esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 6 Results and Discussions
### The Effect of Power Law Index on DNFs for Different Volume Fractions of Porosity  
Figure 8 shows the variation in DNFs against the power law index for different volume fractions of porosity. It has been observed that the DNFs reach their maximum at k = 0. Since the FG shaft is completely composed of ceramic at k = 0, the ratio of Young's modulus to the density of a ceramic is high. However, the DNFs of the system decrease with the increase in the power law index. As the power law index increases from zero to five, the composition of the FG shaft alters from an absolute ceramic to an FG shaft that is comprised of a combination of metal and ceramic. The percentage of metal increases in the FG shaft and hence the ratio of Young's modulus to the density decreases. Eventually, the DNFs of the system are reduced. DNFs with volume fractions that have porosities (\(\alpha\)) = 0.1, 0.2 are slightly higher than the DNFs without porosity. This is due to the fact that the reductions in the material properties lead to an increase in the ratio of stiffness and mass as compared to the ratio of the FG shaft without porosity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) & E (ZrO2) & E (SS) & Density (ZrO2) & Density (SS) \\ \hline
0 & \(1.68\times 10^{11}\) & \(2.078\times 10^{11}\) & 5700 & 8166 \\
0.1 & \(1.492\times 10^{11}\) & \(1.8901\times 10^{11}\) & 5006.7 & 7472.7 \\
0.2 & \(1.304\times 10^{11}\) & \(1.7022\times 10^{11}\) & 4313.4 & 6779.4 \\
0.3 & \(1.116\times 10^{11}\) & \(1.5143\times 10^{11}\) & 3620.1 & 6086.1 \\
0.4 & \(9.284\times 10^{10}\) & \(1.3264\times 10^{11}\) & 2926.8 & 5392.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Young’s modulus and density of ZrO2 and stainless steel (SS) at different \(\alpha\).  
Figure 8: Variation in DNF for different power law indices (k).  
Figure 7: Variation in dimensionless natural frequency (DNF) for different volume fractions of porosity (\(\alpha\)).
## 2 Background
### Technology Acceptance  
The unified theory of acceptance and use of technology comprises eight different models [2; 25; 45] that facilitate comprehension of user-perceived technology adoption [2].  
By incorporating principles from multiple theoretical frameworks--such as the technology acceptance model, theory of reasoned action, and innovation diffusion theory--it enables an all-encompassing comprehension of user behaviour [21; 33]. In addition to performance expectancy, effort expectancy, social influence, and facilitating conditions, UTAUT considers subjective norms, personal beliefs, and attitudes, which are socio-psychological factors [33]. The model also considers the moderating influences of age, gender, experience, and voluntary use, considering the ever-changing attributes of user environments and demographics [25; 45]. The usefulness of UTAUT in adopting blockchain technology in payment systems is limited. An emphasis is placed on individual factors, with network effects and sociotechnical dynamics being disregarded. Furthermore, security concerns, regulatory compliance, and interoperability issues might not be adequately considered.  
A significant amount of studies on using blockchain technology in payment systems highlight the potentials of an integrated approach incorporating factors of trust and acceptance [2; 21; 25]. The relationship between trust and acceptance is closely interconnected, as trust influences the initial adoption of blockchain technology and plays a crucial role in its continued utilisation [21]. The integrated model would provide a thorough understanding of the interdependent relationship between trust and acceptance, revealing all factors influencing the adoption of blockchain technology in digital payment systems [21; 25]. The model will be pivotal in developing strategies that will improve user trust and acceptance, eventually leading to the widespread adoption of blockchain technology.
