esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed DanRETwin Solution
### Overall Framework
On top of that, an optimization approach is integrated with the digital twin solution to select the most efficient and/or cost-effective solution for modification in the specific building. A simplified illustration of the DanRETwin digital twin solution with the main technology components and connections is shown in Figure 6. On this basis, the proposed DanRETwin digital twin will provide the following services:  
1. Building data acquisition, storage, integration, management, and exchange employing a communication-enabling ICT infrastructure preserving privacy, security, and quality. This includes data from sensors, as indoor temperature, CO\({}_{2}\), humidity, PIR, as well as energy meters on various levels, as heating, electricity, and water flow. 2. Energy retrofit decision-making support and the capability of testing and simulating various options, scenarios, and systems integration to optimize the selection and implementation of renovation technologies on the level of the building envelope and the energy generation and supply systems. 3. Post-retrofitting retro-commissioning of the energy systems and the automation and control system to ensure a fault-free and energy efficient restart of the building after the retrofit process and proper integration of various components and units. 4. Data-driven performance optimization platform employing advanced machine learning and artificial intelligence techniques to drive building control and management. Furthermore, it is expected that the implementation of the proposed digital twin platform will: - Improve the energy efficiency of existing buildings and allow around a 15% reduction in buildings' energy demand, with an average reduction of 53 kWh/(m\({}^{2}\).y) and energy savings up to 60% in the case of deep energy retrofitting [1]. - Provide comprehensive retro-commissioning and smarter buildings with data-based operational management and generate up to a 20% reduction in a building's maintenance and energy costs [42]. - Allow an average reduction of 18% and up to 35% for deep energy retrofits in CO\({}_{2}\) emissions in buildings after the retrofit process [1]. Figure 5: Major differences between the current projects and practice and the proposed DanRETwin solution.
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
## 2 Model
### Underwater Vehicle Model
\begin{array}{l}\sum F_{X}=-(W-B)\sin\theta+X_{u|u|}u\left|u\right|+X_{u \dot{u}}\dot{u}+X_{uq}wq+X_{qq}qq+X_{qr}vr+X_{rr}\\ \sum F_{Y}=(W-B)\cos\theta\sin\phi+Y_{v|v|}v\left|v\right|+Y_{r|r}\dot{r}\left| r\right|+Y_{\dot{v}}\dot{v}+Y_{r}r+Y_{ur}ur+Y_{wp}wp+Y_{pq}pq\\ \hskip 14.226378 pt+Y_{uv}uv+Y_{uu}\delta t^{2}\delta_{r}\\ \sum F_{Z}=(W-B)\cos\theta\cos\phi+Z_{w|w|}w\left|w\right|+Z_{q|q}\left|q \right|+Z_{uv}\dot{w}+Z_{uq}uq+Z_{vp}vp\\ \hskip 14.226378 pt+Z_{rp}rp+Z_{uw}uw+Z_{uu}a^{2}\delta_{e}\\ \sum M_{K}=(y_{G}W-y_{B}B)\cos\theta\cos\phi-(z_{G}W-z_{B}B)\cos\theta\sin \phi+K_{P|p|}p\left|p\right|+K_{\dot{p}}\dot{p}+K_{prop}\\ \sum M_{M}=-(z_{G}W-z_{B}B)\sin\theta-(x_{G}W-x_{B}B)\cos\theta\cos\phi+M_{w| w|}w\left|w\right|+M_{q|q|}q\left|q\right|\\ \hskip 14.226378 pt+M_{\dot{w}}\dot{w}+M_{q}\dot{q}+M_{uq}uq+M_{vp}vp+M_{rp}rp+M_{ uw}uw+M_{uu}\delta t^{2}\delta_{e}\\ \sum M_{N}=(x_{G}W-x_{B}B)\cos\theta\sin\phi-(y_{G}W-y_{B}B)\sin\theta+N_{v|v |}v\left|v\right|+N_{r|r}\dot{r}\left|r\right|+N_{\dot{v}}\dot{v}\\ \hskip 14.226378 pt+N_{\dot{r}}\dot{r}+N_{ur}ur+N_{wp}wp+N_{uv}uv+N_{pq}pq+N_{ uu}\delta t^{2}\delta_{r}\end{array}  \tag{4}\]  
