esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.4 Microstructural Analysis  
To examine the micromorphological changes and chemical composition of POFA-based geopolymer stabilized samples and highlight the mechanism of stabilization, field emission scanning electron microscopic (FESEM) along with energy dispersive X-ray (EDX) was carried out on both natural soil samples and some stabilized specimens.  
The specimens were tested using a Hitachi SU8010 machine for the analysis of both FESEM and EDX. For sample preparation, small representative test specimens were collected from the specimens crushed after the UCS test and then installed on aluminum stubs with two sides of carbon tabs. A thin film of platinum was used as a coating material in a sputter coater to achieve surface conductivity.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Test Series**} & \multirow{2}{*}{**OMC from Compaction Test (\%)**} & \multicolumn{2}{c}{**Measured Water**} & \multicolumn{2}{c}{**Measured Water**} & \multirow{2}{*}{**Standard**} \\  & & **Content after** & & **Content after** & & **Deviation (UCS) (\%)** \\  & & **Sample Preparation** & & **Sample Preparation** & & **Deviation (DST) (\%)** \\  & & **(UCS) (\%)** & & **(DST) (\%)** & & \\ \hline Untreated CL & 15.5 & 12.8 & 14.8 & 2.7 & 0.7 \\ CL-G10 PA & 14.6 & 11.3 & 13.5 & 3.3 & 1.1 \\ CL-G20 PA & 13.4 & 11.1 & 13.1 & 2.3 & 0.3 \\ CL-G30 PA & 13 & 12.6 & 12.7 & 0.4 & 0.3 \\ CL-G40 PA & 12.8 & 12.2 & 12.2 & 0.6 & 0.6 \\ Untreated CH & 34.4 & 32.6 & 33.6 & 1.8 & 0.8 \\ CH-G10 PA & 29.9 & 27.8 & 29.4 & 2.1 & 0.5 \\ CH-G20 PA & 24.5 & 22.9 & 22.4 & 1.6 & 2.1 \\ CH-G30 PA & 23 & 22.5 & 21.4 & 0.5 & 1.6 \\ CH-G40 PA & 18.6 & 18.4 & 18.3 & 0.2 & 0.3 \\ \hline \end{tabular}  
* standard deviation was calculated as the difference between the optimum moisture content (OMC) from the compaction test (%) and the measured water content after preparation of samples (%).  
\end{table}
Table 4: Calculated moisture content (%) after unconfined compression (UCS) and direct shear test (DST) samples preparation.
# Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization
## 3 Method
### Task Plan Construction
If the individual benefit variation \(\kappa_{ij}^{+}\) of a task \(j\) exceeds the swarm benefit variation \(\mathbf{B}_{j}^{i}\) (line 3), the optimal task \(j^{*}\) and its insertion position \(n^{*}\) are determined through Formulas (15) and (16) and subsequently incorporated into UAV \(i\)'s task plan \(\mathbf{a}_{i}\) (lines 4-5). Following this, the selected task \(j^{*}\)'s current swarm benefit variation \(\mathbf{B}_{j}^{i}\), and the planned execution times \(\tau_{i}^{j}(\mathbf{a}_{i})\) of task \(j\) after it are updated accordingly (lines 6-7). Ultimately, UAV \(i\) either exhausts all available tasks \(j^{*}\) that meet the criteria for inclusion in its task plan (line 3) or reaches its maximum capacity for selected tasks (line 1), the process of task selection for UAV \(i\) terminates (line 9). After the construction of UAV \(i\)'s task plan is completed, if changes occur to the task plan of UAV \(i\), then UAV \(i\) must update the current benefit variation values for all tasks \(j\) included in its task plan \(\mathbf{a}_{i}\) using Formula (17), to ensure that the benefit variation values of tasks \(j\in\mathbf{a}_{i}\) are up to date (lines 10-12). ```
