esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Through-plane transport was simulated in 25 realizations created by the geometry model, oriented in reverse order to distinguish the GDL/channel surface from the GDL/electrode surface presented by [PERSON] et al. [26]. The operating conditions, according to Section 2.3, are summarized in Table 1. The conditions led to a Reynolds number of \(2.4\times 10^{-4}\). This in turn led to velocity vectors at the GDL exit that were almost parallel to the through-plane direction. The free space downstream of the GDL mentioned in Section 2.3 was required to arrange the velocity vectors properly behind the porous structure.  
The variation of the volumetric characteristics--permeability \(\kappa\) and tortuosity \(\tau\)--is shown in Figure 3a. A comprehensive study of these was presented by [PERSON] et al. [4; 5]. The quantile levels \(z\) were implicitly defined by \(u_{q}(z)\), as introduced by Equation (4). They define the total quantile areas:  
\[s_{\text{tot}}(q)=\left\{x|u_{q}(z)>0\right\} \tag{6}\]  
that can be summarized to the values illustrated in Figure 3b. Like the volumetric characteristics, they showed statistical variation, because the simulation results were based on statistical microstructures. In addition to the total quantile area defined by Equation (6), the size of the largest of these regions is shown in Figure 3c. For this purpose, the function \(U(q,z)\) from Equation (5) needs to be applied on the largest area identified by the contour levels \(u_{q}(z)\) from Equation (4), which was done via the visualization tool paraview and an external R [50] script.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Value** \\ \hline Average current density & 1 A/cm\({}^{2}\) \\ Volumetric flow rate H\({}_{2}\) & \\ (normal conditions) & 7 mL/min \\ Volumetric flow rate O\({}_{2}\) & \\ (normal conditions) & 3.5 mL/min \\ Operating temperature & 160 \({}^{\circ}\)C \\ Superficial velocity H\({}_{2}\)O & 1.8 \(\times\) 10\({}^{-3}\) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating conditions. H\({}_{2}\)O is created as a vapor. The input to the LB algorithm are the species “H\({}_{2}\)O”, its temperature (for density and viscosity), and the velocity.  
Figure 3: Characteristics of the uncompressed GDL. Slip condition at walls: (**a**) permeability and tortuosity; (**b**) relative total quantile areas of the gas flow; total area where \(u_{q}(z)>0\). (**c**) The largest area from (**b**).  
In contrast to the display of the key properties in Figure 3, the detail of the two realizations, Nos. 5 and 15, is depicted in Figure 4. With the decrease of the quantile level \(q\), the areas resulting from the contour lines became smaller, while the number of areas decreased, as well. The contour levels addressed in Section 2.4 look completely different in detail, which depicts the large variation of the areas summarized in Figure 3c.
## 4 Discussion  
### Principal Findings  
The study objective was to investigate the change in patterns of users' problematic smartphone use and their level of anxiety and depression while using MindsCare during the study period. This was done to confirm the effect of MindsCare on problematic smartphone usage associated with mental health and to identify the effectiveness of the application on the problematic user group.  
The results revealed the difference between the two groups in terms of problematic smartphone use and depression and anxiety after a total of 13 weeks of MindsCare use. First, there was no significant change in the SUC group for problematic smartphone use, while in the PSU group, problematic smartphone use scores were reduced to the potential-risk group score from 0 to 8 weeks and it showed a continuous decrease until week 13, nearly reaching the normal group's scores. These results demonstrated that smartphone usage management apps such as MindsCare were effective on the PSU group. Likewise, there was no significant change in the SUC group for GAD, while in the PSU group, there was no change in the subgroup, but it showed a continuous decrease in scores until week 13. Interestingly, it showed a similar pattern in PHQ. There was no significant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent** & **Fixed Effects** & **numDF** & **denDF** & **\(F\)-Value** & **\(p\)-Value** \\ \hline \multirow{4}{*}{PHQ} & (Intercept) & 1 & 383.295 & 380.835 & \textless{}0.001 \\ \cline{2-6}  & Gender & 1 & 186.879 & 0.023 & 0.879 \\ \cline{2-6}  & Group & 1 & 383.243 & 27.707 & \textless{}0.001 \\ \cline{2-6}  & Time & 1 & 203.621 & 18.050 & \textless{}0.001 \\ \cline{2-6}  & Group \(\times\) Time & 1 & 203.621 & 10.068 & 0.002 \\ \hline \hline \end{tabular} Statistically significant associations (\(p\) < 0.05), Degree of Freedom (DF), Smartphone Addiction Proneness Scale (S-scale), Generalized Anxiety Disorder Scale (GAD), The Patient Health Questionnaire (PHQ).  
