esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time 3D Reconstruction Method for Holographic Telepresence
###### Abstract  
This paper introduces a real-time 3D reconstruction of a human captured using a depth sensor and has integrated it with a holographic telepresence application. Holographic projection is widely recognized as one of the most promising 3D display technologies, and it is expected to become more widely available in the near future. This technology may also be deployed in various ways, including holographic prisms and Z-Hologram, which this research has used to demonstrate the initial results by displaying the reconstructed 3D representation of the user. The realization of a stable and inexpensive 3D data acquisition system is a problem that has yet to be solved. When we involve multiple sensors we need to compress and optimize the data so that it can be sent to a server for a telepresence. Therefore the paper presents the processes in real-time 3D reconstruction, which consists of data acquisition, background removal, point cloud extraction, and a surface generation which applies a marching cube algorithm to finally form an isosurface from the set of points in the point cloud which later texture mapping is applied on the isosurface generated. The compression results has been presented in this paper, and the results of the integration process after sending the data over the network also have been discussed.  
3D reconstruction; telepresence; telepresence holographic Article Real-Time 3D Reconstruction Method for Holographic Telepresence  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] 3D Reconstruction Method for Holographic Telepresence. _Appl. Sci._**2022**, 12, 4009. [[https://doi.org/10.3390/app12084009](https://doi.org/10.3390/app12084009)]([https://doi.org/10.3390/app12084009](https://doi.org/10.3390/app12084009))
## 6 Simulation Results
### Robustness Comparison with Existing Watermarking and Zero-Watermarking Schemes  
This section compares the proposed scheme with some recent bio-signal watermarking and zero-watermarking schemes presented in [10; 17; 18; 19; 23; 61; 62] in terms of robustness against different types of attacks (see Table 4).  
It is worth mentioning that the present comparison was performed on a PC (4 GB of RAM and 2.4 GHz CPU) using Python as the programming language. The reason for performing the current comparison on a PC was that the compared schemes were not natively implemented on a Raspberry Pi board. The bio-signals used in the present comparison were the same ones used in Section 6.2 through 6.4. Next, each zero-watermarking technique was applied to the test signals, and the average BER value corresponding to the extracted watermark was computed and then is reported in Table 4. The results presented in this table indicated, on the one hand, that the transform-based watermarking and zero-watermarking schemes presented in [17; 19; 23; 61; 62] are more robust to the variety of attacks compared to the schemes implemented in the spatial domain [10; 18]. On the other hand, we can clearly notice the superiority of the proposed scheme designed in this work over the compared schemes. This superiority can be explained by the fact that our scheme was originally developed to provide strong resistance against different types of attacks, especially cropping, scaling, and noise attacks.  
After validating the strong robustness of the proposed scheme against different types of signal-processing attacks, the efficiency of our scheme in terms of execution speed is investigated in the next section.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 4 Results and Discussion
### Model Training and Evaluation  
In this study, two hidden layers containing ReLU activation functions were setup, with 12 neurons in the first hidden layer and 8 neurons in the second hidden layer. For the experiment, a stratified sampling method was used: 70% of the original dataset was allocated to the training set, 30% to the testing set, and 20% of the training set was further divided into a validation set. The datasets were independent of each other, without duplication. During training, the cross-entropy loss function was employed as the optimization objective function, and parameters were updated using the Adam adaptive optimizer. The learning rate was set to 0.005, and the Dropout layer discard ratio was set to 0.1. After 100 epochs of training, the FCNN model exhibited stable convergence characteristics. The change curves of the loss rate and accuracy are shown in Figure 16a, where the loss function values for the training set showed a monotonically decreasing trend, and the classification accuracy of the validation set stabilized at about 98.7%. To quantitatively evaluate the classification performance of the model, Figure 16b presents the confusion matrix for the test set, showing the classification of each example in the test set. The model demonstrated high classification accuracy and good overall performance.
## 2 Materials and Methods  
### Membrane-Based Desorber  
The desorber uses the configuration presented in Figure 2. The microporous flat membrane separates the vapour from the H\({}_{2}\)O-LiBr nanoparticles flowing through rectangular microchannels. Hot water circulates through a similar set of microchannels, transferring heat to the nanofluid. A metallic wall separates the hot water from the nanofluid. Geometrical and operating data employed in the simulation appear in Table 1.  
