esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
## 1 Introduction
### Review Details  
In this paper, we provide an overview of multimodal cue pseudo-haptics, with an emphasis on the inclusion of auditory cues, focusing on advancements made after the [PERSON] and [PERSON] 2019 review [33]. Our reason to focus on the auditory sensory modality is motivated by the limited research into the role that audio can play in pseudo-haptics, in contrast to the significant amount of research investigating visual-based pseudo-haptics. Furthermore, several reviews focusing on visual-based pseudo-haptics are available, including the review by [PERSON] [44] and the more recent review of by [PERSON] and [PERSON] [40].  
Our review follows the structure of a narrative review, as outlined by [PERSON] et al. [45]. A narrative review serves to examine the literature comprehensively, offering a broad summary of the field. This approach is especially beneficial for readers new to the subject, providing them with valuable insights [46]. When crafting this review, we adhered to the principles delineated by [PERSON] et al. [47], ensuring alignment with established guidelines for preparing a narrative literature review intended for publication in a peer-reviewed journal. The search was carried out using the Google Scholar, RefSeek, PubMed, and Scopus databases between 23 December 2023, and 5 June 2024. The research query was: (\"sound\" OR \"audio\" OR \"auditory\") AND (\"visual\" OR \"graphical\") AND (\"multimodal interaction\" OR \"cross-modal interaction\") AND (\"pseudo haptics\" OR \"pseudo-haptics\") OR \"pseudohaptics\"), which initially yielded 391 articles. A more recent search revealed one additional article. While vision-based pseudo-haptics is an important and relevant area of work, we have restricted our scope to audio-based work in this review. After initially reviewing the abstracts of these 391 articles to assess relevance, 52 articles were identified. Following a detailed examination, of these 52 articles, 17 articles were found to be pertinent to our review. A summary of our search process, outlining the information flow through the phases of our review process is provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [48] shown in Figure 1.  
The remainder of this paper is organized as follows. Section 2 (the following section) summarizes our review. To provide context and to better motivate our audio-based pseudo-haptics discussion, we begin our review with an overview of audio-haptic interactions, and more specifically, the interaction between auditory and haptic cues whereby the haptic cues are provided using some form of haptic device. The discussion then moves to audio-based pseudo-haptics, beginning with a discussion on audio-only pseudo-haptics followed by a discussion of audio-visual pseudo-haptics. In Section 3, a discussion of the review results is presented in addition to a discussion of the potential of pseudo-haptics in surgical training. Finally, concluding remarks are presented in Section 4.
# Ontology in Hybrid Intelligence: A Concise Literature Review
## 5 Gap Identification and Challenges
### A Qualitative Analysis  
From a conceptual point of view, four key aspects have been identified as follows:  
1. _Interoperability as a key factor_. This includes interoperability among systems as well as a shared understanding between humans and machines. Interoperability is a concept classically associated with ontology. However, in the specific field of hybrid intelligence, there is a clear emphasis on human-machine interaction. This may have implications on knowledge engineering, as well as on the evolution of ontology-related technology. 2. _Explainable and transparent models_. The use of ontologies to support some kind of intelligible synthesis of a given analysis or process is not an absolute novelty. For instance, knowledge graphs [101] are extensively adopted in several disciplines and applications and may be underpinned by formal ontologies to deal with the underlying complexity [40]. In most cases, such a presentation level is designed ad-hoc to optimally match the requirements within a system. Explainable and transparent models in AI (and hybrid AI) usually require a more systematic approach. 3. _System Engineering_. The contribution of ontology within system/software engineering is well known and extensively documented in the literature. This review, conducted in the specific context of hybrid intelligence, has highlighted a potential extended and enhanced scope to include a linkage to different aspects, such as design and ethic principles and challenges. 4. _Quality and Accuracy (including evolution)_. This last aspect has a less specific scope than the previous ones as it addresses the contributions of ontology to do \"better\". In practice, it may refer to support advanced functionalities and features or to drive the evolution of a system. A typical practical example that emerged from the conducted review is the collaborative approach which, in general terms, requires extended and more sophisticated semantics, as well as a certain level of interoperability. That is, normally, a value provided by ontologies. Figure 4: Quantitative analysis of the provided value from a conceptual and an application perspective. A more extended qualitative analysis of applications aims to estimate the relevance of ontology in a given context. A summary is reported in Table 3. The relevance of knowledge representation, automated reasoning or other features has been informally evaluated without specific criteria independently for each pertinent system in order to support a more holistic assessment. In at least 70+% of cases, the role of ontology may be considered relevant or critical. This evaluation is merely indicative and reflects a fundamental lack of detail about the semantic infrastructure and its actual role in many proposed systems.