\end{table}
Table 3: \(Cont.\)change in the SUC group, but the PSU group showed a continuous decrease in scores until week 13. Similar to prior research results, this can be attributed to our mainly designing the interventions focusing on reducing on problematic smartphone use (See Figure 4) [35].
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 9 Experimental Results  
We conduct two experiments with three vehicles to verify and evaluate the CL algorithm. The first experiment utilizes relative pose measurements, while the second one only takes relative position measurements. The duration for both experiments is 80 s, and each vehicle follows a predefined path. The actual path data are processed through the CL EKF algorithm as measurements obtained by a vehicle. Each vehicle propagates its own states at a rate of \(\Delta t=0.02\) s. However, relative measurements are obtained every 0.2 s.  
Figure 10 shows an overview of one of the vehicles. All three vehicles have the same generic configuration and possess similar capabilities. The vehicles are differential-drive mobile robots with two motors controlled via a dual motor control and a PI controller implemented on an Arduino Duo board using motor encoder measurements. A Raspberry Pi computer performs most of the computation required to navigate the robot autonomously. Relative positions are calculated via LiDAR measurements of distance and bearing angle. However, the relative orientations are measured using a motion capture system that tracks markers, which are otherwise unused. The 0.2 s measurement rate is the time required for the LiDAR to rotate fully and the required data processing.
# Analysis of the Impact of the New Two-Lane Shield Tunnel Underpass on the Existing Tunnels
## 3 Numerical Simulation of Underpass Construction  
### Project Overview  
The new tunnel segment of Dalian Metro Line 4, spanning from Dongfang Road Station to Suoyuwan Station, starts at Dongfang Road Station and runs along the Suoyuwan Business District Central Green Belt in a northwest-to-southeast direction, passing beneath the existing Metro Line 5. The construction of both the left and right tracks employs the shield tunneling method. The shield's outer diameter is 6.20 m, with an inner diameter of 5.50 m. The width of the tunnel shield is 1.20 m, and the thickness is 0.35 m. The center-to-center distance between the left and right axes is 12.34 m, and the overburden thickness is approximately 25.70 m. The minimum clearance between the arch of the new shield tunnel and the base plate of the existing Line 5 is about 3 m, making this a small-clearance underpass project. Figure 3 shows the spatial location.  
According to the site investigation report, Metro Line 4 will pass beneath the existing Metro Line 5. Figure 4 shows the geological cross-section of the specific underpass area. The tunnel of the existing Metro Line 5 lies mainly between the strongly weathered dolomite and the moderately weathered plate dolomite, while the tunnel of the new Metro Line 4 is in the moderately weathered plate dolomite layer. Based on the strata and overburden  
Figure 3: Plane diagram of new Line 4 and existing Line 5.  
encountered by the shield, we set the soil chamber pressure at 0.30 MPa and the grouting pressure at 0.30 MPa.
## 5 Branch-and-Bound Algorithm for \(\epsilon\)-Proper Equilibrium Computation
### The 0-1 Mixed Linear Master Program  
The following 0-1 mixed linear program \((\mathfrak{L}_{\bullet})\) combines the Nash equilibrium primal and dual conditions, for polymatrix games, and the \(\epsilon\)-properness conditions aforementioned. The optimization of \((\mathfrak{L}_{\bullet})\) using exact arithmetics, provides at optimality a Nash equilibrium \(\hat{X}\) and an \(\epsilon\)-proper equilibrium \(X\), when \(\epsilon\) is set to a fixed value.  
