 0.04 * \\ L1 R to N-perp & \(-\)10.77 & 5.81 & \(-\)9.49 & 4.31 & \(-\)1.27 & 0.31 \\ Interincisal angle & 110.87 & 7.70 & 115.32 & 10.70 & \(-\)4.45 & 0.053 \\ BOP & 11.19 & 3.04 & 10.30 & 3.58 & 0.89 & 0.28 \\ FOP & 11.74 & 3.50 & 11.43 & 4.07 & 0.31 & 0.74 \\ IMPA & 104.81 & 6.95 & 98.94 & 7.46 & 5.87 & 0.00 ** \\ \hline \hline \end{tabular} Independent \(t\)-tests were performed to compare variables between the two groups before treatment. * \(p<0.05\), ** \(p<0.01\). IMPA, incisor mandibular plane angle; ASO, anterior segmental osteotomy; SD, standard deviation; FH, Frankfort Horizontal.  
\end{table}
Table 3: Comparison of skeletal and dental measurements between the ASO and NASO groups before treatment.  
and 3.28 in the ASO and NASO groups implied that the proportions between the maxilla and mandible in the sagittal dimension was normalized in each group.
## 2 Materials and Methods
### Data Processing
#### 2.6.1 Time Windows  
To estimate the space-time \(\rho_{a}\) variation, particular time intervals should be defined depending on the changes to be seen. Therefore, we define three principal time windows: _total window_, _partial window_, and _calculation window_.  
* Total window: It is the complete time interval on which to work (e.g., you want to analyze six days along which there was seismic activity: total window = 6 days);
* Partial Window: It is the time interval in which you want to see changes in the \(\rho_{a}\) of the rocks (e.g., you want to see changes every hour: partial window = 1 h);  
Figure 2: Schematic representation of the location of the orthogonal dipoles, corresponding to the USME station.  
* Calculation window: It is the time interval over which we will calculate the Fourier Transform (FT) and, therefore, the variable used as the period (\(T\)) in the MT method equations (e.g., 10 s, 100 s, or 1000 s, depending on the depth of penetration to be obtained). In this study:
* Total window: 6 days;
* Partial window: 1 h;
* Calculation window: 1000 s.
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 4 Discussion
### Quantitative Evaluation  
The effectiveness of thresholding post-implementation of the k-means clustering is clear from the DSC values ranging from 92.51% to 97.86% and the Jaccard index ranging from 91.39% to 94.36%. Compared to thresholding of non-clustered images where the DSC is 77.99%, the Jaccard index is 63.92% for normal flow conditions [17], the present method scores much better effectiveness for normal flow conditions. The metric values prove better overlap between the manually segmented images and the algorithm segmented images. We expect we can score a much better DSC and Jaccard index for overflow conditions since in overflow conditions the ROI becomes smoother. We encountered the commission and omission errors in the image classification technique in the present work to a small extent. During image classification, at some point, no actual change is classified as a change in the model, which leads to commission errors. While in other instances, we left some actual changes unsegmented in image thresholding, leading to omission errors [41]. We observed the combined effect of these errors in the quantitative evaluation results (Table 4). For the suggested research, the quantitative method can aid in an analysis where qualitative data is mainly in terms of analytic yield. The technique expands qualitative analyses horizons by providing researchers with a systematic way of guiding subgroup analysis within a qualitative data set.
## 1 Introduction
However, the difference is in the application of these architectures. In fPLC, the cooperation of dual architecture channels is solved at the firmware level of each module. However, if we want to create a dual architecture using two sPLCs, their collaboration must be addressed at the application level. The solution to the problem of sPLC interoperability in dual architecture is the focus of this problem (basically, it is the management of dual architecture at the application level). By creating a suitable algorithm controlling the dual architecture, a somewhat universal, affordable platform could be created, enabling the implementation of safety functions based on application code generated from pre-tested models. This paper presents a dual architecture based on sPLC controlled at the application level. The architecture can perform any safety function based on a model created in Matlab Stateflow software (more information about this software can be found in ref. [8]). In this paper, the safety function \"Muting\" is provided as an example. There is currently no procedure that would allow implementing a safety function into the PLC-based control system from a model. However, this trend is proving to be very advantageous (precisely because of the elimination of systematic errors in software development), and the paper is devoted precisely to this issue. The use of a dual architecture is one of the ways to apply redundancy in the system. In the case dealt with in this article, redundancy is used for quick fault detection using comparison. This allows us to detect random failures and respond to them in a predefined way, which will lead to an increased SIL. Another option is to use redundancy so that after a part of the control system fails, the redundant part will perform the control tasks. This leads to an increase in the availability of the control system. The realization of such a system at the application level is dealt with, for example, in ref. [9]. If necessary, redundancy can be used in combination to achieve the required SIL and the control system's requiredavailability. However, this requires a multi-channel architecture (not just a dual-channel one). A more detailed description of this issue can be found in ref. [10].