esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bayesian Techniques--MCMC and Particle Filter
### Markov Chain Monte Carlo Method  
The Markov Chain Monte Carlo method is used to generate samples from the posterior distribution and obtain sample estimates for the characteristics of this distribution using iterative simulation techniques. One of the widely utilized algorithms for implementing the Markov Chain Monte Carlo method is the Metropolis-Hastings algorithm, which was utilized in the present study [30; 31].  
The construction of the Markov Chain involves a sequence of random variables, \(\left\{\mathbf{P^{(1)}},\mathbf{P^{(2)}},\ldots,\mathbf{P^{(n)}}\right\}\) where the next state, \(\mathbf{P^{(i+1)}}\), is derived from a distribution \(q(\mathbf{P^{(i+1)}}|\mathbf{P^{(i)}})\) which only depends on the current state \(\mathbf{P^{(i)}}\) but not explicitly on previous states. This sequential process is called a Markov Chain, and the distribution \(q(\mathbf{P^{(i+1)}}|\mathbf{P^{(i)}})\) is referred to as the transition kernel or transition density of the chain. In this work, a proposed distribution \(q(\mathbf{P^{*}},\mathbf{P^{(i-1)}})\) was used to generate a new candidate parameter vector \(\mathbf{P^{*}}\), dependent only on the current state but not explicitly on the previous states, \(\mathbf{P^{(i-1)}}\). This new parameter vector \(\mathbf{P^{*}}=\mathbf{P^{(i-1)}}(1+\mathbf{w}\epsilon)\) was randomly generated using a Gaussian transition kernel, characterized by a mean \(\mathbf{P^{(i-1)}}\) and standard deviation \(\mathbf{w}\mathbf{P^{(i-1)}}\), where \(\epsilon\) is a random variable drawn from a Gaussian probability distribution with a mean of zero and standard deviation equal to 1, denoted as 1, N (0.1) [27; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41].
## 2 3D LiDAR Fundamentals
### Discrete and Full-Waveform LiDAR  
LiDAR records the energy of the return signal. The reflectance calculated from the energy of the return signal is important to understand and separate the surface properties. A record of a waveform or continuous distribution of light energy is related to the level of detail expected from signals returning to the LiDAR sensor. The return signal may be recorded in two ways, such as discrete or full waveforms.  
A discrete LiDAR system records individual return points for the peaks in the waveform curve. It takes as a measurement peak points in the waveform curve. The peak points are called returns. Five or more returns can be recorded from each laser pulse in discrete systems. A full-waveform LiDAR records a distribution of return energy in the continuous waveform (Figure 2). Thanks to these, it includes more information than discrete LiDAR. However, it is more complex to process full-waveform records. The full-waveform measurement data could have large noise and low SNR. Full-waveform LiDAR is more useful than discrete LiDAR for characterizing vegetation and investigating forests [25; 26].
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 5 Conclusions  
Driving behavior is an important feature to consider in the next generation of ITS, as research has proven that the cultural and educational backgrounds of people can have a major impact on driving environments. In fact, drivers do not always obey the instructions provided by the vehicular systems, which may lead to a misjudgment of driving safety and hinder the efficiency of traffic management. In this paper, we propose a guidance approach that includes several metrics related to human behavior. A pre-filtering procedure based on SVM is thus proposed to identify the class of drivers that is more likely to follow the ITS instructions. A new route guidance algorithm, DWVDFS, was also designed based on WVDFS [12]. The simulation results showed that DWVDFS can alleviate congestion efficiently, whether a pre-filtering procedure is implemented or not. When comparing the configurations with and without pre-filtering, the results proved that the pre-filtering step leads not only to less time spent eliminating congestion but also to a more stable performance.  
The aim of this work is to focus on an aspect rarely considered in the literature. The drivers' obedience to the ITS instructions will become a challenge in the future (unless all driving behaviors are accomplished by artificial intelligence in fully autonomous cars). The uncertainty of drivers' behavior should remain a major concern in all vehicular feedback systems.  
Conceptualization, Z.H., N.L. and L.K.; methodology, Z.H., N.L. and L.K.; validation, Z.H., N.L. and L.K.; writing--original draft preparation, Z.H., N.L. and L.K.; writing--review and editing, Z.H., N.L. and L.K.; supervision, N.L. and L.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No concerned.  
The authors declare no conflict of interest.  
Figure 5: Congestion mean duration time.
