esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Statistical Data Elaboration  
GC-MS, GC\(\times\)GC-TOF-MS, HPLC-DAD-ESI/MS, and sensory analyses data (concentrations of volatile aroma compounds and phenols) were subjected to one-way analysis of variance (ANOVA), and the average values were compared by Least Significant Difference (LSD) test at the level of \(p<0.05\). These data, together with the results of sensory analysis (medians of intensities and grades), were further processed by principal component analysis (PCA) in order to better visualize the differences between the two groups of EVOO and explain them on the basis of the concentrations of volatiles and phenols. Three datasets (GC-MS, GC\(\times\)GC-TOF-MS, and HPLC-DAD-ESI/MS) were separately treated. Prior to PCA analysis, the GC-MS and GC\(\times\)GC-TOF-MS original datasets were reduced to only include the volatiles with the highest discriminative potency (\(F\)-values in one-way ANOVA), combined with those regularly reported as among the most important for EVOO aroma. For the HPLC-DAD-ESI/MS dataset, first PCA was performed including all the identified phenols (Figure S4), and then it was applied on a reduced dataset including only those for which statistically significant differences were observed (\(p<0.05\)), and those being close to that (\(0.05<p<0.10\)). Statistical data elaboration was performed by Statistica v. 13.2 software (Stat-Soft Inc., Tulsa, OK, USA).
## 5 Experiment and Discussion
### Efficient LDA Application Method  
In the existing papers, the method of applying LDA is used to obtain the entire data and to project it with the eigenvector through LDA and to align it with AI technology. Even in the general short-circuit situation of the stator, excellent performance can be observed.  
Figure 12: D-Q synchronous coordinate system according to turn short circuit at no load.  
Figure 13: D-Q min–max coordinate system according to the speed change.  
If LDA is applied to D-Q synchronous min-max coordinate system, which was proposed in this paper, as shown in Figure 13, eigenvector w = [\(-\)0.704105, 0.710096] can be obtained as shown in Figure 14, and an optimal rotation direction of LDA was \(-\)45.243\({}^{\circ}\) to obtain optimal direction information. In this case, since the general turn short was assumed, the ITSC was excluded.  
In Figure 14, the red dotted line shows the eigenvector w line obtained by LDA, and the red solid line shows the optimal critical line when the rotation angle is transformed according to the eigenvector. As shown in Figure 14, a general turn short circuit enables stable diagnosis even at a light load.  
However, if LDA is used to diagnose ITSC, it can be found from Figure 15 that diagnosis is difficult not only at light load but also at full load.  
In Figure 15, the blue solid line indicates the critical line when only the max value of \(I_{d}\) is used at full load, the blue box on the upper right indicates the class of full load at 1690 rpm, black dots indicate the steady state, the red dots in the blue box on the upper right indicate the ITSC, and the red dots overlapping the class of the black dots indicates the ITSC class at 1700 rpm. As shown in Figure 15, it can be effectively applied to LDA at light load. Under full load, however, it can be found that the separation is not efficient compared to the method using the max value of Id, and the existing LDA application method is not efficient.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