Thermal power transmitted from the heating fluid to the nanofluid channel is equal to the sum of the power required for vapour desorption and that stored in the nanofluid:  
\[q_{hw}=\dot{m}_{w}i_{l\upsilon}+q_{nf} \tag{1}\]  
Energy losses to the surroundings have been neglected because the simulated system is surrounded by adiabatic walls. In a real desorber, energy losses can be reduced to a negligible amount if the isolating system is correctly dimensioned. The heat transferred and stored in a differential element \(j\) is, respectively, as follows:  
\[q_{hm\upsilon}^{j}=\dot{m}_{hw}(i_{hw}^{j}-i_{hw}^{j+1})=\dot{m}_{hw}Cp_{hm \upsilon}^{j}(T_{hw}^{j}-T_{hw}^{j+1}) \tag{2}\]  
\[q_{nf}^{j}=(\dot{m}\cdot i)_{nf}^{j+1}-(\dot{m}\cdot i)_{nf}^{j} \tag{3}\]The heat transferred across the metallic wall from the hot water to the nanofluid is as follows:  
\[q_{htw}^{j}=U_{nf\_fn}^{j}l_{nf}dz(T_{htw}^{j}-T_{nf}^{j}) \tag{4}\]  
The global coefficient, \(U\), is estimated considering conduction heat transfer through the metallic wall and convection in the hot water and nanofluid channels. Two possible desorption modes in the nanofluid channels are considered: direct diffusion (single-phase) and boiling (two-phase). The first type occurs if \(T_{nf}-T_{sat}<0\) and the second one when \(T_{nf}-T_{sat}>0\). Single-phase region includes the thermal entrance zone and the fully developed region and correlations of [28; 29] are employed to predict heat transfer. During boiling, the correlation of [30] is employed to estimate the heat transfer. To account for the influence on heat transfer of mass transfer, the nanofluid heat transfer coefficient is multiplied by the Ackermann factor [31]. In the hot water channels, the same equations used to estimate the nanofluid heat transfer in the single-phase region are used. To predict mass transfer, nanofluid and vapour mass balances are employed. Salt mass fraction is calculated as follows:  
\[x^{j+1}=x^{j}\frac{\dot{m}_{nf}^{j}}{\dot{m}_{nf}^{j+1}} \tag{5}\]  
The rate of vapour separation is a function of the desorption rate \(j\):  
\[\dot{m}_{v}^{j}=J^{j}\cdot l_{nf}\cdot dz \tag{6}\]  
\(J\) depends on the difference between the vapour partial pressure, calculated at the bulk nanofluid temperature and concentration, and the pressure in the vapour channel:  
\[J^{j}=K^{j}(P_{nf}^{j}-P_{v})\]
## 4 Applied Methods  
### Autoregressive Integrated Moving Average (ARIMA)  
[PERSON] and [PERSON] [85] developed a general forecasting approach to predict future points in the time series. ARIMA can be applied in some cases where data show signal of non-stationarity. In this approach, the future value of a variable is presumed to be a linear function of periodic past explanations and random errors. This model is known as ARIMA (p, q, d), where p stands for number of time lags of the autoregressive model, q is the instruction of moving-average parameters, and d is the number of times the data have had past values subtracted [86]. The \(d\) parameter is estimated by the number of times the differencing is achieved on the time series [87]. In order to build the best ARIMA model for forecasting of iron ore price, \(p\) and \(q\) parameters have to be efficiently determined for a proper model. To estimate the best model, we use the criteria as follows: Bayesian information criterion (BIC), R-squared, root-mean-square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). Table 2 shows the different parameters \(p\), \(q\), and d in the ARIMA model. ARIMA (0, 1, 1) shows the best performance compared to other presented models in Table 2. The residual plots  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Description** & **Max** & **Min** & **Unit** & **Variable** \\ \hline Gold (UK), 99.5\% fine, London & & & US Dollars per Troy & Gold Price \\ afternoon fixing, average of daily & 1770.95 & 256.08 & \begin{tabular}{c} Outree \\ \end{tabular} & \
\begin{tabular}{c} \\ \end{tabular} &
\begin{tabular}{c} \\ \end{tabular} \\ research.stlouisfed.org & 1.5997 & 0.9553 & - & Exchange Rate \\ Crude Oil (petroleum), simple & & & & \\ average of three spot prices. Dated & 132.55 & 10.41 & US Dollars per Barrel & Oil Price \\ Brent, West Texas Intermediate, and & & & & \\ the Dubai Fateh & & & & \\ www.  
of autocorrelation function (ACF) and partial autocorrelation function (PACF) in Figure 3 shows the model is adequate as it shows a random variation from the origin zero (0), the points below and above are all uneven, hence the model fitted is acceptable.
