esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### The Influence of Volume Concentration on the Rheological Parameters of the Debris Flow  
Using Equation (2) to fit the rheological curves, the rheological parameters of the debris flow, namely, yield stress (\(\tau_{\rm s}\)) and viscosity (\(\eta\)), were obtained at various solid volume concentrations. The specific fitted data are presented in Table 3.  
Figure 5: Changes of the shear stress increment with solid volume concentration at different shear rates.  
A debris flow is a non-Newtonian fluid composed of two phases: liquid and solid. The liquid phase is a slurry formed by the combination of water and fine particles, while the solid phase consists of the solid particles. Due to the complex interaction between the solid and the liquid phases during motion, currently, an effective mechanical model that adequately describes the debris flow movement mechanism is not available. However, through experimental analysis and research, we could quantitatively express the impact of the debris content on the yield stress. Figure 6 illustrates the variation in yield stress with solid volume concentration, demonstrating that as the solid volume concentration in the debris flow slurry gradually increased, the yield stress rose. Notably, on a semi-logarithmic scale, the yield stress exhibited a linear trend in relation to the solid volume concentration, with a correlation coefficient reaching 0.998, indicating a strong correlation between these parameters. The relationship between the yield stress of the debris flow slurry in Jiangjiagou and the volume concentration is expressed by Equation (4)  
\[\rm lg\tau_{s}=10.12082C_{v}-3.39724 \tag{4}\]  
Figure 7 depicts the relationship between the viscosity coefficient and the solid volume concentration. It is evident that with an increase in solid volume concentration, the viscosity coefficient continuously rose. Similar to the trend observed for the yield stress in relation to the volume concentration, the viscosity coefficient also showed a linear relationship with the volume concentration on a semi-logarithmic scale. Consequently, the relationship between the viscosity coefficient of the debris flow slurry in Jiangjiagou and the volume concentration is represented by Equation (5)  
\[\rm lg\gamma=6.89612C_{v}-3.61916 \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline C\({}_{v}\) & 0.350 & 0.375 & 0.400 & 0.425 & 0.450 & 0.475 & 0.500 & 0.525 & 0.550 \\ \(\tau_{s}\) (Pa) & 1.5579 & 2.3315 & 4.6737 & 7.0227 & 14.767 & 25.376 & 44.452 & 84.125 & 155.34 \\ \(\eta\) (Pa-s) & 0.0589 & 0.1039 & 0.1471 & 0.2068 & 0.2959 & 0.4081 & 0.5475 & 1.0686 & 1.7291 \\ R\({}^{2}\) & 0.9997 & 0.9980 & 0.9998 & 0.9995 & 0.9976 & 0.9931 & 0.9987 & 0.9975 & 0.9934 \\ \hline \end{tabular}
\end{table}
Table 3: Fitting parameters of the rheological curves at different solid volume concentrations.  
Figure 6: Curve of yield stress versus solid volume concentration.
# Optimization of Heat Pump Systems in Buildings by Minimizing Costs and CO\({}_{2}\) Emissions
## 3 Case Study
### Assumptions for Prices of Electricity, Natural Gas, and CO\({}_{2}\) Emissions  
The cost of energy and the environmental impact are critical considerations when assessing the performance of a heating system over a mid-term period. In this subsection, the paper focuses on the assumptions and forecasts of electricity, natural gas and CO\({}_{2}\) prices, and their impact on the operating costs of heat pumps and electric heaters over a 10-year period.  
When evaluating the operating costs of HPs and EHs, it is essential to take into account the prices of both electricity and natural gas. According to the tariffs offered by UAB \"Ignitis\", valid from 1 July 2022 to 31 December 2022, the cost of electricity is 0.24 EUR/kWh [51], and the cost of natural gas is 0.77 EUR/m\({}^{3}\)[52]. However, when analyzing a 10-year period, it is important to consider the potential for energy price fluctuations due to various environmental factors.  
In this study, the trend of electricity prices for the period of 2023-2031 was based on a long-term forecast presented in the report in [38], using the 2022 electricity price as a reference point. The prices of natural gas for the same 10-year period were also analyzed. According to the data from [53], gas prices in Europe are predicted to increase from 2021 to 2035, with the gas price in 2035 being approximately 18% higher compared to 2021. Based on this forecast and the 2022 natural gas price in Lithuan, the natural gas prices for the period of 2023-2031 were assessed. The assumptions of electricity and natural gas prices for the 10-year period are illustrated in Figure 6.  
