esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Use Cases/Scenarios  
This tool is used in several courses related to human-computer interaction, entertainment and interactive systems, including gaming and VR. Particular focus on haptic feedback is provided in courses like Virtual Reality Systems and Game Development Process, as it plays a crucial role in these fields. These courses include specific sections aimed at teaching students the fundamental principles, concepts, and techniques for designing, implementing, and applying haptic feedback in various interactive experiences. On the other hand, the tool also proved valuable in the Game Engine Architecture course, where its functionality, design, and implementation are used to demonstrate the purpose and importance of scripting custom editor tools in game engines.  
Within the laboratory environment, students have access to desktop personal computers that have the Unity engine installed, as well as a certain number of gamepad controllers with vibration motors. Currently, the main constraint is the insufficient number of gamepad controllers, resulting in multiple students sharing a single controller. While this might restrict students from getting the full hands-on experience simultaneously, it can be turned into a learning moment. Students can share devices and work in teams, which promotes collaboration, communication, and discussion between them.  
Based on the experience gained during the research, an example of the tool's use in education was created. As illustrated in Table 1, the haptic feedback design tool sup ports a variety of use cases and scenarios for teaching and learning, both in the lab and remotely. These range from designing basic reusable haptic effects to creating more advanced effects synchronized with application scenarios and other feedback systems. This flexibility enables the tool to be adapted to various teaching levels, from introductory to advanced courses.
## 4 Modeling and Validation
### Individual Modeling
#### 4.1.5 Neural Network Multilayer Perceptron (NN)  
A multilayer perceptron is a fully connected class of feedforward artificial neural networks. Neural networks consist of many artificial neurons and layers. Each neuron has its own weight associated with it. There can be any number of hidden layers within a feed-forward network. Multilayer perceptron neural networks have the advantage of learning non-linear models [25]. Optimum classification results for the sklearn MLPClassifier model were achieved with 100 hidden layers and the solver for weight optimization set to the 'Adam optimization algorithm', an extension to stochastic gradient descent.
## 2 Materials and Methods
### Limitations and Future Studies  
The absence of well-defined criteria for evaluating SRE poses significant challenges for researchers in this area [33]. This issue is compounded by the ambiguity surrounding which standards should be applied, as well as the wide range of variables that can impact mechanical performance, such as specimen geometry and fabrication methods. As a result, comparing outcomes across studies is often problematic. For example, [PERSON] and [PERSON] [34] found that cylindrical samples of reconstituted soil exhibited higher compressive strength than prismatic ones. They linked this discrepancy to the angular features of the prismatic molds, which likely hinder compaction during preparation, thereby reducing unconfined compressive strength (UCS). Additionally, [PERSON] and [PERSON] [35] studied how the thickness of soil layers affects compaction and concluded that 100 mm was the optimal thickness under their conditions; greater thicknesses led to lower UCS values. Further, research by [PERSON] et al. [36] and [PERSON] et al. [37] showed that curing conditions could significantly influence compressive strength in lime- and alkali-stabilized SRE. They noted that elevated curing temperatures accelerated both pozzolanic and geopolymerization reactions. Due to the influence of these factors, a study is needed to define the standards and specimen molds that should be used in the SRE building sector to achieve consistency among the results obtained by different authors. Since such a study is currently lacking, the present research was conducted following the steps and specifications outlined in the new methodology to characterize SRE materials explained in [23], as this methodology aims to standardize the results obtained by researchers working in the SRE sector.
# Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis +
###### Abstract
Another important aspect in these approaches is the computation method itself and the choice of its hyper-parameters. For example, choice of simulation budgets in Monte Carlo approaches is crucial, as it may strongly influence the probability density estimation and its confidence. Moreover, Monte Carlo methods with a low number of samples lead to an underestimation of extreme ground impact footprints, which may be of interest to provide more confidence in the risk assessment process for flight preparation and authorization. Knowledge of UAV's extreme fallout zones can also help defining safety levels at very low thresholds, which can be critical for certain high-risk infrastructures. This paper therefore addresses the estimation problem of accurate extreme ground impact probability maps and footprints containing 95%, 99% and 99.9% of the impacts. Multiple importance sampling (MIS) is considered to estimate density minimum volume sets associated with these extreme quantiles. In addition, a study of the sensitivity of hazard parameters is proposed to estimate the most influential uncertain parameters on ground impact positions. This analysis may enable both operators and drone constructors to better understand, design and anticipate fallout zones in the event of a failure. All the results in this paper are obtained for the case of a fixed wing UAV after main engine failure. This paper is organized as follows. The first section focuses on the simulation approach used to compute the ground impact points coordinates. The characterization of uncertainties that are taken into account is discussed in Section 3. The estimation of ground footprints by Multiple Importance Sampling is then presented in Section 4 and an associated sensitivity analysis is proposed in Section 5.