**Proposition 5**.: _Given a polymatrix game, for some \(\epsilon>0\), consider the 0-1 mixed linear program \((\mathfrak{L}_{\bullet})\):_  
\[\sigma(\epsilon)=Maximize_{\hat{X},X,V,\sigma} \sigma \tag{8}\] \[\text{Subject to} e_{m}^{t}\hat{X}_{i}=1, i\in N,\] (9) \[\hat{X}_{i}+V_{i}\leq 1, i\in N,\] (10) \[\alpha_{i}e^{i}-\sum_{j=1,j\
eq i}^{n}A_{ij}\hat{X}_{j}\geq 0, i\in N\] (11) \[\alpha_{i}e^{i}-\sum_{j=1,j\
eq i}^{n}A_{ij}\hat{X}_{j}-LV_{i}\leq 0, i\in N\] (12) \[\hat{x}_{ik}-\epsilon\leq x_{ik}\leq\hat{x}_{ik}+\epsilon,\ \forall k\in 1,\ldots,m_{i}, i\in N,\] (13) \[(X_{1},\ldots,X_{n})\in\Omega_{\epsilon}^{\sigma}\] (14) \[\hat{X}_{i}\geq 0, i\in N,\] (15) \[V_{i}\in\{0,1\}^{m_{i}}, i\in N. \tag{16}\]  
_This 0-1 mixed linear program \((\mathfrak{L}_{\bullet})\) is always feasible for any \(\epsilon>0\) and its optimal objective value \(\sigma(\epsilon)\) is always strictly positive._  
_The mixed strategies vector \(\hat{X}=(\hat{X}_{1},\ldots,\hat{X}n)\) is a Nash equilibrium of the given polymatrix game and the completely mixed strategies vector \(X=(X_{1},\ldots,X_{n})\) is an \(\epsilon\)-proper equilibrium._  
Proof.: Constraints (9)-(12) assert that \(\hat{X}=(\hat{X}_{1},\ldots,\hat{X}_{n})\) is a Nash equilibrium of the given polymatrix game (see [4]). Constraints (13) and (14) assert that \(X=(X_{1},\ldots,X_{n})\) is an \(\epsilon\)-proper equilibrium as shown earlier. For any given polymatrix game, the set of Nash equilibria and the set of proper equilibria are non-empty. Therefore, for any \(\epsilon>0\), there exists a pair of vectors \(\hat{X}\) and \(X\) such that \(\hat{X}\) is a Nash equilibrium, and \(X\) is an \(\epsilon\)-proper equilibrium. Hence, the optimal objective value \(\sigma(\epsilon)\) is always strictly positive.  
The mixed 0-1 linear program \((\mathfrak{L}_{\bullet})\) serves as the master program in the proposed \(\epsilon\)-proper Branch-and-Bound algorithm.
# Analysis of Internal Gas Leaks in an MCFC System Package for an LNG-Fueled Ship
## CFD Simulation Method
### Governing Equations  
The governing equations of mass, momentum, turbulence, and heat transfer were applied in accordance with the buoyancy effect in the internal airflow simulation. In Reynolds averaging, the velocity vector **v** is decomposed into the mean and fluctuating velocity components as \(\textbf{v}=\overline{\textbf{v}}+\textbf{v}\). Likewise, a scalar, \(\phi\) (such as pressure, energy, and other quantities) is decomposed as \(\phi=\overline{\phi}+\phi^{\prime}\). The incompressible Reynolds-averaged Navier-Stokes (RANS) equations are defined as follows [18]:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\overline{\textbf{v}})=0 \tag{6}\]  
\[\frac{\partial}{\partial t}(\rho\overline{\textbf{v}})+\
abla\cdot(\rho \overline{\textbf{v}}\overline{\textbf{v}})=-\
abla\overline{p}+\
abla\cdot(2 \mu\overline{\textbf{s}}-\rho\overline{\textbf{v}}\overline{\textbf{v}} \overline{\textbf{v}}\overline{\textbf{v}})+\rho\texttt{g} \tag{7}\]  
where \(\rho\) is the density calculated from the ideal gas equation of state, \(t\) is time, \(\overline{p}\) is the mean pressure, \(\mu\) is the molecular viscosity and \(\overline{\textbf{s}}=\frac{1}{2}\big{(}\overline{\textbf{v}}\overline{ \textbf{v}}+\
abla\overline{\textbf{v}}^{\overline{T}}\big{)}\) is the mean strain-rate tensor. The quantity \(-\rho\overline{\textbf{v}}\overline{\textbf{v}}\overline{\textbf{v}}\) is defined as the Reynolds stress tensor, and \(\rho\texttt{g}\) is the gravitational body force. To close the RANS equations, the Reynolds stress tensor is modeled on the basis of the Boussinesq approximation as follows:  
\[\rho\overline{\textbf{v}}\overline{\textbf{v}}\overline{\textbf{v}}=2\mu_{t} \overline{\textbf{s}}-\frac{2}{3}\rho\textbf{K}\textbf{I} \tag{8}\]  
where \(\mu_{t}\) is the turbulent viscosity, the turbulent kinetic energy is defined as \(K=\frac{1}{2}\overline{\textbf{v}}\overline{\textbf{v}}\overline{\textbf{v}}\), and **I** is the unit tensor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**HV249**} & \multicolumn{2}{c}{**HV302**} \\ \cline{2-5}  & **Mole Fraction** & **Mass Fraction** & **Mole Fraction** & **Mass Fraction** \\ \hline