Figure 6: Forecasted trend of natural gas and electricity prices in Lithuanía for the period of 2022–2031.  
The price of CO\({}_{2}\) emissions and its potential fluctuations during the analyzed period are important considerations in the analysis, as the presented methodology aims to minimize the costs associated with such emissions. The study uses the average price of CO\({}_{2}\) emissions in 2022, which was 83 EUR/t, as a reference point, based on the latest data of [54]. It is important to note that the price of CO\({}_{2}\) emissions is subject to change and may fluctuate due to various factors, such as economic conditions, energy policies, and technological advancements.  
The CO\({}_{2}\) emission price forecast in this study is based on the report in [55], which predicts that it will significantly increase by 2050 (more than six times) as a result of increased energy efficiency and the promotion of less CO\({}_{2}\)-emitting energy sources. The trend of CO\({}_{2}\) emission prices for each year during the analyzed period, assuming that the price for CO\({}_{2}\) emissions was 83 EUR/t in 2022, is presented in Figure 7.
## 4 Experiments  
### Datasets and Metrics  
**Datasets.** We demonstrate the performance of our methods on three human parsing datasets, including the Look Into Person (LIP) dataset [13], Fashion Clothing Dataset [32] and Active Template Regression (ATR) [33].  
The LIP dataset is the largest single human dataset that provides 50,462 images with 19 semantic human part labels and 16 body key points. The 19 semantic human part labels contain the hat, hair, glove, sunglasses, upper-clothes, dress, coat, socks, pants, jumpsuits, scarf, skirt, face, left-arm, right-arm, left-leg, right-leg, left-shoe, and right-shoe. The images in the LIP were collected from realistic scenes with challenging poses, heavy occlusions and complex backgrounds. Images in this dataset are divided into 30,462 for training, 10,000 for validation and 10,000 for testing.  
The Fashion Clothing Dataset is a collection of 4371 images from Colorful Fashion Parsing [34], Fashionista [35] and Clothing Co-Parsing [36]. It is split into 1716 and 1817 images for training and testing. One background and 17 pixel-level labels are annotated. A total of 17 pixel-level labels mainly focus on clothing details, i.e., jewelry, bags, coats, belts, dresses, glasses, hair, pants, shoes, shirts, skin, skirts, upper-clothes, vests and underwent, scarves, socks and hats. Following the label set defined by [PERSON] et al. [34], we merge the labels of the Fashionista and CFPD datasets into 18 categories: faces, sunglasses, hats, scarves, hair, upper-clothes, left-arm, right-arm, belts, pants, left-leg, right-leg, skirts, left-shoe, right-shoe, bags, dresses and background.  
The ATR dataset is the first large dataset that appeared in the field of human parsing. There are 6000 images for training, 700 images for validation and 1000 images for testing. The ATR contains 18 categories including background, hats, hair, sunglasses, upper-clothes, skirts, pants, dresses, belts, left/right-shoes, face, left/right-legs, left/right-arms, bags and scarves.  
**Metrics.** For the LIP dataset, we follow three metrics to evaluate human parsing results, pixel-wise accuracy (Pixel Acc.), mean accuracy (Mean Acc.) and mean pixel Intersection-over-Union (mIoU). The pixel accuracy, foreground accuracy (F.G. Acc.), average precision (Avg. P.), average recall (Avg. R.) and average F-1 are leveraged as the evaluation metrics for the ATR and Fashion Clothing datasets.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 3 Results & Discussion
### Result
#### 3.1.3 Modified Iterative Closest Point (MICP) Registration Results: Normal Correction  
Modified iterative closest point (MICP) was performed on the four data sets to register the volume data and surface data. The volume data was set as the fixed data and the surface data was set as the moving data. The result of the MICP method is a 3D rigid transformation from dental scan data to CBCT volume data. To compare the results to those of the conventional registration method, the average point distance of the two surface data after registration was calculated (D).  
