esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Modal Work Approach for Forced Response Analysis of Bladed Disks
## 3 Results and Comparisons
### Dummy Test Case
#### 3.1.4. Tremor-ANSYS Comparison  
The Tremor program requires the blade mode shape, the control nodes modal displacements and the forcing function distribution for a single traveling wave as inputs. The modal work integration was performed on the CFD blade surface mesh, which is usually finer than the corresponding FEM discretization, ensuring a high computational accuracy. Control nodes were used to select the traveling wave direction of the mode shape and to take the mode complex conjugate in case the analysis of the opposite traveling wave is requested. Moreover, an automatic overlapping of the forcing function on the CFD blade surface was implemented to avoid the manual mapping of the forcing (as required in the ANSYS solver). All these aspects make the tool attractive for its integration in automatic procedures like aeromechanical optimization processes.  
For this preliminary validation, the nodal diameters \(\text{ND}=0,\pm 4,+8\) are considered with the same total damping equal to 0.01. The validation was performed by comparing the maximum amplitude of total deformation between ANSYS and Tremor results in resonance and out of resonance conditions for the different forcing functions. In the resonance condition, Tremor computes the scaling factor by using the natural frequency related to the nodal diameter under investigation. In a tuned system, this excitation acts only on nodal diameters that show a crossing in the interference diagram (ZZENF). The comparison of the maximum amplitude for the more complex forcing function (case 3) at \(\text{ND}=0,\pm 4,+8\) is reported in Table 1 shows an excellent agreement, as did the other pressure loads. The additional comparisons are not shown here for the sake of brevity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Maximum Amplitude of Total Displacement (m)**} \\ \hline  & **ND = 0** & **ND = +4** & **ND = \(-4\)** & **ND = +8** \\ \hline Tremor & 9.36 \(\times\) 10\({}^{-8}\) & 8.95 \(\times\) 10\({}^{-8}\) & 8.60 \(\times\) 10\({}^{-8}\) & 8.12 \(\times\) 10\({}^{-8}\) \\ ANSYS & 9.36 \(\times\) 10\({}^{-8}\) & 8.95 \(\times\) 10\({}^{-8}\) & 8.60 \(\times\) 10\({}^{-8}\) & 8.12 \(\times\) 10\({}^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tremor–ANSYS comparison (1 st family, case 3).  
Figure 5: Forcing function distribution mapped on the FEM surface (front side, case 3).  
The validation is further extended to out-of-resonance conditions, comparing the amplitude of total deformation due to the forcing function at EO = 4 for different nodal diameters, listed in Table 2. It is clear that the maximum amplitude of total deformation occurs at the crossing (ND \(=+4\)), whereas when moving away, the amplitude decreases.  
The agreement is excellent for out-of-resonance conditions as well, so the tool validation must be extended to a more complex model reproducing a realistic low-pressure turbine bladed rotor.
## 3 Results and Discussion  
### Verification of the Simulation Method  
In order to verify the reliability of the proposed numerical method, the Venturi tube geometry model utilized in the [PERSON] et al. [19; 30] experiment was employed as the CFD calculation model. The inlet pressure was fixed at 0.4 MPa, whereas the outlet pressure ranged between 0.091 MPa and 0.378 MPa, with the temperature set at 19 \({}^{\circ}\)C, resulting in a  
Figure 4: Absolute pressure profile of Venturi in four different mesh sizes.  
Figure 3: Distribution of Y plus at different inlet pressures.  
saturation vapor pressure of 2200 Pa. In addition, the pressure ratio is defined as the ratio of outlet pressure to inlet pressure, which can be written as follows:  
\[\mathrm{P_{ratio}}=\frac{P_{\mathrm{out}}}{P_{\mathrm{in}}} \tag{10}\]  
where \(P_{\mathrm{in}}\) is the inlet pressure.  