## 3 Effect of Cyclic Loading  
The numerical model established above was utilized to perform a set of numerical calculations. The numerical results were analyzed to examine how cyclic amplitude and cycle count affect the natural frequency of offshore wind turbine structures. Subsequently, an empirical formula was put forward to describe how long-term cyclic loading impacts the natural frequency.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 3 Analytical Applications in Environmental Samples  
The environmental applications of graphene-based nanomaterials and nanocomposites are classified into two categories: in the extraction and determination of (i) organic pollutants and (ii) metal ions and rare earth elements. Some of the organic compounds that are examined with these materials are Polycyclic Aromatic Hydrocarbons (PAHs), estrogens, sulfonamides, chlorophenols and so forth, whilst the second category consists of Hg, Cr(III), Cd, Cr(VI), Au, Co, Cu, Zn, Pb(II), Ce(III), Tl(III) and other [57; 58].
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 3 Results and Discussion
### Comparison Test Between ReLU and Mish Activation Functions  
In this study, the ReLU activation function used in ShuffleNet V2 was replaced with the Mish activation function. The accuracy curves of ReLU and Mish activation functions are shown in Figure 13, where Mish demonstrates a certain advantage over ReLU, enhancing the model's representational capacity.  
The results analyzed in Table 3 indicate that the Mish activation function achieved an accuracy, precision, recall, and F1 score of 87.35%, 87.37%, 87.33%, and 87.33%, respectively. These metrics were 0.15%, 0.04%, 0.13%, and 0.08% higher than those achieved by the ReLU activation function. Although the improvement is relatively small, it is still notable. This is especially true given that the dataset used in this study features clean backgrounds and minimal noise. Even with ReLU, features can be effectively extracted. Therefore, the performance gain provided by Mish demonstrates a meaningful advantage.
# Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography
## 2 Theory
### Random Forest  
Machine learning has become a powerful tool for analyzing and extracting information from big data, with Random Forest being one of the most effective algorithms for exploring relationships within large datasets. It exhibits excellent classification and regression capabilities, and existing studies have demonstrated that the Random Forest model offers low computational cost and high accuracy. This algorithm can solve both linear and nonlinear regression problems by utilizing regression functions in high-dimensional feature spaces, thereby reducing model complexity [20]. Essentially, the Random Forest algorithm is an ensemble classifier based on decision trees, where each tree is independent and relies on its random vector.  
Decision trees, as the fundamental components of random forests, rely on attributes (features) to partition nodes, thereby generating a tree structure [21]. Common decision tree classification algorithms include ID3, C4.5, and CART. Random forests consist of tree classifiers formed by multiple independent and identically distributed random variables, with each tree voting on classification based on the input variables. Random forests train each tree by generating a random vector independent of the previous tree, with the same distribution. During training, the generalization error and interdependence between trees can be obtained as parameters by extracting the upper limit of the random forest. The process structure of the random forest is shown in Figure 2.  
Since [PERSON] proposed the Random Forest algorithm in 2001, numerous scholars have further explored its applications. The Random Forest model has been successfully applied across various scientific domains, including physics, chemistry, and biology. For instance, [PERSON], [PERSON] employed the Random Forest model to predict critical superconducting temperatures [22], [PERSON] utilized it to forecast the rejection of organic compounds by nanofiltration and reverse osmosis membranes [23], and [PERSON] applied it for the classification of bacteriophage capsid proteins [24]. These studies demonstrate that the Random Forest model exhibits superior resistance to overfitting compared to other models. It is also more robust to noise in the dataset, enhancing its ability to predict data and explain data correlations. The Random Forest algorithm employs the bootstrap sampling method, where only a subset (typically \(1/4\)) of the data is used for generating each decision tree. This data is referred to as OOB (Out-of-Bag) data, primarily used to quantify parameter importance and validate the accuracy of the generated model [25]. Consequently, cross-validation is generally unnecessary when using the Random Forest algorithm for classification or regression tasks, and its algorithmic process is easier to interpret.  
