esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Verification
### Measurement Error Analysis
#### 4.2.2 Single Point Dynamic Displacement Measurement Error Analysis  
To reveal the dynamic displacement measurement error of a single point on the beam, the displacement-time history curves of the mid-span point by STMM and laser ranger are shown in Figure 19, and the results show that the displacement-time history curves obtained by the two methods are in good consistency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Template Width**} & \multirow{2}{*}{**Data Types**} & \multicolumn{2}{c}{**RMSE/mm**} \\ \cline{3-4}  & & **STMM** & **Studies** \\ \hline
1 & & 0.204 & \\
3 & Original data & 0.191 & 0.1–0.2 [15] \\
5 & & 0.182 & 0.7–2.6 [21] \\
11 & & 0.169 & 0.1–0.3 [22] \\
11 & Fitted data & 0.052 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE of STMM under mid-span load.  
Figure 18: Measurement error of STMM deflection under mid-span load.  
From the two displacement-time history curves in Figure 19, it can be deduced that the dynamic displacement measurement error is not constant but varied over time. To clarify the measurement error over time, the measurement errors of two curves over time are shown in Figure 20, and results indicate that the maximum error is approximately 0.5 mm or 0.3 pixels.  
The errors of two displacement-time history curves are characterized by correlation coefficient \(\rho\) and determination coefficient \(\mathrm{R}^{2}\)[16], formulated as follows:  
\[\rho=\frac{\left|\sum\limits_{l}\left(r_{L}(i)-\mu_{r_{L}}\right)\times\sum \limits_{l}\left(r_{v}(i)-\mu_{r_{v}}\right)\right|}{\sqrt{\sum\limits_{l} \left(r_{L}(i)-\mu_{r_{L}}\right)^{2}}\sqrt{\sum\limits_{l}\left(r_{v}(i)-\mu_ {r_{v}}\right)^{2}}} \tag{6}\]  
\[R^{2}=1-\frac{\sum\limits_{i}\left(r_{v}(i)-r_{L}(i)\right)^{2}}{\sum\limits_ {i}\left(r_{v}(i)-\mu_{r_{v}}\right)^{2}} \tag{7}\]  
where \(r_{L}(i)\) and \(r_{v}(i)\) are displacement measured by the laser ranger and the STMM of measurement point at time \(i\), respectively, and \(\mu_{r_{L}}\), \(\mu_{r_{v}}\) are the mean values of each data sets. The correlation coefficient \(\rho\) and determination coefficient \(R^{2}\) of the two displacement-time history curves are listed in Table 3.  
Figure 19: Mid-span dynamic deflection–time history curves.  
Figure 20: Measurement error of mid-span dynamic displacement–time history curves.  
From Table 3, the correlation coefficient and the determination coefficient of the two mid-span displacement-time history curves are 0.9994 and 0.9986, respectively; both are superior to the values reported in the literature [14].
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 2 Materials and Methods
### Retrieval of Dust  
The method for retrieving the dust mass loading utilizes the residual power loss due to the atmosphere similar to the retrieval of sulfuric acid vapor in Venus' atmosphere. However, in the case of Mars, suspended dust particles are the major contributors to signal loss. The residual power loss due to atmospheric attenuation is calculated by subtracting free space loss, refractive defocusing loss, and antenna mispointing errors from the total signal loss [40]. In the next step, the residual power loss is converted into absorptivity using the inverse Abel transform.  