The simulated flow rate results were subsequently compared with the experimental data. Figure 5 displays a comparison between the measured flow rate and the predicted values obtained from numerical simulations using the Venturi tube model employed in the experiment. As depicted in the figure, while there exists a discrepancy of 4.49% between the critical pressure ratio prediction from numerical simulation and its corresponding experimental value at the inception of cavitation, the numerical simulation results agree well with the experimental values for pressure ratios below 0.8. Figure 6 shows a comparison of the distribution of the vapor phase obtained from the experiment [30] and numerical simulations at an outlet pressure value of 0.188 MPa. It is noteworthy that there is a high degree of consistency in the extent of cavity development between the two approaches. And from the detailed local flow streamlines, it can be observed that there is a recirculation region at the downstream end of the cavitation region. The emergence of this structure effectively inhibits further extension of the cavity. This observation indicates a good agreement between the numerical simulation results and the experimental measurements under cavitation conditions.  
Figure 5: Comparison of experimental flow rate and simulation results.
## 3 Mathematical Formulation
### Mathematical Model  
Equation (1) is an objective function that minimizes the total labor cost. It consists of the sum of regular employees' total labor costs, the sum of irregular employees' total labor costs, and the sum of total overtime work costs:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Meaning** \\ \hline \(I\) & Set for employees, including regular and irregular workers, \(i\in I\) \\ \(Pt\) & Set for irregular employees, such as foreign workers and \\ sub-contractors, \(pt\in Pt\) \\ \(Ep\) & Set for regular employees, \(ep\in Ep\) \\ \(M\) & Set for managers, \(m\in M\) \\ \(P\) & Set for workplaces, \(p\in P\) \\ \(F\) & Set for floors excluding any other workplaces, \(f\in F\) \\ \(J\) & Set for jobs, \(j\in J\) \\ \(D\) & Set for days, \(d\in D\) \\ \(N\) & Set for weeks, \(n\in N\) \\ \(W_{n}\) & Set for days of the nth week \\ \(D_{sun}\) & Set for Sundays \\ \(D_{work}\) & Set for workdays \\ \(cost_{id}\) & Cost for regular employees \(i\) on day \(d\) (USD/day) \\ \(hours_{id}\) & Workhour for regular employees \(i\) on day \(d\) (hour) \\ \(partcost_{pt,d}\) & Cost for irregular employees \(pt\) on day (USD/day) \\ \(parthours_{pt,d}\) & Workhour for irregular employees \(pt\) on day \(d\) (hour) \\ \(overcost_{id}\) & Overtime work cost for irregular employees \(i\) on day \(d\) (USD/day) \\ \(overhours_{i,d}\) & Overtime workhour for irregular employees \(i\) on day \(d\) (hour) \\ \(min_{d}\) & Minimum number of needed workers on day \(d\) during daytime \\ \(overmin_{d}\) & Minimum number of needed workers on day \(d\) during nighttime \\ \(A_{d}\) & The average percent of irregular employees’ absences on day \(d\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Known parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Meaning** \\ \hline \(X_{i,j,p,d}\): & It becomes 1, if regular employee \(i\) is assigned to job \(j\) at workplace \(p\) on day \(d\), \\  & otherwise 0 \\ \(Y_{i,j,p,d}\) & It becomes 1, if irregular employee \(i\) is assigned to job \(j\) at workplace \(p\) on day \(d\), \\  & otherwise 0 \\ \(R_{i,j,p,d}\) & It becomes 1, if irregular employee \(i\) is assigned to job \(j\) at workplace \(p\) on day \(d\) \\  & after considering absence, otherwise 0 \\ \(O_{i,j,p,d}\) & It becomes 1, if regular employee \(i\) is assigned to job \(j\) at workplace \(p\) on day \(d\) \\  & during nighttime, otherwise 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Decision Variables.  
\[\begin{array}{c}\sum_{i\in I}\sum_{j\in I}\sum_{p\in P}\sum_{d\in D}Cost_{id}\times X _{ijpd}+\sum_{i\in Pt}\sum_{j\in I}\sum_{p\in P}\sum_{d\in D}Partcost_{id}\times R _{ijpd}+\\ \sum_{i\in I}\sum_{j\in I}\sum_{p\in P}\sum_{d\in D}Overcost_{id}\times O_{ijpd }\end{array} \tag{1}\]
## 5 Discussion
### Limitations  
We single out two limitations of our current work.  