Figure 2: Flow structure diagram of the Random Forest algorithm.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 3 Materials and Methods
### UAV Remote Sensing Image Acquisition  
Common methods of UAV image acquisition include tilt photogrammetry, orthophoto mapping, strip-area mapping, and close-range photogrammetry. In this study, tilt photogrammetry is employed to capture remote sensing images of the study area, characterized by significant terrain undulations and steep slopes. Tilt photogrammetry offers multiple tilt-angle lenses in addition to vertical downward shots, enabling simultaneous image collection from various angles, such as vertical and oblique. This method allows for the acquisition of side texture and geometric information of features, providing a true representation of the area. It enables the rapid acquisition of large-scale terrain data, overcoming terrain obstacles, and capturing multi-angle image data in a single flight, thereby reducing external work time and efficiently gathering data.  
Five routes were planned for tilt photogrammetry, with one route having a gimbal pitch angle of \(-90^{\circ}\) and four routes set to a gimbal pitch angle of \(-45^{\circ}\). The camera lenses were aligned with the flight direction to ensure the collection of detailed surface morphology information of the objects being photographed. Each route in this study was set at a height of 400 m, approximately 35 m above the highest point of the detected slope, with images captured at equal time intervals. The terrain in this study area is characterized by significant undulations, cliffs, and steep slopes. To ensure adequate coverage of the aerial photography images, it is necessary to increase the route overlap rate. This adjustment ensures comprehensive image acquisition and improves the accuracy of subsequent air-to-ground calculations and three-dimensional model generation, thereby enhancing the overall quality of the work. Therefore, the route overlap rate is set to 75% in the heading direction and 85% in the side direction to balance aerial photography efficiency and accuracy [29; 30; 31].  
In this study, slope image data were acquired during two periods, in December 2023 and December 2024. The two routes used for data acquisition were identical and chosen to be conducted at midday under sunny conditions, effectively minimizing issues such as unclear texture and low brightness in the aerial images. Additionally, the coordinates were automatically adjusted to correct any bias, preventing large-scale shifts in aerial image coordinates due to the limited positioning accuracy of GPS and other factors in mainland China. The two aerial photography missions were conducted with a 33-day interval between them. Each aerial photography mission lasted 33 min, during which 74 photographs of the study area were collected, meeting the quality requirements for the missions.
## 4 Result
### Doppler Spread  
The Doppler spreads for an omnidirectional and a bidirectional antenna are shown in Figure 9. The average Doppler spreads for the omnidirectional antenna were 48.00-61.22 Hz at the rural crossings and 72.57-81.00 Hz at the suburban crossings. The average Doppler spreads for the bidirectional antenna were 40.78-44.71 Hz at the rural crossings and 42.49-43.16 Hz at the suburban crossings. Interestingly, the Doppler spreads for both antenna types at rural-like crossings, crossing #1, 2, and 5, are almost constant. Additionally, more constancy was observed with the bidirectional antenna scenario. We assumed the more condensed signal power due to narrower beamwidth can minimize the noise effect. Since our measurements were performed with a 10 mph train operating speed, we could not find proper previous vehicular channel studies to compare. With a faster moving train speed, higher Doppler spread values may occur than what we present in this paper.
## 2 The Basic Theory
### Gear Coupling Model  
Figure 2 shows a schematic diagram of the gear coupling. When the gear teeth are meshing tightly to transmit a large torque, deformation of gear teeth is inevitable and the following meshing force is related to the deformation amount, meshing distance and meshing rigidity [13]. The torque-induced meshing force is given by  
\[F_{Ti}=\phi L_{i}K_{Li}, \tag{5}\]  
\[\phi=T/\sum_{i=1}^{z}[L_{i}K_{Li}(R+L_{i})], \tag{6}\]  
where \(\phi\) is the deformation angle of the gear tooth, \(T\) is torque, \(L_{i}\) and \(K_{Li}\) are the equivalent meshing distance and stiffness of each gear tooth respectively, \(R\) is the radius of the root circle.  