where \(W\) and \(B\) are the gravity and buoyancy of REMUS vehicle. \(\left|v\right|\) denotes the absolute value of \(v\), and so on for the other state items. The coefficients have been achieved from REMUS vehicle's experimental data successfully. Explanations and details for coefficients in Equation (4) can be referred to [PERSON]'s thesis [30]. \(X_{u|u|},X_{\dot{u}},X_{uq},X_{\dot{v}qr},X_{qq},X_{rr}\) are the cross flow drag and added mass coefficients in \(x\) direction. Similarly, we can have cross flow drag and added mass coefficients for other five directions. It should be noted that \(Y_{ur},Z_{uq},N_{ur},M_{uq}\) stand for resultants of added mass and Fin lift. \(M_{uw},N_{uv},Y_{uw},Z_{uw}\) represent coefficients which take fin lift, body lift force and munk moment into account. \(Y_{uu\dot{u}},Z_{uu\dot{u}},N_{uu\dot{u}},M_{uu\dot{u}},X_{T},K_{prop}\) are hydrodynamic coefficients related to control fins and thrusters. Coupling disturbances are the interference of various degrees of freedom in underwater vehicle due to dynamic coupling. Vibrations in the state of one freedom such as pitch channel will bring oscillations for other freedoms such as roll channel, and vice versa. Hence, the vehicle system has to consume more energy to keep the vehicle balanced in the water. In Equation (4), changes in pitch channel are transmitted to the roll by the variation of the pitch angle \(\theta\), the oscillation in \(p\) affect items in pitch freedom that are related to the roll rate \(p\) and the roll angle \(\phi\) (i.e., \((x_{G}W-x_{B}B)\cos\theta\cos\phi\), \(M_{vp}vp\), \(M_{rp}rp\)).
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 3 Results
### Case 1: Study of Anode Shapes
#### 3.2.2 Velocity Variations  
Figure 8a shows axial velocity along the symmetry axis from the cathode tip to the outlet for both anode types, under input powers of 6.2 kW and 18.4 kW. For a given cathode shape, the peak velocity near the cathode tip remains similar across the two anode geometries. However, as the jet moves toward the outlet, differences emerge; cylindrical nozzles show an 18% velocity drop, while stepped nozzles exhibit a sharper 42% reduction. This accelerated decay is attributed to flow separation in the stepped region, which disrupts the velocity profile.  
Figure 6: Comparison of (**a**) temperature and (**b**) velocity of plasma jet inside plasma torch for different cathode shapes.  
Figure 8: (**a**) Axial velocity variation inside plasma torch. (**b**) Radial velocity variation at the plasma torch outlet.  
Figure 7: Streamlines of the velocity field for (**a**) cylindrical and (**b**) stepped nozzle anodes. (**c**) Velocity vectors in a stepped anode.  
Figure 8b compares radial velocity at the outlet and it could be observed that the stepped nozzle yields a radial velocity approximately 38% lower than the cylindrical one. This difference is primarily influenced by inlet velocity and thermal expansion, which shape the outlet jet profile. To further characterize the flow, Reynolds numbers at the nozzle exit were calculated: 488 and 406 for cylindrical nozzles, and 364 and 300 for stepped nozzles. According to [PERSON] et al., a turbulent shear layer typically forms when the Reynolds number exceeds 400 [36]. These results suggest that cylindrical nozzles produce more turbulent and faster jets than stepped nozzles.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.1 Latent Class Cluster Analysis (LCCA)  
LCCA is an unsupervised learning algorithm based on a probability model [42]; it sorts data with similar properties into potential clusters, by classifying them into maximally heterogeneous data groups. It analyzes the complex interrelationships between the observed variables and applies these to a comprehensive dataset irrespectively of the data type (categorical, binary, or continuous), to derive maximum heterogeneity [43]. To determine the number of clusters in LCCA, the optimal number of layers is first determined by analyzing various statistical criteria (i.e., the Akaike information criteria (AIC), Bayesian information criteria (BIC), and consistent AIC (CAIC)) and the entropy R-squared value. The more constant the statistical criteria values are, the larger the entropy R-squared value, and the more appropriate the potential grouping [44]. In this study, LCCA was first used as a form of contribution analysis between variables; then, it was used to conduct a detailed analysis of the construction accident data.