## 4 Experiments
### Implementation and Evaluation
For the performance of the DT model, which is related to the use of SMOTE and nested CV methods, high discriminatory power was achieved for all folds, increasing confidence in the results presented with ROC curves. The source codes of the experiments are available in our repository [54]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **ACC** & **PR** & **Recall** & **Weighted F1** & **Macro F1** & **MCC** & **FM** \\ \hline KNORA-U & 92.66 & 0.91 & 0.92 & 0.91 & 0.86 & 0.89 & 0.89 \\ KNORA-E & 91.33 & 0.89 & 0.91 & 0.89 & 0.82 & 0.87 & 0.88 \\ \hline META-DES & 93.00 & 0.92 & 0.93 & 0.91 & 0.86 & 0.90 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **ACC** & **PR** & **Recall** & **Weighted F1** & **Macro F1** & **MCC** & **FM** \\ \hline
**Manual Augmentation Only** & & & & & & & \\ \hline Hold-out & 83.33 & 0.77 & 0.83 & 0.79 & 0.61 & 0.74 & 0.77 \\ Multiple stratified CV & 92.33 & 0.92 & 0.92 & 0.91 & 0.88 & 0.90 & 0.86 \\ Nested CV & 92.33 & 0.92 & 0.92 & 0.91 & 0.90 & 0.90 & 0.82 \\ \hline
**Manual Augmentation + Augmentation with SMOTE** & & & & & & & \\ \hline Hold-out & 83.33 & 0.86 & 0.83 & 0.84 & 0.80 & 0.74 & 0.78 \\ Multiple stratified CV & 98.99 & 0.99 & 0.99 & 0.98 & 0.98 & 0.98 & 0.98 \\ Nested CV & 98.99 & 1.00 & 0.99 & 0.99 & 0.98 & 0.98 & 0.99 \\ \hline
**Manual Augmentation + Augmentation with Borderline-SMOTE** & & & & & & & \\ \hline Hold-out & 88.88 & 0.88 & 0.88 & 0.88 & 0.84 & 0.82 & 0.84 \\ Multiple stratified CV & 98.00 & 0.98 & 0.98 & 0.97 & 0.96 & 0.97 & 0.98 \\ Nested CV & 95.00 & 0.95 & 0.95 & 0.95 & 0.94 & 0.93 & 0.88 \\ \hline
**Manual Augmentation + Augmentation with Borderline-SMOTE SVM** & & & & & & & \\ \hline Hold-out & 94.44 & 0.95 & 0.94 & 0.93 & 0.90 & 0.91 & 0.91 \\ Multiple stratified CV & 95.00 & 0.93 & 0.95 & 0.93 & 0.91 & 0.93 & 0.91 \\ Nested CV & 96.00 & 0.94 & 0.96 & 0.95 & 0.91 & 0.94 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Decision tree results for the hold-out, multiple stratified CV, and nested CV methods without using the framework proposed by [PERSON] et al. [23]. Figure 3: ROC curves of the DT model using SMOTE and the nested CV method for the five first folds. Each graphic represents one of the ten folds.
# Application of Dimension Extending Technique to Unified Hardening Model
## 3 Incremental Constitutive Integration
### The Dimension-Extending Technique and Constitutive Integration  
For hardening/softening behaviors, the yield surface in the stress space will shrink or expand with the development of plastic deformation. The hardening parameter \(\varpi\) in the yield equation \(\varphi(\sigma_{ij},\varpi)\) characterizes this change. \(\varpi\) is a vector form with a dimension greater than one and is suited to complex hardening behaviors, such as the mixed hardening model. The current model adopts a scalar form. The dimension-extending technique regards \(\varpi\) as the same position as the components of \(\sigma\), and then the yield function in two-dimensional space, principal stress space, or general stress space is expanded to the \"increasing dimensional stress\" space. Taking the MCC model as an example, the yield function of the increased dimensionality MCC model is as follows:  
\[\overset{\sim}{F}(p,q;p_{c})=q^{2}+M^{2}p(p-p_{c}) \tag{24}\]  
\(p\) and \(q\) are the mean stress and generalized shear stress, respectively.  
The elastic domain enclosed by the yield function in the \((p,q;p_{c})\) space thus turns into:  
\[\overset{\sim}{E}\triangleq\{(p,q;p_{c})\in R^{3}|\overset{\sim}{F}(p,q;p_{ c})\leq 0\} \tag{25}\]  
In the \"increasing dimensional stress\" space, the stress points of the MCC model can only be on or in the \(\overset{\sim}{E}\) without jumping out of it, as shown in Figure 3.  