## 2 System Model
### RF Channel Model  
All RF links as well as the interference links experience the Nakagami-m fading because it can effectively model channel fading. According to [20] the PDF and CDF for the instantaneous SNR \(\gamma_{k}\) of the RF link are as follows:  
\[f_{\gamma_{k}}(\gamma)=\frac{1}{\Gamma(m_{k}N_{k})}(\frac{m_{k}}{\Omega_{k}})^ {m_{k}N_{k}}\gamma_{k}^{m_{k}N_{k}-1}\exp(-\frac{m_{k}}{\Omega_{k}}\gamma_{k}) \tag{5}\]  
\[F_{\gamma_{k}}(\gamma)=1-\exp(-\frac{m_{k}}{\Omega_{k}}\gamma_{k})\sum_{t=0}^{ m_{k}N_{k}-1}\frac{1}{t!}{(\frac{m_{k}}{\Omega_{k}}\gamma_{k})}^{t} \tag{6}\]  
where \(\Omega_{SR}=\frac{P_{S}\lambda_{SR}}{\sigma_{R}^{2}d_{SR}^{2}},\Omega_{SE}= \frac{P_{S}\lambda_{SE}}{\sigma_{E}^{2}d_{SE}^{2}},\Omega_{JR^{\prime}}=\delta \frac{\lambda_{IR^{\prime}}}{d_{IR^{\prime}}^{2}},\Omega_{R^{\prime}E}=\delta \frac{\lambda_{RF^{\prime}}^{2}}{d_{RF^{\prime}E}^{2}}\), and \(k\in\{SR,SE,JR^{\prime},R^{\prime}E\}\), \(\lambda_{k}\) are the corresponding channel average power channel gains, and \(\delta=\frac{\lambda_{P}P_{J}M_{j}}{(1-\lambda)\sigma_{E}^{2}d_{S}^{2}}\).
## 3 Results from the Numerical Calculations
### Results from Calculation of Additional Lateral Forces in Wheel-Rail Contact  
The next step is to determine the additional forces acting on the central bearings of the bogies from forces calculated in the previous subsection. They are used to assess the safety against derailment of the entire specialized train composition. The calculation is carried out only for the wagon with the fixing blocks and the front wagon.  
The forces at points A\({}_{0}\) and A\({}_{1}\) of the wagon with fixing block with their respective values from Table 2 are applied to the wagon, as shown in Figure 9.  
The reason for analyzing the front wagon is that it is at risk of derailment due to the incomplete vertical load from the rails' weight (leading to a reduction in the vertical force \(Q\) from Equation (1)) and due to the asymmetric distribution of the load in the structure resulting from the placement of the support frames on the wagon (Figure 3). The forces at points A\({}_{11}\) and A\({}_{12}\) of the front wagon with their respective values from Table 2 are applied to the wagon, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Point** & **Coordinate \(x\) (m)** & **Coordinate \(y\) = \(u_{iy}\) (m)** & **Reaction Identifier (Unit)** & **Value** \\ \hline A\({}_{0}\) & 0 & 0 & M\({}_{A0}\) (kN-m) & \(-\)21.8257 \\ A\({}_{0}\) & 0 & 0 & F\({}_{A0}\) (kN) & \(-\)21.8257 \\ A\({}_{1}\) & 3.000 & 0 & F\({}_{A1}\) (kN) & 22.8997 \\ A\({}_{2}\) & 8.190 & \(-\)0.1436 & F\({}_{A2}\) (kN) & \(-\)1.0583 \\ A\({}_{3}\) & 12.690 & \(-\)0.4350 & F\({}_{A3}\) (kN) & 1.3180 \\ A\({}_{4}\) & 17.190 & \(-\)0.8733 & F\({}_{A4}\) (kN) & \(-\)1.0934 \\ A\({}_{5}\) & 22.380 & \(-\)1.5400 & F\({}_{A5}\) (kN) & \(-\)1.1026 \\ A\({}_{6}\) & 26.880 & \(-\)2.2530 & F\({}_{A6}\) (kN) & 1.1627 \\ A\({}_{7}\) & 31.380 & \(-\)3.1000 & F\({}_{A7}\) (kN) & \(-\)0.0952 \\ A\({}_{8}\) & 36.570 & \(-\)4.2410 & F\({}_{A8}\) (kN) & \(-\)0.2744 \\ A\({}_{9}\) & 41.070 & \(-\)5.3690 & F\({}_{A9}\) (kN) & \(-\)0.4341 \\ A\({}_{10}\) & 45.570 & \(-\)6.6274 & F\({}_{A10}\) (kN) & 1.2717 \\ A\({}_{11}\) & 50.760 & \(-\)8.2450 & F\({}_{A11}\) (kN) & 4.8339 \\ A\({}_{12}\) & 56.260 & \(-\)10.1100 & F\({}_{A12}\) (kN) & \(-\)5.6022 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adjusted coordinates of points A\({}_{i}\) along the bending line and values of reaction forces (and moment \(M_{A0}\)) in points A\({}_{i}\) with horizontal clearance between the rails and the support stanchions (\(\Delta h\) = 90 mm).  