## 3 Synergistic Effects on Benzene Formation  
The synergistic effect resulting from the interaction between process parameters influences the behavior of the blends. This phenomenon is demonstrated by the higher concentration of MAH and PAHs formed in blends compared to their corresponding pure fuels, attributable to species crosslinking arising from various fuel component interactions. Some authors have reported the total synergistic effect on aromatic formation across different systems, as summarized in Table 1. Both diagnostic methods and numerical methods have been adopted to investigate the synergistic effects among hydrocarbons and hydrocarbons with oxygenated fuels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Facility** & **Methods** & **Fuels** \\ \hline [PERSON] et al. [91] & Flow reactor & SVUV-PIMS \({}^{\text{a}}\) & toluene/propylene; toluene/allenes \\ [PERSON] et al. [92] & Shock tube & GC \({}^{\text{b}}\)/GC-MS & toluene/propylene; toluene/propylene \\ [PERSON] et al. [93] & Shock tube & GC/GC-MS & toluene/ethylene; toluene/acetylene \\ [PERSON] et al. [11] & Flow reactor & SVUV-PIMS & toluene/acetylene \\ [PERSON] et al. [93] & Shock tube & GC/GC-MS & benzene/ethylene; benzene/acetylene; benzene/indenes \\ [PERSON] et al. [67] & JSR & GC/GC-MS & toluene/n-heptane \\ [PERSON] et al. [31] & Counterflow flames & LII \({}^{\text{c}}\), LIF \({}^{\text{d}}\) & ethylene/ethanol \\ [PERSON] et al. [94] & Laminar premixed flames & ILI, LIF & benzene/ethylene \\ [PERSON] et al. [95] & Laminar diffusion flame & PLIF \({}^{\text{e}}\) & toluene/n-heptane; toluene/iso-octane \\ [PERSON] et al. [96] & Counterflow flames & LS \({}^{\text{f}}\) & ethylene/dimethyl ether \\ [PERSON] et al. [97] & Counterflow flame & LII, LIF & toluene/n-heptane; toluene/iso-octane \\ [PERSON] et al. [98] & Counterflow flames & LII & ethylene/propane \\ [PERSON] et al. [99] & Counterflow flames & LIF & n-heptane/ethylene, ethanol/ethylene, \\ [PERSON] et al. [12] & Flow reactor & GC & 1,3 butadiene/propylene \\ [PERSON] et al. [100] & Co-flow flames & LII & ethylene/dimethyl ether \\ [PERSON] et al. [101] & Counterflow flame & LII, LIF & toluene/n-heptane; toluene/iso-octane \\ [PERSON] et al. [102] & Flow reactor & HPLC \({}^{\text{g}}\), GC & catechol/propylene \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature on synergistic effect on benzene and PAH formation.  
Figure 12: Reaction pathway for HAPaA for (**a**) zigzag and (**b**) armchair (reprinted from [PERSON] et al., 2021) [90].
## 4 Multi-User MIMO System Model
### SLNR Precoding and MMSE Combining
 \tag{79}\]  
i.e., \(\mathbf{w}_{k,l}\) is a column vector while \(\mathbf{u}_{k,l}^{T}\) is a row vector, we can express the estimated signal for user \(k\) and layer \(l\) as:  
\[\hat{s}_{k,l}=\mathbf{u}_{k,l}^{T}\,\mathbf{\tilde{H}}_{k}\,\mathbf{w}_{k,l} +\sum_{ \begin{subarray}{c}d=1\\ d\
eq l\end{subarray} }^{L}\mathbf{u}_{k,l}^{T}\,\mathbf{\tilde{H}}_{k}\, \mathbf{w}_{k,d}+\sum_{ \begin{subarray}{c}i=1\\ i\
eq k\end{subarray} }^{K}\sum_{d=1}^{L}\mathbf{u}_{k,l}^{T}\,\mathbf{\tilde{H }}_{k}\,\mathbf{w}_{i,d}+\mathbf{u}_{k,d}^{T}\,\mathbf{n}_{k}. \tag{80}\]  
The Signal-to-Interference-plus-Noise Ratio for user \(k\) and layer \(l\) can be defined as:  
\[SINR_{k,l}=\frac{\left|\mathbf{u}_{k,l}^{T}\,\mathbf{\tilde{H}}_{k}\,\mathbf{ w}_{k,l}\right|^{2}}{\left|\mathbf{u}_{k,d}^{T}\right|^{2}N_{r}\,\sigma_{k}^{2}+ \sum_{ \begin{subarray}{c}d=1\\ d\
eq l\end{subarray} }^{L}\left|\mathbf{u}_{k,l}^{T}\,\mathbf{\tilde{H}}_{k}\, \mathbf{w}_{k,d}\right|^{2}+\sum_{ \begin{subarray}{c}i=1\\ i\
eq k\end{subarray} }^{K}\sum_{d=1}^{L}\left|\mathbf{u}_{k,l}^{T}\,\mathbf{ \tilde{H}}_{k}\,\mathbf{w}_{i,d}\right|^{2}}. \tag{81}\]  
By recalling that all matrices and vectors in (81) are subcarrier dependent, i.e., \(SINR_{k,l}=SINR_{k,l}(q)\), we can express the achievable rate for user \(k\) on subcarrier \(q\) as:  
\[R_{k}(q)=\Delta\sum_{l=1}^{L}\log_{2}(1+SINR_{k,l}(q)) \tag{82}\]  
the total achievable rate for user \(k\) is the sum of rates along all active subcarriers \(Q\) normalized by the rate loss due to the CP:  
\[R_{k}=\frac{T_{sym}}{T_{sym}+T_{CP}}\sum_{q=1}^{Q}R_{k}(q) \tag{83}\]where \(T_{sym}\) and \(T_{CP}\) the OFDM symbol time and CP duration respectively. Finally, we define the average per-user achievable rate \(C=\mathrm{E}[R_{k}]\) where expectation \(\mathrm{E}[\cdot]\) is firstly computed w.r.t. random channel realizations with fixed users' positions and secondly w.r.t. random users' positions. Results are going to be presented with \(C\) as key metric.
