esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Cost Analysis  
Based on the data provided, the costs associated with the heat demand for the building were calculated using an average price of 0.25 Euro per kWh of electricity. Table 3 lists the monthly operating costs of the heat pump related to the electricity demand for the heat pump compressor (NAHP) and the costs associated with activating the electric heater (EHM) during periods when the heat pump achieves low _COP_ values. EHP represents the percentage of the electric heater's demand in the total electricity demand. For the entire year, the power of the electric heater accounts for 12.5% of the total electricity demand for the discussed system. The annual costs associated with providing heat for the building for the aforementioned purposes amount to 2947 Euro/a. This is a relatively high cost due to the low external air temperatures from October to January, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & **NAHP** & **EHM** & **EHM + NAHP** & **EHP** \\ \hline  & **kWh/m** & **kWh/m** & **Euro/m** & **\%** \\ \hline
1 & 633 & 3163 & 949 & 83.3 \\ \hline
2 & 1030 & 34 & 266 & 3.2 \\ \hline
3 & 1082 & 19 & 275 & 1.8 \\ \hline
4 & 570 & 0 & 142 & 0.0 \\ \hline
5 & 414 & 0 & 104 & 0.0 \\ \hline
6 & 222 & 0 & 55 & 0.0 \\ \hline
7 & 186 & 0 & 47 & 0.0 \\ \hline
8 & 174 & 0 & 44 & 0.0 \\ \hline
9 & 303 & 0 & 76 & 0.0 \\ \hline
10 & 754 & 0 & 189 & 0.0 \\ \hline
11 & 959 & 76 & 259 & 7.4 \\ \hline
12 & 1001 & 1169 & 542 & 53.9 \\ \hline Total & \begin{tabular}{c} 7328 \\ (26.4) * \\ \end{tabular} & \begin{tabular}{c} 4462 \\ (16.1) * \\ \end{tabular} & \
\begin{tabular}{c} 1947 \\ \end{tabular} &
\begin{tabular}{c} 149.6 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monthly electricity demand for the NAHP compressor and the EHM electric heater, and the percentage of electricity demand covered by the EHP electric heater. Figure 2: Change in the heat pump’s COP (Coefficient of Performance) value depending on the external temperature. the high price of electricity. Therefore, it was decided that an alternative bivalent system where the AHP works in conjunction with a gas boiler (GB) would be analyzed. Figure 3 shows the diagram of the aforementioned system. The price of 1 kWh of gas is assumed to be 0.11 Euro in Poland, but in Ukraine for example, only 0.065 Euro. This seems to be a more favorable solution compared to the monoenergetic variant operating in a parallel system due to the lower price per kWh of gas compared to electricity. Table 4 presents the calculation results, where the heat demand obtained from the AHP and the gas boiler GB is determined. The AHP operates up to a _COP_ of 2.8; below a COP of 2.8, the system provides heat solely from the GB due to the lower cost of gas compared to the price of electricity needed for the NAHP compressor. The total annual cost associated with the electricity for AHPM and the gas fuel for GBM amounts to 2321 Euro/a, which is 21.3% lower than the monovalent heat pump system (AHP) with an electric heater (EH). This indicates that the cooperation between
# Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm
## 2 Methods  
### Expression and Similarity Measure of Rock Discontinuity Occurrence Data  
The occurrence of random discontinuities of rock mass are usually represented by two random variables of dip direction \(\alpha\) (\(0\leq\alpha\leq 360^{\circ}\)) and dip angle \(\beta\) (\(0\leq\beta\leq 90^{\circ}\)). The actual field investigation of rock discontinuity is also described by measuring the dip direction and dip angle of discontinuity. The dip direction of the discontinuity represents the inclined orientation of the discontinuity in space. Generally, the north is set to \(0^{\circ}\), and the angle between the clockwise rotation and the north is the inclination value, which ranges from \(0^{\circ}\) to \(360^{\circ}\). The dip angle is the angle between the discontinuity and the horizontal plane, and its value range is \(0^{\circ}\)-\(90^{\circ}\). Assuming that the discontinuity is a plane, its occurrence can be expressed by the unit normal vector of the discontinuity. As shown in Figure 1, the positive \(x\) and \(y\) axes represent the north and east, respectively, and the positive \(z\) axis is vertically upward. Through geometric analysis, the expression of the unit normal vector of the discontinuity is \(e_{i}=(x_{i},y_{i},z_{i})\)[34; 35], where  
\[\left\{\begin{array}{l}x_{i}=\cos\alpha\sin\beta\\ y_{i}=\sin\alpha\sin\beta\\ z_{i}=\cos\beta\end{array}\right. \tag{1}\]  