## 3 Results and Discussion
### Raman Spectroscopy Measurement Results  
As visible in the DSC spectra, orange samples were suspected to have another component or compound which was not visible in the EDS images. To reveal this compound and the difference in the composition or the bond structure of the orange material compared to the other three, the green and orange samples were examined by Raman microscopy. Figure 11 reports the Raman spectra of the investigated samples. It is necessary to identify both spectra: the red dashed line belongs to the orange sample, while the black continuous line belongs to the green sample.  
Figure 11: Raman spectra of the green and orange samples.  
Figure 10: The DSC curves.  
During the measurement, counts were registered at wavenumbers from 50 to 3600 cm\({}^{-1}\). The changes of the main peaks, the Raman bands, are shown in Table 6.  
It was established that the boson peak (connected with the metallic content of the samples), the metallic oxide bonds, basic bonds of the glassy network, some organic bonds, and OH modes could be observed in both samples. However, the intensities of the peaks at the same investigation parameters were larger for the orange sample. This could be connected with the higher density of the material. Moreover, the Raman spectra of the orange sample contain peaks associated with acrylic acid: a band at the wavelength of 1637 cm\({}^{-1}\) is reported in Table 6. All these structural components and differences could influence the previously shown experimental results. From the Raman spectra, it can also be concluded that all of the samples had an amorphous structure.
# Flash Floods Hazard to the Settlement Network versus Land Use Planning (Lublin Upland, East Poland)
## 3 Methods
### GIS Analysis
After the catchments were assigned scores for each criterion, they were summed (Formula (1)), and the catchments were classified according to the criteria summarized in Table 3. Categories were distinguished by dividing the set into five equal ranges:  
\[FF=\sum_{i=1}^{n}(w_{i}x_{i}) \tag{1}\]  
where FF is susceptibility to flash flood formation, \(w_{i}\) is weight of parameter, \(x_{i}\) is parameter, and n is number of parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Area** & \multirow{2}{*}{**Pts**} & \multicolumn{2}{c}{**Share of**} & \multirow{2}{*}{**Pts**} & \multirow{2}{*}{**Geomorphous ***} & \multirow{2}{*}{**Pts**} & \multicolumn{2}{c}{**Mean**} \\ \cline{4-9} (**km2**) & & & & & & & & **Slope (\({}^{\circ}\))** & **Pts** \\ \hline
0.0–0.25 & 5 & 0–20 & 2 & 0–10 & 5 & Ridge, flat & 2 & \textless{}2.0 & 2 \\
0.26–0.50 & 4 & 21–40 & 4 & 11–20 & 4 & Spur, shoulder & 4 & 2.1–4.0 & 4 \\
0.51–0.75 & 3 & 41–60 & 6 & 21–30 & 3 & Slope, footslope & 6 & 4.1–6.0 & 6 \\
0.76–1.00 & 2 & 61–80 & 8 & 31–40 & 2 & Hollow & 8 & 6.1–8.0 & 8 \\
1.01–10.0 & 1 & 81–100 & 10 & 41–50 & 1 & Valley, depression & 10 & \textgreater{}8.0 & 10 \\ \hline
**Local relief** & \multirow{2}{*}{**Pts**} & \multirow{2}{*}{**Circularity**} & \multirow{2}{*}{**Pts**} & \multirow{2}{*}{**Drainage Slope (\({}^{\circ}\))**} & \multirow{2}{*}{**Pts**} & \multirow{2}{*}{**Roads Density (km/km2)**} & \multirow{2}{*}{**Pts**} \\ \cline{1-1}
1.05–15 & 1 & \textgreater{}0.4 & 1 & \textless{}2.0 & 1 & 0–2.5 & 1 \\
15–30 & 2 & 0.3–0.4 & 2 & 2.0–4.0 & 2 & 2.6–5.0 & 2 \\
30–45 & 3 & 0.2–0.3 & 3 & 4.0–6.0 & 3 & 5.1–7.5 & 3 \\
45–60 & 4 & 0.1-0.2 & 4 & 6.0–8.0 & 4 & 7.6–10.0 & 4 \\ \textgreater{}60 & 5 & \textless{}0.1 & 5 & \textgreater{}8.0 & 5 & \textgreater{}10.0 & 5 \\ \hline \hline \end{tabular}  
* this criterion does not take into account the distribution of values throughout the catchment and refers only to the location of the pour point in relation to the geomorphon type; Pts—points assigned to the range of values.  