**Decision maps:** Although they significantly simplify the understanding of how a trained ML model works, decision maps require significant effort to become _actionable_, that is, leading to concrete insights that explain and/or improve the working of a given ML model. In other words, it is still challenging for users to make decisions using decision maps. Our work partially helps in this direction by helping users to make decisions about (a) how and where they trust a given decision map, and (b) which decision-map technique to use in practice in a given context. Improvements can, and should, be made to the understandability of decision-map visualizations, e.g., explaining which parts of a given training set, training process, or model are responsible for visible patterns in such a map. Such extensions are not the scope of this work but are very promising directions for future work.  
**Evaluation:** Our evaluation, which covered only five datasets and seven classifiers, is necessarily limited in its generalizability. More models and architectures exist in machine learning. How the three decision-map techniques studied (DBM, SDBM, and DeepView) cope with these models is not necessarily the same as for the models we have studied so far, so our evaluation can be extended by considering additional models. However, we argue that our choice for our _initial_ evaluation, which consisted of the aforementioned datasets and classifiers, was as follows: (1) As no similar evaluation of decision maps existed before our work, we had to start with relatively simple cases, that is, datasets and classifiers with known behavior. This way, we could use these datasets and classifiers as 'ground truth' to actually assess the produced decision maps. (2) Our limited evaluation pointed out several key insights, such as the very limited computational scalability of DeepView and the fact that _all_ decision-map methods only visualize a surface subset that passes close to the training samples in the data space. These limitations will exist for any more complex model visualized using the current methods. As such, future work should focus on removing these limitations before applying decision-map techniques to more complex models.
## 4 Results
### Ablation Study with Other Datasets
#### 4.10.2 Ablation Dataset-2
**Comparison with different classification models:** Table 10 details the performance of the proposed model with other classifiers in classifying BSL features. The CNN achieves the highest classification accuracy of 90%, next to the GCN (proposed model), which achieves 94%. SVM stands next to the CNN, with 89%, and the lowest classification accuracy is with a k-NN (62%). Figure 16: Classification accuracy. (**a**) classification accuracy of female subjects. (**b**) classification accuracy of male subjects for ablation-dataset-2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **19-Channel** & **21-Channel** & **32-Channel** & **256-Channel** \\
**Bands** & 74.8 & 75.6 & 66.8 & 23.5 \\ \hline
**Theta** & 90.69 & 85.67 & 72.9 & 39 \\
**Alpha** & 93.86 & 92.68 & 75.34 & 46 \\
**Beta** & 82.98 & 77.86 & 54.38 & 22 \\
**Gamma** & 89.67 & 79.82 & 62.83 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance (accuracy %) of the proposed method with selected channels for ablation-dataset-2. Figure 17: Sensitivity and specificity of the model in alpha, theta, and gamma frequency bands for ablation-dataset-2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Accuracy (\%)** & **Accuracy (\%)** \\  & **(Best Classification)** & **Average of All Subjects** \\ \hline AEC-c & \(86\pm\) 0.79 & \(82\pm\) 1.76 \\ ImCoh & \(67\pm\) 0.42 & \(56\pm\) 2.23 \\ PTE & \(77.5\pm\) 0.52 & \(72\pm\) 1.59 \\ Graphical LASSO & \(82\pm\) 1.26 & \(77\pm\) 1.64 \\ BSL (Theta) & \(91\pm\) 0.89 & \(86.25\pm\) 1.03 \\ BSL (Alpha) & \(94\pm\) 1.29 & \(87.35\pm\) 1.27 \\ BSL (Gamma) & \(90\pm\) 1.98 & \(84\pm\) 2.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of functional-connectivity metrics using the proposed classification model (GCN) for ablation-dataset-2. Figure 18: ROC curves of top classifications in alpha, theta, and gamma frequency bands for ablation-dataset-2.
