esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### N\({}_{2}\) adsorption-Desorption Isotherms: Porosity and Surface Area
This is demonstrated not only in the increase in microporosity just mentioned, but also in the mean pore size estimated with the BJH theory (Table 3), showing a reduction in pore size of almost 40%, from 118 A in Dt to 71 A in Dt-Z (Figure 3). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multirow{2}{*}{\(S_{\text{BET}}\) (m\({}^{2}\)/g)} & \multirow{2}{*}{
\begin{tabular}{c} **DFT Micro** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} } & \multicolumn{2}{c}{**BJH Cumulative Pore Volume (cm\({}^{3}\)/g)**} & \multicolumn{2}{c}{**BJH Average Pore Width (\(\AA\))**} \\  & & & **Acsorption** & **Desorption** & **Average** & **Adsorp.** & **Desorp.** & **Average** \\ \hline Dt & 39.38 & 0.002 & 0.11 & 0.13 & 0.12 & 121.8 & 114.0 & 117.9 \\ Dt-Z & 62.57 & 0.005 & 0.10 & 0.13 & 0.11 & 67.8 & 74.3 & 71.0 \\ \(\Delta\%\) & 58.9 & 124.6 & \(-10.9\) & 0.5 & \(-4.9\) & \(-44.4\) & \(-34.8\) & \(-39.7\) \\ \hline \hline \end{tabular} NOTE: \(S_{\text{BET}}\) = specific surface area calculated according to the BET theory; DFT micro = microporosity volume calculated according to the DFT theory, considering the cumulative pore volume < 20 Å. BJH results embrace both micro- and mesoporous.  
\end{table} 
Table 3: Main properties deduced from N\({}_{2}\) adsorption–desorption isotherms. Although there is a predominance of mesoporous over microporosity, as explained in the previous section, according to the results of Figure 1 and Table 2, zeolitization of Dt has been effectively carried out. In fact, this is consistent with other works already published that show the obtaining of mesoporous zeolites from diatomite, such as that of [PERSON] et al. [11] or the previously cited of [PERSON] et al. [9]. However, it should be noted that the volume of sediment obtained was significantly lower than that of the raw material used. This would indicate that zeolitization has been restricted, a priori, by the low aluminum content of the diatomite and that, therefore, further investigation would be required including some source of aluminum to react with the dissolved silicon.
## Appendix B Proof of Lemma 1  
We first prove the convexity. The Hessian matrix of \(\zeta\) w.r.t. \(\mathbf{d}_{P}\) is computed as  
\[\boldsymbol{\
abla}_{\mathbf{d}_{P}}^{2}\zeta=\text{Diag}\bigg{\{}\frac{2x_{ Q,1}x_{T,1}^{3}}{(d_{P,1}x_{T,1}+x_{Q,1})},\ldots,\frac{x_{Q,s}x_{T,s}^{3}}{(d_{P,s}x_{T,s }+x_{Q,s})^{3}}\bigg{\}},\] (A12)  
where \(x_{T,i}=\lambda_{T,i}+d_{T,i}\), \(i=1,\cdots,M_{t}\) and \(x_{Q,i}=\lambda_{Q,i}+d_{Q,i}\), \(i=1,\cdots,L\). For fixed \((\mathbf{d}_{T},\mathbf{d}_{Q})\in\mathcal{D}_{\mathcal{T}}\times\mathcal{D}_ {\mathcal{Q}}\), the diagonal elements of the Hessian matrix \(\boldsymbol{\
abla}_{\mathbf{d}_{P}}^{2}\zeta\) in (A12) are non-negative for \(\mathbf{d}_{P}\in\mathcal{D}_{\mathcal{P}}\) because the values of \(\{d_{T,i}\}\), \(\{x_{T,i}\}\), and \(\{x_{Q,i}\}\) are all non-negative. This means that \(\boldsymbol{\
abla}_{\mathbf{d}_{P}}^{2}\zeta\) is positive semi-definite. Therefore, the function \(\zeta\) in (17) is convex in \(\mathbf{d}_{P}\in\mathcal{D}_{\mathcal{P}}\) for fixed \((\mathbf{d}_{T},\mathbf{d}_{Q})\in\mathcal{D}_{\mathcal{T}}\times\mathcal{D}_ {\mathcal{Q}}\).  