\[T_{tot}(r_{j}) = L(r_{j})+\tau(r_{j})+m(r_{j}) \tag{10}\] \[\alpha(r_{j}) = -\frac{n(r_{j})}{\pi\alpha(r_{j})}\frac{d}{da}[\int_{a_{j}}^{ \infty}\frac{\tau(a)a}{\sqrt{a^{2}-a(r_{j})^{2}}}\,da] \tag{11}\]  
\[L_{dB}=10 log_{10}(\frac{I}{I_{0}}) \tag{12}\]  
\[\tau=ln(\frac{I}{I_{0}}) \tag{13}\]  
\[\alpha_{dust}(r_{j}) = \alpha(r_{j})-\alpha_{background}(r_{j}) \tag{14}\] \[\alpha_{dust}(r_{j}) = 4.343Q_{ext}pN\pi r_{eff}^{2}\] (15) \[q_{dust} = \frac{4}{3}\frac{\rho_{dust}\pi r_{eff}^{3}N}{\rho} \tag{16}\]  
where \(T_{tot}\) is the total signal loss minus the free space loss, \(L\) is refractive loss, \(m\) is the mispointing error, and \(\tau\) is the atmospheric transmission loss. The absorptivity due to dust (\(\alpha_{dust}\)) is obtained by removing the contributions from the background gas mixture \(\alpha_{background}\). \(q_{dust}\) is the mass mixing ratio of dust, which can be obtained from the dust number density N, the average density of the dust particle \(\rho_{dust}\), and the density of the atmosphere at that location \(\rho\). Decibel losses (\(L_{dB}\)) can be derived from the signal intensity at the transmitter (\(I_{0}\)) and receiver (I) ends. The conversion from decibel losses to absorption and scattering losses involves a conversion factor of 4.343, as expressed in Equation (16). The background contributions may be subtracted by assuming an appropriate mixing ratio of absorbers. More details about the concentration of absorbers and their contribution to gas absorption are explained in the next section.
## 3 Joint Power Control and Resource Allocation
### Optimal JoTAPS Scheme
Thus, we can conclude that \(g_{2}(t,\rho)\) is concave with respect to \(\rho\), and so is \(\mathcal{C}4\) with respect to \(\rho\). Now, we analyze the case when \(\rho\) is fixed. In this case, \(t\cdot g_{1}(t,\rho)\) is a linear function. The second derivative of \((1-t)\cdot g_{1}(t,\rho)\) is  
\[\text{C2:}\where  
\[C(\hat{\Upsilon}_{rl})=\log_{2}\left(1+\frac{|h_{i}|^{2}\eta\rho(\gamma_{\Sigma}+ \sigma_{u,r}^{2})\frac{t}{1-t}}{\sigma_{u,i}^{2}+\sigma_{i}^{2}}\right) \tag{49}\]2  
where \(b=\frac{|h_{i}|^{2}\eta\rho(\gamma_{2}+\sigma_{u}^{2})}{\sigma_{u}^{2}+\sigma_ {u}^{2}}\). Thus, OP4 is a concave function with respect to \(\rho\) (when fixing \(t\)) or with respect to \(t\) (when fixing \(\rho\)). Theorem 2 is proved. Based on Theorem 2, we propose an alternating iterative optimization algorithm to determine optimal value of \(t\) and \(\rho\). The first step of the algorithm is to solve for the optimal \(\rho\) with a given \(t\). Let \(k\) be the iteration number. We can calculate the optimal value \(\rho\) of the \(k+1\) iteration by solving OP5-a, which is written as  
\[ \begin{split}\text{OP5-a:}\quad\max&\ R_{\min,a} \\ s.t.&\ \text{C2}:\ \rho^{(k+1)}\leq\frac{Q}{|l_{r}|^{2}} \cdot\frac{1-t^{(k)}}{\eta(\gamma_{2}+\sigma_{u,r}^{2})t^{(k)}}\\ &\ \text{C4}:\ \ R_{\min,a}\leq t^{(k)}\cdot g_{1}(t^{(k)},\rho^{(k+1 )})\\ &\ \text{C4}:\ \ R_{\min,a}\leq(1-t^{(k)})\cdot g_{2}(t^{(k)}, \rho^{(k+1)})\\ &\ \text{C6}:0<\rho^{(k+1)}<1.\end{split}  \tag{60}\]  
Then, by substituting the optimal value \(\rho^{(k+1)}\) of the \(k\)th iterations into OP5, the optimal value \(t^{(k+1)}\) of the \(k\)th iterations can be calculated by solving OP5-b, which is written as  
\[ \begin{split}\text{OP5-b}:\quad\max&\ R_{\min,b} \\ \text{s.t.}&\ \text{C2}:t^{(k+1)}\leq\frac{Q}{Q+|l_{r}|^{2} \eta(\gamma_{2}+\sigma_{u,r}^{2})\rho^{(k+1)}}\\ &\ \text{C3}:R_{\min,b}\leq t^{(k+1)}\cdot g_{1}(t^{(k+1)},\rho^{(k+1 )})\\ &\ \text{C4}:R_{\min,b}\leq(1-t^{(k+1)})\cdot g_{2}(t^{(k+1)},\rho^{(k+ 1)})\\ &\ \text{C5}:0<t^{(k+1)}<1.\end{split}  \tag{61}\]  