## 4 Experimental Results
### Experimental Results Analysis
#### 4.2.3 SVR Experimental Results  
The outcomes of the SVR model across different feature combinations are encapsulated in Table 8 and visualized in Figure 7. The SVR-RB model, which integrates regional features, demonstrates significant improvements across all metrics. The MRE is reduced to 0.0702, the MAE decreases to 817,575, the SMAPE is lowered to 0.0718, the \(R^{2}\) increases to 88.60%, and the RMSE drops to 990,176, outperforming all other feature combinations. Incorporating usage-based features into the SVR-UB model also yields positive results, although these enhancements are not as pronounced as those achieved by the SVR-RB model. The SVR-RB-UB model, which amalgamates both regional and usage features, does see some uptick in performance, yet it still lags behind the SVR-RB model in terms of overall effectiveness. This variance in performance can be attributed to the added complexity and potential noise that come with an expanded feature set. It appears that while feature integration can be beneficial, the judicious selection and combination of features truly drive predictive power.  
Overall, the predictive performance of the models--whether Informer, MLR, or SVR--significantly improves with the inclusion of regional and usage features. The inclusion of these features not only boosts the \(R^{2}\) values, indicating stronger explanatory power of the models, but also substantially reduces metrics such as MAE, MRE, SMAPE, RMSE, and MAPE, thereby decreasing prediction errors.  
These results clearly demonstrate the importance of feature engineering in enhancing the performance of natural gas purchase volume prediction models.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Approach** & **MRE** & **MAE** & **SMAPE** & \(R^{2}\) **(\%)** & **RMSE** & **MAPE (\%)** \\ \hline SVR & 0.0976 & 1,074,106 & 0.0957 & 79.18 & 1,338,196 & 9.76 \\ SVR-RB & 0.0702 & 817,575 & 0.0718 & 88.60 & 990,176 & 7.02 \\ SVR-UB & 0.0978 & 1,121,888 & 0.1018 & 80.30 & 1,301,718 & 9.78 \\ SVR-RB-UB & 0.0787 & 895,598 & 0.0799 & 83.31 & 1,198,170 & 7.87 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance evaluation results of SVR.  
Figure 6: Comparison of actual and predicted gas purchase volumes using the MLR model with different feature combinations. (**a**) MLR; (**b**) MLR-RB; (**c**) MLR-UB; (**d**) MLR-RB-UB.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Channel Model  
In the study of the model, validation, and robustness phases, the number of bits per slot \(\text{m}_{\text{k}}\) that a mobile device can receive is assumed to depend on the selected Modulation and Coding Scheme (MCS), which is determined by the user's radio channel conditions. The selection of the MCS is based on the Signal to Interference plus Noise Ratio (SINR)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\overline{x}_{\text{on}}\) & 3 Mbits \\ \(\overline{t}_{\text{off}}\) & 3 s \\ \(MSTR\) for throttling mode & 2048 Kbps \\ Pareto parameter \(\alpha\) & 1.2 \\ Pareto lower cutoff \(q\) & 300 Mbits \\ Pareto higher cutoff \(q\) & 3000 Mbits \\ Pareto parameter \(b\) for lower cutoff & 712,926 bits \\ Pareto parameter \(b\) for higher cutoff & 611,822 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic parameters.  
measurements and predefined thresholds [24]. Table 3 summarizes the wireless channel parameters, including the MCS states (and outage) along with the corresponding number of bits transmitted per slot for each state [25].  
A method to describe the channel between the base station (BS) and a mobile station (MS) is to model the transitions between MCS levels as a finite-state Markov chain (FSMC). This chain is fully characterized by its transition matrix \(P_{T}=(p_{ij})_{0\leq i,j\leq K}\). It operates in discrete time, with transitions occurring every \(L\) frame, where \(LT_{F}\)\(<\)\(t_{coh}\) (coherence time).  
To validate the model, we set \(L=1\), meaning that an active mobile device will draw a new MCS for each frame. The discrete distribution is given by \((p_{ij})_{0\leq i\leq K}\), where \(p_{ij}=p_{i}\) for all i, simulating the memoryless channel model used in the analytical model.  