When the torque is getting transmitted by gear teeth, a relative movement between the two meshing teeth takes place, which is induced by the system vibration at the same time. As a result, the displacement of meshing nodes causes a deformation of each tooth so that another meshing force is produced, which is expressed as:  
\[F_{Di}=(e^{\prime}\sin{\varphi_{i}})K_{Li}, \tag{7}\]  
\[\varphi_{i}=2\pi(i-1)/z, \tag{8}\]  
\[e^{\prime}=\sqrt{x^{2}+y^{2}}. \tag{9}\]  
Figure 2: Schematic diagram of the gear coupling.  
In the formula, \(\varphi_{i}\) is the position angle of the gear tooth, and \(e^{\prime}\) is the misalignment of gear coupling.  
From the above analysis, it can be concluded that the meshing force of coupling misalignment can be obtained as:  
\[F_{Li}=(\phi L_{i}+e^{\prime}\sin\varphi_{i})K_{Li}. \tag{10}\]  
Then, the reactions of meshing force in the X and Y directions of the rotor system are as follows:  
\[F_{LX}=\sum\limits_{i=1}^{z}{(\phi L_{i}+e^{\prime}\sin\varphi_{i})K_{Li}\cos \theta_{i}}, \tag{11}\]  
\[F_{LY}=\sum\limits_{i=1}^{z}{(\phi L_{i}+e^{\prime}\sin\varphi_{i})K_{Li}\sin \theta_{i}}. \tag{12}\]
# Single-Lens Imaging Spectral Restoration Method Based on Gradient Prior Information Optimization
## 3 Methods
The expression for SSIM is:  
\[\text{SSIM}=\frac{2(\mu_{r}\mu_{o}+A)(2\sigma_{ro}+B)}{(\mu_{r}^{2}+\mu_{o}^{ 2}+A)(\sigma_{r}^{2}+\sigma_{o}^{2}+B)} \tag{26}\]  
In the above equation, \(A\) and \(B\) are constants related to the pixel range of the image, where \(A=(0.01L)^{2}\) and \(B=(0.03L)^{2}\), with \(L\) being the maximum pixel value (e.g., 255 for 8-bit images). \(\mu_{r}\) and \(\mu_{o}\) represent the mean values of the restored image and the original image, respectively. \(\sigma_{r}\) and \(\sigma_{o}\) represent the standard deviations of the restored image and the original image, respectively, while \(\sigma_{ro}\) represents the covariance between the restored image and the original image. For evaluating spectral restoration quality, the spectral correlation coefficient (\(SCC\)) and spectral mean square error (\(SMSE\)) were used [33; 34]. Let \(u\) represent the original spectral data and \(v\) represent the reconstructed spectral data, with \(n\) denoting the data size. Descriptions of the various evaluation methods are as follows:  
The formula for calculating _SCC_ is:  
\[SCC(u,v)=\frac{\sum_{i=1}^{n}{(u_{i}-\overline{u})(v_{i}-\overline{v})}}{\sqrt{ \sum_{i=1}^{n}{(u_{i}-\overline{u})^{2}(v_{i}-\overline{v})^{2}}}} \tag{27}\]  
\(\overline{\mu}\) and \(\overline{v}\) represent the mean values of the original spectral data and the reconstructed spectral data, respectively. The _SCC_ (spectral correlation coefficient) takes values between \(-1\) and \(1\), where a larger _SCC_ value indicates higher spectral similarity. The formula for calculating _SMSE_ is:  
\[SMSE(u,v)=\frac{\sqrt{\sum\limits_{i=1}^{n}{(u_{i}-v_{i})^{2}}}}{n^{2}} \tag{28}\]
## 3 Results
### Impact Test
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Impact Test** \\ **90 J** \\ ** ASTM D7136** \\ \end{tabular} } & \multicolumn{2}{c}{**T300/EP**} & \multicolumn{2}{c}{**Sandwich**} & \multicolumn{2}{c}{**Sandwich**} & \multicolumn{2}{c}{**T300/EP + HHZ**} & \multicolumn{2}{c}{} \\ \cline{3-11}  & \multicolumn{2}{c}{**T300/EP**} & \multicolumn{2}{c}{**T300/EP + SUT**} & \multicolumn{2}{c}{**T300/EP + HHZ**} & \multicolumn{2}{c}{} \\ \cline{3-11}  & \multicolumn{2}{c}{**6 m/s**} & \multicolumn{2}{c}{**1.5 m/s**} & \multicolumn{2}{c}{**6 m/s**} & \multicolumn{2}{c}{**1.5 m/s**} & \multicolumn{2}{c}{**6 m/s**} & \multicolumn{2}{c}{**6 m/s**} & \multicolumn{2}{c}{**6 m/s**} \\ \cline{2-11}  & \multicolumn{2}{c}{**Untreated**} & \multicolumn{2}{c}{**Aged**} & \multicolumn{2}{c}{**Untreated**} & \multicolumn{2}{c}{**Aged**} & \multicolumn{2}{c}{**Untreated**} & \multicolumn{2}{c}{**Aged**} & \multicolumn{2}{c}{**Untreated**} & \multicolumn{2}{c}{**Aged**} & \multicolumn{2}{c}{**Untreated**} & \multicolumn{2}{c}{**Aged**} \\ \hline Peak force & Avg. & 1.6 & 1.7 & 2.6 & 1.8 & 3.6 & 1.2 & 2.8 & 1.7 & 3.5 & 1.4 \\ \cline{2-11}