Since OP5-a and OP5-b are convex optimization problems, the value of \(\rho^{(k+1)}\) and \(t^{(k+1)}\) in each iteration can be solved by using CVX toolbox. With the solved \(\rho^{(k+1)}\) and \(t^{(k+1)}\), an alternative algorithm named jointly-optimum time allocation and power splitting (JoTAPS) is designed as shown in Algorithm 2, where \(\varepsilon\) is the given allowable deviation. ```
1:Initial \(t=\frac{1}{2}\), \(R_{\min}^{pre}=0\), \(k=1\)
2:While \(|R_{\min}^{cur}-R_{\min}^{pre}|>\varepsilon\)
3:Solve OP5-a, and obtain \(\rho^{(k+1)}\), \(R_{\min,a}\)
4:Solve OP5-b, and obtain \(t^{(k+1)}\), \(R_{\min,b}\)
5:Update \(R_{\min}^{cur}=R_{\min,b}\), \(k=k+1\)
6:End
```  
**Algorithm 2** The jointly-optimum time and power splitting scheme with fixed power allocation algorithm
# LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
## 4 Experimental Results and Discussion
### Mobile Device Deployment and Testing  
To build our app we designed an Android-based mobile application that is user friendly and deployment ready. The application comprises a mobile interface and a backend cloud database, along with an EfficientNetB2V2 classification model quantized using QAT. The database was developed using Cloud Firestore, which serves as an easy yet secure way to store and access data throughout the application. The application is named \"LungVision\", as it encapsulates the purpose of the app in a simple and clear manner. As mentioned previously, a clear description of the app's functionalities is provided in Appendix A.  
In terms of inference time on the mobile app, which generally varies across different quantization methods and models as shown in Figure 6, we randomly selected and classified five images per class from the testing split. The model achieved an F1-Score of 91.31%, with an accuracy of 88.89%, as well as 99.18% specificity and a 100% sensitivity score. While the test data on the phone is small, it is nevertheless reasonably good enough to show that the application is capable of distinguishing different respiratory system diseases. Figure 7 shows the confusion matrix obtained using the mobile phone.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Quantization Technique**} \\ \cline{3-6}  & & **FP32** & **FP16** & **QAT** & **INT-REP** \\ \hline \multirow{4}{*}{NasNetMobile} & Accuracy & 68.67 & 93.75 & 24.62 & 31.34 \\  & Specificity & 75.59 & 95.42 & 27.90 & 40.40 \\  & Sensitivity & 68.67 & 93.75 & 24.63 & 31.34 \\  & F1-Score & 69.16 & 93.72 & 23.56 & 33.54 \\ \hline \multirow{4}{*}{EfficientNetV2B2} & Accuracy & 98.00 & **93.75** & **97.52** & **94.03** \\  & Specificity & 98.05 & 95.42 & **97.53** & **95.01** \\  & Sensitivity & 98.00 & **93.75** & **97.52** & **94.03** \\  & F1-Score & 97.99 & 93.72 & **97.51** & **94.03** \\ \hline \multirow{4}{*}{EfficientNetB2} & Accuracy & **98.59** & **93.75** & 96.14 & 56.72 \\  & Specificity & **98.60** & **95.83** & 96.45 & 76.71 \\ \cline{1-1}  & Sensitivity & **98.59** & **93.75** & 96.14 & 56.72 \\ \cline{1-1}  & F1-Score & **98.58** & **93.85** & 96.15 & 56.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Technique/Model** & **ResNet50** & **MobileNetV2** & **DenseNet121** & **NasNet** & **EfficientNetB2** & **EfficientNetV2B2** & **Avg.** \\ \hline
**Original (FP32)** & 1.230 & 0.240 & 1.160 & 0.520 & 0.700 & 0.500 & N/A \\
**FP16** & 1.037 & **0.102** & 0.607 & 0.379 & 0.687 & 0.505 & 0.1723 \\
**QAT** & 1.030 & 0.140 & 0.850 & **0.340** & **0.410** & **0.470** & 0.2017 \\
**INT-REP** & **0.652** & 0.193 & **0.512** & 0.504 & 0.604 & 0.519 & **1.3830** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of quantization techniques on model inference time (in seconds).