Let \(P_{T}(0)\) be the transition matrix for the memoryless model. To test the robustness of the model, two additional channel models are introduced: the average channel model and the combined channel model. For these models, the MCS of a mobile station during a frame depends on the MCS used during the previous frame. The transition matrix is derived from the following equation:  
\[P_{T}\left(a\right)=al+(I-a)P_{T}(0)\quad 0\leq a\leq 1 \tag{33}\]  
where \(I\) is the identity matrix, and a is an indicator of the channel memory. The average duration for which a mobile maintains the same MCS is given by [21]:  
\[t_{coh}=1/(1-a) \tag{34}\]  
If \(a=0\), the channel model becomes memoryless. When \(a=1\), the channel has infinite memory, meaning the mobile always keeps the same MCS [15, 16].  
For our robustness study, we consider an intermediate value \(a=0.5\), meaning the channel remains constant for two frames. The \(a=0.5\) value corresponds to two types of channel models:
## 2 Materials and Methods
### Volatile Compound Profile  
A gas chromatographic 6890N (Agilent Technologies, Santa Clara, CA, USA) fitted with a DB-624 capillary column (30 m, 0.25 mm i.d., 1.4 \(\upmu\)m film thickness; J&W Scientific, Folsom, CA, USA) coupled to a mass selective detector 5973N (Agilent Technologies) was used to determine the volatile profile. For this, the volatile compounds were extracted using a solid-phase microextraction (SPME) device (Supelco, Bellefonte, PA, USA), including a fused silica fibre (10 mm length) coated with a 50/30 \(\upmu\)m thickness layer of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) and following the method explained by [PERSON] et al. (2016) with some modifications. Initially, 1 g of minced sample was weighed into a 24-ml glass vial. This vial was screw-capped with a laminated Teflon-rubber disc. Then, it was maintained during 15 min in an oven at 35 \({}^{\circ}\)C to equilibrate the volatile compounds in the headspace, guarantying a homogeneous temperature for both sample and headspace. Afterwards, the fibre (previously prepared by heating in a gas chromatograph injection port at 270 \({}^{\circ}\)C during 60 min) was inserted into the glass vial across the septum and exhibited to headspace for 30 min, at 35 \({}^{\circ}\)C in the oven. Once removal was complete, the fibre was transferred to the injection port of the gas chromatograph-mass spectrometer (GC-MS) system. The SPME fibre was desorbed and kept in the nozzle port at 260 \({}^{\circ}\)C for 8 min in splitless mode. Helium was employed with a linear velocity of 40 cm s\({}^{-1}\) as a carrier gas. The temperature programme was initially isothermal at 40 \({}^{\circ}\)C during 10 min, then lifted to 200 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\) and after to 250 \({}^{\circ}\)C at 20 \({}^{\circ}\)C min\({}^{-1}\), and lastly maintained for 5 min (the total run time was 49.5 min). Injector and detector temperatures were both supported at 260 \({}^{\circ}\)C.  
The mass spectra were achieved employing a mass selective detector working in electronic impact at 70 eV, with a multiplier voltage of 1953 V and collecting data at 6.34 scans s\({}^{-1}\) over the range \(m/z\) 40-300. Volatile compounds were identified by comparing their mass spectra with those shown in the NIST05 (National Institute of Standards and Technology, Gaithersburg) library (\(>\)80% of coincidence), and/or by comparing their mass spectra and retention time with authentic standards (Supelco, Bellefonte, PA, USA), and/or by the calculation of the retention index relative to a series of standard alkanes (C\({}_{5}\)-C\({}_{14}\)) (for calculating Kovats indexes, Supelco 44585-U, Bellefonte, PA, USA) and matching them with data relayed in literature. The aftermaths obtained are showed as area units (AU) \(\times\) 10\({}^{6}\) per g of dry matter.
## 3 Results and Discussion
### Distribution of Air Quality Index (AQI)  
The daily AQI based on the concentrations of PM\({}_{2.5}\) and PM\({}_{10}\) in Seoul and Daegu is shown in the Figure 8. NO\({}_{2}\) could only be calculated from an AQI above 200, but no day exceeded 200 in this study. The AQI results, shown in contrasting colors between the COVID-19 pandemic period and the control period showed a dramatic improvement in air quality. Interestingly, the AQI for PM\({}_{2.5}\) has not been classified as unhealthy (red) since the COVID-19 outbreak in either city. During the control period, the values unhealthy for the sensitive group (SG) accounted for the majority, with 91.2% and 92.8% in Seoul and Daegu, respectively, whereas in 2020, the proportions were moderate at 68.3% and 65.9%, respectively.  
The AQI for PM\({}_{10}\) also showed a significant reduction. In both cities, it was moderate during the control period at 86.4-98.4%, while good at 75.4-86.5% in 2020.  
Figure 6: Comparison of average NO\({}_{2}\) concentrations by week between 2020 and the previous three years in (**a**) Seoul and (**b**) Daegu. Highlighted shadow represents the level of social distancing. The error bar means standard deviation.  