Taking into account the discontinuity orientation similarity evaluation of a special problem, that is, when the discontinuity of the inclination of about 180\({}^{\circ}\) difference between the high and steep dip angle (dip angle of nearly 90\({}^{\circ}\)) is the same set of discontinuities, for the purpose of solving this issue reasonably, this paper used two discontinuities clipped by the acute angle of the sine value to measure the similarity of two discontinuities distance [36; 37]. Assuming that the unit normal vector of one rock discontinuity is \(e_{1}=(x_{1},y_{1},z_{1})\) and the unit normal vector of another rock discontinuity is \(e_{2}=(x_{2},y_{2},z_{2})\), then the acute angle \(\theta\) between the normal vectors of two discontinuities is:  
\[\theta=\arccos\left|e_{1}\cdot e_{2}^{\top}\right| \tag{2}\]  
The distance \(d(e_{1},e_{2})\) between two discontinuities can be expressed as  
\[d(e_{1},e_{2})=\sin^{2}\theta=1-\left(e_{1}\cdot e_{2}^{\top}\right)^{2} \tag{3}\]
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 9 Zeros in the Short-Term Predictor  
The G.726/727 codecs have 2 poles and 6 zeros. When these codecs were being standardized, the order of the all-pole predictor was reduced so that stability tests could be easily performed and the predictor stabilized, but the zeros were needed to compensate for any loss in prediction performance from using only 2 poles but without risking instabilities. However, there are other important reasons for including zeros in a backward adaptive tree-coding structure.  
First, it is well known that when estimating the parameters of an autoregressive (AR) sequence in additive noise, the estimation procedure should include zeros [35,36]. The inclusion of zeros can make the estimation more accurate and can reduce the flattening of the estimated spectral envelope of the AR model.  
Second, rate distortion theory indicates that for optimal encoding using the mean squared error distortion measure, the code generator should include zeros as well as poles when achieving small distortion [29].  
Third, from studies of backward adaptive prediction with a quantizer in the loop, it is known that an all-pole model can end up tracking itself rather than the input speech signal. The inclusion of zeros in the adaptive predictor can prevent this behavior and can couple the backward adaptive predictor to the codec input signal more directly [37].  
For all these reasons, we include zeros in our short-term backward adaptive predictor structure.
## 2 Literature Review
### Research Gap  
Most existing research relies on statistical data to measure how convective weather affects traffic operations, mainly analyzing the overall effects in terminal areas. However, these studies have some important limitations:  
(1) Do not take into account the spatial distribution of convective weather in the terminal area and its relative position to the flight trajectory.  
(2) Current methods focus on single-airport or flight-level delay prediction, neglecting comprehensive evaluation of multi-airport collaborative delay levels under terminal-area operational scenarios.  
(3) Conventional models rely on manually designed static features (e.g., visibility, wind speed), which inadequately capture spatial correlations between weather patterns and flight paths, resulting in suboptimal prediction accuracy.  
We summarized the above studies in four aspects, including study area, research focus, key features, and unaddressed gaps, as shown in Table 1.  
Therefore, this paper proposes a method for predicting airport delay level based on deep metric learning. The method focuses on airport delay levels as the main research object. First, the FCM algorithm is used to cluster delay-related features, extract classification rules for delay levels, and assign delay level labels. Then, actual data from the terminal area, including weather avoidance areas (WAF) and radar tracks (ADS-B), are converted into multi-channel image features that retain spatial location information. After that, a delay-level prediction model is built using a convolutional neural network. The aim of this method is to provide a comprehensive and objective way to describe and monitor the overall operational status of the terminal area. By accurately predicting the airport's delay conditions during convective weather, the method aims to support traffic management decisions in the pre-tactical phase and improve the operational efficiency of the terminal area.