The constitutive equation of the model is generally given in the rate form. If we want to find the stress-strain relationship, we need to integrate the rate equations. The definition of the constitutive integration is to calculate the stress state at time \(n+1\) by knowing the stress state \(\mathbf{\sigma}_{n}\) and strain state \(\varepsilon_{n}\) at the time \(n\), as well as the strain increment \(\Delta\varepsilon\) at this time step [29].  
Figure 3: The dimension expansion technique of the MCC model. (**a**) The yield surface of MCC in \(p\)-\(q\) coordinates increases with \(p_{c}\) increasing; (**b**) The MCC model in \(p\)-\(q\)-\(p_{c}\) coordinate.  
The constitutive integration of the elasto-plastic model can be reduced to the MiCP mentioned earlier, such as the Mohr-Coulomb (MC) model [19] and the MCC model [20]. The UH model is similar to the MCC model, so the hardening parameters can also be extended to the \"increasing dimensional stress\" space. The elastic properties of the model are pressure-dependent. \(K\) and \(G\) are the bulk modulus and shear modulus, respectively.  
\[K=\overline{K}_{0}p\qquad\quad G=\overline{G}_{0}p \tag{26}\]  
\[\overline{K}_{0}=\frac{(1+e_{0})}{\kappa}\qquad\quad\overline{G}_{0}=\frac{3( 1-2v)}{2(1+v)}\overline{K}_{0} \tag{27}\]  
\(v\) is the Poisson ratio, and \(e_{0}\) is the initial void ratio.  
There are two methods to deal with this variable elastic property; one is Euler integral approximation, and the other is precise integration [9]. This study mainly uses the two methods to discuss the constitutive integration of the UH model in the \"increasing dimensional stress space\".
# Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO
## 2 Results and Discussion
### Sensor Responses
Consequently, the number and amount of the VOCs differ from sample to sample, leading to dissimilar responses of the sensors. However, the use of sensors that respond in a similar way are redundant, since they provide the same information. For this reason, some of them are discarded. Dimension reduction of the dataset was done through the analysis of loadings. Starting from 14 parameters, 7 were not considered for the following analysis, since they gave the same contribution to the first two principal components (PCs). Figure 2: First derivative signals of a SnO\({}_{2}\)-RGTO sensor. On the x-axis is time (s), on the y-axis are first derivative values. The remaining features were \(\Delta\)R/R0 values of two SnO\({}_{2}\)-RGTO, SnO\({}_{2}\)Au-RGTO, CuO, SnO\({}_{2}\)Au-NW and the minimum of 1 st derivative of SnO\({}_{2}\)-RGTO and SnO\({}_{2}\)Au+Au-NW. Selected parameters were used as input for k-Nearest Neighbors (k-NN) classification algorithm. It was found that the optimal k = 4, with a classification rate equal to 94.3%. Three Garda PDO samples were confused with non-PDO EVOOs, and one olive oil specimen was misclassified as PDO EVOO. All the samples of non-PDO EVOOs were correctly identified. Once the system was tested and we had noted that it was able to distinguish three different types of oil with a good accuracy, we tried to evaluate whether S3 was able to distinguish among two  
Figure 3: PCA score plot (top) and loading plot (bottom) of phase 1. PC1 variance = 72.04%, PC2 variance = 16.51%. different PDO EVOOs. Four different Garda products were considered; from here on, they are called Western Garda and Eastern Garda. The same steps were used as in the first phase of data analysis. PCA in Figure 4 shows the separation between the two different lake shore EVOOs (red and black clusters), while the other two seem to be more similar, since they are partially overlapped (blue and green groups). The total explained variance is equal to 80.65% in the first two PCs. Also, in this case, the loading analysis led to a selection of 7 parameters out of the 20 used (10 sensors x 2 features): \(\Delta\)R/R0 of MQ8, TGS2611, TGS2620 and SnO\({}_{2}\)Au-RGTO and the minimum of first derivative of MQ3, SnO\({}_{2}\)-RGTO and SnO\({}_{2}\)Au+Au-NW. These 7 inputs in k-NN algorithm were enough to obtain a classification rate of 94.7%. Misclassified samples belonged to Western Garda 1 (identified as Eastern Garda 2) and Eastern Garda 1 (confused with Western Garda 2 samples). It is evident that this is only a preliminary result that must be confirmed through further analysis, but this outcome seems encouraging.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 6 Discussion
