esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed DETD Algorithm and Simulation Results
### Additional Theft Case Detection  
Power utility companies could face certain theft cases that are very much specific to a particular smart grid, which may be different (varied or expanded) from proposed mathematical cases. On such an occasion, the utilities may need to retrain (adapt) the presented theft detector with those specific theft cases.  
As a simple example, we consider additional ToU theft cases, where the malicious consumer decides to report 0 kWh usage during the high price period.  
The following defines such specific window-based theft cases, while the high-price period consumption is set to 0, i.e., \(t\left(x_{high\_price}\right)=0\):  
1. \(t1\left(x_{np}\right)=x_{np}*random(0.0,0.9)\);
2. \(t2\left(x_{np}\right)=x_{np}*random[0,1]\);
3. \(t3\left(x_{np}\right)=x_{np}*r_{np}\left(r_{np}=random(0.0,0.9)\);  
where \(x_{np}\) indicates the normal price period. The remaining low-price consumption is assumed to stay unaffected by malicious behavior.  
Via simulation, we generated a separate testing dataset using the above equations for such cases only and found that 98% of those cases were detected without extra need for retraining the entire classifier.  
The discarded theft cases 4, 5, and 6 from Section 2.3 were also tested while using DETD without additional retraining and detected with an accuracy of up to 97% in the average sense. This shows that DETD is at par with current ML-based electricity theft detectors performance wise, even for conventional theft cases.
# One-Dimensional Modeling of the Pressure Loss in Concrete Pumping and Experimental Verification
## 6 Conclusions  
1. In this paper, based on the characteristics of concrete pumping flow, considering the viscous force of mortar and the blocking effect of coarse aggregate particles in the lubricating layer, a one-dimensional model of concrete pumping pressure loss was established.
2. In this paper, the HandySCAN noncontact scanner and reverse modeling software Geomagic Design X (v.19.0.2) were used to reconstruct the complex geometrical shapes of the aggregate particles, and the blocking effect of coarse aggregate particles on the flow state of concrete was measured by equivalent particle size.
3. Based on a comparison of the values calculated with empirical equations and the measured values in the field, the relative error between the calculated pressure loss value and the measured value is about 20.2%. The results indicate that the sources of pressure loss considered in this study are basically the same as those in real situations.  
**Author Contributions:** X.Z.: Methodology, formal analysis, writing--original draft. G.G.: Conceptualization, funding acquisition, writing--review and editing. M.W.: Resources, project administration. J.D.: Writing--review and editing, project administration, supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by National Natural Science Foundation of China (grant no. 51805163).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest to report regarding the present study.
## 6 Results
### Comparison of Speech Models  
In the undertaken studies, an attempt was made to cross-test the most accurate models: Parallel 2D CNN-Bidirectional LSTM with Attention and Parallel 2D CNN-Transformer Encoder. Models trained on the RAVDESS set were tested on the nEMO test set and vice versa. However, in both cases, the results did not exceed 30% efficiency, which indicates diversity in the datasets, especially in tones and manners of speech. The manner and characteristics of speech are culturally and, to some extent, nationally conditioned. Factors that can affect accuracy during the cross-test are temporal characteristics of speech such as the length of the speech, tempo, intonation, rhythm, or pauses (punctuation). There are several ways to create a more accurate model for recognizing emotions in speech for different languages. One of them is to maintain the aforementioned features at a similar level between languages or people of different nationalities. However, this creates another problem, which is the unnaturalness of speech. A model trained on such data may have a problem with spontaneous speech in different languages. The second, more universal way is to create a new data set (or combine several existing ones). This set should represent data collected in several of the most popular languages from people with different ways of  
Figure 4: Confusion matrices for emotion analysis models in speech based on the Polish-language nEMO dataset.  