## 3 Method Design
### Dependency Information Fusion Attention Graph Convolutional Network
#### 3.2.4 Classification Layer  
After obtaining the output representation of each word, the vector representation of the entity \(h_{E_{q}}\) is obtained by the max pooling method:  
\[h_{E_{q}}=MaxPooling(h_{i}^{(L)}|x_{i}\in E_{q}) \tag{11}\]  
Finally, we concatenate together the entire sentence and the vector representations of the two entities (i.e., \(h_{X},h_{E_{1}}\) and \(h_{E_{2}}\)) to obtain the output vector **O**:  
\[\textbf{O}=W^{R}\cdot(h_{X}\oplus h_{E_{1}}\oplus h_{E_{2}}) \tag{12}\]  
where \(W^{R}\) is a trainable matrix and \(h_{X}\) is a whole sentence vector representation, obtained directly from the \([CLS]\) label in BERT without going through the graph convolutional network module. **O** is an R-dimensional vector whose each value represents a relation type in the set R of relation types. The softmax function is then applied to predict the relation \(\hat{r}\) between two entities:  
\[\hat{r}=\arg\max\frac{\exp(\textbf{O}^{s})}{\sum_{s=1}^{|R|}\exp(\textbf{O}^{ s})} \tag{13}\]  
where \(\textbf{O}^{s}\) represents the value of dimension \(s\) in **O**. The model of this paper is shown in Figure 7. The above is the model method studied in this paper for biomedical relation extraction, which will be evaluated with two biomedical datasets in the next experiments.
## 2 Literature Review
### Limitations of Traditional and Supervised Approaches  
Historically, predictive models have relied on physics-based simulations and supervised machine learning. While physics-based models benefit from domain knowledge [9; 10; 11], they often require explicit failure definitions, are time-consuming to develop, and may not generalize well to new failure modes [12; 13]. On the other hand, supervised machine learning approaches require extensive labeled datasets, often unavailable in industrial settings due to the difficulty of accurately labeling large volumes of sensor data or the scarcity of failure events.  
This scarcity introduces a bottleneck in supervised learning applications and limits the scalability of PdM systems. The labeling process is labor-intensive and prone to inconsistencies, especially in cases where failure progression is gradual or ambiguous. Furthermore, real-world operational data rarely includes run-to-failure records, complicating the application of supervised learning frameworks [14].
## 3 Results and Discussion
### Volatiles Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & **Identification 1** & **RI** & \multicolumn{3}{c}{**Content (ug/g) 2**} & \multicolumn{2}{c}{**Reference**} \\ \hline  & & & **0 h** & **48 h** & **1 st Week** & **4 th Week** & \\ \hline \multicolumn{8}{c}{Alcohols} \\ \hline Ethanol & A & 932 & 10.0 \(\pm\) 2.23 d  & 55.0 \(\pm\) 3.47 d  & 69.0 \(\pm\) 4.71 b  & 99.0 \(\pm\) 5.35 a  & [45] \\
3-Methyl-1-butanol & A & 1213 & 6.3 \(\pm\) 1.33 a  & 2.5 \(\pm\) 0.77 b  & 1.7 \(\pm\) 1.05 b  & 1.6 \(\pm\) 0.93 b  & [45] \\
2-Ethyl-1-hexanol & A & 1494 & 0.4 \(\pm\) 0.05 b  & 0.2 \(\pm\) 0.05 c  & 0.4 \(\pm\) 0.08 b  & 0.6 \(\pm\) 0.03 a  & [45] \\