Now, we prove the concavity. Let us define the augmented vector \(\mathbf{r}\) as \(\mathbf{r}=[\mathbf{d}_{T}^{T},\mathbf{d}_{Q}^{T}]^{T}\). Then, the Hessian matrix of \(\zeta\) w.r.t. \(\mathbf{r}\) is computed as  
\[\boldsymbol{\
abla}_{\mathbf{r}}^{2}\zeta =-\Bigg{[}\begin{pmatrix}\mathbf{G}_{1}(\boldsymbol{\Lambda}_{Q} +\mathbf{D}_{Q})^{2}\mathbf{G}_{1}&-\mathbf{G}_{1}(\boldsymbol{\Lambda}_{Q} +\mathbf{D}_{Q})(\boldsymbol{\Lambda}_{T}+\mathbf{D}_{T})\mathbf{G}_{2}\\ -\mathbf{G}_{2}(\boldsymbol{\Lambda}_{T}+\mathbf{D}_{T})(\boldsymbol{\Lambda}_ {Q}+\mathbf{D}_{Q})\mathbf{G}_{1}&\mathbf{G}_{2}(\boldsymbol{\Lambda}_{T}+ \mathbf{D}_{T})^{2}\mathbf{G}_{2}\end{pmatrix}\Bigg{]}\] \[=-\Bigg{[}\begin{pmatrix}\mathbf{G}_{1}(\boldsymbol{\Lambda}_{Q} +\mathbf{D}_{Q})\\ -\mathbf{G}_{2}(\boldsymbol{\Lambda}_{T}+\mathbf{D}_{T})\end{pmatrix}\Bigg{]} \Bigg{[}\begin{pmatrix}\mathbf{G}_{1}(\boldsymbol{\Lambda}_{Q}+\mathbf{D}_{Q}) \\ -\mathbf{G}_{2}(\boldsymbol{\Lambda}_{T}+\mathbf{D}_{T})\end{pmatrix}\Bigg{]}^{H},\] (A13)  
where  
\[\mathbf{G}_{1}=\text{Blkdiag}\Big{(}\mathbf{G},\mathbf{0}_{(M_{t}-v)\times(M_ {t}-v)}\Big{)},\]  
\[\mathbf{G}_{2}=\text{Blkdiag}\Big{(}\mathbf{G},\mathbf{0}_{(L-v)\times(L-v)} \Big{)}\]  
and the matrix \(\mathbf{G}\) is given by  
\[\mathbf{G}=\text{Diag}\bigg{(}\sqrt{\frac{2d_{P,i}}{(d_{P,1}x_{T,1}+x_{Q,1})^{ 3}}},\ldots,\sqrt{\frac{2d_{P,x}}{(d_{P,x}x_{T,s}+x_{Q,0})^{3}}}\bigg{)}.\]  
For fixed \(\mathbf{d}_{P}\in\mathcal{D}_{\mathcal{P}}\), it can be shown from (A13) that \(\mathbf{a}^{T}\boldsymbol{\
abla}_{\mathbf{r}}^{2}\zeta\mathbf{a}\leq 0\) for any vector \(\mathbf{a}\in\mathbb{R}^{M_{t}L\times 1}\). This means that the Hessian matrix \(\boldsymbol{\
abla}_{\mathbf{r}}^{2}\zeta\) in (A13) is negative semi-definite. Thus, \(\zeta\) is concave in \(\mathbf{d}_{T}\in\mathcal{D}_{\mathcal{T}}\) and \(\mathbf{d}_{Q}\in\mathcal{D}_{\mathcal{Q}}\) for fixed \(\mathbf{d}_{P}\in\mathcal{D}_{\mathcal{P}}\).
# Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
## 4 Design Method
### Design Procedure  
A design method for the new CSPS was proposed, as shown in Figure 11. The design procedure is summarized as follows.  
Step 1: Select the cross sections of cables 1 and 2 (\(S_{1,2}\)) under the ultimate limit state in Case 180\({}^{\circ}\).  
Step 2: Select the cross sections of cable 3 (\(S_{3}\)) and the outer diameter (\(D\)) of the rods under the ultimate limit state in Case 0\({}^{\circ}\), and check the strength of cables 1 and 2.  