The results of the MICP using row scan data converged well and show good results compared to those of the conventional registration process. After normal correction, even more accurate registration  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Input Data**} & \multicolumn{1}{c}{**Metal Artifact**} & \(E_{ICP}\) (mm) & \(SD_{ICP}\) (mm) \\ \hline \multirow{2}{*}{Set no.1} & Maximillary & No & 0.3612 & 0.1308 \\  & Mandible & No & 0.3357 & 0.1140 \\ \hline \multirow{2}{*}{Set no.2} & Maximillary & Yes & 0.4170 & 0.1574 \\  & Mandible & No & 0.3812 & 0.1380 \\ \hline \multirow{2}{*}{Set no.3} & Maximillary & Yes & 0.3178 & 0.1242 \\  & Mandible & Yes & 0.3464 & 0.1420 \\ \hline \multirow{2}{*}{Set no.4} & Maximillary & Yes & 0.5025 & 0.1914 \\  & Mandible & no & 0.4834 & 0.1823 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The result of conventional registration.  
Figure 6: Conventional registration result. ((**a**): segmented surface data, (**b**): maxillary, (**c**): mandible) (**1**) set no.1 (**2**) set no.2 (**3**) set no.3 (**4**) set no.  
results are obtained (Figure 7). Table 2 shows the registration numbers in detail both before and after normal correction.
## 1 Introduction
### A Carousel Wind Rotor Design  
An alternative to the above designs of vertical axis wind rotors is the carousel wind rotor presented by [PERSON] in study [27]. The kinematic diagram and cross-sectional view of this rotor is presented in Figures 2 and 3. A planetary gear is the main component of the carousel wind rotor, which is connected with the vertical drive shaft and three blades. The blades should be properly balanced to minimize the resistance on the bearings during operation. Their planetary motion is synchronized by a gearbox with a gear ratio of 1:2. The blades rotate at half speed and in the opposite direction to the direction of rotor rotation. A self-adjusting system determines the angular orientation of the rotor with respect to the wind direction. The self-adjusting system is directly coupled to a worm gear located under the rotor housing. The gear ratio causes the blade surfaces to work alternately. Therefore, the blade profile must have point symmetry. One of the typical features of the carousel wind rotor is the large starting torque, which is forced mainly by the drag force, and less by the lifting force acting on the blades. Another characteristic feature of the carousel rotor is the low speed of the turbine. Due to the kinematics of the gearbox, the rotor reaches and maintains maximum speed despite the increasing wind speed. The kinematic diagram of the carousel wind rotor is shown in Figure 2a, whereas its cross-sectional view is shown in Figure 2b. The carousel wind rotor consists of: at least three blades (7) mounted on vertical pins (10) and coupled with gears z2; a rotor mast (9); a rotor (1) coupled to the drive shaft (5) and rotating relative to a stationary housing (2); a sleeve (4) mounted in the housing with a bearing; the blades axles (6); the planetary gearbox (8) with a gear ratio (z1/z2) of 1:2; and a worm gear (3) to attach the self-adjusting system to the wind direction. A full-scale physical model of a carousel wind rotor built to verify the movement of the blades, positioned by a planetary gear, is shown in Figure 3.  
The gear ratio determining the position of the blades during operation and the profile of the blades are of great importance in determining the most favorable design parameters of the carousel wind rotor. A gear ratio of 1:2 was assumed in this study, and the main focus was on the comparison of blades with single and dual coherent cross-sections.