Figure 7: Map of wind speed and direction analysis in the Northeast Asia region in January and February. (**(a)**: Seoul and (**b**): Daegu)  
The AQI analysis showed a significant improvement in air quality centered on PM. In particular, it was rare that air quality deteriorated to the level of unhealthy for the SG, and significant health benefits would have been seen in at-risk groups (e.g., the elderly, children, and people with lung disease) due to the effects of air quality improvements in 2020.  
In previous studies, a decrease in the AQI level of 14.9-32.2% was observed in central China where the COVID-19 lockdown was implemented [24]. In addition, significant improvement in air quality was also found in cities that implemented measures to prevent the spread of coronavirus [11], although not lockdowns, consistent with our study. These improvements in AQI have been observed worldwide, including the United States [44, 60], Italy [61], and India [8, 23] experiencing COVID-19.
# Long Length Document Classification by Local Convolutional Feature Aggregation
## 4 Experiment Analysis
### Experiment Results
#### 4.3.3 Model 2: CNN_LSTM_Agg  
For model CNN_LSTM_Agg, we performed the following experiments by changing the three parameters: window size, total number of words, and number of hidden unit. For each fixed window size and total number of words, the result for the 256 hidden unit is at the top and 512 is at the bottom. The experimental results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Total Words** & **Window Size** & **10** & **20** & **50** & **100** & **200** & **400** & **500** \\ \hline
200 & 86.37\% & 86.53\% & 86.98\% & 87.03\% & 87.21\% & & \\ \hline
400 & 87.48\% & 90.11\% & 90.33\% & 90.60\% & 92.08\% & 92.12\% & \\ \hline
600 & 88.36\% & 91.21\% & 91.88\% & 92.16\% & 93.35\% & & \\ \hline
800 & 89.14\% & 92.13\% & 92.32\% & 92.63\% & 93.38\% & 93.41\% & \\ \hline
1000 & 90.26\% & 92.21\% & 92.59\% & 93.78\% & 93.89\% & 94.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model CNN_Random_Agg experimental results.  
Figure 5: Experimental results of these models (ACC is the accuracy of classification): **(a)** Fixing the number of extracted words to 1000, vary the window size; **(b)** Fixing the window size to 40 words, vary the total number of extracted words.  
From Table 3, we can see that the accuracy increases gradually with the increase of the window size and the total number of words. When the window size is 10 and the total number of words is 200, that is, the number of blocks extracted is 20, the accuracy is only about 88%; and when the total number of words increases to 1000 and the window size increases to as large as 200, that is, the number of blocks is 5, the accuracy of the model reached more than 94%. In addition, we also investigate the effect of the number of hidden units by setting the number to 256 and 512, respectively. Experiments show that setting hidden units to 256 is better than the case of 512. This may because the current collected arXiv dataset is still too small relative to a larger RNN network, which could be easily overfit. We hope to enlarge our dataset in future work.
## 5 Interference Analysis
### Jammers Interference  
The MCP-distributed jamming clusters, \(\lambda_{J}\), with Poisson-distributed jammers, \(J\), in a circular radius, \(r_{J}\), can degrade the efficiency of a UAV-assisted C-V2X network. The interference of the jamming clusters at the recipient node is derived in Appendix B and is given as  
\[L_{I_{J}}=\exp\Bigg{\{}-\frac{2\pi\lambda_{J}J}{\alpha_{J}\mathfrak{r}_{J}^{2 }}\tau^{2/\alpha_{J}}\csc\left(\frac{2\pi}{\alpha_{J}}\right)z_{d}^{2}-\pi \lambda_{J}\tau^{2/\alpha_{J}}z_{d}^{2}\int_{0}^{z_{d}}\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\  