## 3 Preliminaries
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) is a multivariate statistical projection technique applied for dimensionality reduction [39]. The key idea of PCA is to represent the statistical distribution of data vector using the basis vectors, defined by the eigenvectors of the sample covariance matrix. Let the zero-mean data matrix \(\mathrm{S}\) be defined as the row concatenation of \(\mathrm{n}\) samples of \(\mathrm{d}\)-dimensional row data vectors shifted as zero-mean. Then, the sample covariance matrix is computed as \(\Sigma=S^{T}S\). The PCA learns the transform:  
\[T_{q}=SW_{q} \tag{2}\]  
where \(W_{q}\) is the column concatenation of the \(\mathrm{q}\) largest eigenvectors of \(\Sigma\). Each column of \(T_{q}\) is called the score vector of the \(d\)-dimensional data vector. The PCA is known to be robust against noises and frequently utilized as a feature extraction method [43].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parts** & **Variable** & **Attribute Type** \\ \hline RTK–GPS & Inertial Position and Velocity & \\ INS & Gyro (Body frame rate and their integrals) & Continuous \\  & Accelerometer (\(xyz\)-axis values and integrals) & \\ Setpoint & Position (\(x_{s}\), \(y_{s}\), \(z_{s}\)) & \\ Vehicle status & Navigation state & Discrete \\ \hline \hline \end{tabular}
\end{table}
Table 1: Extracted parameters for flight analysis.
## 1 Introduction
Their work indicated that the empirical formula between micro and macro is not reliable. [PERSON] et al. [26] introduced a strategy based on random forests, which offers advantages in terms of time complexity and incorporates classical machine learning methods into this field. These methods start to possess global search capability and randomness indicators in the parameter space. However, these methods struggle with handling practical constraints such as parameter value ranges and parameter sensitivity, leading to higher computational costs and time consumption. Furthermore, improved heuristic algorithms tend to be dependent on specific problems. In the third aspect, data-driven techniques centered around deep learning showcase its superiority. Deep learning [17] successfully overcomes the prior difficulties by leveraging automated feature extraction and nonlinear modeling. Researchers have applied DNNs (Deep Neural Networks), CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and RL (reinforcement learning) to parameter calibration [8; 27; 28; 29], and deep learning frameworks based on Bayesian inference have also been utilized [30]. The existing methods rely on static sampling of the response process to calibrate model parameters. Despite the improving robustness of these methods, the understanding of time-series data from the response process remains inadequate. By utilizing sequence data rather than static sampling points, it becomes possible to learn high-dimensional coupled patterns present in the response data [8]. The generalization of deep learning for sequential data, such as RNNs, is constrained by the long-term dependencies within the data. It is essential to combine the attention mechanism to achieve the ultimate generalization of deep learning in DEM calibration tasks. This paper proposes an attention retention fusion with a deep learning mechanism, PartsNet, which addresses long-term dependencies by incorporating an improved sparse multi-head self-attention mechanism with a GRU (gated recurrent unit). PartsNet takes the sequence data generated in simulations as the input and the constitutive parameters that need to be calibrated as the output. We study the parameter calibration of the Edinburgh elasto-plastic adhesion (EEPA) model to describe the triaxial compression of wet sand. The calibrated model accurately captures wet sand's constitutive relationship with exceptional precision. PartsNet obtains the relationship between the macroscopic behavior of wet sand and the microscopic parameters in the EEPA model. The rest of this paper is organized as follows: The principles, network topology, and implementation details of PartsNet are elucidated in Section 2. In Section 3, we introduce the simulations and experiments conducted on elastoplastic wet sand, along with the calibration results and verification tests of PartsNet for these experiments. In Section 4, we present the parameter calibration results obtained by PartsNet, the ablation experiments, and the error analysis. Finally, some conclusions are given in Section 5.