1:while\(\overline{\overline{\mathbf{a}}_{i}}\leq L_{i}\)do
2: Calculate the list \(\mathbf{K}_{i}^{+}=\left[\kappa_{i1}^{+},\kappa_{i2}^{+},\ldots,\kappa_{iN_{l }}^{+}\right]\) by Formula (13). 3:if\(\max_{j=1}^{N_{l}}\left\{\kappa_{ij}^{+}-\mathbf{B}_{j}^{i}\right\}>0\)then
4: Calculate the optimal task \(j^{*}\) and its insertion position \(n^{*}\) by Formulas (15) and (16)
5: Add the optimal task \(j^{*}\) to UAV \(i\)'s task plan \(\mathbf{a}_{i}\): \(\leftarrow\)\(\mathbf{a}_{i}+\left\{j\right\}\)
6: Update the selected task \(j^{*}\)'s current swarm benefit variation \(\mathbf{B}_{j^{*}}^{i}=\kappa_{ij^{*}}^{+}(\mathbf{a}_{i})\). 7: Update the planned execution times \(\tau_{i}^{j}(\mathbf{a}_{i})\) of task \(j\) (\(j\) \(\succ\)\(j^{*}\in\mathbf{a}_{i}\))
8:endif
9:endwhile
10:while\(\mathit{IsChanged}(\mathbf{a}_{i})\)do
11: Update \(\mathbf{B}_{j}^{i}\) for all tasks \(j\) in UAV \(i\)'s task plan \(\mathbf{a}_{i}\) through Formula (17). 12:endwhile
```  
**Algorithm 1** Individual task plan construction
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & & **Pre-PHV** & **Mid-PHV** & **Post-PHV** & **F** & \(p\) & **np\({}^{2}\)** \\ \hline LH & n & 28 & 47 & 54 & & & \\ \cline{2-8}  & time (min) & 8.57 \(\pm\) 1.26 & 8.67 \(\pm\) 1.26 & 8.61 \(\pm\) 1.25 & 0.06 & 0.943 & \textless{}0.001 \\  & vIFT (\%) & 82.56 \(\pm\) 2.52 & 82.66 \(\pm\) 2.52 & 82.59 \(\pm\) 2.52 & 0.10 & 0.902 & 0.002 \\  & EL (u.a.) & 710.71 \(\pm\) 110.23 & 717.29 \(\pm\) 110.72 & 711.34 \(\pm\) 106.77 & 0.05 & 0.953 & \textless{}0.001 \\  & RPE (1–10) & 7.07 \(\pm\) 0.72 & 6.68 \(\pm\) 1.16 & 5.82 \(\pm\) 1.30 \({}^{\text{ab}}\) & 13.20 & \textless{}0.001 & 0.173 \\  & IL (u.a.) & 60.27 \(\pm\) 8.48 & 57.45 \(\pm\) 11.28 & 50.00 \(\pm\) 13.16 \({}^{\text{ab}}\) & 8.94 & \textless{}0.001 & 0.124 \\ \hline SH & n & 30 & 50 & 54 & & & \\ \cline{2-8}  & time (min) & 8.83 \(\pm\) 1.27 & 8.65 \(\pm\) 1.26 & 8.57 \(\pm\) 1.25 & 0.44 & 0.644 & 0.007 \\  & vIFT (\%) & 92.50 \(\pm\) 2.54 & 92.50 \(\pm\) 2.53 & 92.13 \(\pm\) 2.50 & 0.35 & 0.706 & 0.005 \\  & EL (u.a.) & 817.08 \(\pm\) 119.51 & 800.25 \(\pm\) 119.38 & 788.66 \(\pm\) 113.93 & 0.57 & 0.567 & 0.009 \\  & RPE (1–10) & 7.93 \(\pm\) 0.94 & 7.02 \(\pm\) 1.24 \({}^{\text{a}}\) & 6.20 \(\pm\) 1.32 \({}^{\text{ab}}\) & 19.94 & \textless{}0.001 & 0.233 \\  & IL (u.a.) & 70.58 \(\pm\) 15.62 & 60.45 \(\pm\) 13.10 \({}^{\text{a}}\) & 53.19 \(\pm\) 13.92 \({}^{\text{ab}}\) & 14.92 & \textless{}0.001 & 0.186 \\ \hline \end{tabular}
\end{table} 
Table 4: Descriptive data (M \(\pm\) SD) and means comparison (ANOVA) of EL and IL variables according to the players’ maturity groups. \begin{table}