\end{table} 
Table 2: Evaluation criteria. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Points** & **Category** & **Susceptibility** \\ \hline
22–28 & 1 & Very low \\
29–35 & 2 & Low \\
36–42 & 3 & Moderate \\
43–49 & 4 & High \\
50–56 & 5 & Very high \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rules for determining catchment susceptibility categories. In the next step, the number of catchments was reduced by combining those that lay on the same runoff line and had outlets (closing points) that were close to each other (Figure 4). The smaller catchments were removed, leaving the larger catchments with the lowest outlets. However, any smaller catchments located inside larger ones but not supplied from the main runoff line were left as separate sites for analysis. In this way, the number of catchments was reduced from 12,024 to 5468.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 4 Transparent Armor Nano-Ceramics
### Summary  
At present, it is difficult for MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics to achieve the theoretical density required for transparency by conventional sintering methods because they are very sensitive to powder size, agglomerates, impurities, and additives. The defects of the stoichiometric ratio, impurities, particle size, and agglomerates of the starting nano powders are generally difficult to improve by adjusting the process parameters, which affect the optical properties of the prepared samples. To obtain MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics with high optical transparency, high-quality starting powders are indispensable. Meanwhile, it is also necessary to optimize the concentration of sintering aids or add some rare earth elements. Existing commercial MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics are sintered by pressureless sintering/HIP or hot-pressing sintering/HIP. Thereafter, the manufacturing cost of these processes is still high, and it is difficult to produce large plates.  
The AlON powder synthesized in the liquid phase has relatively high purity, small particle size, uniform distribution, and relatively high chemical activity, but the synthesis  
Figure 14: Transparent armor nano-ceramics; (**a**) MgAl\({}_{2}\)O\({}_{4}\) transparent nano-ceramics, 12 mm thick, created through pre-sintering in air and HIP methods [83]. Reprinted with permission from Ref. [83]. Copyright 2014, copyright ScienceDirect. (**b**) a photograph revealing the transmittance of the Mg-\(\gamma\)-AlON transparent ceramic after HIP treatment [96]. Reprinted with permission from Ref. [96]. Copyright 2019, copyright ScienceDirect.  
Figure 15: Comparison of transparent armor materials for STANAG 4569; (**a**) Level 2 and (**b**) Level 3 [65]. Reprinted with permission from Ref. [65]. Copyright 2017, copyright ELSEVIER.  
process conditions are not suitable for large-scale production. The solid-phase pulverization method can ball-mill micron-sized AlON powder to the nano-scale; the preparation process is relatively simple, and it is easy to achieve large-scale production, but ball milling causes powder lattice distortion increases the defect concentration, and introduces impurities. Therefore, the sintered ceramics have special structures such as impurities, which affect the optical properties. As reviewed in Section 4.3, AlON transparent nano-ceramics usually need to be sintered for a long time above 1800 \({}^{\circ}\)C without external field assistance; otherwise, it is difficult to make these materials fully dense. Although preparation methods have undergone great progress in the past few decades, there are still some difficulties in the large-scale preparation process, such as shrinkage during drying and cracks forming inside the green body. In addition, the sintering of large-sized AlON transparent nano-ceramics also causes an uneven microstructure due to uneven temperatures, which in turn leads to stress birefringence and reduces their optical properties.
## 3 Results
### Optimization and Modeling in Energy Systems
#### 3.1.3 Comparative Analysis  
Co-optimization in distributed networks represents a substantial improvement over centralized systems, offering enhanced flexibility, resilience, and adaptability [34]. One key distinction lies in the superior capacity of distributed systems to handle uncertainties through advanced methodologies such as stochastic and robust optimization. Unlike centralized systems, which often rely on deterministic models and historical data, distributed frameworks like ADMM-based models dynamically address uncertainties in real-time, significantly enhancing the operational reliability [25]. Another advantage of distributed approaches is their ability to integrate multiple objectives and diverse stakeholders. For instance, Nash Bargaining-based models also ensure equitable benefit distribution among participants--an aspect often overlooked in centralized systems [35].  
Decentralization also brings improved data privacy and security. Blockchain-enabled platforms eliminate the need for intermediaries, creating a more transparent and secure environment for P2P energy transactions [18]. This contrasts sharply with centralized systems, which are more vulnerable to cybersecurity risks and often lack transparency in operations. Then, distributed systems are better positioned to adopt emerging technologies such as AI and evolutionary algorithms. These tools enable dynamic resource optimization in decentralized networks, overcoming the rigidity and scalability issues inherent to centralized structures [24]. The inherent adaptability of distributed systems positions them as a practical solution for the dynamic and multifaceted challenges of modern energy systems.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 2 Materials and Methods
### Feature Fusion  
An attention-based fusion method enhances the model's focus on critical spatial and temporal features. The method weights the spatial feature \(\mathbf{v}_{i}\) and temporal feature \(\mathbf{k}_{i}\) of \(\mathbf{x}_{i}^{\mathbf{v}}\) according to their contributions to load forecasting accuracy in the Logical Range. The weight computation follows Equation (10):  
\[q_{i}=\text{softmax}(\frac{\mathbf{v}_{i}\mathbf{k}_{i}^{T}}{\sqrt{\lambda_{k_{i}}}}) \mathbf{k}_{i}, \tag{10}\]  
where \(\sqrt{\lambda_{k_{i}}}\) is the dimension weight for \(\mathbf{k}_{i}\), and \(\mathbf{q}_{i}\) is the attention-fused feature vector.  