**CH4** & 0.938 & 0.8934 & 0.1498 & 0.1751 \\ \hline
**H2O** & 1.134 & 2.651 & 0.3196 & 0.4196 \\ \hline
**H2** & - & - & 0.4127 & 0.0606 \\ \hline
**CO2** & - & - & 0.0874 & 0.2803 \\ \hline
**Air** & 0.062 & 0.1066 & 0.0305 & 0.0644 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Leaked gas composition.  
Figure 4: Leaked valve positions in the MCFC stack package.  
chamber, as shown in Figure 6a,b. The low concentration of 1% which accumulated behind the stack because of stagnant flow is not dangerous, but it must be circulated by a blowing or suction fan.
# Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
## 2 Related Work and Background
In general, these existing works can neither capture the potential of coordination among D2D communications nor take fairness in payoff or NRF-EH into consideration under various network conditions in order to achieve the benefits and efficiencies of MDO. [PERSON] [53] established a basic two-person bargaining framework between two rational players, and proposed an axiomatic solution concept--NBS--which is characterized by a set of predefined axioms, and does not rely on a detailed bargaining process of the players. Since [PERSON]'s pioneering work, researchers have extended the bargaining analysis to cases with more than two players. In the multi-player scenario, some players may form groups and bargain jointly to improve their payoffs [54; 55; 56]. The NBS is a type of cooperative game that has been used for solving resource allocation problems among competing players. [PERSON] proposed four axioms that should be satisfied by a reasonable bargaining solution [53]: Pareto efficiency, symmetry, invariance to affine transformations, and independence of irrelevant alternatives. The bargaining problem can be described as follows [54]. There are \(I\) players competing for a resource. Each player, \(i\) (\(i\in\{1,2,\ldots,I\}\)), requires a minimal payoff \(U_{i}^{min}\); let \(\mathbf{U}^{min}=\left(U_{1}^{min},\ldots,U_{i}^{min},\ldots,U_{l}^{min}\right)\) denote a set of the minimal payoffs for player \(i\in\{1,2,\ldots,I\}\) over the reservation payoff or disagreement point of player \(i\). Defining \(\mathbf{U}=\left(U_{1},\ldots,U_{i},\ldots,U_{I}\right)\), \(\mathbf{U}\) is a closed and convex set of payoffs over all possible agreements in order to present the set of feasible payoff allocations that the players can get when they cooperate. Since the minimal payoff of each player must be guaranteed, \(\left\{U_{i}\in\mathbf{U}|U_{i}\geq U_{i}^{min},\forall i\in\{1,2,\ldots,I\}\right\}\) is a nonempty set. The NBS can be represented in a very simple form: it corresponds to an outcome that maximizes the product of both players' payoff gains upon a disagreement outcome. **Definition 1** ([PERSON] bargaining solution [53; 54]). : _a set of payoffs \(\mathbf{U}=\left(U_{1},\ldots,U_{i},\ldots,U_{I}\right)\), is an NBS (i.e., satisfying [PERSON]'s four axioms) if it solves the following problem:_  
\[\mathbf{U}^{*}=\arg\max_{\mathbf{U}}\prod_{i=1}^{I}\left(U_{i}-U_{i}^{min}\right) \tag{1}\]  
\[\text{s.t. }U_{i}\geq U_{i}^{min} \tag{2}\]  
According to [56], if \(U_{i}\) is a concave upper-bounded function that has convex support, there exists a unique and optimal NBS.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 5 Effectivity of the Method in Model Problems
### Numerical Examples  
In the analysis of the locking phenomena, the following data were treated as independent: the problem type (the type of the strain dominance), and the structure length \(l\) and thickness \(t\) resulting in the thinness ratio \(l/t\). In the numerical analysis, also the initial mesh data, the relative value of the target admissible error \(\gamma_{T}\), and the ratio \(\gamma_{I}/\gamma_{T}\) of the intermediate (after \(h\)-step) error to the target (after \(p\)-step) error were treated as independent quantities.  