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 8 Conclusions  
An adaptive neuro-fuzzy inference system (ANFIS)-based model was developed to predict the punching shear strength of two-way flat concrete slabs without shear reinforcement. A database of 207 test results available in the literature was used to train and test the model. The database covered a rather wide range of two-way flat slab parameters, including slab thickness, concrete strength, reinforcement ratio, yield strength of reinforcement, and width of square loaded area. Five variables were selected as inputs into the ANFIS, with punching shear strength as the output variable. Within the framework of ANFIS, different models may be developed using different learning algorithms with different membership functions and epochs. After experimenting with several of these different models, a model was chosen that had the best potential to predict experimental results. An ANFIS model with a hybrid learning algorithm, 200 epochs, and 10 Gaussian membership functions was selected and then tested. The results from the ANFIS model were compared to the experimental results and to those from the equations recommended in ACI 318-14, BS-8110-97, Model Code 2010, and Euro-Code 2. For these comparisons, the correlation coefficient (\(R\)), the root mean square error (RMSE), and the average and standard deviations of the ratios of predicted (\(V_{ni}\)) to experimental (\(V_{ne}\)) punching shear strength were used as evaluation criteria. The values of \(R\), RMSE, and average and standard deviations of \(Vn_{i}/V_{ne}\) for the training set were found to be 0.996, 0.45, 1.0, and 0.11, respectively, and for the testing set were 0.995, 0.52, 1.1, and 0.13, respectively, for the ANFIS model. This demonstrated that (i) the ANFIS model was capable of making highly reliable predictions of experimental results, (ii) the ANFIS model outperformed the equations recommended in four design codes currently used in practice, and (iii) the ANFIS model showed that it was a good tool for developing parametric studies to assess the influence of each parameter on the shear strength. In summary, the model developed in this study may serve as an economical, efficient, and reliable tool for the prediction of punching shear strength of flat concrete slabs.  
M.M.: Data curation, formal analysis, methodology, software, writing--original. A.M.: Writing--review and editing.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 23: Effect of yield strength of reinforcement on the punching shear strength.
### Human Health Footprint: The Case of Nitrosamines in Ham Production  
Meat products may contain nitrites that are used for their technological role (color and oxidation prevention), sensory attributes, and preservation of those products. Nitrites are precursors of nitrosamines: nitrosamines are formed from the reaction between nitrites and amino acids or secondary amines. Nitrosamines are molecules with known toxicity, especially their carcinogenic effects. In the environment, the main sources of nitrosamines are food, cigarettes, and occupational activities (rubber manufacturing), and they are thus pollutants of water sources [24; 25]. Indeed, an exposure pathway is the consumption of meat products [26]. This is why the authorized amount of sodium nitrite in meat products is regulated and limited to a maximum of 150 mg.kg\({}^{-1}\) product [27] by the European Food Safety Authority (EFSA).  
The nitrosamines most commonly found in the meat matrix are N-nitrosodimethylamine (NDMA), N-nitrosodiethylamine (NDEA), N-nitrosopiperidine (NPIP), and N-nitrosopyrrolidine (NPYR) [28]. Among these substances, NDMA and NDEA are recognized as probable carcinogens by the International Agency for Research on Cancer (IARC) [29; 30; 31]. NPIPs and NPIYRs also have the potential to pose cancer risks. However, some agents, such as ascorbic acid and alpha-tocopherol, influence the reaction and decrease the formation of nitrosamines.  
Daily consumption of nitrites may increase the risk of gastrointestinal cancer due to the in vivo formation of nitrosamines. The endogenous formation of N-nitroos compounds in the stomach is complex and depends on several factors, such as gastric pH, bacteria, and the presence of antioxidants [32]. The amount of those components is therefore very complex to evaluate, model, and predict [33]. In addition, the processing and storage of meat can increase the amount of amines available for nitrosamine formation [34]. Therefore, many alternatives have been developed to limit the use of nitrites in cured and processedmeat products [35]. Figure 1 illustrates the general risks and benefits associated with nitrite treatment of ham and, more generally, of cooked and processed meat products.  
Cooked ham has been widely used as a marker of the exposure of nitroso compounds because it is largely consumed, and thus exposure to nitrosamines could be considered significant [36; 37; 38; 39].
## 2 Results and Discussion
### \(N_{2}\) Adsorption-Desorption Measurement  
As shown in Figure 5A, the N\({}_{2}\) physisorption isotherms of pristine and amino-functionalized WMS and NR/WMS materials were classified as type IV with a hysteresis loop according to the IUPAC classification, which are characteristics of framework confined mesoporous materials [16]. The large hysteresis loop observed at \(P/P_{0}>0.8\) originated from N\({}_{2}\) condensation inside the interparticle voids of particle agglomerates. Table 2 summarizes the textural properties of the materials. The functionalized WMS and NR/WMS series exhibited lower \(S_{\text{BET}}\), \(V_{\text{p}}\) and \(V_{\text{t}}\), but with an increased \(W_{\text{t}}\), than the corresponding pristine ones due to the chemical grafting of the organo-functional group onto the mesoporous silica surface. With increasing APS loading onto the NR/WMS, the change was systematically  
Figure 4: XRD patterns of the WMS and NR/WMS functionalized with different amine loadings.  
extended to a higher degree. A decrease in \(S_{\text{ext}}\) after the functionalization suggested that the particle size of the WMS-NH\({}_{2}\) and NR/WMS-NH\({}_{2}\) was larger than that of the WMS and NR/WMS, respectively, which was explained by a catalytic effect of the basic amino group on the hydrolysis and condensation of the silicate species [17; 35].  