Figure 9: Forces and moment acting on a wagon with fixing block when entering the curve.  
The determination of the additional horizontal forces \(H_{\text{ri}}\) is trivial, and their values are given in Table 3.
## 3 Results
### Surface Roughness (Ra)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Control** & **Glazing** & **Grinding** & **Repolishing** & **Test Statistic** & \(p\) \\ \hline \multirow{3}{*}{CT} & 0.19 \(\pm\) 0.01 & 0.13 \(\pm\) 0.02 & 0.81 \(\pm\) 0.08 & 0.16 \(\pm\) 0.01 & \multirow{3}{*}{\(\chi^{2*}\) = 29.727} & **\textless{}0.001** \\ \cline{2-5}  & 0.18 (0.17–0.21) BC & 0.13 (0.11–0.16) A & 0.82 (0.67–0.93) B & 0.17 (0.15–0.18) AC(a) & & \\ \hline \multirow{3}{*}{LS Block} & 0.19 \(\pm\) 0.03 & 0.12 \(\pm\) 0.01 & 0.84 \(\pm\) 0.08 & 0.18 \(\pm\) 0.01 & \multirow{3}{*}{\(\chi^{2*}\) = 27.12} & **\textless{}0.001** \\ \cline{2-5}  & 0.19 (0.16–0.24) BC & 0.13 (0.11–0.14) A & 0.84 (0.71–0.96) B & 0.18 (0.17–0.2) AC(b) & & \\ \hline \multirow{3}{*}{EC} & 0.19 \(\pm\) 0.02 & 0.12 \(\pm\) 0.03 & 0.77 \(\pm\) 0.12 & 0.17 \(\pm\) 0.02 & \multirow{3}{*}{\(\chi^{2*}\) = 26.04} & **\textless{}0.001** \\ \cline{2-5}  & 0.19 (0.15–0.23) BC & 0.13 (0.09–0.18) A & 0.76 (0.61–0.98) B & 0.17 (0.15–0.2) AC(ab) & & \\ \hline \multirow{3}{*}{KAT} & 0.24 \(\pm\) 0.05 & 0.11 \(\pm\) 0.03 & 0.72 \(\pm\) 0.13 & 0.16 \(\pm\) 0.02 & \multirow{3}{*}{\(\chi^{2*}\) = 30} & **\textless{}0.001** \\ \cline{2-5}  & 0.24 (0.17–0.35) BC & 0.11 (0.07–0.14) A & 0.74 (0.47–0.9) B & 0.17 (0.11–0.18) AC(a) & & \\ \hline Test Statistic & \multicolumn{1}{c}{\(\chi^{2}\) = 8.693} & \multicolumn{1}{c}{\(\chi^{2}\) = 5.587} & \multicolumn{1}{c}{\(\chi^{2}\) = 6.461} & \multicolumn{1}{c}{\(\chi^{2}\) = 12.459} & \multirow{3}{*}{\(\chi^{2*}\) = 30} & **\textless{}0.001** \\ \hline \(p\) & 0.051 & 0.134 & 0.091 & **0.006** & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the roughness values according to the ceramic and the process applied to each ceramic (glazing, grinding, and repolishing).