speaking. This diversity will cause the neural network to have more patterns for a given emotion, which may ultimately increase the effectiveness of such a model. For a multimodal system for recognizing emotions in Polish, such a model is sufficient. In future studies, one can focus on creating a more universal model based on datasets in different languages.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 3 System Model and Problem Formulation  
### Scenario Definition  
As shown in Figure 1, a downlink AmBC system assisted by RIS is described, which consists of an ambient radio-frequency (RF) source with single antenna, a reader equipped with \(K(K\geq 1)\) antennas, a tag equipped with single antenna, and a RIS with \(N(N\geq 1)\) reflectors [17]. The RF signal to the backscatter tag is offered by the ambient RF source, while its own information is transmitted. The backscatter tag's information is modulated by on-off modulation to the ambient RF signal. A large number of reflective elementsand passive beamforming are used to enhance the tag-to-reader wireless transmission link, while counteracting the direct link from ambient RF sources to the reader at the RIS. Multiple antennas are utilized at the reader to linearly combine the received signals to enhance the signal's reception ability.  
In this section, the flat-fading channel is assumed. The channel from the ambient RF source to the tag, from the RF source to the \(k\)-th antenna of reader, from the RF source to the \(n\)-th reflector of the RIS, from the backscatter tag to the \(k\)-th antenna of the reader, from the tag to the \(n\)-th reflector of RIS, and from the \(n\)-th reflector of RIS to the \(k\)-th antenna of the reader, is denoted by \(h_{0}\in\mathbb{C}^{1\times 1}\), \(\mathbf{h}_{sd}=\begin{bmatrix}h_{sd}^{1}&h_{sd}^{2}&\cdots&h_{sd}^{k}\end{bmatrix} ^{T}\in\mathbb{C}^{K\times 1}\), \(\mathbf{h}_{sr}=\begin{bmatrix}h_{sr}^{1}&h_{sr}^{2}&\cdots&h_{sr}^{n}\end{bmatrix} ^{T}\in\mathbb{C}^{N\times 1}\), \(\mathbf{h}_{td}=\begin{bmatrix}h_{td}^{1}&h_{td}^{2}&\cdots&h_{td}^{k}\end{bmatrix} ^{T}\in\mathbb{C}^{K\times 1}\), \(\mathbf{h}_{rt}=\begin{bmatrix}h_{rt}^{1}&h_{rt}^{2}&\cdots&h_{rt}^{k}\end{bmatrix} ^{T}\in\mathbb{C}^{N\times 1}\), \(\mathbf{h}_{rd}^{k}=\begin{bmatrix}h_{rd}^{1}&h_{rd}^{2}&\cdots&h_{rd}^{n}\end{bmatrix} ^{T}\in\mathbb{C}^{N\times 1}\) and \(\mathbf{H}_{rd}=\begin{bmatrix}h_{rd}^{1}&\mathbf{h}_{rd}^{2}&\cdots&\mathbf{h}_{rd}^{k} \end{bmatrix}\in\mathbb{C}^{N\times K}\), respectively. Assume that all the channels follow the independent and identically distributed circularly symmetric complex Gaussian (CSCG) random variables with a mean value of zero and a variance of \(\mathbb{E}\left[\left|h_{k}\right|^{2}\right]=\sigma_{h}^{2}\). Assume also that the channel state information (CSI) at the reader is not known, which is consistent with the actual situation. In practice, due to the complexity of the device, the channel often cannot be observed by the reader.
###### Abstract  
A nanofiber membrane with a high surface-to-volume ratio has advantages in applications such as those used for particulate matter filtration and gas detection. To maximize the potentials of the membrane structure, recent research has been attempted to control nanofiber geometries. In this paper, surface modification of a nanofiber membrane with a metal/ceramic nanostructure is performed to improve multi-functional filter performance, enhancing fine particle filtration and toxic gas absorption. Here, a smart filter is fabricated by electrospinning polyvinylidene diffuoride (PVDF) nanofiber onto a nylon mesh and hydrothermal synthesis of ZnO nanoparticles onto a nanowire array on a PVDF nanofiber surface. On the ZnO nanowires-PVDF nanofiber layer filter, the pressure difference (\(\Delta P\) = 4.13 kPa) is higher than the pure PVDF nanofiber layer. However, the filtration efficiency is 94.3% for a 0.3 \(\upmu\)m particle size, which is higher than that of other sizes. Additionally, a ZnO nanowire array with high density on a PVDF nanofiber layer affects sensitivity (\(S\) = 39.37), with high resolution. The photocurrent characteristics of a smart filter have the potential for a photo-assisted redox reaction to detect toxic polar molecules in continuous airflow in real-time in indoor environments.  