## 3 Dataset Analysis and Description
### Statistical Figures on Seriousness and Non-Seriousness  
For a more granular understanding, we segregated the dataset based on the severity of the adverse events into serious and non-serious categories. Subsequently, we analyzed the frequency of these categories in association with the top substances, providing insights into which substances are commonly linked with severe adverse reactions and which ones predominantly report milder, non-serious events.
## 3 Results and Discussion
### Viscoelastic Properties
#### 3.2.1 Particle Tracking Microrheology
The same six differences were found for group a versus group b, group a vs. group c and group b vs. group c mucus samples. The analysis validated the classification. In sum, the absolute MSDs and the diffusive exponents \(\alpha\) increased from group a over group b to group c. The subdiffusive movement (\(\alpha<\) 1, see Table 2) of the microspheres, revealed the viscoelastic character of the human laryngeal mucus samples. Vice versa, the storage (G') and loss moduli (G\") decreased from group a over group b to group c. For all mucus samples of all groups, tan\(\delta\) is smaller than 1 at the lower evaluation limit. One can conclude that the rigidity and the mucin-network properties of the investigated samples differ. We assume that the differences were caused by variations of the hydration level of the mucus samples. The trends of G' and G\" including the crossover, can be interpreted in the context of entangled polymer solutions. Entangled polymer solutions show a glassy (G' > G\"), a rubbery (G' > G\") and a flow regime (G' < G\") with increasing temperature or decreasing frequency. Additionally, a transition regime is found between the rubbery and glassy region, which is characterized by (G' < G\") [29]. The solid-like character in our findings can be related to the rubbery regime and the liquid-like character to the transition regime. The convergence (group a) and the crossover (group b, group c) of G' and G\" may lead to the assumption that mucus has probable viscous characteristics at high shear, serving as a lubricant for the vocal folds during oscillation but exhibiting viscoelastic solid characteristics at rest. Although this would presuppose a fast regeneration of the gel after destruction. A rapid reformation of the gel of pig gastric mucus after the destruction was already found [30]. However, further investigations would be necessary to substantiate these assumptions due to the high forces acting on mucus during oscillation of the vocal folds. The relationship of the demographic data of the patients belonging to the mucus samples and groups is captured in Figure 4. Group a, group b and group c mucus samples were found for both gender and both larynx status. For patients older than 70 years, no group c mucus samples were found, but group a and group b mucus samples. For the other age ranges, all three groups were present. Group c mucus samples were only found for current smokers, not for non-smokers or former smokers. Group a and group b mucus samples were related to all smoking behaviors. This leads to the assumption that there are no tendencies with respect to gender and laryngeal status. A firm correlation with respect to age cannot be firmly established due to the limited samples. It is remarkable that group c mucus was only found for current smokers. Nevertheless, this is not in accordance with a study done before, where nicotine was found to lead to higher mucus viscosity [24]. However, a relationship between smoking and the rigidity of laryngeal mucus from the vocal folds seems reasonable.