A major difference in the effect of functionalization on the textural properties of the WMS and NR/WMS materials was observed when taking to account the BJH pore size distribution (Figure 5B). Compared to the corresponding pristine materials, \(D_{\text{p}}\) was increased for WMS-NH\({}_{2}\) but reduced in the case of NR/WMS-NH\({}_{2}\). During WMS-NH\({}_{2}\) synthesis, the presence of a hydrogen bonding interaction between the amino groups existing in the aminosilane, and those of the head groups of the DDA molecules, possibly expanded the confined space of the micellar template [17]. This type of interaction during mesophase formation was useful in the synthesis of mesostructured silica via an anionic-surfactant templating route in the presence of aminopropylsiloxane as a co-structure-directing agent [36]. On the other hand, the NR/WMS-NH\({}_{2}\) series showed a decrease in \(D_{\text{p}}\) from 2.63 nm to 2.48 nm when increasing the APS loading level from 5 mol% to 15 mol%. The expansion of the template micelle might be traded off with a hydrophobic interaction between the propyl moieties of the organo-functional groups and the rubber molecules. This explanation was supported by a degree of \(V_{\text{p}}\) reduction at the same 10 mol% APS loading level, where the NR/WMS-0.10 (56%) exhibited a more reduced \(V_{\text{p}}\) than the WMS-0.10 (44%). Nevertheless, the introduction of NR into the mesopores should be limited by the mesostructured confined space due to its large molecular size. Our previous study demonstrated that the rubber phase was majorly entrapped in the silicate wall of the nanocomposite and highly dispersed throughout the mesostructured framework [13].
# Spatial Evolution Analysis of Tailings Flow from Tailings Dam Failure Based on MacCormack-TVD
## 2 Tailings Pond Breach Modeling  
### Tail Sand Flow Control Equation  
In this paper, a three-dimensional model is employed for simulations. Given that the variation in the tailings sand flow in the vertical direction is much smaller than that in the horizontal direction, the three-dimensional Navier-Stokes equations are integrated vertically. From this, the mass equations, momentum equations, and hydrodynamic control equations that must be satisfied are derived. As illustrated in Figure 1, the model's schematic design is depicted. According to the theory of continuous media, the fluid must satisfy the following control equations [24]:  
\[\frac{\partial(h)}{\partial t}+\frac{\partial(h\bar{v}_{x})}{\partial x}+\frac {\partial(h\bar{v}_{y})}{\partial y}=0 \tag{1}\]  
\[\frac{\partial(h\bar{v}_{x})}{\partial t}+\frac{\partial\left(h\bar{v}_{x}+k_{ ap}g_{z}h^{2}/2\right)}{\partial x}+\frac{\partial\left(h\bar{v}_{x}\bar{v}_{y} \right)}{\partial y}=g_{x}h-k_{ap}g_{z}h\frac{\partial(z_{b})}{\partial x}- \frac{(\tau_{zx})_{b}}{\bar{\rho}} \tag{2}\]  
\[\frac{\partial\left(h\bar{v}_{y}\right)}{\partial t}+\frac{\partial\left(h \bar{v}_{y}^{2}+k_{ap}g_{z}h^{2}/2\right)}{\partial y}+\frac{\partial\left(h \bar{v}_{x}\bar{v}_{y}\right)}{\partial x}=g_{y}h-k_{ap}g_{z}h\frac{\partial (z_{b})}{\partial y}-\frac{(\tau_{zy})_{b}}{\bar{\rho}} \tag{3}\]  
In the formula, t represents time; \(\bar{v}_{x}\) and \(\bar{v}_{y}\) represent average velocity in the x and y directions; \(z_{b}\) is the height of the basement, and \(z_{s}\) the height of the free surface; h is the depth of the fluid; the terms \(g_{x}\), \(g_{y}\), \(g_{z}\) are the gravitational accelerations in the X, Y, and Z directions; the symbol \(\bar{\rho}\) represents the average mass density in the vertical direction; the term \(\tau_{i,j}\) signifies the conventional mechanical component of the mechanics of the continuum; \(k_{ap}\) is the coefficient of lateral earth pressure.