1-Penten-3-ol & B & 1165 & 0.2 \(\pm\) 0.04 & tr & tr & tr & [45] \\
1-Hexanol & A & 1358 & 18.8 \(\pm\) 3.14 a  & 1.7 \(\pm\) 0.05 c  & 2.0 \(\pm\) 0.10 b  & 1.9 \(\pm\) 0.05 b  & [45] \\
3(25)-3-Hexen-1-ol & B & 1367 & 0.1 \(\pm\) 0.03 a  & 0.1 \(\pm\) 0.04 a  & 0.1 \(\pm\) 0.03 a  & [46] \\
3(2)-3-Hexen-1-ol & B & 1387 & 0.7 \(\pm\) 0.14  & 0.1 \(\pm\) 0.04 a  & 0.1 \(\pm\) 0.03 a  & [46] \\
2(25)-2-Hexen-1-ol & B & 1409 & 3.7 \(\pm\) 0.39 a  & 0.1 \(\pm\) 0.04 a  & 0.3 \(\pm\) 0.04 b  & 0.2 \(\pm\) 0.03 c  \\
2(27)-2-Penten-1-ol & B & 1316 & 0.1 \(\pm\) 0.04 a  & 0.1 \(\pm\) 0.03 a  & tr & [45] \\ Benzyl alcohol & B & 1875 & 35.3 \(\pm\) 4.81 a  & 0.3 \(\pm\) 0.05 a  & 0.1 \(\pm\) 0.04 c  & [45] \\ Phenylethyl Alcohol & A & 1912 & 1.2 \(\pm\) 1.6 a  & 0.2 \(\pm\) 0.05 b  & 0.1 \(\pm\) 0.02 c  & 0.1 \(\pm\) 0.03 c  & [45] \\
2-Methylprogan-2-ol & B & 907 & 10.1 \(\pm\) 2.09 a  & 4.1 \(\pm\) 0.89 b  & 4.8 \(\pm\) 1.34 b  & 4.8 \(\pm\) 1.07 b  \\
2-Methylbutan-2-ol & B & 1018 & 3.9 \(\pm\) 0.63 a  & 1.7 \(\pm\) 0.35 c  & 1.9 \(\pm\) 0.27 c  & 2.8 \(\pm\) 0.34 b  & \\
1-Propanol & A & 1043 & 0.3 \(\pm\) 0.05 & tr & tr & tr & \\
2-Methyl-3-buten-2-ol & B & 1045 & tr & 0.1 \(\pm\) 0.02 a  & 0.1 \(\pm\) 0.06 a  & \\
2-Methyl-1-propanol & A & 1103 & 5.4 \(\pm\) 1.23 a  & 1.9 \(\pm\) 0.47 b  & 1.4 \(\pm\) 0.38 b  & 1.6 \(\pm\) 0.34 b  & [47] \\
2-Methyl-1-propanol & B & 1113 & 0.8 \(\pm\) 0.02 a  & 0.4 \(\pm\) 0.03 c  & 0.5 \(\pm\) 0.02 b  & 0.5 \(\pm\) 0.01 b  & [47] \\
3-Pentanol & A & 1117 & 0.1 \(\pm\) 0.04 & tr & tr & tr & \\
2-Pentanol & A & 1129 & 3.3 \(\pm\) 0.04 a  & 0.6 \(\pm\) 0.05 b [FONOTE:b]Footnote a: footnotemark: [ENDFOOTNOTE] & 0.3 \(\pm\) 0.04 c  & 0.3 \(\pm\) 0.07 c  & [48] \\
1-Butanol & A & 1151 & 0.4 \(\pm\) 0.07 a  & 0.1 \(\pm\) 0.04 b  & 0.1 \(\pm\) 0.03 b  & 0.1 \(\pm\) 0.03 b  & [48] \\
4-Methyl-2-pentanol & B & 1174 & 0.2 \(\pm\) 0.04 a [FOOTNOTE:a]Footnote a: footnotemark:
The occurrence of organic esters during fermentation at 4 \({}^{\circ}\)C is considered as a positive fact, as they may pose fruity and floral notes to the fermented juice even at low concentrations [27; 48]. Aldehydes on the other hand are considered as unpleasant flavors in foods, while juices may be enhanced in aldehyde content via polyphenols oxidation [51].
## 3 Simulation Scheme Description
### Description of the SWAN Scheme  
The SWAN model uses a structured grid, with a grid resolution set to 0.1\({}^{\circ}\), and a time step of 10 min. The simulation period aligns with that of the WRF model, with effective wave height data output every hour. Figure 4 shows the SWAN simulation domains for Typhoon Surigae and Typhoon Neperatk. The driving wind field for the SWAN model will use wind field data from both the WRF simulations and the empirical parameter typhoon models. The \"cubic\" interpolation method is employed to interpolate the wind speed data to the SWAN simulation domain. The bathymetry data used in the SWAN simulations come from the ETOPO1 global topographic elevation dataset, published by the National Geophysical Data Center (NGDC) of the United States.