Step 3: Check whether the deformation satisfies the requirements under the serviceability limit state in Cases 0\({}^{\circ}\) and 180\({}^{\circ}\).  
Step 4: Increase the sectional area of cable 3 (\(S_{3}\)) if the deformation in Case 0\({}^{\circ}\) exceeds the requirement under the serviceability limit state.  
Step 5: Increase the sectional area of cables 1 and 2 (\(S_{1,2}\)) if the deformation in Case 180\({}^{\circ}\) exceeds the requirement under the serviceability limit state.  
Step 6: Determine the product specifications of the cables and triangular brackets according to the parameters of the final structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \(\gamma_{\rm G}\) & \(\gamma_{\rm P}\) & \(\gamma_{\rm w}\) \\ \hline \multirow{2}{*}{Ultimate limit state} & Case 0\({}^{\circ}\) & 1.2 & 1.0 & 1.4 \\  & Case 180\({}^{\circ}\) & 1.0 & 1.0 & 1.4 \\ \hline \multicolumn{5}{c}{Serviceability limit state} & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design coefficient of the structure [32; 37].
## 2 Theoretical Analysis
### Interference-Type CGH  
To capture an object light field \(\widetilde{U}(x,y)=A(x,y)\exp[j\varphi(x,y)]\), it is typically interfered with a reference beam \(\widetilde{R}(x,y)=\text{Rex}p(j2\pi\alpha x)\).  
From Equation (9), the maximum transmittance \(h(x,y)\) of the hologram occurs when:  
\[\varphi(x,y)-2\pi\alpha x=2n\pi \tag{17}\]  
and the minimum transmittance occurs when:  
\[\varphi(x,y)-2\pi\alpha x=2(n+1/2)\pi \tag{18}\]In the equation, \(n=0,\pm 1,\pm 2\ldots\). Based on the formula of amplitude transmission coefficient for holograms, the computer-generated interferometric hologram function can be expressed as:  
\[H(x,y)=\left\{\begin{array}{ll}1&\cos[\varphi(x,y)-2\pi\alpha x]\geq\cos\pi q( x,y)\\ 0&\text{Other situations}\end{array}\right. \tag{19}\]  
where \(q(x,y)=\arcsin[A(x,y)]/\pi\), and the amplitude \(A(x,y)\) is normalized.  
From this function, the bright fringe positions (0\(\rightarrow\)1 transition points) satisfy:  
\[\cos[\varphi(x,y)-2\pi\alpha x]=\cos[\pi q(x,y)] \tag{20}\]  
where \(\varphi(x,y)-2\pi\alpha x=2n\pi\mp\pi q(x,y)\).  
In this expression, n denotes the bright fringe order, and \(\alpha\) represents the carrier spatial frequency, where the negative sign corresponds to the equation of leading edges (H(\(x\), \(y\)) transitions from 0 to 1) and the positive sign describes trailing edges (H(\(x\), \(y\)) transitions from 1 to 0). By calculating the coordinates of bright fringes at different n values, a binary computer-generated interferometric hologram composed of width-modulated bright fringes can be effectively constructed [21].
## 4 Experimental Evaluation
### SI Cancellation by Adaptive Filter
#### 4.3.3 BER Evaluation  
Finally, to provide a comprehensive assessment of the overall performance of the proposed IBFD system, we examine the BER derived from the RX UL signals. The BER curve for the proposed IBFD system is depicted in Figure 16. A close examination of the results in Figure 16 reveals that the BER reaches the order of \(10^{-3}\) when the SNR exceeds approximately 22 dB. This achievement aligns with the initial usability requirements of a wireless system.  
As previously mentioned, the proposed system prototype incorporates a three-stage SI cancellation method, totally achieving about 100 dB of SI cancellation performance. This AF-based SI cancellation process results in a synthesized outcome capable of achieving a BER in the order of \(10^{-3}\) under reasonable SNR conditions, which significantly validates the effectiveness and practicality of the proposed system design.