smart filter; particulate matter; hybrid structure; electrospinning; pvdf; hydrothermal synthesis; zinc oxide; photo conductivity; quality factor +
Footnote †: journal: Applied
## 5 Potential Use Cases
### Emergency Communication Critical System
The application metrics that could be reported from the mission critical service to the monitoring module, NWDAF, include among others: users in a call, registered users in the service, number of private calls, number of group calls, maximum number of potential dialogs, etc.
# Systematic Mapping Study on Remote Sensing in Agriculture
## 3 Results of the Systematic Mapping Study
### MQ6. What Are the Platforms Used to Capture the Images for RSA?  
Figure 5 depicts the most frequent types of systems used in RSA to capture the images. In some cases, different capture devices are used, so the total number of systems is higher than the number of papers. Moreover, the capture process should not necessarily be done by the authors, as the research could be based on existing datasets.  
Concerning the obtained results, it is remarkable the small number of research works that are based on in-field low cost cameras. Although most research has been done in this area, this may be due to the fact that, in some contexts, they are not considered to be included in the remote sensing category. On the other hand, the most frequent type of platform used in the research are satellites, followed by drones and manned aircraft, and finally other types of vehicles.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Techniques** & **Ref.** & **Total** \\ \hline Classification systems & [101; 104; 106; 108; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159] & 54 \\ Feature extraction & [41; 91; 107; 108; 111; 155; 160; 161; 162; 163; 164; 165; 166; 167; 168; 169] & 16 \\ Similarity and maximum likelihood & [92; 117; 118; 170; 171; 172; 173; 174; 175; 176; 177; 178; 179; 180] & 14 \\ Preprocessing and segmentation & [108; 112; 113; 114; 115; 181; 182; 183; 184; 185; 186] & 11 \\ Recognition systems & [96; 187; 188; 189; 190; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 191; 192; 193; 194; 195; 196] & 11 \\ Other machine learning algorithms & [79; 110; 116; 197; 198; 199; 200; 201; 202; 203] & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of the types of techniques used in the selected papers.
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 5 Performance Analysis
### Probability of False Alarm  
The probability of false alarm \(P_{FA}\) is given as\[P_{FA} = P\{\Delta(m)>\gamma|\text{no device is present}\} \tag{21}\] \[= \int_{\gamma}^{\infty}\frac{1}{\sqrt{2\pi\sigma_{a}}}\exp\left(- \frac{\left(x-\mu_{a}\right)^{2}}{2\sigma_{a}^{2}}\right)dx\] \[= Q\left(\frac{\gamma-\mu_{a}}{\sigma_{a}}\right),\]  
where \(Q(\cdot)\) is the standard Gaussian Q-function, given as  
\[Q(t)=\frac{1}{\sqrt{2\pi}}\int_{t}^{\infty}\exp\left(-\frac{t^{2}}{2}\right)dt. \tag{22}\]  
and \(\mu_{a}\) and \(\sigma_{a}^{2}\) are calculated in (A2) and (A7), respectively, as shown in Appendix A.