## 2 Scattering Rate
### Inelastic Scattering
The scattering rate \(\tau^{-1}(E)=\tau_{\mathrm{sc}}^{-1}(E)+\tau_{\mathrm{imp}}^{-1}(E)+\tau_{ \mathrm{sp}}^{-1}(E)\) as a function of the carrier energy calculated from Equations (1), (3), and (4) for graphene in the air at a distance of \(d=0.34\,\mathrm{nm}\) above a semi-infinite SiO\({}_{2}\) substrate is shown in Figure 1a. The physical parameters of graphene used in the calculation are \(E_{\mathrm{F}}\equiv\mu=100\) meV, \(T=300\) K, and charged impurity density \(n_{\mathrm{i}}=4.4\times 10^{11}\,\mathrm{cm}^{-2}\) on the same order of magnitude as the carrier density given by \(k_{\mathrm{F}}^{\ 2}/\pi\), where \(k_{\mathrm{F}}=E_{\mathrm{F}}/\hbar v_{\mathrm{F}}\). The physical parameters of SiO\({}_{2}\) for the calculation of \(\tau_{\mathrm{sp}}^{-1}(E)\) can be found in Reference [32]. As can be seen from the data plotted in Figure 1a, \(\tau_{\mathrm{imp}}^{-1}\left(E\right)\) contributes the most to the total scattering rate. For carriers of large carrier energy \(E\), \(\tau_{\mathrm{imp}}^{-1}\left(E\right)\) decreases with increasing \(E\), approaching the behavior described by Equation (3). The second important scattering mechanism is \(\tau_{\mathrm{sp}}^{-1}(E)\), which is contributed by two surface phonon modes \(\hbar\omega_{1}\) and \(\hbar\omega_{2}\) of SiO\({}_{2}\). The overall \(\tau_{\mathrm{sp}}^{-1}(E)\) increases with \(E\) mostly due to the fact that the density of states increases with the carrier energy. Most notably \(\tau_{\mathrm{sp}}^{-1}(E)\) begins to increase rapidly for \(E>\hbar\omega_{2}\equiv 156\,\mathrm{meV}\), which marks the onset of the intraband phonon emission of \(\hbar\omega_{2}\). The scattering rate \(\tau^{-1}(E_{\mathrm{F}})\) contributed by different scattering mechanisms as a function of \(E_{\mathrm{F}}\) is shown in Figure 1b. To better see the dependence of the scattering rate on \(E_{\mathrm{F}}\), the curves in Figure 1b are normalized to \(\tau^{-1}(E_{\mathrm{F}})\) and are shown in Figure 1c. As can be seen, the relative weight of phonon scattering increases due to the increase of the density of states with \(E_{\mathrm{F}}\). By contrast, \(\tau_{\mathrm{imp}}^{-1}\left(E_{\mathrm{F}}\right)\) decreases with \(E_{\mathrm{F}}\) because of the enhanced screening by the increasing carrier density \(n=\sqrt{\pi E_{\mathrm{F}}/\hbar v_{\mathrm{F}}}\).
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 2 Experimental Procedure
### Specimens and Materials  
In the current study, 34 prismatic reinforced concrete specimens, each with dimensions of 80 \(\times\) 80 \(\times\) 320 (mm) were cast with concrete class C20/25 and a centrally positioned ribbed reinforcing bar of nominal diameter (d\({}_{0}\)) equal to 16 mm. The total length of the reinforcing bars was equal to 340 mm, while the technical class of steel was B500c, the chemical composition of which is summarized in Table 1. Moreover, two adjacent surfaces of each prismatic specimen were protected with a special anticorrosive coating (epoxyresin), so as to simulate the reinforcing bars positioned in the corners of RC elements in existing structures. The materials were appropriately selected to represent a typical structure of the period 1990-2010 located in coastal and earthquake prone areas of the Mediterranean basin (Greece, Turkey, Italy, Egypt), designed and built by neglecting the more recent guidelines of EN 206 on the use of concrete class of C25/30 or C30/37.  
Preceding the concrete mixing into the molds, the initial mass of all reinforcing steel bars was recorded. Afterwards, all reinforcing steel bars were positioned into the molds to cast the concrete, which was compacted via a table vibrator (Figure 1). For the concrete mixture, a Portland cement was used, with a water-cement ratio equal to 0.55 and a 20 mm maximum size coarse aggregate. The total amount of RC specimens was kept for three days in a wet environment at 22 \({}^{\circ}\)C. Upon the completion of wetting, the molds were carefully removed, and the specimens were cured for 28 days, once again in a wet environment under the same temperature of 22 \({}^{\circ}\)C (Figure 2). The mean value of 28-day recorded compressive strength was measured on concrete cube specimens of 200 mm\({}^{3}\) with an average value of f\({}_{\rm c}\) equal to 30 MPa.