# ALReg: Registration of 3D Point Clouds Using Active Learning
## 1 Introduction
Instead of decreasing the time budget allocated to manually label the dataset, our main aim is to decrease the training time of the network model. Since learning the rigid transform between two point clouds is equivalent to learning the transform between patches of these point clouds, the learning procedure can be altered to its superpoint variant. It is unnecessary to use similarly structured point cloud patches that have resembling features. In this study, we present ALReg, an active learning approach to efficiently use the informative regions for point cloud registration to decrease the training time. As the predictor, the baseline registration networks are modified to use Monte Carlo DropOut (MCDO) [30] to efficiently calculate uncertainty. The guide works on each point cloud separately and calculates acquisition scores for a given point cloud's unlabeled superpoints. The oracle provides a superpoint chosen by the guide. Our main motivation is that, by using fewer point clouds or point cloud parts in the training phase of any point cloud registration network, a similar accuracy score could be obtained. However, it depends to the efficient selection of these training samples/parts. By the usage of ALReg, we aim the involve the most effective point cloud parts to the training procedure. To demonstrate the effect of the ALReg, we took three deep learning-based methods as our baselines: Deep Best Buddies (DeepBBS) [31], Feature Metric Registration (FMR) [32] and Deep Closest Point (DCP) [33]. As in the official papers, we evaluated FMR on 7 Scenes [34] and the other methods on ModelNet40 [35]. To further investigate our method we also evaluated our trained FMR networks on 3 DMatch [18] as unseen data. To demonstrate the performance of ALReg extensively, we used datasets from different domains and structures. We observe in our experiments, that our method matches or outperforms other existing methods, but with significantly reduced computation time. The three main contributions of this study are as follows:* We propose ALReg, an active learning pipeline that can be used to drastically decrease trainingtime for registration networks with either a similar performance or even an increase in terms of accuracy. Regarding the drawbacks of the overcalculations for network training for whole-to-whole point cloud registration, ALReg focuses on using only a relevant and adequate subset of superpoints during the process. * A novel uncertainty-based acquisition function that could be used to calculate superpoint uncertainties is presented. In the previous studies focusing on active learning for point cloud data, class labels were used for uncertainty calculations. * ALReg is tested on three popular registration methods (DCP, FMR, DeepBBS) for both real (7 Scenes, 3 DMatch) and synthetic (ModelNet) point cloud datasets. Overall, an improvement over the existing methods in terms of accuracy scores is obtained.
## 3 Results and Discussions
### Secondary Particle Yield Difference between Neutron and Proton  
The secondary particle species can not only produce a different LET but also change the semiconductor properties. In this study, the secondary particles with Z = 2-80 were analyzed. Figure 6 shows the secondary particle yields in structures without SiGe when it is irradiated by 63, 105, 150, 200, and 230 MeV protons and LANSCE neutron, respectively.  
In Figure 6, five groups can clearly be identified, namely alpha group, O group, Si group, Cu group, and W group, from left to right. Between copper and tungsten groups, there are some events from the cleavage fragments of W. In the secondary particle yields, the highest peak is at Z = 2, which is helium or alpha particles resulting from elastic interaction. The next two peaks, at Z = 6 and 14, are from SiO\({}_{2}\).  
It can be seen in Figure 6 that the secondary particles distribution of the neutrons is narrower than that of the protons. The reason is that the energy of most neutrons is too low to have a spallation event. Another difference is that for Z > 14, 200 MeV protons generate more secondary particles than neutrons, but for 8 < Z < 14, neutrons give more events. The difference is mostly from the last layer of the structure, which is SiO\({}_{2}\). In the Z = \(\sim\)70, which are made of tungsten, high-energy protons have much higher potential to generate secondary particles than the low-energy protons or even neutrons. For secondary particle yields, the C\({}_{\rm v}\) is dependent on the secondary particle species. For Z < 14, the C\({}_{\rm v}\) is less than 10%, but for heaver ions, some yields are less than 10 counts per 10\({}^{9}\) incidents, and this makes the C\({}_{\rm v}\) go up to 60%. For a few channels, the C\({}_{\rm v}\) is even 100% because only one count is observed.  
EI evaluation of the secondary particles yield is shown in Table 2, and again 200 MeV protons and the LANSCE neutron have the most similar secondary particle yields. Similar to the case of LETs, 63 MeV proton has the worst EI in secondary particle yields. In Figure 6, the 63 MeV proton in the Z range between 18 and 25 contributed fewer secondary ions, which are copper spallation fragments. In Z between 65 and 70, the 63 MeV proton also has fewer secondary yields. [PERSON] et al. reported that secondary particle yields are higher for protons with higher energy [23].  