\[C =\int_{0}^{\infty}\Pr\{\mathrm{SIR}_{I2r}\geq\tau\}f_{r}(z)dz\] \[\stackrel{{ a}}{{=}}\int_{0}^{\infty}\Pr\{\frac{P_{t} \eta g_{t}G_{t}G_{r}\|z_{r}-z_{t}\|^{-a_{r}}}{I_{X}}\geq\tau\}f_{r}(z)dz\] \[\stackrel{{ b}}{{=}}\int_{0}^{\infty}\Pr\{g_{t}\geq \frac{\tau(I_{X})}{P_{t}\eta G_{t}G_{r}\|z_{r}-z_{t}\|^{-a_{r}}}\}f_{r}(z)dz \tag{41}\] \[\stackrel{{ c}}{{=}}\int_{0}^{\infty}\exp\{-s(I_{X} )\}f_{r}(z)dz,\]  
where (a) yields by inserting \(\mathrm{SIR}_{I2r}\), (b) yields by straightforward mathematical concepts, and (c) yields by \(g_{t}\sim\exp(1)\) and \(s=\tau(P_{t}\eta G_{t}G_{r}\|z_{r}-z_{t}\|^{-a_{r}})^{-1}\). For direct mode transmission, the V2V link is considered to be within coverage whenever the SIR acquired at the recipient V-N surpasses the set limit. This can be obtained as  
\[C_{V} =\int_{0}^{\infty}\Pr\{\mathrm{SIR}_{V2V}\geq\tau\}f_{V}(z)dz\] \[\stackrel{{ a}}{{=}}\int_{0}^{\infty}\Pr\{g_{V}\geq \frac{\tau(I_{V_{o}}+I_{V})}{P_{V}\eta G_{V,I}\theta_{V,I}\|z_{V}\|-a_{V}}\}f_ {V}(z)dz\] \[\stackrel{{ b}}{{=}}\int_{0}^{\infty}\exp\{-s(I_{V_ {o}}+I_{V})\}f_{V}(z)dz\] \[\stackrel{{ c}}{{=}}\int_{0}^{\infty}\Bigl{(}L_{I_{V }}(s)\times L_{I_{V_{o}}}(s)\Bigr{)}f_{V}(z)dz, \tag{42}\]  
where (a) is obtained by simplification and inserting \(\mathrm{SIR}_{V2V}\), (b) is derived by presuming \(g_{V}\sim\exp(1)\), and (c) is implied by the description of the Laplace transform. V2V connection's coverage is calculated by entering the values of \(L_{I_{V}}(s)\), \(L_{I_{V_{o}}}(s)\), and \(f_{V}(z)\) in (42). The direct-mode coverage incorporating jamming can be evaluated as  
\[C_{V,J}=\int_{0}^{\infty}\Bigl{(}L_{I_{V}}(s)\times L_{I_{V_{o}}}(s)\times L _{I_{I}}\Bigr{)}f_{V}(z)dz. \tag{43}\]  
Substituting the values of \(L_{I_{V}}(s)\), \(L_{I_{V_{o}}}(s)\), \(L_{I_{I}}\), and \(f_{V}(z)\) in (43), the expression is obtained and expressed in (  
sending via M2V, L2V, and N2V connections. By substituting the values of \(L_{I_{V}}(s)\), \(L_{I_{V_{0}}}(s)\), \(f_{M}(z)\), \(L_{I_{M}}(s)\), \(L_{I_{L}}(s)\), \(L_{I_{N}}(s)\), and \(f_{V}(z)\) in (45), MBS coverage is obtained.
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions
### Ultrasonic Pulse Velocity  
UPV test was carried out on the mortars at 7, 28, and 90 days of hydration. Figure 8 depicts the results of UPV test. UPV values augmented with growing curing age. For instance, the control (REF) specimens demonstrated a UPV value of 3650, 3986, and 4078 m/s at 7, 28, and 90 days of curing. It is due to the formation of hydration products with curing age causing a densified microstructure. On the contrary, the inclusion of fine-sized WSA particles caused an enhanced UPV value. This enhancement was much higher for finer ash particles. For example, a UPV value of 3768 m/s, 4101 m/s, and 4200 m/s was measured for W1/20 mortar specimens at 7, 28, and 90 days of curing.  
Figure 8: UPV test results for mortar specimens.  
Figure 7: Total porosity of mortar specimens.  
W2/20 and W3/20 mortar specimens demonstrated UPV value of 3500 and 3822 m/s at 7 days; 3834 and 4231 m/s at 28 days; 3966 and 4282 m/s at 90 days, respectively. It is evident from the UPV results that the addition of W2 type ash particles (Sp. Surface area: 7251 cm\({}^{2}\)/g) caused a reduction in the pulse velocity. A reduction of 4.1%, 3.8%, and 2.7% in the UPV values was witnessed for W2/20 mortar specimens compared to control mortars. It indicates that the incorporation of finer ash particles refined pore structure of the mortars and led to increased UPV values. This refinement was caused by the pozzolanic as well filler effect of WSA. Comparable findings were reported in the previous research studies. [PERSON] et al. has reported that increase in the UPV values was due to the filling effect of fine particles of SCBA [40]. Similarly, in another study, it has been concluded that pozzolanic and micro-filling effect of RHA particles caused a densification in the microstructure which resulted in an enhanced UPV values [41].