# Competitive Analysis of Algorithms for an Online Distribution Problem
## 6 Computational Results
### Randomly Generated Instances
& 1.7343 & 1.3771 & 1.1441 & 5.2822 & 5.2924 & 1.7440 & 1.3806 & 1.1584 & 5.3333 & 5.3333 \\ \hline  & & \multicolumn{4}{c}{Normal(150,5)} & \multicolumn{4}{c}{Normal(150,15)} \\ \(h\) & \(\text{ALG}_{1}^{0}\) & \(\text{ALG}_{3}^{P_{5}^{4}}\) & \(\text{ALG}_{4}^{P_{5}^{4}}\) & \(\text{ALG}_{3}^{1}\) & \(\text{ALG}_{5}^{5}\) & \(\text{ALG}_{1}^{0}\) & \(\text{ALG}_{3}^{P_{5}^{4}}\) & \(\text{ALG}_{4}^{P_{5}^{4}}\) & \(\text{ALG}_{5}^{1}\) & \(\text{ALG}_{5}^{5}\) \\ \hline
0.01 & 1.1959 & 1.1550 & 1.0011 & 1.1617 & 1.1701 & 1.7190 & 1.3894 & 1.0014 & 1.6195 & 1.6195 \\
0.1 & 1.1850 & 1.1557 & 1.0115 & 1.1715 & 1.1793 & 1.7097 & 1.3837 & 1.0138 & 1.6318 & 1.6318 \\
1 & 1.1667 & 1.1633 & 1.1145 & 1.2701 & 1.2701 & 1.6226 & 1.3942 & 1.1271 & 1.7545 & 1.7545 \\
10 & 1.0922 & 1.0922 & 1.0922 & 2.2901 & 2.2901 & 1.4655 & 1.4655 & 1.4655 & 2.9818 & 2.9818 \\
100 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 12.5954 & 12.5954 & 1.0000 & 1.0000 & 1.0000 & 15.2545 & 15.2545 \\ Av. & 1.1279 & 1.1132 & 1.0439 & 3.6978 & 3.7010 & 1.5034 & 1.3266 & 1.1216 & 4.6485 & 4.6485 \\ \hline  & & \multicolumn{4}{c}{Triangular} & \multicolumn{4}{c}{Beta} \\ \(h\) & \(\text{ALG}_{1}^{0}\) & \(\text{ALG}_{3}^{P_{5}^{4}}\) & \(\text{ALG}_{4}^{P_{5}^{4}}\) & \(\text{ALG}_{4}^{P_{5}^{4}}\) & \(\text{ALG}_{5}^{5}\) & \(\text{ALG}_{1}^{0}\) & \(\text{ALG}_{3}^{P_{5}^{4}}\) & \(\text{ALG}_{4}^{P_{5}^{4}}\) & \(\text{ALG}_{5}^{5}\) \\ \hline
0.01 & 1.8909 & 1.3956 & 1.0015 & 1.7443 & 1.8853 & 1.9685 & 1.3647 & 1.0015 & 1.8279 & 1.8314 \\
0.1 & 1.8793 & 1.3921 & 1.0146 & 1.7571 & 1.8918 & 1.9529 & 1.3565 & 1.0147 & 1.8093 & 1.8426 \\
1 & 1.8148 & 1.3810 & 1.1316 & 1.8857 & 1.9537 & 1.8091 & 1.4135 & 1.1316 & 1.9151 & 1.9533 \\
10 & 1.5366 & 1.5289 & 1.4643 & 3.1714 & 3.1714 & 1.  
the online distribution problem. Finally, we note that the Secretary problem algorithms become more competitive when the penalty cost is low.
## 5 Case Study
### Impacts of RES and DR Percentage and Rebate Rate Factor in the DR Scheme  
Additionally, we test the impact of RES percentage, DR percentage and total rebate value on load shedding. We assume RES can be consumed totally, and if there is too large a fluctuation of the pure load, the load shedding of customers has to occur to ensure frequency stability. First, we change the RES percentage at the 7 th time slot from 40% to 60%. The DR percentage means the ratio of DR participants to all of the 3000 customers, which is changed from 10% to 60%, while the rebate rate factor \(\sigma=0.5\). The simulation result (Figure 11) shows that a larger RES percentage leads to more load shedding because of the rapid random changing and limited generation's ramp rate. On the other  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of DR Customers** & **Iteration** & **Number of DR Customers** & **Iteration** \\ \hline
150 & 16 & 750 & 8 \\
300 & 15 & 900 & 7 \\
450 & 12 & 1050 & 7 \\
600 & 10 & 1200 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Iteration in the game process vs. number of DR customers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Iteration** & **Estimation Value** & **Actual Value** & **Iteration** & **Estimation Value** & **Actual Value** \\ \hline
1 & 2.76 & 2.94 & 6 & 411.98 & 412.45 \\
2 & 7.90 & 7.78 & 7 & 569.36 & 569.67 \\
3 & 21.53 & 21.66 & 8 & 591.64 & 591.76 \\
4 & 59.68 & 59.89 & 9 & 593.13 & 593.27 \\
5 & 164.87 & 165.26 & 10 & 593.23 & 593.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimation value of Gossip algorithm vs. actual value.  
