esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 4 Experimental Results
In theory, the receptive field size of the TCN depends on the network depth \(n\) (number of stacks), filter size \(k\), and dilation factor \(d\); making the TCN deeper and larger is important to obtain a sufficiently large receptive field. Empirically, making the network deep and narrow, which means stacking a large number of layers and choosing a thin filter, results in an effective architecture [31]. (i) Number of stacks  
First, we need to decide the number of stacks for the TCN model. In essence, the network architecture of a TCN is an extension of a 1D CNN in which a series of 1D convolutional layers are stacked on one another. [24] In the experiment, we analysed three different stack numbers \(n\) (i.e., 2, 3, 4) from transferring learning cases in the TCN predictor. (ii) Filter size  
Similarly, we analysed three different filter sizes \(k\) (i.e., 8, 16, 32) for the receptive field size of the TCN for the designed TCN-based prediction model using iterations of the Q-learning computation. (iii) Dilatation coefficient  
As described before, the residual block (\(d\)) was used between each layer in the TCN to accelerate convergence and enable the training of deeper models. From [14], we analysed three different sets of combinations for the dilatation coefficient \(d\) (i.e., 1, 2, 4; 1, 2, 4, 8; 1, 2, 4, 8, 16) in the receptive field size of the model input. (iv) Activation function  
We analysed two major types of nonlinear activation functions, that is, Norm_relu and Tanh*Sigmoid activation functions used in Wavenet. Then, we calculated the corresponding convergence error of the cost function for the two activation functions. (v) Optimiser  
To minimise loss during model training, an optimiser was adopted to improve the predictive accuracy of the model by adjusting the filter weights. We assessed three popular optimisers for the TCN model: Adam, SGD, and RMSprop. In summary, the computation process for the proper selection of TCN model parameters is as follows:  
1. Initialize the parameters for the model. The architectural components of the proposed TCN model from GitHub [24] were regarded as the initial values and are listed in Table 5.  
2. Repeat the computation of Algorithm 1 for the developed model until \(R\) < 0.1. Select the appropriate parameter values for the five model parameters in Table 5.  
1. The parameter Q table is iterated until the termination condition. 2. Compute Reward R using Equation (14). 3. Update the Q-table using Equation (15) as shown in Table 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Stacks** & **Filter** & **Dilatations** & **Activation Function** & **Optimizer** \\ \hline Initial value & 2, 3, 4 & 8, 16, 32 & \
\begin{tabular}{c} [1, 2, 4] \\ [1, 2, 4, 8] \\ [1, 2, 4, 8, 16] \\ \end{tabular} & norm\_relu, wavenet &
\begin{tabular}{c} Adam, SGD, \\ RMSprop \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Initial parameters for the proposed TCN model.
## 3 Results and Discussion
### Burn Defect and Phenol Profile Quantification Using an Electronic Device  
A partial least squares (PLS) model was established to quantify the burn defects in the odours perceived by the panellists and the total phenol profiles (\(\Sigma\) phenols) for the samples from the different table olive categories (Figure 6). The results showed good relationships between both variables studied and the electronic device. It was found that the different thermal treatments applied to the samples were related to the aromatic profiles and, indirectly, to the representative phenolic profiles of each of the categories studied. These results were reflected in the parameters in the models established. Thus, the \(R_{CV}^{2}\) values for the burn defects and total phenol profiles for the models established were 0.89 and 0.92, respectively. The results for the RMSECV values were 0.65 for burn defects and 0.55 for total phenols. The model was also validated with external samples, obtaining \(R_{P}^{2}\) values of 0.94 for burn defects and 0.95 for total phenols. The RMSEP values were less than 0.52 in both cases.  
The characteristic aromas of these olives could be predicted with the electronic device in a fast and efficient way. This model also made it possible to satisfactorily predict the total phenol profiles of the samples studied. This would be interesting for the prediction of the content of substances with health properties. In fact, higher phenol content was observed in the extra category and, therefore, these olives would be healthier. In the scientific literature,relationships between electronic devices and aromatic parameters of samples have been established. [PERSON] et al. [2] predicted the strength of the odour defects in Californian-style black olives with no flavoured filler added. Another study [22] established an indirect prediction model for an electronic device and the quantity of acrylamide, although this toxic substance does not smell. However, other researchers have used this method for the classification of roasted coffee samples into different categories depending on their quality with an E-nose [44].