## 2 Materials and Methods
### Procedures  
The participants (athletes) visited the laboratory as teams based on their respective sports disciplines. During their visit, anthropometric measurements, including lower limb length (tibia length and femur length) and body composition assessments (height, weight, body fat mass, etc.) were conducted. After a sufficient warm-up, tests for basic physical performance (muscular strength, muscular endurance, muscular power, cardiorespiratory endurance, agility, and flexibility) and anaerobic power (peak power) were performed. The collected data were categorized according to the classification table of sports disciplines based on activity level, as outlined by [PERSON] et al. (2017) and [PERSON] et al. (2018). Participants were grouped into three categories: low dynamic sports (LD; n = 41), moderate dynamic sports (MD; n = 87), and high dynamic sports (HD; n = 184). The participant information is presented in Table 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Variable**} & **Low Dynamic Sports** & **Moderate Dynamic Sports** & **High Dynamic Sports** & **Total Athletes** \\  & **(LD; n = 41)** & **(MD; n = 87)** & **(HD; n = 184)** & **(N = 312)** \\ \hline Age (years) & 17.46 \(\pm\) 0.60 & 17.87 \(\pm\) 1.34 & 18.04 \(\pm\) 1.93 & 17.92 \(\pm\) 1.67 \\ Height (cm) & 166.94 \(\pm\) 7.37 & 171.36 \(\pm\) 8.62 & 169.41 \(\pm\) 7.33 & 169.63 \(\pm\) 7.81 \\ Weight (kg) & 67.89 \(\pm\) 15.81 & 65.85 \(\pm\) 15.74 & 68.54 \(\pm\) 12.92 & 67.70 \(\pm\) 14.15 \\ BMI (kg/m\({}^{2}\)) & 24.20 \(\pm\) 4.43 & 22.37 \(\pm\) 4.58 & 23.77 \(\pm\) 3.39 & 23.43 \(\pm\) 3.95 \\ Career (years) & 7.46 \(\pm\) 0.60 & 7.55 \(\pm\) 1.53 & 7.94 \(\pm\) 1.48 & 7.84 \(\pm\) 1.73 \\ \hline \multirow{6}{*}{I. Low static} & \multicolumn{3}{l}{Baseball} & \multicolumn{1}{l}{Badminton} \\  & & \multicolumn{1}{l}{Fencing} & \multicolumn{1}{l}{Field hockey} \\ \cline{1-1}  & & \multicolumn{1}{l}{Seppak takraw} & \\ \cline{1-1}  & & \multicolumn{1}{l}{Running (sprint)} & \\ \cline{1-1}  & & \multicolumn{1}{l}{Taekwondo} & \\ \cline{1-1}  & & \multicolumn{1}{l}{Tennis} & \\ \hline \multirow{6}{*}{II. Moderate static} & \multicolumn{1}{l}{Diving} & \multicolumn{1}{l}{Fin swimming} \\  & \multicolumn{1}{l}{Gymnastics} & \multicolumn{1}{l}{Sovemming} \\ \cline{1-1}  & \multicolumn{1}{l}{Judo} & \multicolumn{1}{l}{Team handball} \\ \cline{1-1}  & \multicolumn{1}{l}{Shooting} & & \\ \hline \multirow{6}{*}{III. High static} & \multicolumn{1}{l}{Weightlifting} & \multicolumn{1}{l}{Wrestling} & \multicolumn{1}{l}{Boxing} \\ \cline{1-1}  & & \multicolumn{1}{l}{Cycling} & \\ \cline{1-1}  & & \multicolumn{1}{l}{Modern pentathlon} \\ \cline{1-1}  & & \multicolumn{1}{l}{Rowing} \\ \hline \hline \end{tabular} Values are presented as Mean \(\pm\) SD, Abbreviations: BMI: body mass index. The lengths of the leg were normalized with body height to calculate relative leg lengths, which were expressed as percentages (i.e., the % of body height).  
\end{table}
Table 1: The characteristics of the subjects and their classification according to activity level.
## 3 Hardware Implementation  
### Hardware Setup and Tests  
A R-SpaceFibre implementation has been tested and validated on a Xilinx ZC706 evaluation board [21]. The System on a Chip (SoC) mounted embeds one Zynq-7000 XC7Z045-2 FFG900C FPGA, one Cortex A9 processor and up to 16 GTX transceivers. The hardware setup is shown in Figure 4: the programmable logic of the FPGA is used to implement two SpFi CODECs, which communicate using two GTXs. The transceivers are externally connected through a Xilinx FMC XM104 [22] expansion board, using e-SATA cables. The Cortex A9 generates and consumes packets to be transmitted or received over the SpaceFibre link, as described in [18]. The system automatically checks whether all received packets are correct.  