## 2 Thin Film Deposition and Characterization by X-ray Diffraction  
The (Bi\({}_{1-x}\)Pr\({}_{x}\))(Fe\({}_{0.95}\)Mn\({}_{0.05}\))O\({}_{3}\) thin films with (x = 0.05, 0.10, 0.15, 0.20) and (x = 0.05, 0.10, 0.125, 0.15, 0.175, 0.20) were deposited onto Si (100) and LAO (100) single-crystalline substrates, respectively, using chemical solution deposition by spin coating, as was described in detail earlier [12; 13] and in Appendix A. The nominal final thicknesses of BPFMO films were expected to be about 600 nm and 500 nm, respectively. The structures of BPFMO films were characterized by X-ray diffraction (XRD). Earlier, XRD was used to demonstrate the transition in the BPFMO films from rhombohedral to tetragonal structure (from R3c to P4 mm) for Pr concentrations above 15% [13]. In Appendix B, we present the XRD data demonstrating the difference between the films deposited on Si and on LAO substrates. The films on both substrates are polycrystalline, however the films on the LAO substrate exhibit preferential orientation of the crystallites, i.e., texturing.  
Time-Domain Brillouin Scattering Experiments and Characterization of Multiferroic (Bi\({}_{1-x}\)Pr\({}_{x}\))(Fe\({}_{0.95}\)Mn\({}_{0.05}\))O\({}_{3}\) Films on Si Substrates
# Factors Affecting the Stability of Loess Landslides: A Review
## 2 Relevant Measured Parameters
### Creep
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analytical Factors** & **Measured Parameters** & **Methods** & **Application** & **References** \\ \hline thickness, volume, open stacking structure & bulk properties, subsidence, mass movements, erosion & analysis of data and artifacts & & [4] \\ \hline fracture system & resistivity & ERT & & [7] \\ \hline depth, volume, angle & rainfall, coefficient of friction, static liquefaction & field investigation, geotechnical in situ testing and indoor testing & survey (loess) & [56] \\ \hline internal structure & water infiltration, physical state & penetration test, undrained triaxial test, particle analysis, scanning electron microscope test & & [37] \\ \hline structural strength & moisture content & double line method humidification test & & [89] \\ \hline landslide thickness, fracture, stratification, porosity, failure surface, saturation & seismic wave velocity, resistivity, isotope, displacement and hydrogeological parameters & ERT, SRT, mass spectrometer, Liquid Water Isotope Analyser & survey and monitor (non-loess) & [41,42,45,48,51] \\ \hline subsoil complexity, sliding surface, fractures, water flow within formation & seismic wave velocity, resistivity & SRT, ERT, HVSR and MASW & & [43,46] \\ \hline spatial orientation of rock layers & seismic wave velocity, permittivity & SRT, GPR, geotechnical surveys & & [44] \\ \hline underground geological conditions & seismic wave velocity, Bouguer gravity anomaly & SRT, ERT, Bouguer gravity anomaly modeling & & [47] \\ \hline landslide surfaces, paleochannels & seismic wave velocity, resistivity & SRT, ERT, SSI & & [58] \\ \hline structure of landslide deposition & s-wave velocity & HVSR & (non-loess) & [11] \\ \hline underground images, depth of landslide surfaces & seismic wave velocity, resistivity & ERT, MASW, SRT & & [49] \\ \hline spatial variability, surface area of landslides & seismic wave velocity, resistivity & ERT, MASW, SRT, and Digital Terrain Models (DTMs) & & [50] \\ \hline landslide geometry & seismic wave velocity, resistivity, lithology logging data & SRT, ERT, and borehole data & & [52] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of methods for obtaining measured parameters for landslide stability analysis.