Figure 6: The secondary particle yields in structure without SiGe irradiated by 63, 105, 150, 200, and 230 MeV protons and LANSCE neutron.  
In Figure 6, 200 and 230 MeV protons typically gave more yields than the LANSCE neutron, but 63, 105, and 150 MeV protons gave less. It is possible that mixing different proton energies could lead to better neutron equivalence.  
Therefore, from both Tables 1 and 2, 200 MeV proton radiation is closer to neutron radiation, and this concurs with suggestions from NASA (Washington, DC, USA).
# Electrochemical Properties of NdCl3 and CeCl3 in Molten LiCl-KCl Eutectic Salt
## 2 Experimental  
### Electrochemical Apparatus and Electrodes  
The eutectic LiCl-KCl was prepared by mixing LiCl (Alfa Aesar, Daejeon, Korea, 99.95% purity) and KCl (Alfa Aesar, 99.99% purity) in a 58.2:41.8 eutectic molar ratio followed by drying for more than 48 h at 572 K to remove residual water and melted under a high-purity argon atmosphere. This eutectic mixture was heated at 753 to 793 K with several LiCl-KCl mixtures containing NdCl\({}_{3}\) and CeCl\({}_{3}\) with concentrations ranging from 4 to 10 wt%. All the electrochemical measurements were carried out with Autolab/PGSTAT128N potentiostat. The data acquisition was controlled by NOVA 2.0 software for cyclic voltammetry. A three-electrode electrochemical cell was assembled in an alumina crucible, which was positioned in a graphite vessel and heated in an electric furnace. A chromel-alumel thermocouple was used for temperature measurement with an accuracy of \(\pm\)1 \({}^{\circ}\)C. The reference electrode (RE) was self-assembled by a silver wire (1 mm diameter, 99.99% purity). The reference electrode consisted of a closed-end porous quartz tube, in which the LiCl-KCl eutectic salt containing 5 mol% AgCl was placed, and an Ag wire of 1.0 mm diameter was immersed into the molten bath. A glassy carbon rod of 2.0-mm diameter was used as a counter electrode (CE). The surface area of the glassy carbon counter electrode in contact with the salt phase was about 43.1 mm\({}^{2}\). The working electrode (WE) consisted of a metallic tungsten (W) wire (1.0-mm diameter, 99.9% purity) dipped into a molten salt bath (Figure 1E).  
Figure 1: Experimental setup and instruments. (**A**) Ar atmosphere glove box. (**B**) Schematic diagram of experimental apparatus. (**C**) Oxygen and moisture monitoring. (**D**) Autolab/PGSTAT128N potentiostat (**E**) Electrode setup in glove box.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 7 Dynamic Precision Reliable Analysis of Multilink Structure with Clearance  
### Influence of Clearance Values on Reliable Dynamic Accuracy of Hybrid Clearance Mechanisms  
This subsection investigates impact of clearance values on dynamic precision reliability of mechanisms with clearances. Friction coefficient at clearance is set to 0.2, driving speed is  
Figure 26: Poincaré map at the clearance of revolute pair: (**a**) Poincaré map of clearance A\({}_{1}\) in the \(X\)-direction; (**b**) Poincaré map of clearance A\({}_{1}\) in the \(Y\)-direction; (**c**) Poincaré map of clearance A\({}_{2}\) in the \(X\)-direction; (**d**) Poincaré map of clearance A\({}_{2}\) in the \(Y\)-direction.  
uniformly 60 rpm. Clearance values are taken as 0.1 mm, 0.2 mm, and 0.3 mm, respectively. Displacement and velocity curves do not change much due to the effect of the clearance, so the selection phenomenon is more intuitively accelerated. The reliability indices and reliability of slider acceleration are illustrated in Figure 27.  