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Layout Optimization Research
#### 3.3.1 Optimization of Pitch of 22 mm Spherical Tooth Ring  
Based on the principle that rock strength decreases under stress, an analysis was conducted using a stress threshold of 98 MPa obtained from single button bit impact tests [29]. As shown in Figure 11, the stress distribution of the rock under the impact of two button bits exhibits a wave-like pattern. Stress reaches its peak directly beneath the two button bits and diminishes progressively on both sides of the bits. As the spacing between the button bits increases, the peak stress values at the bits decrease. When the spacing is 43.2 mm, the stress values in the rock between the two bits are entirely above the stress threshold, indicating a significant reduction in rock strength throughout the region. At a spacing of 47.2 mm, most of the stress values in the intermediate region remain above the stress threshold, suggesting a moderate reduction in rock strength over a smaller area. However, when the spacing exceeds 51.2 mm, some portions of the intermediate region fall below the stress threshold, implying minimal stress impact and a less effective reduction in rock strength.  
From the above analysis, it can be concluded that a spacing of approximately 47.2 mm strikes an optimal balance between cost and efficiency. At this spacing, most of the intermediate rock stress values exceed the stress threshold, enabling significant strength reduction over a large portion of the rock. This spacing minimizes button bit costs while effectively facilitating rock fragmentation.
## 2 Materials and Methods
### Model Creation  
The DL models used combinations of parameters (loss function, sampling method, type of layer); they are compared in Figure 5. Firstly, raw data were extracted from the.txt files from forces and markers, using either up-sampling or down-sampling depending on the sampling rate. This was done to solve granularity and then merge both datasets into a single dataset that contained XYZ markers and forces for each velocity and subject. Then, an FIR filter and min-max scaling pre-processed data for each combination of subject and velocity. Data were further split into training, validation, and testing datasets according to the data division in Figure 2. Thereafter, data passed through a process of model creation,where the training and validation datasets trained and hence validated the model, based on \(n_{epoch}=25\) to train, compute the loss function, and update its weights. According to the loss on the final epoch, for training and validation datasets, the model could over-fit, under-fit, or ideal-fit the training dataset; only when the model had a perfect fit could it be used with the testing dataset to compute the performance. Hence, we chose the best model using these metrics.  
As for the evaluation of the model using training and validation datasets, the model at the ith epoch, \(e_{i}\), computed training and validation loss. These computations across \(n_{epochs}\) created a line plot with two curves. These curves validated the model before testing with the testing dataset, as they first required parameters' modifications. The three types of \"fits\" are:  
Figure 5: Flow diagram of the DL models’ creation. The process starts at the top when solving granularity and merging the raw.txt files of forces and markers. Then, data were pre-processed using FIR and min-max based on the combination of subject and velocity. Afterwards, the model was created using \(n_{epoch}\) and regularized (over-fit, under-fit), and performance metrics were used to determine the best model.  
Under-fit: The validation curve is under the training curve, as the model is not complex enough to learn the training dataset. The parameters are not good enough to model the relationship between source and target features, so the loss function does not decrease at a stable rate. This model would not be valid, as the relationship between variables is not completely understood; the model should be more complex, adding more layers of neurons.
2. Over-fit: The training curve is under the validation curve, as the model is over-learning from the training dataset by analyzing the tiniest details that do not appear when testing the model's performance on the validation dataset. The model becomes too specific for the training dataset, making it not helpful in a real-life scenario on an unseen dataset (validation or testing dataset).
3. Ideal fit: Training and validation curves are similar; neither under-fits data nor over-fits them. The learning rate is relatively equal to the speed of generalization, so it can learn new information while still understanding the training dataset.
## 4 Results: Analysis of the Integration of VR, AR, and MR in the Design Process
### Results of Usability Indicator Analysis
#### 4.1.6 Perceived Creativity  
Perceived creativity was evaluated on the number of design variants generated. The results show a progressive increase in the production of proposals as greater mastery of the virtual environment is acquired. The products with stronger expressive and ergonomic components, Design Units 6 (Figure 9a,b) and 7 (Figure 9c,d), registered the highest levels of perceived creativity. This may indicate that immersive design stimulates idea generation in Design Units where formal language plays a central role. It may also be influenced by the ease of creating new models within the immersive environment of 'Gravity Sketch'.  