# An Improved Differential Quadrature Time Element Method
## 4 Numerical Analysis
### Frequency Comparisons
#### 4.1.2 Euler Beam
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{\(M=10\)} & \multicolumn{4}{c}{\(M=100\)} \\ \cline{3-10}  & & \multicolumn{2}{c}{**DQTEM0**} & \multicolumn{2}{c}{**Present**} & \multicolumn{2}{c}{**DQTEM0**} & \multicolumn{2}{c}{**Present**} \\ \cline{3-10}  & & \(NE=10\), \(m=2\) & \multicolumn{2}{c}{\(NE=1\), \(m=11\)} & \(NE=100\), \(m=2\) & \multicolumn{2}{c}{\(NE=1\), \(m=101\)} \\ \cline{3-10}  & & \(\Lambda\) & **RelErr1** & \(\Lambda\) & **RelErr1** & \(\Lambda\) & **RelErr** & \(\Lambda\) & **RelErr1** \\ \hline
1 & 1.5708 & 1.5724 & 0.1028 & 1.5708 & 6.3611 \(\times\) 10\({}^{-8}\) & 1.5708 & 0.0010 & 1.5708 & 7.2083 \(\times\) 10\({}^{-11}\) \\
2 & 4.7124 & 4.7561 & 0.9277 & 4.7124 & 1.2722 \(\times\) 10\({}^{-7}\) & 4.7128 & 0.0093 & 4.7124 & 6.4515 \(\times\) 10\({}^{-12}\) \\
3 & 7.8540 & 8.0571 & 2.8589 & 7.8540 & 2.5289 \(\times\) 10\({}^{-6}\) & 7.8560 & 0.0257 & 7.8540 & 7.7932 \(\times\) 10\({}^{-12}\) \\
4 & 10.9956 & 11.5542 & 5.0803 & 10.9956 & 1.3213 \(\times\) 10\({}^{-4}\) & 11.0011 & 0.05103 & 10.9956 & 9.0158 \(\times\) 10\({}^{-13}\) \\
5 & 14.1372 & 15.3203 & 3.8689 & 14.1355 & 0.0117 & 14.1489 & 0.0833 & 14.1372 & 1.4325 \(\times\) 10\({}^{-12}\) \\same degrees of deflections (see Table 8), in which the dimension of DQTEM0 model is nearly twice of that of the improved DQTEM. Although the results of DQTEM0 in Table 8 are more accurate than those in Table 7, it is also obvious that the improved DQTEM is much more accurate than DQTEM0. In other words, the improved DQTEM is more accurate than DQTEM0 when the same spatial DOFs are used. That is, the dimensions of \(G\) can be reduced by using the improved DQTEM without sacrifice of accuracy.
# Structured Data REST Protocol for End to End Data Mashup
###### Abstract  
Due to the exponential growth of the data and its services, visiting multiple webs/apps by a user raises three issues--(1) consumption of extra bytes; (2) time killing process of surfing inside the webs/apps; (3) tedious task of remembering address of webs/apps with their credentials. The data mashup is a set of techniques and user-friendly approaches which not only resolves above issues but also allows ordinary user to fetch required data from multiple disparate data sources and to create the integrated view in his defined digital place. In this paper, we have proposed an extension of existing REST protocol called Structured Data REST (SDRest) protocol and user-friendly novel approach which allows even ordinary users to develop end to end data mashup, using the innovative concept of Structured Data Mashup Box (SDMB) and One Time Configuration (OTC)-Any Time Access (ATA) models. Our implementation shows that pre-mashup configuration can easily be performed by an ordinary user and an integrated user interface view of end user data mashup can be created without any technical knowledge or programming. We have also evaluated the proposed work by comparing it with some of the related works and found that the proposed work has developed user friendly configurable approach using the current state of the art techniques to involve not only the ordinary user but also the mashup service provider and the data service provider to develop public, private and hybrid data mashup.  