A fully connected layer transforms \(\mathbf{q}_{i}\)'s dimensions as shown in Equation (11):  
\[y_{i}=\mathbf{W}\mathbf{q}_{i}, \tag{11}\]  
where \(\mathbf{W}\) is the weight matrix and \(y_{i}\) is the final forecasting result.
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 2 Total Uplift Force on Pile in Expansive Soils
Some research has suggested that the lateral constant-volume swelling pressure can be considered by a reduction factor, \(\beta\), ranging from approximately 0.7 [26] to 1 [27] times the vertical constant-volume swelling pressure. This reduction factor, \(\beta\), allows for an accurate consideration of the differences between horizontal and vertical swelling pressures, thereby facilitating more precise design guidelines for piles and foundation structures in expansive soils. The uplift forces, \(P_{FS}\), can be calculated using the given equations, considering parameters such as pile diameter, \(d\), pile length in the active zone, \(L\), and matric suction, \((u_{a}-u_{w})\). \[P_{FS}=\pi dL\bigg{\{}C_{a}^{\prime}+(K\sigma_{v}^{\prime})tan\phi^{\prime}+ \big{[}\beta\big{(}\sigma_{cv}^{\prime\prime}\big{)}_{v}\big{]}tan\phi^{\prime} +(u_{a}-u_{w})tan\bigg{[}\phi^{\prime}\bigg{(}\frac{\theta-\theta_{r}}{\theta_{ s}-\theta_{r}}\bigg{)}\bigg{]}\bigg{\}} \tag{8}\]  
Considering that \(C_{a}^{\prime}\) and \((K\sigma_{v}^{\prime})tan\phi^{\prime}\) are relatively small compared to the swelling pressure, the values of \(C_{a}^{\prime}\) and \((K\sigma_{v}^{\prime})tan\phi^{\prime}\) shown in Equation (8) can be considered negligible. Therefore, the uplift forces can be further simplified as:  
\[P_{FS}=\pi dL\bigg{\{}\big{[}\beta\big{(}\sigma_{cv}^{\prime\prime}\big{)}_{v }\big{]}tan\phi^{\prime}+(u_{a}-u_{w})tan\bigg{[}\phi^{\prime}\bigg{(}\frac{ \theta-\theta_{r}}{\theta_{s}-\theta_{r}}\bigg{)}\bigg{]}\bigg{\}} \tag{9}\]  
Or  
\[P_{FS}=\pi dL\bigg{\{}\alpha\big{(}\sigma_{cv}^{\prime\prime}\big{)}_{v}+(u_ {a}-u_{w})tan\bigg{[}\phi^{\prime}\bigg{(}\frac{\theta-\theta_{r}}{\theta_{s} -\theta_{r}}\bigg{)}\bigg{]}\bigg{\}} \tag{10}\]  
where the value of \(\alpha\) is the coefficient of uplift and is equal to \(\beta\) times the tangent of \(\phi^{\prime}\). As shown in Equation (10), the coefficient of uplift, \(\alpha\), plays a critical role in calculating the pile uplift force. Researchers have proposed various empirical values for this parameter. [PERSON] (1988) [28] suggested that \(\alpha\) should range from 0.09 to 0.18, while [PERSON] and [PERSON] (1992) [14], building on the work of [PERSON] [28] and [PERSON] (1988) [29], recommended a broader range of 0.10 to 0.25. A survey conducted by the Colorado Association of Geotechnical Engineers (CAGE) [9] on pier designs for three expansive soil profiles revealed that 10 respondents predominantly used an \(\alpha\) value of 0.15, except for one respondent who used 0.05 and another who used 0.07. CAGE (1999) [9] indicated that an \(\alpha\) value of 0.15 is commonly adopted in the Front Range area of Colorado, USA. However, [PERSON] (2005) [30] indicated that the \(\alpha\) value ranges from 0.4 to 0.6 based on a long-term research study conducted at the Expansive Soils Field Test Site at Colorado State University (CSU), Colorado, USA.