\begin{tabular}{l l l l l l} \hline  & **LH** & **SH** & **t** & \(p\) & **Hedge’s \(g\) (95\% CI)** \\ \hline time (min) & 8.62 \(\pm\) 1.25 & 8.66 \(\pm\) 1.25 & 0.21 & 0.832 & 0.03 (\(-\)0.22; 0.27) \\ vIFT (\%) & 82.67 \(\pm\) 2.50 & 92.35 \(\pm\) 2.50 & 31.33 & \textless{}0.001 & 3.85 (3.44; 4.26) \\ EL (a.u.) & 713.37 \(\pm\) 108.15 & 799.35 \(\pm\) 116.85 & 6.19 & \textless{}0.001 & 0.76 (0.51; 1.01) \\ RPE (1–10) & 6.40 \(\pm\) 1.25 & 6.90 \(\pm\) 1.38 & 3.03 & 0.003 & 0.37 (0.13; 0.62) \\ IL (a.u.) & 54.94 \(\pm\) 12.31 & 59.80 \(\pm\) 15.42 & 2.82 & 0.005 & 0.13 (0.10; 0.59) \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive data (M \(\pm\) SD) and means comparison (paired samples \(t\)-test) of the EL and IL variables according to the kind of HIIT. The RM ANOVA revealed an overall positive effect of the intervention on the 30 m sprint and vIFT (F = 10.81 and 11.60, respectively, \(p\) < 0.010). Specifically, the pre-PHV group exhibited significant improvements in the 30 m sprint (t = 2.91, \(p\) = 0.031, ES(g) = 0.41) and vIFT (t = 2.89, \(p\) = 0.048, ES(g) = 1.28) between AS1 and AS2. However, no significant changes were observed in the post-PHV group for any variable (t = 0.45-2.01, \(p\) > 0.050, ES(g) = 0.22-1.04). Likewise, no significant interaction effect (time \(\times\) maturational group) was detected for any variable (F = 0.12-0.24, \(p\) = 0.632-0.732, \(\eta\)p\({}^{2}\) = 0.01).
## 4 Interpretable Analysis of Prediction Model Based on SHAP
### Global Interpretation Based on SHAP
When the length is between 20 and 30 mm, the SHAP value is positive, indicating a slight enhancement in compressive strength. For lengths between 30 and 40 mm, the SHAP value tends to be neutral, and the improvement in strength becomes more apparent. However, when the length exceeds 50 mm, the excessive increase in fiber length negatively affects the workability and compaction of the concrete, ultimately weakening its compressive strength. As shown in Figure 7d, when the silicon powder content is higher than 30 kg/m\({}^{3}\), the SHAP value of silicon powder is larger than 0, which has a positive contribution to the predicted compressive strength. However, when the silicon powder content is higher than 40 kg/m\({}^{3}\), the SHAP value decreases slightly. Excessive silicon powder, due to its large surface area, may reduce compressive strength; therefore, the silicon powder content should not be too high. Figure 7e shows that when the sand content is higher than 600 kg/m\({}^{3}\), the SHAP value is less than 0, which indicates that the further addition of sand content will lead to a decrease in the compressive strength of SFRC. Figure 7f,g show that the water content is negatively correlated with the compressive strength of SFRC. When the water content is low, higher dosage of high-range water reducer is required to achieve high strength. However, when the dosage of high-range water reducer exceeds 8 kg/m\({}^{3}\), it begins to have a positive effect on compressive strength, reaching its  
Figure 7: Feature dependence plots by SHAP: (**a**) SHAP value for cement content; (**b**) SHAP value for age; (**c**) SHAP value for steel fiber content; (**d**) SHAP value for silicon powder content; (**e**) SHAP value for sand content; (**f**) SHAP value for water content; (**g**) SHAP value for dosage of high-range water reducer; (**h**) SHAP value for steel fiber length. maximum effect at around 18 kg/m\({}^{3}\). It should be noted that the results in this study are based on the experimental data from the established literature database, and the situation outside the database (see Table 1 for parameter range) may need further discussion.