As results of the analysis, we present the meshes corresponding to three performed courses of adaptation: standard \(hp\)-adaptation and such adaptations preceded by the modification of the initial mesh with the increased longitudinal order of approximation \(p\), equal either to the maximum or optimized value. The results are completed with the adaptive convergence curves corresponding to these three courses of adaptation. The convergence curves present the approximation error as a function of the number \(N\) of degrees of freedom (dofs). The absolute error is defined as a negative difference of the total strain energy \(U\) corresponding to the global solution under consideration and the reference energy \(U_{r}\) replacing the unknown exact value. The energies are calculated in accordance with the strain energy definition from Section 2. Due to the exponential character of \(hp\)-convergence, the curves are plotted as \(\log(U_{r}-U)\) versus \(\log N\). The reference energy \(U_{r}\) is obtained numerically from calculations performed on over-killed meshes with the global discretization parameters equal to: \(p=9\), \(q=2\), \(m=9\), where \(m=l/2h\). Apart from the absolute error values, the relative error values \((U_{r}-U)/U_{r}\) are also presented and discussed.
## 4 Conclusions  
### Significance and Contributions  
This study proposes an innovative framework for disaster situation awareness by integrating computer vision, natural language processing, and spatiotemporal analysis to identify disaster-related content from social media. By extracting multimodal information from social media, this approach facilitates the understanding of disaster evolution patterns. While textual data provide descriptions of disasters, images complement these by depicting the extent of damage, and the integration of both modalities enhances the efficiency of resource allocation. The effectiveness of this method was validated through a case study on the disaster situation awareness of Typhoon Haikui in Fuzhou, Fujian Province, from 3 September to 10 September 2023. The study conducted a detailed analysis of the temporal evolution of typhoon-related disasters and examined the inter-relations among different  
Figure 15: Disaster driving factor rising sun map.  
types of disasters from a holistic perspective. This provides valuable insights for predicting disaster developments and improving disaster prevention systems. Using the proposed disaster situation awareness method, 119 instances of waterlogging were extracted from social media data, significantly exceeding the 80 instances recorded in official data. Additionally, the disaster damage categories identified in this study include waterlogging, stranded individuals, building damage, vehicle inundation, traffic restrictions, landslides, fallen trees, suspension of public transportation, and power and water outages. These categories, while overlapping with official records, also provide significant complementary information. Moreover, the method in this paper can collect disaster information in near real-time and improve the timeliness of official disaster investigations. These validation results demonstrate the effectiveness and scientific contributions of this method. It not only extends the scope of official disaster data but also captures the diversity of disaster damage categories. This comprehensive information supports post-disaster emergency management and resource allocation, offering a more complete and nuanced understanding of disaster impacts.  
Several important conclusions were drawn. First, social media offers a pathway to acquire near-real-time disaster information with fine-grained spatiotemporal resolution; time can be precise to the minute, and location can be detailed to roads, intersections, and points of interest (POI), providing specific disaster information. Second, social media multimodal data help reveal the overall process of disaster situational awareness, uncovering spatiotemporal patterns in disaster evolution. Lastly, in terms of heterogeneous driving factors, population density has the most significant explanatory power for the four types of disasters, followed by building density. The interaction of multiple geographic factors plays a key role in the formation and development of disasters.
## 3 Results and Discussion
### Assessment of Energy Generation Potential
The energy self-sufficiency of companies obtained through the incineration of their own waste also seems to us more beneficial and logical compared to the construction of other alternative energy sources such as PV plants. This presented paper does not aim to elaborate and model in detail all energy/heat recovery technologies for all investigated materials, as their efficiency will vary considerably according to the conditions of each individual operation. However, our results give the professionals and interested companies some idea of how waste can be utilized and how the amount of waste needed to be disposed of can be reduced. From a pan-European perspective, waste by-products from the manufacturing industry represent about 15 million tonnes per year [17]. If at least 20% of this amount were to be used as energy recoverable waste, and with an average LHV of 14 MJ\(\cdot\)kg\({}^{-1}\), this would represent 4.2 \(\times\) 10\({}^{7}\) GJ of energy, which is 11.6 TWh. This energy corresponds to about 2.36 million tonnes of wood pellets, while in 2022 Europe consumed 24.8 million tonnes of pellets [67]. Targeting only the Czech market, where about 50,000 tonnes of fruit and 92,000 tonnes of grapes are processed annually, and again with an average production of 20% of the waste available for combustion, this represents 4\(\times\)10\({}^{5}\) GJ, which is 0.11 TWh of energy. To put this into perspective, all PV plants in the Czech Republic produced 2.15 TWh of electricity in 2022 [68].