structured data; REST protocol; structured data mashup box; one-time configuration model; any time access model; end to end data mashup; ordinary user; mashup service provider; data service provider +
Footnote †: journal:  
_Future Internet_**2018**, _10_, 98; doi:10.3390/610100098
## 3 Design Optimization
### Mechanical Stress Analysis  
It is important to check the mechanical stress of the rotor when the geometry of the rotor core and the positions of the permanent magnets change. When the permanent magnets inserted inside the rotor rotate at high speed, a strong centrifugal force is generated; thus, the strength of the rotor core should be verified. Therefore, mechanical stress analysis of the optimal model was performed using ANSYS Mechanical. The material properties of the electrical steel and permanent magnets used for the analysis are shown in Table 4. The maximum stress of the optimal model was 88.493 MPa at 8000 rpm, which was lower than the tensile yield strength of the electrical steel, and it occurred in the protrusion of the rotor core used to fix the permanent magnets, as shown in Figure 12. The calculated safety factor was 4.69, which proves the feasibility of the proposed shape optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Unit** & **Electrical Steel (35 PN210)** & **Permanent Magnet (N38 UH)** \\ \hline Density & kg/m\({}^{3}\) & 7600 & 7500 \\ Poisson’s ratio & - & 0.3 & 0.24 \\ Young’s modulus & GPa & 170 & 160 \\ Tensile yield strength & MPa & 415 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties for mechanical stress analysis.  
Figure 11: The back EMF of the initial model and optimal model: (**a**) waveform; (**b**) harmonic.  
Figure 10: Flux distribution under the no load condition: (**a**) initial model; (**b**) optimal model.
# Aiding Dictionary Learning Through Multi-Parametric Sparse Representation
## 2 The Dictionary Learning Problem  
Given the signal \(\mathbf{y}\in\mathbb{R}^{m}\) and the fixed dictionary \(\mathbf{D}\in\mathbb{R}^{m\times n}\), the sparse representation problem imposes an error bound \(\varepsilon\) and seeks the sparsest representation  
\[\min_{\mathbf{x}}\quad\|\mathbf{x}\|_{0}\quad\text{s.t.}\quad\|\mathbf{y}-\mathbf{D}\mathbf{x}\| \leq\varepsilon, \tag{1}\]  
where \(\left\|.\right\|_{0}\) is the \(\ell_{0}\) pseudo-norm counting the non-zero entries of \(\mathbf{x}\). The process is depicted in Figure 0(a), where the full signal \(\mathbf{y}\) uses \(s=3\) dictionary atoms for its sparse representation \(\mathbf{x}\).  
Due to the non-convex nature of the \(\ell_{0}\)-norm, we prefer its convex \(\ell_{1}\) relaxation  
\[\min_{\mathbf{x}}\quad\|\mathbf{y}-\mathbf{D}\mathbf{x}\|^{2}\quad\text{s.t.}\quad\|\mathbf{x}\|_ {1}\leq\theta, \tag{2}\]  
which is known as the _lasso_ problem [16]. Here, \(\theta\) is a sparsity promoting bound for the 1-norm of the solution. The focus in (2) is on the approximation, such that the objective is minimized up to a point where \(\mathbf{x}\) has too many large non-zero elements. If instead we desire the sparsest \(\ell_{1}\) solution satisfying a given approximation quality \(\varepsilon\), the optimization problem becomes  
\[\min_{\mathbf{x}}\quad\|\mathbf{x}\|_{1}\quad\text{s.t.}\quad\|\mathbf{y}-\mathbf{D}\mathbf{x}\|^ {2}\leq\varepsilon. \tag{3}\]  
Given a set of \(\mathbf{Y}\in\mathbb{R}^{m\times N}\) training signals, the dictionary learning (DL) problem [15] is  
\[\min_{\mathbf{D},\mathbf{X}}\quad\|\mathbf{Y}-\mathbf{D}\mathbf{X}\|_{F}^{2}\quad\text{s.t.}\quad \|\mathbf{x}_{\ell}\|_{1}\leq\theta,\ \ell=1:N,\quad\left\|\mathbf{d}_{j}\right\|=1,\ j=1:n, \tag{4}\]  
where \(\mathbf{D}\in\mathbb{R}^{m\times n}\) is the dictionary, whose columns are also called atoms, and \(\mathbf{X}\in\mathbb{R}^{n\times N}\) are the resulting sparse representations. Note that (4), in contrast to (1), does not only find the sparse representation \(\mathbf{X}\) but also learns the dictionary \(\mathbf{D}\).  