To validate R-SpaceFibre, two configurations were tested:  
* CODEC 0 is a R-SpaceFibre CODEC and CODEC 1 is a SpaceFibre CODEC (CONFIG1);
* CODEC 0 and CODEC 1 are both R-SpaceFibre CODECs (CONFIG2).  
The system was intensively stimulated through an appropriate hardware validation plan, focusing on Data Link layer functionalities. In particular, QoS features, VCs scheduling and configurations with and without data scrambling were tested. Furthermore, errors (i.e., bit flips) were injected into the data stream in order to verify the behaviour of the CODECs both in CONFIG1 and CONFIG2.  
In CONFIG1 tests, a deadlock arises when the SpaceFibre node send too many retry request to the R-SpaceFibre node, as described in Section 2.2. In particular, tests show that the time between two consecutive deadlocks increase decreasing packet dimension. In fact, the FCT counter is incremented of a number equal to the number of words composing a correctly received packet. CONFIG2 tests show data packet loss as expected, but no deadlock situation arises because R-SpaceFibre considers valid all received packets. The performed tests validated R-SpaceFibre: the reduced CODEC demonstrated to be able to communicate with a version of the CODEC complaint with the SpaceFibre standard, as well with another reduced implementation.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-9}
**Layer** & \multicolumn{2}{c}{**Last**} & \multicolumn{2}{c}{**First + Last**} & \multicolumn{2}{c}{**-**} \\ \hline avg. & 25.0 & 30.9 & 35.9 & 43.5 & 41.9 & 14.8 & 20.2 & 39.2 \\ + normalize & 25.4 & 30.7 & 36.9 & 42.8 & 42.3 & 15.0 & 20.7 & 39.0 \\ + quantile-u & 27.8 & 32.7 & 38.3 & 45.9 & 45.1 & 14.7 & 20.5 & 38.6 \\ + quantile-u\({}^{W}\) & 25.7 & 32.4 & 38.4 & 46.9 & 47.9 & 15.0 & 20.4 & 40.0 \\ + whiten\({}^{W}\) & 39.8 & 39.7 & 34.2 & 17.8 & 13.0 & 9.8 & 12.6 & 12.2 \\ + whiten\({}^{W}\) & 28.9 & 33.9 & 49.4 & 57.3 & 62.0 & **21.7** & **23.5** & 56.6 \\ + zscore & 26.7 & 33.1 & 37.3 & 44.2 & 43.6 & 14.0 & 19.8 & 39.2 \\ \hline id\({}_{t}^{W}\) & 28.6 & 29.8 & 30.6 & 37.7 & 39.1 & 16.1 & 17.5 & 55.4 \\ + normalize & 29.0 & 30.3 & 31.1 & 37.2 & 38.0 & 16.8 & 18.6 & 62.7 \\ + quantile-u & 29.8 & 31.1 & 32.0 & 38.1 & 40.7 & 16.8 & 17.5 & 57.7 \\ + quantile-u\({}^{W}\) & 30.0 & 30.9 & 32.1 & 39.9 & 40.8 & 16.3 & 18.0 & 58.8 \\ + whiten & 30.6 & 30.1 & 26.1 & 15.4 & 12.2 & 9.7 & 9.7 & 12.5 \\ + whiten\({}^{W}\) & 29.8 & 31.4 & 40.0 & 50.3 & 55.8 & 16.2 & 17.1 & 56.8 \\ + zscore & 30.2 & 30.6 & 31.1 & 37.4 & 38.7 & 16.1 & 16.8 & 54.1 \\ \hline id\({}_{t}^{T}\) & 36.2 & 39.0 & 54.2 & 63.4 & 60.5 & 14.8 & 20.2 & 61.6 \\ + normalize & 36.9 & 38.6 & 55.1 & **66.6** & 62.4 & 15.0 & 20.7 & **70.6** \\ + quantile-u\({}^{W}\) & 38.7 & 40.7 & 57.1 & 64.3 & **63.1** & 14.7 & 20.5 & 63.7 \\ + quantile-u\({}^{W}\) & 38.6 & 40.0 & 57.7 & 65.6 & 63.0 & 15.0 & 20.4 & 65.2 \\ + whiten & **43.5** & **44.6** & 32.1 & 19.7 & 16.6 & 9.8 & 12.6 & 16.7 \\ + whiten\({}^{W}\) & 35.7 & 36.7 & **59.9** & 63.1 & 58.7 & 21.7 & 23.5 & 61.6 \\ + zscore & 38.3 & 40.6 & 54.5 & 62.0 & 58.9 & 14.0 & 19.8 & 60.7 \\ \hline -biases & 28.5 & 34.5 & 44.8 & 56.7 & 59.7 & 14.3 & 18.2 & 53.9 \\ + normalize & 28.4 & 35.3 & 45.2 & 57.0 & 58.7 & 14.6 & 19.4 & 63.6 \\ + quantile-u & 31.0 & 36.8 & 44.5 & 56.7 & 59.3 & 14.0 & 19.0 & 58.0 \\ + quantile-u\({}^{W}\) & 