The general analysis suggests that immersive technologies support divergent thinking. The possibility to work at full scale, modify geometries in real time, and move around the product enhances spatial exploration and creative flow. This could help overcome some limitations of traditional design tools, such as paper sketching and 2D screen CAD modelling. VR encourages a more intuitive, embodied, and iterative process. However, this advantage is strengthened after an initial familiarisation phase with the physical and digital interfaces of the software and hardware. In other words, the learning curve also affects the full development of creative potential.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 2 Materials and Methods
### Sequence Coding  
\"A\", \"G\", \"C\", and \"U\" are nucleotides in RNA sequences. To capture RNA sequence features, nucleotides were represented using one-hot encoding. To ensure that the inputs to the CNN had the same lengths, \"N\" was used to pad all sequences to the longest length, which was used as the standard in this experiment. Given an RNA sequence containing \(n\) nucleotides and a detector with a length of \(m\), the one-hot encoding rules for the sequence were as follows.  
\[\mathrm{M_{ij}}=\left\{\begin{array}{ll}0.25\ \mathrm{if}\ \mathrm{s_{i-m+1}=N\ \mathrm{or}\ \mathrm{i}<m\ \mathrm{or}\ \mathrm{i}>n-m}\\ 1\ \mathrm{if}\ \mathrm{s_{i-m+1}\ \mathrm{is}\ (A,C,G,U)}\\ 0\ \mathrm{otherwise}\end{array}\right. \tag{1}\]  
where \(i\) represents the nucleotide position, \(j\) represents the nucleotide category, and the positions filled with \"N\" are expressed as a mean value of 0.25.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 4 IMM-TPMB Filter Driven by Model and Data Collaboration  
Addressing the particle degeneration issue identified in [20], this paper proposes a SCKF-based particle importance sampling method for IMM-TPMB-GBP. The improved IMM-TPMB-GBP particle filter employs the ASCKF to obtain state estimates and their covariance matrices at each moment, using these as the mean and variance in the original filter to establish a proposal distribution function. Innovatively, in scenarios where noise statistics are unknown, the ASCKF provides an adaptive noise ASCKF, enhancingthe accuracy of particle weights. While particle filters typically face degeneration, the ASCKF performs moderately in systems with non-Gaussian noise estimation. Utilizing the advantages of ASCKF in estimating unknown noise and nonlinear estimation, this paper combines the improved ASCKF with IMM-TPMB-GBP, proposing a new state estimation method. Further, the ASCKF in this paper adaptively estimates the covariance of process noise and measurement noise in interacting multiple models, not relying on noise system metrics, thus offering robust performance.  
We omit the auxiliary variables \(l\) of the target state and use the node \(i\) to represent the highest posterior probability density. In a nonlinear model, the target state equation and the measurement equation for a motion model \(\xi\) are formulated as follows:  
\[\left\{\begin{array}{l}X_{k}=F\Big{(}X_{k-1}^{\xi_{f}}\Big{)}X_{k-1}^{\xi_{ f}}+G\omega_{k-1}\\ \\ Z_{k}=\left[\begin{array}{cc}\sqrt{x_{k}^{2}+y_{k}^{2}}\\ \arctan(y_{k}/x_{k})\end{array}\right]+v_{k}\end{array}\right. \tag{58}\]  
where \(G=\begin{bmatrix}T^{2}/2&T&0&0\\ 0&0&T^{2}/2&T\end{bmatrix}\) denotes the input matrix, assuming the position and velocity components are \((x_{k},y_{k})\) and \(\big{(}\dot{x}_{k},\dot{y}_{k}\big{)}\), respectively, \(T\) represents the sampling interval, \(X_{k}\) can be expressed as \(X_{k}=\left[\begin{array}{cc}x_{k},\dot{x}_{k},y_{k},\dot{y}_{k}\end{array} \right]^{\mathrm{T}}\), \(\omega_{k}\) is process noise with zero mean and the covariance matrix \(Q_{k}\). \(Z_{k}\) represents the measurement set at time step \(k\), \(V_{k}\) is the measurement noise with zero mean and the covariance matrix \(R_{k}\).