Figure 10: The dynamic process for customer 1 to estimate \(\hat{K}_{i=1}(t=7)\) based on the proposed Gossip algorithm.  
hand, a higher DR percentage means a larger voluntary and flexible adjusting capacity, which helps decrease load shedding and consumes RES. When the DR percentage is large enough, there will be no load shedding even if there is more RES. Then, the RES percentage is also changed from 40% to 60%, while the rebate rate factor \(\sigma\) changes from 0.2 to 1.8 but DR percentage is set as constant 20%. The simulation result is shown in Figure 12. When the RES percentage is not very high, the higher total rebate can play the same role as the higher DR percentage. However, in regard to a high RES percentage, increasing the total rebate cannot ensure zero (0) load shedding. This phenomenon is shown in the red circle in Figure 12 and means that in the high proportion RES integration power system, increasing DR percentage is very important but increasing rebates may not work well due to the regulation capacity limit of customers.
## 5 Results and Discussion
### The Effectiveness of the Proposed Methodology  
Finally, the \(iwp_{1}^{2}\) band normalized spectrum in 2-D format was used as the input of the 2-D CNN. The data set with a total sample size of 4800 was randomly divided into the training set, validation set and test set. The convolutional neural network was trained with the first two subsets, with 150 epochs. After the training was completed, the model's recognition ability was tested with the test set that was not involved in the training process. The test accuracy was 98.65%, and the confusion matrix of the recognition results is shown in Figure 20. The confusion matrix was an effective visualization tool to estimate the performance of classification algorithm. Each element in the matrix represents a type of sample. The abscissa of the element is the real label of the sample. The ordinate of the element is the label of the CNN output. The value of the element is the number of such elements.  
Figure 19: Performance comparison of data sets constructed with different frequency bands.  
Figure 20: Confusion matrix.  
As can be seen from the figure, the recognition accuracy of all these six types was fairly high. The recognition accuracy of the fourth type (tool wear state label is state #4) was relatively low, but it also reached 95.2%, which met the requirements of the manufacturing site, which proved the effectiveness of the method given in this paper.
# Virtual Try-On Systems in Fashion Consumption: A Systematic Review
## 3 Results and Discussion
### Antecedents and Consequences
#### 3.4.2 Comparative Studies of Media  
Of the reviewed papers, 22 research articles focused on media and its related variables. The design of VTO systems significantly impacted the adoption intention [91]. Additionally, VTO on a website can increase offline purchase intentions [49]. Personalized VTO boosted online sales in experiments [14]. However, vividness alone did not impact technology adoption [91].  
Nineteen works compared different types of media, finding that each has unique advantages, though none was clearly superior. While VR and AR provided richer experiences than standard interfaces, the latter remained more convenient and enjoyable than AR-based VTO [23; 34]. Additionally, incorporating 3D models into traditional displays enhanced the sense of presence, usefulness, and ease of use, while reducing perceived risk [44; 45; 78; 80].  
While many studies argued that VR and AR enhanced the shopping experience over traditional websites, their effectiveness varied significantly depending on the product category [10; 23; 34]. For example, VTO for shoes struggles to replicate real-world interactions, raising questions about whether they can fully replace physical trials [65]. Consequently, virtual options currently serve as supplements rather than substitutes.  
Personalized simulations using AR proved more effective than static images alone [10; 23]. Allowing customers to see personalized avatars significantly increased purchase intentions compared to general models [62]. However, AR's advantages were not universal, and were less impactful for those satisfied with their appearance when stacked up against conventional 2D displays [76].  
In contrast to the overlay capabilities of AR, VR immersed users in fully customizable virtual worlds with extensive interactive avatars. VR trials demonstrated tangible effects on subsequent in-store purchases [48; 49]. Research indicated that highly engaging VR outperformed less immersive implementations, promoting usage, entertainment, utilitarian value experiences, and time spent [68; 72]. While AR applications only require a camera-equipped phone, VR demands complex equipment like headsets and controls for deep immersion, complicating widespread adoption. As a result, retailers implementing VR must carefully weigh technological investments against the benefits of maximum immersion.