## 3 Results and Discussion
### Conclusions  
In this work, a symmetrical cell of the La\({}_{4}\)Ni\({}_{3}\)O\({}_{10\pm 5}\)-BaCe\({}_{0.9}\)Y\({}_{0.1}\)O\({}_{3\text{-}6}\) composite cathode on the BaCe\({}_{0.9}\)Y\({}_{0.1}\)O\({}_{3\text{-}6}\) electrolyte substrate was successfully analysed with respect to the dominant electrode mechanism under wet (\(p_{\text{H2O}}\sim 10^{-2}\) atm) and low humidity (\(p_{\text{H2O}}\sim 10^{-5}\) atm) conditions in O\({}_{2}\) atmosphere (400-550 \({}^{\circ}\)C). Equivalent circuit modelling was revealed to be challenging on the analysis and correct interpretation of the individual processes of the polarisation resistance, leading to large fitting errors for each distributed element. In this respect, a detailed analysis of the stability of the impedance data against _Kramers-Kronig_ (KK) relations and _Bayesian_ Hilbert transform (BHT) simulations allowed to detect significant deviations from stability. To try to accommodate these concerns, a combination of three different methodologies for the distribution function of relaxation times (DFTR) analysis permitted the identification of the main processes governing the electrode mechanism. Data were also corrected for short-circuiting due to an increase of electronic p-type conductivity from the electrolyte substrate with increasing temperature and decreasing humidity. In a more global conclusion, the current work highlights that the excellent performance of the La\({}_{4}\)Ni\({}_{3}\)O\({}_{10\pm 5}\)-BaCe\({}_{0.9}\)Y\({}_{0.1}\)O\({}_{3\text{-}6}\) composite cathode can be maintained on reducing water vapour partial pressure to the very low humidity conditions of \(p_{\text{H2O}}\sim 10^{-5}\) atm, for operation in nominally dry conditions in the intermediate temperature range (350-600 \({}^{\circ}\)C).  
Conceptualization, F.J.A.L. and D.P.F.; methodology, F.J.A.L., D.P.F. and D.R.; formal analysis, F.J.A.L., D.P.F., D.R., V.C.D.G., L.I.V.H. and S.M.M.; investigating, F.J.A.L., D.P.F., D.R., V.C.D.G., L.I.V.H. and S.M.M.; writing--original draft preparation, F.J.A.L., D.R., V.C.D.G., L.I.V.H. and S.M.M.M.; writing--review and editing, F.J.A.L. and D.P.F.; funding acquisition, F.J.A.L. and D.P.F. All authors have read and agreed to the published version of the manuscript. This research was partially funded by FCT (Fundacao para a Ciencia e a Tecnologia), grants numbers PTDC/CTM-CTM/2156/2020, PTDC/QUI-ELT/3681/2020, POCI-01-0247-FEDER-039926, POCI-01-0145-FEDER-032241, UIDB/00481/2020 and UIDP/00481/2020; and also, by Centro Portugal Regional Operational Programme (Centro2020), under the FORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), grant number CEN-TRO-01-0145-FEDER-022083. The data supporting this research article are available upon request to the corresponding authors. The authors declare no conflict of interest.
## Materials, Methods, and Concept  
### Materials  
Current basic hardware/technologies and gadgets presented in this paper are shown in Figure 2:  
1. The dental monitoring scan box;
2. Retractor;
3. The patient's smartphone.  
The hardware shown in Figure 2 represents the tools necessary for telehealth applications (Dental Monitoring(r) (DM)) (Dental Monitoring Co., Paris, France) to record an intraoral situation in a patient's home. What is central is that the software, which is powered by AI, can recognize various parameters from video datasets. With the age of AI technology and telecommunications, the healthcare system is changing rapidly. Several telecommunications systems have been applied to hospitals, and over time, a new term, \"telemedicine\" now represents a real thing in clinical practice [29].  
Figure 2: The Dental Monitoring® (DM) (Dental Monitoring Co., Paris, France) set up—from the left is the retractor, the middle top is the dental monitoring scan box, and the right is the patient’s phone.