30.8 & 35.5 & 46.4 & 59.9 & 60.6 & 14.8 & 19.3 & 57.4 \\ + whiten\({}^{W}\) & 35.1 & 36.1 & 55.4 & 62.8 & 61.0 & 21.0 & 20.9 & 52.2 \\ + whiten & 42.6 & 38.7 & 21.4 & 15.7 & 11.3 & 8.0 & 11.6 & 11.3 \\ + zscore & 31.5 & 36.9 & 45.2 & 57.5 & 57.9 & 13.4 & 18.5 & 53.0 \\ \hline [MASK] & 26.4 & 35.5 & & & & & & \\ + normalize & 26.5 & 36.1 & & & & & & \\ + quantile-u & 26.0 & 37.7 & & & & & \\ + quantile-u\({}^{W}\) & 24.9 & 37.0 & & & & & & \\ + whiten & 22.7 & 41.0 & & & & & \\ + whiten\({}^{W}\) & 18.0 & 32.2 & & & & & \\ + zscore & 27.3 & 37.4 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Clustering accuracy dependence on token aggregation and post-processing techniques for the stackoverflow dataset. The best result for each model is bolded, while underlined result is the best across all the models.
## 4 Results and Discussion
### Results of the Survey and Statistical Analysis
Therefore, the auditory design of the metaverse environment should be minimalist, with sounds that can be useful to the user, rather than importing all the sounds of the real world. Comprising QNs 13, 8, 17, and 7, 'Component 5' explains 6.371% of the total factor and can be described as 'auditory consistency', considering the coherence of sound and visual motion and atmosphere in the metaverse environment, as well as the coherence of sound changes in the content. 'Component 5' is grouped into'sound matching visual motion timing' (QN 7),'sound experience meeting visual expectations' (QN 8),'relevance of sound to ideas' (QN 17), and 'degree of consistency of overall sound across sound variations' (QN 13), which is a strong tendency of AUX evaluation factors, suggesting that the unified experience of sight and sound covered by traditional auditory presence should be harmonized with the auditory experience across the metaverse environments. Based on these results, it was identified that five main components, including'realistic auditory background' and 'acoustic aesthetics', explained more than 70% of the variance in auditory presence in metaverse environments. While previous studies have mainly focused on the spatial characteristics of auditory presence in metaverse environments or the congruence of auditory and visual atmospheres [36; 37; 38; 39; 40; 41; 42], the study focuses on the importance of AUX and its impact on presence. As expected from the above research questions, it was confirmed that auditory presence and AUX factors can enhance presence when they act complementarily. These findings suggest that users do not experience a sense of presence solely through the reproduction of the auditory environment, but that it can be enhanced by preventing factors that inhibit the sense of presence and amplifying acoustic quality through AUX factors. A limitation of the study is that it focuses on virtual worlds, which are the most popular metaverse platforms, and therefore does not consider aspects of the other three types of metaverses (augmented reality, lifelogging, and mirror worlds), especially auditory interaction through VR devices. As VR devices reflect the unique characteristics of metaverse environments, further research on the auditory presence of VR devices should be conducted, and such research will have a significant impact on improving the presence of metaverse environments in the future.