[kN] & St.Dev. & 0.3 & 0.2 & 0.1 & 0.1 & 0.6 & 0.1 & 0.1 & 0.1 & 0.7 & 0.3 \\ \hline Energy & Avg. & 29.1 & 12.7 & 33.7 & 22.8 & 32.3 & 15.2 & 31.8 & 18.2 & 21.8 & 9.7 \\ \cline{2-11}
[J] & St.Dev. & 1.0 & 0.8 & 2.2 & 1.1 & 2.5 & 3.7 & 2.9 & 0.6 & 1.1 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Averaged results with the standard deviation of the impact tests. Figure 5: Energy-displacement mean curves for untreated or aged specimens of sandwich with Kraibon. In the velocity effect evaluation, it is possible to note that all of the untreated specimens show a reduction of absorbed energy. This fact could be attributed to strain-rate effects, as described in the literature [15; 17]. However, it is interesting to note that SUT samples are more influenced by aging and speed than HHZ, despite the opposite seems during low energy absorption. In fact, in SUT samples, the loss of energy absorption capacity is tripled by the synergistic effect of aging and speed, while in HHZ samples it is only increased by 50%. The aging effect is uniform for all materials; a 50% decrease in energy absorbed at 6 m/s is noted. The addition of the elastomer layer involves an increase in Maximum Load (probably due to the increase in thickness of the composite) but there is an opposite effect in energy absorption between SUT and HHZ; the former increase their ability to absorb energy by 11% while the latter reduce it by 25%. This can be traced back to the interfaces created by the reinforcers within the composite.
## 4 Experiment
### Model Analysis  
In order to verify the key role played by the feature interaction layer and meta-path aggregation layer in model optimization, we analyze the impact of user and item embedded representations incorporating attribute features and meta-path aggregation operations with feature information in the recommendation model. And then, we design the following comparison baselines for model efficiency verification.  
**MIMA-F**: This method has user and item embedded representations incorporating attribute features but not meta-paths in the aggregation operation. **MIMA-P**: This approach has user and item embedded representations incorporating attribute features but not user and item attribute information in meta-path aggregation operations.  
As shown in Figure 5, experimental results demonstrate that MIMA achieves the best Precision@20, Recall@20 performance, and average execution time per round among the three approaches. Specifically, MIMA-P obtains better results than MIMA-F, which indicates that integrating meta-path aggregation into model optimization can lead to better results than not using meta-paths. MIMA obtains better results than MIMA-P, which demonstrates that meta-path aggregation that integrates attribute information can achieve better performance than not incorporating user and item features in meta-path aggregation. Hence, the multi-feature interaction meta-path aggregation heterogeneous graph neural network achieves significant improvements in relevance ranking and recommendation accuracy. Meanwhile, its execution time is not much different than that of the other models. The higher density and greater number of nodes of the Amine dataset lead to more complex feature extraction and also increase the execution time due to feature interactions and meta-path aggregation with nodes and their attributes. The results show that our proposed algorithm can better utilize the attribute information of users and items and meta-path semantic contexts with node attribute information, thereby improving the performance of the recommendation system.