## 3 Results and Discussion
### Microrheological Parameters During Fermentation Process
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **1** **SLB-BF** & **2** **SLB-AF** & \\ \hline CM + 0\% CSF & 0.61 \(\pm\) 0.03 & 0.47 \(\pm\) 0.02 & * \\ CM + 0.25\% CSF & 0.57 \(\pm\) 0.05 & 0.35 \(\pm\) 0.01 & * \\ CM + 0.5\% CSF & 0.71 \(\pm\) 0.09 & 0.23 \(\pm\) 0.02 & ** \\ CM + 0.75\% CSF & 0.70 \(\pm\) 0.06 & 0.27 \(\pm\) 0.01 & ** \\ CM + 1\% CSF & 0.78 \(\pm\) 0.08 & 0.28 \(\pm\) 0.03 & ** \\ AM + 0\% CSF & 0.63 \(\pm\) 0.05 & 0.17 \(\pm\) 0.02 & ** \\ AM + 0.25\% CSF & 0.77 \(\pm\) 0.03 & 0.28 \(\pm\) 0.03 & ** \\ AM + 0.5\% CSF & 0.95 \(\pm\) 0.02 & 0.21 \(\pm\) 0.04 & ** \\ AM + 0.75\% CSF & 0.90 \(\pm\) 0.01 & 0.21 \(\pm\) 0.02 & ** \\ AM + 1\% CSF & 0.70 \(\pm\) 0.01 & 0.33 \(\pm\) 0.07 & ** \\ SM + 0\% CSF & 0.62 \(\pm\) 0.03 & 0.12 \(\pm\) 0.01 & ** \\ SM + 0.25\% CSF & 0.77 \(\pm\) 0.01 & 0.32 \(\pm\) 0.02 & ** \\ SM + 0.5\% CSF & 0.93 \(\pm\) 0.03 & 0.42 \(\pm\) 0.03 & ** \\ SM + 0.75\% CSF & 0.61 \(\pm\) 0.01 & 0.47 \(\pm\) 0.07 & * \\ SM + 1\% CSF & 0.57\(\pm\)0.01 & 0.49\(\pm\)0.06 & \\ \hline \end{tabular}
\end{table} 
Table 2: Solid–liquid balance (SLB) before and after fermentation process. The results are the mean of three experiments \(\pm\) standard deviation. (* \(p\) value \(<\) 0.05; ** \(p\) value \(<\) 0.001). Figure 1: Elasticity index (EI) and macroscopic viscosity index (MVI) values for almond milk (**A**,**A**), soy milk (**B**,**B**) and coconut milk (**C**,**C**) during the fermentation process. The results are the mean of three experiments. As can be seen from the results, both MVI (see Table 3) and EI (see Table 4) values are maintained during the storage at 4 \({}^{\circ}\)C after 5 days, for almost all samples with the highest percentages of carob seed flour, thus demonstrating that the obtained samples were stable. Furthermore, as already mentioned, MVI increased as the amount of the carob seed flour increased; therefore, a higher content of carob seed flour allows one to obtain yogurt-like products with more suitable macroscopic viscosity values to be consumed by man. These results are in agreement with a recent work of [PERSON] and collaborators in which carob flour added to wheat bread dough caused an increase in the viscosity, which was probably caused by the water absorption of the caroubin, which led to the formation of a gel structure [21]. It is interesting to notice that the MVI (Table 3) and EI values (Table 4) of almond milk without carob seed flour after 5 days of storage are not shown because this sample has lost its characteristics undergoing a phase separation. On the other hand, almond-milk-based yogurt-like products with 0.25%, 0.5%, 0.75% and 1% (% _w/v_) of carob seed flour showed very similar MVI values after 24 h, 48 h and 5 days storage, thus demonstrating that carob seed flour at these percentages increased the stability