## 2 Materials and Methods
### In Silico Analysis  
To support our results and confront them with online available data, we additionally assessed _SATB1_, _SMAD3_, _EZR_ and _CTNNB1_ expression in the TCGA cohort. Gene expression data for The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts from 177 patients with pancreatic adenocarcinoma (146 with ductal histology) and 165 of non-cancerous pancreatic tissues were obtained from www.cBioPortal.org and UCSC Xena Viewer ([[http://xena.ucsc.edu/](http://xena.ucsc.edu/)]([http://xena.ucsc.edu/](http://xena.ucsc.edu/)), accessed on 3 July 2020). The RNA sequencing datasets (RNA-seq) were normalized via DESeq2 normalization, and data were divided into low- and high-expression groups according to the cutpoints established in Evaluate Cutpoints software. The cut-off values for positive (high) and negative (low) expression of _SATB1_, _SMAD3_, _EZR_, _CTNNB1_ mRNA were as follows: \(<\)10.99; \(\geq\)10.99; \(<\)12.67; \(\geq\)12.67, \(<\)14.35; \(\geq\)14.35, \(<\)14.68; \(\geq\)14.68, respectively. We created the expression combination of the factors tested. Cases with the co-expression of SATB1\({}^{\text{low}}\)SMAD3\({}^{\text{high}}\)EZR\({}^{\text{high}}\)CTNNB1\({}^{\text{high}}\) were analyzed in comparison with the opposite expression pattern, whereas \"other\" defined all else expressive cases.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 2 Related Works
### Edge-Computing Task Offloading Based on Resource Prediction
The authors of [36] optimized the edge offloading model based on the task prediction of the LSTM algorithm, the computing offloading strategy of mobile devices based on task prediction, and the task migration of the edge cloud scheduling scheme. The authors of [37] used LSTM to track the resource characteristics of IoT over time and, based on the deep deterministic policy-gradient algorithm to assist in online calculation offline decision-making and task migration, and resource allocation, effectively reduced task turnaround time and system energy consumption. The research content of the above papers involves the combination of task offloading and prediction technology and has achieved certain results, effectively improving the task offloading efficiency, but it is not combined with the complex scenario of the dynamic changes of multi-user concurrent resources and lacks the close integration of actual scenarios. Therefore, the follow-up research should focus on task offloading and prediction technology in the environment of multi-user concurrency and dynamic resource changes so as to better adapt to actual needs and further improve the performance and efficiency of the system. In summary, in the smart IoT environment, it is still necessary to conduct in-depth research and improve the algorithms and mechanisms from multiple perspectives, such as timing dependency among tasks, dynamic changes of global resources, and node load in multi-user concurrent scenarios, to optimize the current application of mobile edge computing in the smart IoT. Therefore, this study focuses on the offloading of timing-dependent tasks in multi-user concurrent scenarios and examines collaborative offloading mechanisms among users, considering resource competition in dynamic environments as a starting point. It improves the learning efficiency of agent algorithms by combining prediction techniques with reinforcement learning, thereby reducing task offloading latency and improving the utilization of computing resources.
## 6 Conclusions  
The vibration test of the sectional model at two wind fairing angles--sharp and blunt--was used to evaluate the VIV performance of the box girder section with different wind fairing angles. Meanwhile, the VIV, flutter, and buffeting of the bridge, which is a typical asymmetric flat steel box girder cable-stayed bridge, were investigated using a full bridge aeroelastic model test. According to the results of the dynamics analysis and wind tunnel test, the following key conclusions may be drawn:  
1. The modification of wind fairing angles will affect the VIV performance of the flat steel box girder cable-stayed bridge, according to the sectional model wind tunnel test. The vortex energy created by the sharper wind fairing under the influence of the incoming flow is smaller and has a higher frequency. The VIV can be efficiently mitigated by using sharper wind fairing. In this paper, however, just two distinct wind fairing angles are used to demonstrate how they affect VIV. It is necessary to do a more in-depth quantitative investigation of the law of VIV as they change with wind fairing angles.
2. The bridge's mode shapes are different from symmetrical structure, according to numerical calculations and mode tests. The maximum displacement of the of first torsion mode shape is at the closure rather than the mid-span due to the asymmetric structure. This conclusion was also supported by the results of the full bridge aeroelastic model testing.
3. The bridge sectional model has a positive slope lift coefficient and moment coefficient, and the ratio of torsional and vertical frequency in the service state is larger than 2, indicating that it has stronger wind resistance stability. Furthermore, the flutter  
Figure 20: The root mean square (RMS) value of the displacement of each measure point of the service state in turbulent wind. (**a**) Vertical; (**b**) torsional.  
critical wind speed is substantially greater than the flutter checking wind speed under uniform and turbulent wind, according to the full bridge aeroelastic model testing. As a result, the bridge has enough aerodynamic stability in the service state.
4. Uniform wind causes minimal amplitude vertical and torsional VIV that are substantially below the limits, while there is no VIV in the turbulent wind. Consequently, the odds of vertical or torsional VIV exceeding the permissible amplitude in the service state are exceedingly remote.  