### Comparison with Existing Methods  
Our dynamic downsampling algorithm demonstrates significant improvements over traditional downsampling techniques, such as voxel, random, uniform, grid, and cluster downsampling, as well as farthest-point sampling (FPS). The key advantages include a balance between high simplification rates and rapid processing times, crucial for real-time processing applications. Unlike previous methods that often require labor-intensive parameter tuning to optimize performance, our algorithm's adaptive nature significantly reduces this burden, offering a more user-friendly approach. These enhancements align with the growing demand for efficient point cloud processing in applications like autonomous driving, robotics, and smart city planning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Number of Down-sampled Points**} & \multicolumn{2}{c}{**Simplification Rate**} & \multirow{2}{*}{**Time (s)**} & \multirow{2}{*}{**Parameters**} \\ \hline \multirow{3}{*}{Voxel downsampling} & 94,743 & 0.5169 & 0.1025 & leafsize = 0.01 \\ \cline{2-5}  & 4718 & 0.9759 & 0.0125 & leafsize = 0.05 \\ \cline{2-5}  & 1284 & 0.9934 & 0.0085 & leafsize = 0.1 \\ \hline \multirow{3}{*}{Random downsampling} & 1961 & 0.9900 & 0.0143 & leafsize = 0.01 \\ \cline{2-5}  & 9806 & 0.9500 & 0.0126 & leafsize = 0.05 \\ \cline{2-5}  & 19613 & 0.9000 & 0.0135 & leafsize = 0.1 \\ \hline \multirow{3}{*}{Uniform downsampling} & 1962 & 0.9899 & 0.1129 & every\_k\_points = 100 \\ \cline{2-5}  & 7846 & 0.9599 & 0.3493 & every\_k\_points = 25 \\ \cline{2-5}  & 19,614 & 0.9000 & 1.0615 & every\_k\_points = 10 \\ \hline \multirow{3}{*}{Grid downsampling} & 5000 & 0.9745 & 3.1347 & \begin{tabular}{c} target\_number\_of \\ \_triangles = 5000 \\ \end{tabular} \\ \cline{2-5}  & 10,000 & 0.9490 & 3.5045 & \begin{tabular}{c} target\_number\_of \\ \_triangles = 10,000 \\ \end{tabular} \\ \cline{2-5}  & 15,000 & 0.9235 & 4.0243 & \begin{tabular}{c} target\_number\_of \\ \_triangles = 15,000 \\ \end{tabular} \\ \hline \multirow{3}{*}{Cluster downsampling} & 95,240 & 0.5144 & 1.0808 & leafsize = 0.01 \\ \cline{2-5}  & 4830 & 0.9753 & 1.007 & leafsize = 0.05 \\ \cline{2-5}  & 1248 & 0.9936 & 0.9958 & leafsize = 0.1 \\ \hline \multirow{3}{*}{FPS} & 1000 & 0.9745 & 34.8717 & \begin{tabular}{c} target\_number\_of \\ \_triangles = 5000 \\ \end{tabular} \\ \cline{2-5}  & 5000 & 0.9490 & 65.9702 & \
\begin{tabular}{c} target\_number\_of \\ \_triangles = 10,000 \\ \end{tabular} \\ \cline{2-5}  & 10,000 & 0.9235 & 109.9000 &
\begin{tabular}{c} target\_number\_of \\ \_triangles = 15,000 \\ \end{tabular} \\ \hline Our method & 15,906 & 0.9189 & 0.0129 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental comparison table of various downsampling methods.
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 4 Case Studies and Simulation Results
### Accuracy Comparison in a Single Day  
This paper compares the results of the two models on a certain sunny day (September 4) and rainy day (September 1) in 2015. The results including the predicted PV power data and actual data are shown in Figures 3-6.  
Figure 3 shows that the measured data of PV power, the output data of the prediction model based on the SVM algorithm, and the output data of the proposed prediction model based on the GBDT algorithm on September 4. The abscissa represents the predicted periods in one day and the ordinate represents the power value. As shown in Figure 3, the predictive stability of the GBDT model is higher than that of the SVM model and the ARMA model at the peak of the sunny PV output. The fluctuation range of the SVM model's prediction curve is wide and that means the SVM model lacks deficiency in stability. The forecast accuracy of the ARMA model is also lower than that of the GBDT model, evidently.  
The prediction errors of the two models at each moment are intuitively shown in Figure 4. The values are the differences between the predicted values and actual values. The prediction error curve of the prediction model based on the GBDT algorithm is smoother and stable most of the time.  
Figure 2: The statistical data of the solar power plant in Ashland in 2015.  