As illustrated in the Figure 27, clearance values are 0.1 mm, 0.2 mm, and 0.3 mm, respectively, and the corresponding reliability indices of the minimum acceleration of the slider are 2.4244, 1.7258, and 1.4850. The figures also indicate that the reliabilities of the minimum acceleration of slider corresponding to different clearance values are 0.9923, 0.9578, and 0.9312, respectively. This trend arises from the amplification of kinematic uncertainty at larger clearances. Increased clearance allows for greater free motion between joints, leading to impulsive forces during contact transitions. These forces induce higher-frequency vibrations and transient accelerations, which degrade the repeatability of the slider's motion. Therefore, for multilink mechanisms with mixed clearances, higher values of clearance result in lower reliability of the dynamic accuracy of the slider. A reliability of 0.93 implies a 7% probability that the slider's acceleration will fall below the required minimum during operation. This level is sufficient to meet the requirements for mechanism accuracy and reliability in most industrial production. However, in high-precision industrial applications such as aerospace, there is a high demand for mechanism accuracy, and further improvement and optimization of mechanism design and motion are still needed. Design changes--such as reducing clearance tolerances or implementing active damping--should be considered when reliability falls below application-specific safety margins.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 5 Simulation and Results of Impact of Parameters on Bridge Traffic Operation
### Introduction to Simulation Environment  
This article chooses to simulate the section of road from Yujiawan in Yubei District to Beibei, with a length of approximately 23 km, including the Ma'anshi Bridge. The simulation experiment is conducted using TransModeler software (version 6.0) [21,22], with a simulation time of 1 h (10:00-11:00). The occurrence of each obstacle started at 10:20 and ended at 10:40. The free flow speed of the Chongqing Wuhan Expressway is set to 120 km/h, with a free flow speed of 70 km/h for the bridge. The simulation time is from 10:00 to 11:00, and the entrance flow of the simulated section is 2500 vehicles/h and the exit flow is 2500 vehicles/h.  
To simulate and analyze the impact of changes in road infrastructure on traffic capacity, changes in sensor data are used to simulate changes in bridge status on simulated road sections. Three traffic conditions are set for road capacity, excellent, good, and poor, and the impact of changes in traffic operation influencing factors on traffic operation status is verified. The specific meanings of the different road traffic capacities are as follows:  
1. Excellent: The road and bridge facilities have not undergone any changes, or have undergone minor changes, and these changes have almost no impact on the vehicles driving on the road. Vehicle traffic is basically smooth, and variables such as traffic flow, speed, and lane occupancy rate change steadily.
2. Good: The road and bridge facilities have undergone certain changes. By changing the sensor data on the road, the state of the bridge facilities can be simulated to change. For example, obstacles can be set between sensors, and the maximum speed for passing through this obstacle is set to 60 km/h. However, the impact is not significant, and vehicle traffic is relatively smooth. The variables of traffic flow, speed, and occupancy rate change steadily, with occasional small changes, but the fluctuations are not significant.
3. Poor: There have been significant changes in road and bridge facilities, such as a malfunction in a section of the road between sensors, resulting in a speed of 0 km/h and a significant impact on vehicle traffic, leading to poor traffic flow. The variables of traffic volume, speed, and occupancy rate have shown obvious abrupt changes.
## 2 Contemporary Model- and Data Driven (Crowd) Forecasting Methods
### Model-Driven Forecasting of Crowd Movements  
From the 1970s onwards a number of pedestrian simulation models have been presented. In the early years predominantly microscopic multi-agent simulation models were presented, such as Cellular Automata [17], the Social Force Model [18] and the Velocity-Obstacle model [19; 20]. Microscopic models compute the movements of each pedestrian in a certain space as a reaction to the presence of other objects, pedestrians and their current movement goal in a discrete or continuous fashion. The computational effort of microscopic pedestrian simulation models tends to be very high and increases rapidly when modelling large crowds. Real-time applications of microscopic pedestrian simulation models are non-existent, given their high computational effort. Yet, they are often used to perform scenario analyses of current and/or future pedestrian infrastructure designs.  
More recently, multiple macroscopic crowd simulation models have been presented, which treat a pedestrian crowd as a continuous moving medium (e.g., water). While earlier versions, such as [21; 22], could only model crowd movements of a single homogeneous population with one movement goal, contemporary macroscopic simulation models, for instance [23; 24; 25], can handle heterogeneous populations and interacting flows. Also macroscopic pedestrian simulation models have not yet been used for forecasting purposes, as they are, at this moment, not equipped to simulate the unfolding of events based on a current data-driven state estimation based on real-time data from sensors.