## 3 Proposed Methodology
### Self-Attention and Co-Attention Mechanisms  
To enhance the joint model architecture, self-attention and co-attention mechanisms are introduced. Self-attention captures relationships between different parts of the input data, selectively focusing on the most relevant elements. Co-attention allows the model to jointly attend to multiple input sources, learning their interdependencies. The self-attention mechanism is applied to the output of each feature encoder, weighing different parts of the feature sets according to their relevance. The co-attention mechanism is applied to the concatenated feature representation from the four parallel encoders, capturing relationships between features extracted from different encoders.
# Application of the Residue Method in Steam Superheater Fault Detection
## 3 A Mathematical Modelling Method of the Proposed System
### Testing Equivalent Models of Steam Superheaters for Actuator Faults
#### 3.1.2 Steam Superheaters Model for Experiments  
It is essential to be aware of the faults and perturbations that can occur in processing equipment or auxiliary equipment, as they can significantly impact the plant's performance and safety. These faults can lead to reduced productivity and increased costs, so it is crucial to evaluate the system's performance and diagnose any issues that may cause a loss or decrease in performance. To detect and identify faults, a detection and identification model is needed.  
In the case of steam superheater fault detection and localization, various methods have been explored. However, it is important to conduct experiments to test and verify the residual method generator in this instance. Previous experience has shown that this approach could be effective in similar situations and could be a potential solution to this problem.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 5 Attitude Maneuvering for Downlink Connection
### Monte Carlo Analysis  
A Monte Carlo analysis, composed of 100 simulations, is performed for the purpose of ascertaining effectiveness of the control and actuation architecture in nonnominal flight conditions. The final goal is in proving convergence toward the desired attitude in the presence of displacements on the initial conditions, i.e., the spacecraft angular rate and attitude. The latter is randomly generated by assuming an eigenaxis with uniform distribution over the unit sphere, whereas the principal angle has uniform distribution in the interval \([0,\pi]\),  
\[\Phi =\mathcal{U}(0,\pi) \tag{59}\] \[\xi =\mathcal{U}(0,2\pi)\] (60) \[\phi =\sin^{-1}r\] (61) \[r =\mathcal{U}(-1,1) \tag{62}\]  
In the preceding relations, symbol \(\mathcal{U}\) denotes a uniform distribution, with bounds reported in parenthesis, \(\Phi\) is the eigenangle, whereas the two auxiliary angles \(\xi\) and \(\phi\) identify the stochastic direction of the eigenaxis \(\hat{a}\) both in the inertial and in the body axes frame, i.e.,  
\[\hat{a}^{(N/B)}=\left[\cos\phi\cos\xi\quad\cos\phi\sin\xi\quad\cos\xi\right]^{T} \tag{63}\]  
The initial angular rate components of the spacecraft are assumed to obey a normal distribution, with zero average value and standard deviation equal to \(0.5\,^{\circ}/s\), i.e.,  
\[\omega_{i}=\mathcal{N}(0,\sigma^{2})\,\,(i=1,2,3) \tag{64}\]  
where \(\mathcal{N}\) is the normal distribution.  
Figure 13: Net motor torques \(g_{1}^{(j)}(t)\); \(j=1,\,\ldots,\,4\). during a repetition period.  
The mean value and the standard deviations of two quantities are reported in Table 3, i.e., (a) average value of the pointing error (angle \(\bar{\Phi}_{e}\)) and (b) average magnitude of the angular velocity error. Figures 14 and 15 illustrate these two quantities for all the simulations. The average pointing error is less than 0.5\({}^{\circ}\) for the great majority of the simulations, whereas the average magnitude of the angular velocity error never exceeds 7\(\cdot\)10\({}^{-3}\)\({}^{\circ}\)/s.