Figure 5, Figure 6 show the measured data of PV power, the output data of the prediction model based on the SVM algorithm, and the output data of the proposed prediction model based on the GBDT algorithm on a rainy day on September 1. As we can see in Figure 5, Figure 6, the fluctuation of photovoltaic power generation on rainy days is more severe than that of the sunny day. Faced with such severe power fluctuations, the prediction stability of the GBDT model is still higher than that of the SVM model and ARMA model.  
Figure 4: Comparison of model prediction errors on sunny day.  
Figure 3: Comparison of model prediction accuracy on sunny day.
# Experimental Study on an Optimized-Section Precast Slab with Structural Aesthetics
## 4 Conclusions  
This study presented the newly developed OPS system that has structural aesthetics resulting from the optimization process of the cross-section to effectively resist external forces. An experimental investigation was conducted to analyze the structural performance and the behavior characteristics of the OPS and the test results were compared with the nonlinear analysis results to examine the applicability of the current design codes. Below are the conclusions that were derived from this study.  
1. The OPS has an optimized cross-section that can effectively resist both positive and negative moments with the flange positioned on the top in the center and on the bottom at the end portion of the PC unit and is a member that has been developed to introduce a curved shape into the variable cross-section and thus, to highlight the structural aesthetics.
2. According to the test results, no damage was observed at the interface between the PC unit and the topping concrete, and the composite specimens exhibited full-composite behavior.
3. In the PC unit shear specimens, critical cracks progressed near the variable cross-section and shear cracks were formed at an angle that was higher than that of the typical horizontal members. On the other hand, in the composite PC shear specimens, the flexural performance was improved due to the topping concrete and shear dominant failure modes were more apparent as the influence of the variable cross-section was relatively small compared with that of the PC unit specimens.
4. The current design codes were found to provide a very accurate evaluation of the flexural capacities of the PC unit and the composite PC members and, in particular, could also properly evaluate the negative moment resistance capacity at the continuous ends. However, it should be mentioned that to secure the required negative moment resistance capacity, the location of the variable cross-section and the cutting point of the tensile reinforcement that was placed at the continuous ends should be properly determined considering the applied loads and the flexural moment distribution.
5. It is expected that the OPS can be utilized to long span structures, such as underground parking lots and logistics warehouses which require high load carrying capacities with economic feasibility and structural aesthetics.  
Original draft manuscript, H.J.; Validation, S.-J.H. and M.-K.P.; Investigation, I.S.C. and S.C.; Supervision and Review Writing, K.S.K.  
This research was supported by a grant (18 TBIP-C125047-02) from the Technology Business Innovation Program that was funded by the Ministry of Land, Infrastructure and Transport of the Korean government.  
The authors declare no conflict of interest.
###### Abstract  
Aim: Polyurethane-based aligners, created through photoinitiated free-radical polymerization, have been the subject of numerous studies focusing solely on their mechanical properties. In contrast, we investigate their thermomechanical properties, which are crucial for their efficacy. This paper aims to investigate the effects of different UV light exposure durations on the complex modulus of elasticity, tan delta, glass transition temperature, and the degree of conversion (DC). Methods: Aligners were printed using Tera Harz TC-85 and NextDent Ortho Flex resin with specific exposure times (2, 2.4, 3, 4, and 4.5 s for Tera Harz; 5, 6, 7, and 8 s for NextDent) and processed per manufacturer guidelines. The degree of conversion was analyzed using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, while Dynamic Mechanical Analysis (DMA) characterized the mechanical properties (complex modulus and tan delta) and the glass transition. Results: Tera Harz TC-85 showed a higher degree of conversion (90.29-94.54%), suggesting fewer residual monomers, which is potentially healthier for patients. However, its lower glass transition temperature (35.60-38.74 \({}^{\circ}\)C) might cause it to become rubbery in the mouth. NextDent Orto Flex, with a higher storage modulus (641.85-794.55 MPa) and \(T_{S}\) (49.36-50.98 \({}^{\circ}\)C), offers greater rigidity and stability at higher temperatures (greater than temperature in the oral cavity), ideal for orthodontic forces, though its lower degree of conversion raises health concerns. Conclusions: Tera Harz TC 85 generally achieves higher DC and more stable polymerization across different UV exposure times than NextDent Orto Flex. Optimal polymerization times significantly impact both the mechanical and thermal properties of these dental resins, with NextDent showing optimal properties at 7 s and Tera Harz benefiting from both very short and extended exposure times.  
aligers; 3D printing; Fourier Transform Infrared spectroscopy; dynamic mechanical analysis +
Footnote †: journal: Applied Sciences