As depicted in Algorithm 1, problem (4) is usually solved by alternative optimization: fixing \(\mathbf{D}\) we enter the sparse representation stage (step 2) where we compute the representations \(\mathbf{X}\) by solving either of (1)-(3) for each signal \(\mathbf{y}\in\mathbf{Y}\), and then we enter the dictionary update stage (step 3) where we fix \(\mathbf{X}\) and update \(\mathbf{D}\) by taking each atom \(\mathbf{d}\in\mathbf{D}\) and refining it based on the signals that use it in their sparse representation. The process is repeated \(K\) times (step 1) until the objective in (4) converges or the error becomes satisfactory.  
Figure 1: Sparse/csparse representation of a signal ([15], Chapter 1 and 10). The used atoms are red and the nonzero coefficients are blue. The unused atoms are pink.
# Change and Detection of Emotions Expressed on People's Faces in Photos
## 4 Results
### Emotions Change  
Due to the lack of clearly determining performance metrics of the correctness of emotion change for deepfake technology, the emotion classifiers discussed later in this section were trained. Examples of emotion changes in photos by EmoDNN are presented in Figure 3. More results on emotion changes by EmoDNN are provided in Appendix A.  
By analyzing the results obtained for individual cases in detail, small graphic artifacts are visible to the human eye. An example that may raise suspicion of the use of deepfake technology in Figure 3 is the change in emotion from Disgust to Surprise, for which the lower lips may or may not be a cause for suspicion of the use of deepfake technology. Moreover, the appearance of a smile from the change of emotion from Angry to Happiness may or may not be a cause for suspicion of the use of deepfake technology.  
Figure 3: EmoDNN emotion change preview.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Annotated Training Set**} & \multicolumn{3}{c}{**Validation Set**} \\ \hline
**Automatically** & **Manually** & **Total** & **Emotion** & \\ \hline
143,142 & 74,874 & 218,016 & Neutral & 500 \\
246,235 & 134,415 & 380,650 & Happy & 500 \\
20,854 & 25,459 & 46,313 & Sad & 500 \\
17,462 & 14,090 & 31,552 & Surprise & 500 \\
3799 & 6378 & 10,177 & Fear & 500 \\
890 & 3803 & 4693 & Disgust & 500 \\
28,000 & 24,882 & 52,882 & Anger & 500 \\
2 & 3750 & 3752 & Contempt & 500 \\ \hline
460,384 & 287,651 & 748,035 & & 4000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of images for each emotion type from the AffectNet database.  
The backbone of the generator, consisting of an encoder and a decoder for image generation is U-Net [41]. Applying a U-Net-like network enhanced facial expression recognition by segmenting key regions on faces such as the eyes, mouth, and brows. These regions are critical for understanding subtle emotional cues. By focusing on these regions, the model can identify micro-expressions and subtle emotion indicators with improved accuracy.  
The presented approach omits the cycle-consistency loss employed in models such as StarGAN [49], instead using a direct reconstruction loss limited to cases where the target and ground-truth emotions match. This allows for a higher level of realism in emotion modifications, reducing artifacts commonly associated with unintended source features. Although some facial features may diminish in this process, the resulting images retain a more authentic appearance of the intended emotion.  
Furthermore, compared to FEGAN, which uses region-specific editing to alter emotions precisely while preserving identity, the presented approach achieves a balance by modifying the entire facial structure. FEGAN's region-specific editing can yield nuanced expressions, but it sometimes struggles with texture consistency during subtle adjustments. In contrast, the presented method performs broader modifications, achieving smoother transitions without introducing artifacts, especially during more intense emotional shifts.