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Toutin’s Rigorous Model** & **Min** & **Max** & **Mean** & **St. dev.** & **RMS** \\ \hline \multirow{6}{*}{DEM 1 m} & GCPs: 45 & 0.090 & 4.766 & 1.610 & 1.129 & 1.967 \\  & CPs: 15 & 0.176 & 2.132 & 1.196 & 0.604 & 1.340 \\  & GCPs: 60 & 0.029 & 4.985 & 1.615 & 1.070 & 1.937 \\  & CPs: 15 & 0.270 & 1.813 & 1.083 & 0.494 & 1.190 \\  & GCPs: 75 & 0.110 & 5.256 & 1.511 & 1.065 & 1.848 \\  & CPs: 15 & 0.306 & 1.872 & 1.074 & 0.490 & 1.181 \\ \hline \multirow{6}{*}{DEM 20 m} & GCPs: 45 & 0.379 & 16.654 & 3.124 & 2.952 & 4.298 \\  & CPs: 15 & 0.535 & 4.535 & 1.733 & 1.116 & 2.061 \\  & GCPs: 60 & 0.098 & 17.002 & 2.919 & 2.724 & 3.993 \\  & CPs: 15 & 0.603 & 4.600 & 1.724 & 1.072 & 2.030 \\  & GCPs: 75 & 0.095 & 17.746 & 2.594 & 2.596 & 3.670 \\  & CPs: 15 & 0.106 & 4.173 & 1.585 & 1.056 & 1.905 \\ \hline \multirow{6}{*}{DEM 75 m} & GCPs: 45 & 0.414 & 15.820 & 3.007 & 2.764 & 4.084 \\  & CPs: 15 & 0.755 & 8.795 & 3.241 & 2.460 & 4.068 \\  & GCPs: 60 & 0.264 & 20.095 & 3.256 & 3.454 & 4.747 \\  & CPs: 15 & 0.450 & 8.138 & 3.435 & 2.399 & 4.190 \\  & GCPs: 75 & 0.104 & 20.111 & 3.089 & 3.267 & 4.496 \\  & CPs: 15 & 0.507 & 7.992 & 3.343 & 2.358 & 4.091 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Residuals (in meters) obtained for GCPs (45-60-75) and CPs using [PERSON]’s rigorous model. Figure 6: The combination of 3D RPFs and 2D polynomial functions (PFs): (**a**) 75 GCPs used for 3D RPFs without RPCs; (**b**) 15 GCPs used for 2D PFs first order. The coordinates reported are referred to as UTM/WGS84 (33 N—zone T) and expressed are in meters.
# Numerical Calculation of Slosh Dissipation
## 2 SPH Method: The SPH-Flow Solver  
### Governing Equations Adopted for the SPH Model  
The main simplifications adopted for the SPH model are:  
* Modeling of only the liquid phase;
* Neglect of thermal conductivity and surface tension;
* Liquid is assumed to be a weakly compressible media through an artificial speed of sound.  
Figure 1: Experimental snapshots of the SDOF vertical sloshing water experiments carried out in [4] for \(50\%\) filling level.  
Figure 2: Tank motion recorded in the experiment of [4]: tank with water (dash-dotted line) and tank with oil (solid line).  
The use of a single-phase model obliges the neglect of all physics related to the air contained in the fluid domain. This assumption may appear inappropriate for violent sloshing simulations where air entrapment is unavoidable. However, in [26], it was shown that although the air phase plays a relevant role in the flow evolution, the evaluation of the energy dissipation in violent flows, even under the single-phase hypothesis, is still accurate enough. A further validation can also be found in [24; 25], where the study of the mechanical energy dissipation induced by sloshing and wave breaking in a fully coupled angular motion system was investigated. In this work, a single-phase SPH model is demonstrated to be able to predict the experimental results of the fluid dissipated energy with reasonable accuracy.  
The role of surface tension is negligible according to the velocity and length scales considered, as also explained in [21], since the Weber number, \(\text{We}=\rho\,U_{max}^{2}\,D/\sigma\) (where \(\sigma\) is the surface tension coefficient of the liquid considered) is larger than 4000 for both the liquids considered in this work. However, the liquid menisci due to the contact angle between the lateral walls and the liquid surface need to be considered in the initial conditions of the simulations presented in this work, since they form the main perturbation mechanism of the interface leading to the Rayleigh-Taylor instability during the initial flow stage (see also [23]).  