## 2 New Model Biodiesels and Their Fuel Properties
### Biodiesel Fuel Properties  
Biodiesel is produced by different feedstocks, such as soybean oil in US, rapeseed oil in EU, palm oil in Southeast Asia countries and so on, according to their planting production [22]. China is known to feed one forth global population by too less farm land, so biodiesel is thought to be made by nonedible oil, waste cooking grease oil. The most developed oils were from jatropha curcas, pistacia chinensis, et al., which can be planted in the mountain areas such as Guizhou and Hainan Provinces [21]. Different stocks have different length, structure and content of mono-alkyls. Some 11 common feedstocks of biodiesel are showed in Table 1. The length of most chain is 16 or18, some contain 1 or 2 or 3 unsaturated olefinic bonds. The number of olefinic bond and their containing percentage are used to calculate Degree of Unsaturation (DU). The available researches proved that there is a negative correlation between the DU and CN of biodiesel. Biodiesels are used in CI engines,  
Figure 5: Biodiesel producing processes.  
their CNs are very important parameters. It was correlated in Equation (3). According to their Degree of Unsaturation, their Cetane Number can be estimated [24], and the results are listed in the Table 2, as well.  
\[\text{CN}=65.0958-0.1209\times\text{DU} \tag{3}\]  
The oxygen content is generally measured by element analysis method. Available data are given in Table A1 in Appendix A[21; 22; 23; 24; 26; 27; 31].  
It can be seen that the cetane number and oxygen content of FAME are around 50 and 11%, which are less affected by oil stocks. To improve these fuel properties, new types of biodiesel were developed. Ethylene glycol monomethyl ether was mostly used to react with different vegetable oils to synthesize different biodiesel, so that their effects of ether group and fatty acids on engine combustion and emissions can be compared. Meanwhile ethylene glycol ethyl ether and propyl ether types biodiesel and diethylene glycol and triethylene glycol monomethyl ether types of biodiesel were developed to understand their effects. Table 1 lists some of new types of biodiesel and their fuel properties our group developed in recent years [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50].  
The new types of biodiesel have a CN around 70, oxygen content about 17%, which are higher than FAME.
## 4 Results
### Hydrodynamic Load  
In the first set of experiments, only the hydrodynamic load's effect on the dynamics of the tower and TLP were investigated, there is no generated wind present. The TLP is exposed to an irregular wave series, which is generated from a scaled down JONSWAP wave spectrum [44]. The JONSWAP spectrum is an empirical relationship that defines the distribution of energy with frequency of the wave climate. The theoretical scaled down JONSWAP wave spectrum was used as a target spectrum for the wave generator in AwaSys. The average wave height time series from the four wave gauges were then used to was used to calculate the experimental generated wave spectrum which affected the FOWT. The target and experimentally obtained spectrum can be seen in Figure 6.  
In Figure 6b, it can be seen that the experimentally obtained spectrum differs from the target spectrum. This can be seen clearly at the peak frequency of the spectrum, w  
Figure 6: (**a**) The mean wave height data measured by the four wave gauges. (**b**) The wave spectra calculated from experimental and simulated wave height data. The black wave spectrum was the target wave spectrum for AwaSys to generate in the wave basin.  
higher amplitude was seen in the experiment. The cause of this is not clear, but it was thought to be due to poor absorption of waves at this frequency interval in the wave basin. To take this into account in the simulations, the experimentally found wave spectrum was used for wave generation in both the theoretical model and FAST.  
The experiments were conducted for 50 min, while the simulation time was set to 30 min. A segment of the experimental displacement time series in surge of the TLP and tower top can be seen in Figure 7.  
To compare the experiment and simulations, the distribution of power are the displacement timeseries is compared. Therefore, the PSD of the displacement time series from both the experimental and simulation data is calculated.  
By first inspecting the PSD of the displacement of the TLP in Figure 8a two peaks can be seen. The first peak occurs at the first natural frequency of the TLP and the other at the wave loading from the JONSWAP wave spectrum. In Figure 8b two peaks are seen in the PSD of the displacement of the tower top. The first peak occurs at the wave loading from the JONSWAP wave spectrum. The second peak occurs at the first natural frequency of the tower. The PSDs from the simulations tend to underestimate the energy located at the first natural frequency of the tower, compared to the experiments. The reason for this discrepancy can be due to reflected waves, which have not been captured by the wave probes, that excites the tower at its first natural frequency.