Conceptualization, G.Z.; methodology, G.Z.; formal analysis, Z.W., J.H. and J.W.; writing--original draft preparation, Z.W. and S.Z.; writing--review and editing, Z.W. and J.H.; supervision, [PERSON], and J.H. All authors have read and agreed to the published version of the manuscript.  
This study is sponsored by the Fundamental Research Funds for the Central Universities, Chang'an University (No. 300102210108), which are greatly acknowledged. The Center for Computational Research at the University at Buffalo are greatly acknowledged.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Results
### Quantitative Evaluation
#### 3.4.4 F-score, Precision, and Recall Results on 20 Test Samples  
The F-score (left), Precision (middle), and Recall (right) between the proposed method and REF for 20 test images are shown in Figure 14. From the radar charts in this figure, it can be observed that TongueNet outperforms REF in Precision and Recall on almost all 20 test samples. This reflects the high reliability as well as the comprehensiveness of tongue region prediction using the proposed method. Although some Recall values of TongueNet in Figure 14 (right) are lower than REF (the 11 th sample and the 14 th sample), the F-score of each test sample is still higher, indicating the general superiority of TongueNet over REF.
## 3 Experimental Validation
### AlSi10 Mg Validation  
This Section focuses on the validation of the Probability ML and LEVD ML algorithms on SLM AlSi10 Mg datasets [30; 33; 34; 35; 36]. In Figure 5, the experimental datasets considered for the validation are reported in a Gumbel plot.  
According to Figure 5, the experimental defects show a large scatter, thus highlighting the influence of the investigated process parameters on the fatigue response and justifying the use of ML algorithms for solving this problem concerning the structural integrity of SLM parts. Defects are characterized by sizes ranging from 40 \(\upmu\)m up to 1000 \(\upmu\)m, even if only two datasets out of twenty show defects with sizes above 400 \(\upmu\)m. The defects in [30] were experimentally obtained by the authors. The data in [34; 35] were available in tabular form. The experimental data in [36] were randomly simulated starting from the parameters of the LEVD distribution for machined specimens. Therefore, for these experimental data, a \"digitization error\" is excluded. On the other hand, the experimental datasets in [33] were retrieved by using the Engauge Digitizer software. A digitization error cannot be excluded for these datasets. However, it should be noted that the objective of Section 3 is to validate Machine Learning algorithms and a small error in the digitization of the defect size can be accepted, having limited influence on the validation process.  
Table 1 summarizes the process parameter and the risk-volume ranges of the investigated datasets.
## 3 Learned Separation  
### Distance Function  
The parametrized model \(z[k,l]\) from (11) should match the time-frequency representation \(Z[k,l]\) from (5) as closely as possible. To formalize this, we need to define a distance function. While the \(\ell_{2}\) distance would be simple and \"canonical,\" the problem is that it overemphasizes the correctness of the high-volume parts of the representation rather than the structural similarity. A common alternative is the \(\beta\)-divergence, cf. [36] (which is a generalization of the \(\ell_{2}\) distance, the Kullback-Leibler divergence, and the Itakura-Saito divergence), but this leads to problems with unexplained noise in the spectrum. Instead, we use the distance measure introduced in [20]; for a given time frame \(k\), we set \(y=z[k,\cdot]\) and \(Y=Z[k,\cdot]\) and define:  
\[d^{\text{\tiny{d,abs}}}_{2,\delta}(Y,y)=\frac{1}{2}\sum_{l}\Bigl{(}\bigl{(} \lvert Y[l]\rvert+\delta\bigr{)}^{q}-\bigl{(}\lvert y[l]\rvert+\delta\bigr{)} ^{q}\Bigr{)}^{2},\hskip 28.452756 ptq\in(0,1],\quad\delta>0. \tag{12}\]  
The \(q\) exponent has the purpose of _lifting_ the low-volume parts of the representation in order to increase their relevance. The canonical choice is \(q=1/2\), since this is the lowest value to keep the expression convex in \(y\). The value for \(\delta\) can be low, as it is merely there to ensure differentiability at \(y[l]=0\). Unlike the \(\beta\)-divergence, the distance function in (12) is symmetric, but it is still not a metric in the mathematical sense.