In the SPH model, the flow evolution is considered governed by the weakly compressible Navier-Stokes equations:  
\[\left\{\begin{array}{rcl}\frac{D\rho}{Dt}&=&-\,\rho\,\text{div}( \boldsymbol{u})\,,&&\frac{D\boldsymbol{u}}{Dt}&=&\boldsymbol{g}\,+\,\boldsymbol {f}_{NI}\,+\,\frac{\text{div}(\boldsymbol{\tau})}{\rho}\\ \frac{D\boldsymbol{e}}{Dt  
tank accelerations applied as an acceleration source term \(\mathbf{f}_{NI}\) to the fluid in the governing Equation (1).
## 1 Introduction
Consequently, to address this gap, this study puts forward and empirically examines an integrated model that incorporates perceived value into TAM. This model is anticipated to offer a more comprehensive explanation of the adoption of this new technology, LLMs, in green product consumption, particularly with regard to continue using intention (CUI), where the continuous balance of benefits and costs users derive from LLMs is of crucial importance. Methodologically, this study uses a two-stage analysis method to survey university students participating in online green shopping. First, structural equation modeling (SEM) is used to verify linear relationships, and an artificial neural network (ANN) is used to capture potential non-linear relationships and rank the importance of predictors. This mixed method approach can enhance the robustness of the results [24]. This study primarily contributes to two aspects. First, it proposes an extended model integrating perceived value with TAM. This model expands the utilitarian boundaries of the traditional TAM, is more suitable for research on the adoption and continuous use of novel technologies, and captures the significant trade-offs between benefits and costs in sustainable online shopping. Second, through the rigorous analysis method of combining SEM and an ANN, it demonstrates how PU, PEU, and green self-identity value (GSV) significantly and positively influence CUI. In conclusion, this study provides actionable guidance for designers of LLMs and policymakers seeking to accelerate sustainable consumption. By clarifying the utilitarian and value-based acceptance levers, this study helps explain the diverse findings reported thus far and indicates the direction for leveraging advanced artificial intelligence to further a country's green procurement goals. Subsequently, this study consists of three major parts. The first part involves modeling the specific application of the perceived value theory and TAM in the context of this study, and hypotheses are proposed accordingly. The second part presents the quantitative research methodology and provides a detailed explanation of the data collection results using SEM and an ANN. The third part discusses the research results and further summarizes them.
## 2 Results  
Low Light's Effects on the Densities of Peltate Glandular Trichomes and Chemical Properties of Essential Oil Composition of Mitt Leaves  
We assessed the peltate glandular trichome densities on five separate zones, from the basal to the top regions, of the abaxial leaf surface of _Mentha canadensis_ under different light conditions. The results showed that the peltate glandular trichome (PGTs) densities were reduced by half a percent in all five zones under the low light condition (Figure 1C). Basal zone 1 had a maximum of 27.75 \(\pm\) 0.95 glands number per mm\({}^{2}\), which decreased to 16.63 \(\pm\) 2.67 glands per mm\({}^{2}\) under the low light condition (Figure 1A,B). The top zone (zone 5) had a minimum PGT density of 11.5 \(\pm\) 2.23 glands per mm\({}^{2}\), which was significantly reduced to 4.87 \(\pm\) 0.83 per mm\({}^{2}\) under the low light condition (Figure 1A,B). An abundance of wrinkled PGTs were observed under low light conditions using scanning electron microscopy, indicating low essential oil accumulation in the subcuticular cavities of peltate glandular trichomes (Figure 1D). The organic compounds volatilized from mint leaves were collected via headspace sorption and analyzed via GC-MS. Fifteen volatile compounds were identified, accounting for 99.87% and 97.13% of the total volatile exudates in mint leaves under control and low light conditions, respectively. The changes in total volatile compounds and monoterene compositions were investigated. The relative contents of volatile compounds were dramatically reduced compared to the internal standard substance, which was bornanone. The level of the main oxygenated monoterpene, menthol (constituting 32.02% of total volatile compounds), decreased to 12.85% following low light treatment. The proportion of pulegone increased from 0.95 to 8.70%, and that of piperitone increased from 1.23 to 28.31% (Figure 2). The low light condition caused the accumulation of pulegone and piperitone, which are intermediate compounds in mint leaves, but had no reductive effect on the proportions of total monoterpene hydrocarbons or oxygenated monoterrenes (Table 1). Figure 1: Phenotypes of _Mentha canadensis_ after 4 weeks of growth under low light conditions. (**A**) The morphology of leaves at the same developmental stage under normal light conditions (left) and low light conditions (right). (**B**) The abaxial surface of leaves under fluorescence light. Red spots show the spontaneous fluorescence of pellate glandular trichomes. (**C,D**) Peltate glandular trichome densities of the abaxial surface—five leaf zones from basal to top regions of young leaves. Bars indicate the densities of pellate glandular trichomes. Error bars represent SD. (**E**) SEM micrographs showing PGTs on the abaxial leaf surface. (**a**) Morphology of pellate glandular trichomes and non-glandular trichomes; (**b**) abaxial leaf surface under normal light condition; (**c**) abaxial leaf surface under low light growth conditions; the red triangles indicate wrinkled PGTs. The analysis of trichome densities was repeated five times.