## 5 Machine Learning Algorithms
### The Deep Autoencoder  
The DAE trains in such a way that it tries to reduce the reconstruction error between the data given as the input and the data obtained as the output. The DAE in the HD-DNCAE includes the inner DAE and the outer DAE. A single DAE consists of an encoder and a decoder. The encoder takes inputs \(\mathbf{X}\) and maps these inputs from the input space to the latent space using the function \(\mathbf{F}(\cdot)\), while the decoder tries to reconstruct the output \(\mathbf{\hat{X}}\) from the latent space into the output space using the function \(\mathbf{G}(\cdot)\). The Equations (1) and (2) are a general representation of the encoder and decoder functions:  
\[\mathbf{H}=\mathbf{F}_{\Theta}(\mathbf{X}) \tag{1}\]  
\[\mathbf{\hat{X}}=\mathbf{G}_{\Phi}(\mathbf{H})=\mathbf{G}_{\Phi}(\mathbf{F}_{ \Theta}(\mathbf{X})) \tag{2}\]  
In the HD-DNCAE, the first outer encoder takes the input \(\mathbf{X}\) and maps it into the latent space \(\mathbf{H}=\mathbf{F}_{\Theta}(\mathbf{X})\). Then, the inner encoder takes \(\mathbf{H}\) and maps it into a second latent representation \(\mathbf{Y}=\mathbf{F}_{\Theta}(\mathbf{H})\). The output of the first encoder serves as input for the second encoder. From this point, the inner decoder takes \(\mathbf{Y}\) (a latent space representation) and reconstructs \(\mathbf{\hat{H}}=\mathbf{G}_{\Phi}(\mathbf{Y})\), and the outer decoder takes \(\mathbf{\hat{H}}\) (a latent representation) and reconstructs \(\mathbf{\hat{X}}=\mathbf{G}_{\Phi}(\mathbf{\hat{H}})\).  
The DAE is a deep neural network with a non-linear activation function. \(\Theta\) represents the weights of the encoder, while \(\Phi\) represents the biases of the decoder. The HDBSCAN clustering algorithm is integrated into the latent layer of the outer DAE. This algorithm clusters the data points within this layer into optimal subclusters. The inner DAE refines the important and core features while simultaneously trying to reduce the normal region in the latent layer.
# Impact of Retention Agents on Functional Properties of Recycled Paper in Sustainable Manufacturing
To improve retention and dewatering, dual-component systems with a high electrostatic charge and low molecular weight of the cationic material (dual polymeric retention system) have been developed to coagulate fibers and solid particles, followed by the use of a high-molecular-weight polymer with weak cationic charge (usually polyacrylamide) [26] (microparticle retention-aid system). The danger of using the first system is either excessive cationization of the suspension, leading to the electrostatic repulsion of fibers and solid particles and deterioration of retention, or excessive flocculation. Large flocs improve dewatering but significantly compromise the uniformity of transparency and often affect the strength properties by creating weak areas in the web. So, the microparticle retention aid system has become one of the most successful products available on the market. Compared to conventional retention systems, it not only provides outstanding retention of wet-end chemical additives, fine fibers, and fillers but also decreases the concentration of white water and boosts the speed of the paper machine, thereby offering both environmental and economic advantages [27; 28]. The proper dosing and optimization of retention agents are necessary to ensure the desired benefits without compromising other aspects of the paper manufacturing process. By improving process efficiency and reducing raw material losses, retention aids contribute to both the economic viability and environmental sustainability of paper manufacturing. However, optimizing their use requires careful balancing to avoid adverse effects on the physical and mechanical properties of the paper. While the influence of cationic polyelectrolytes on papermaking processes has been widely studied [29; 30], this study focuses on a novel aspect--the optimization of retention aid dosage specifically for recycled pulp under industrial conditions. This study evaluated the impact of a retention aid on the properties of recycled paper and selected the optimal addition of the aid to the recycled fiber stock. This practical application distinguishes our work from previous studies by directly addressing the challenges faced by the paper industry, including balancing the retention efficiency with the mechanical and surface properties of paper. By providing insights into real-world implementation, this research contributes to both academic understanding and practical advancements in sustainable papermaking.