## 2 Preparations for Listening Test
### Rules Tested  
Using the recordings obtained, different tunings were generated for notes above F4 (key #45), as the goal of this study was to focus on the tuning of high tones of the piano. The aim was to compare some simple rules on how to match the partials of the two tones in an octave. These rules were adapted from previous approaches [3; 5; 6]. The different rules tested for two tones an octave apart were as follows:  
1. Matching first partial (m1): the upper tone was modified such that the first partial of the upper tone overlapped exactly with the second partial of the lower tone, i.e., they had the same frequency.
2. Matching second partial (m2): the second set of matching partials exactly overlapped, in a manner similar to the previous one.
3. Geometric mean (GM): the geometric mean (GM) of the frequencies of the first two sets of matching partials were made to be equal. The GM of the second and fourth partial of the lower tone was calculated and the upper tone was modified such that the GM of the first and second partial was made equal to it.
4. Matching third partial (m3): the third partial of the upper tone and the sixth partial of the lower tone were made to overlap. As the amplitudes of the partials drop off very quickly at high octaves, this rule was expected to be worse than the others. It was included as a low-quality anchor for the listeners, to define the worst performance in each trial.  
For the listening test, samples were chosen primarily from the sixth and seventh octave. In the last octave (the eighth octave) the decay of such high tones is very quick and this makes it difficult to hear beating. According to a study conducted, it is difficult to identify beating cues for tones shorter than 1 to 2 s [9]. The same study also showed that identifying mistuning in very high frequencies is difficult [9]. This is why, often larger intervals like double and triple octaves are used to tune the highest octave [8].  
Below the chosen range, in the octaves just above the reference octave, the results always seem to be pretty good. Deviations occur only in the higher octaves, and thus the two octaves just beyond the reference octave are not used in this listening test. The lowest tone used was F6, with a fundamental frequency of about 1.4 kHz, and the highest tone was D7, with a fundamental frequency of about 2.4 kHz. Eight different tones were chosen in this range.
## 3 Signal Processing and Artificial Intelligence for DT Development  
The integration of signal processing (SP) and artificial intelligence (AI) is critical for deploying effective digital twin (DT) systems to enable reliable predictive monitoring of gearboxes. Figure 10 illustrates this through a simplified workflow showcasing their combined roles in a DT-based approach.  
The process begins with real system-to-DT alignment, where physical parameters are synchronized with their virtual counterparts. Since sensor data often contain noise, gaps,  
Figure 10: Signal processing and artificial intelligence for gearbox monitoring.  
or interference, SP techniques clean the signals and extract meaningful vibrational patterns from healthy gearbox operation. When direct measurements are unavailable, AI fills these gaps, and generative models reconstruct missing data from historical patterns, enabling accurate DT initialization. With clean, synchronized data, the DT can then simulate various fault conditions (cracks, misalignments, wear) to train diagnostic models.  
During fault signature analysis, complex interactions between multiple defects can create non-physical signal patterns. For instance, vibrations from a cracked gear tooth may overlap with those from a failing bearing, producing obscured or combined frequency signatures. SP methods like time-frequency analysis and blind source separation isolate these overlapping components, while AI automatically detects subtle fault characteristics even in noisy environments.  
The final decision-making phase leverages both technologies: AI models classify fault types and predict remaining useful life, enabling preventive maintenance before failures occur. Simultaneously, SP techniques like spectral correlation validate these findings against known reference patterns. This dual approach ensures robust diagnostics across varying operating conditions and data limitations.