## 2 Constellation Design and Data Simulation  
In order to evaluate the performance of an LEO-constellation-augmented BDS POD with consideration of the measurement errors of onboard BDS observations, five different types of LEO constellations including Walker 2/1/1, Walker 6/2/1, Walker 12/3/2, Walker 24/4/2, and Walker 60/6/4 are designed. The BDS and LEO orbits are generated and the observations of ground stations and spaceborne BDS data are simulated to evaluate the BDS/LEO POD under different observation conditions.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 3 The Random Padded OTFS
### Generation of RP-OTFS Pilots  
The generation of OFDM pilot symbols starts in TF domain. The pilot carriers are modulated using pseudorandom numbers \(P_{k}\) originating from quadrature phase-shift keying (QPSK), i.e., \(\Re(P_{k})\) and \(\Im(P_{k})\in\{-1,+1\}\). The real and imaginary parts of the modulating sequence are distributed uniformly; therefore, \(\Re(P_{k})\) and \(\Im(P_{k})\) are i.i.d. with the Radamacher distribution. After switching to TD or DD, the pilot samples will have an approximately normal distribution. The last statement is based on two facts:  
* the discrete ZT is based on the DFT with some rearrangements;
* the DFT of the random variable has approximately normal distribution (Theorem 4.4.1 in [35]) with some limitations that are not applicable for our case.  
The Radamacher distribution of complex pilot values \(P_{k}\) in TF ensures a uniform energy distribution within OFDM-based pilot symbols [36]. This prevents the situation of having zero magnitude carriers in the OFDM-based pilot zone in TF, which causes division by zero in some types of channel equalizers, for example, in the least squares channel estimator (LS) [37].  
After the generation of OFDM-based pilot symbols \(P_{k}\) in TF, they are transformed into TD by IDFT/IFFT, values \(p_{k}\) are obtained and CP is added. The CP length in the RP-OTFS is equal to the length of the OFDM symbol, that are used as pilot. The reason for this is that the pilot symbols do not contain any data carriers and are used only to estimate instantaneous CIR. This means that the maximum CIR length that can be estimated using an OFDM symbol is equal to the length of the symbol itself. As mentioned above, OFDM-based pilot symbols should not go through the Zak transformation from the DD domain to the TD or from the TD domain to the DD domain. They are directly placed on the TD grid (see Figure 3).  
Some additional requirements regarding RP-OTFS pilots are discussed later in Section 3.3 on ICI between pilot and data carriers.
# Research on Effective Design Methods of Core Beam of Full Bridge Aeroelastic Model
## 3 Numerical Solution of Torsion Constant  
The cross-section of a core beam commonly used in aeroelastic models is various, rectangular, circular, U-shaped, \(\pi\)-shaped sections and so on. It is difficult to obtain an analytical solution of the torsion constant, so a series of numerical solutions are proposed here. In fact, the finite element method and generalized difference method can deal with the torsion constant for any complex simply connected section, while the five-point difference method is not. For simple simply connected sections, such as rectangular sections, the torsion constant can be obtained by the five-point difference method. For complex simply connected sections, such as U-shaped cross-sections, the torsion constant can be obtained by the generalized difference method or the finite element method.
## 4 Discussion
One notable factor is the stand availability, which directly determines the route an arriving aircraft takes to its assigned parking position, adding complexity to the process [46; 47]. By integrating real-time data on stand availability, the model's predictive capabilities could be significantly improved. Predicting aircraft movement to parking positions post landing, along with the availability of these positions, is inherently complex due to the multitude of influencing factors. To address these challenges, future research could focus on developing two complementary models: one for predicting taxi-in time and another for forecasting parking position availability. These models would enhance the existing predictions for \(\delta_{IBT}\) and \(\delta_{LT}\), offering a more holistic view of the gate-to-gate arrival process. A taxi-in prediction model could incorporate variables such as runway occupancy time, taxiway configuration, preceding traffic, runway usage, assigned parking position, and adverse conditions. This integrated approach would provide deeper insights into the factors shaping the arrival process [48; 49; 50]. The development of a second predictive model focusing on parking position availability would require addressing several intricate factors. These include the occupancy status of parking stands, anticipated stand availability, allocation policies, and external influences like weather conditions [51]. Much of the input data for such models could be drawn from the Airport Operational Database (AODB). Among these factors, predicted stand occupancy and current allocation processes are likely to demand the greatest attention. While enhancing existing models or creating new ones to improve \(\delta_{IBT}\) predictions remains a relevant research avenue, the primary focus should be on exploring methods to seamlessly integrate multiple models for more accurate and cohesive predictive results. It is also important to note that the model did not incorporate data related to holding patterns, vectoring, or shortcuts, which impact aircraft landing times. The absence of such data was due to limitations in data availability, and future work could benefit from including these variables to enhance prediction precision. In outlining the next phase of research, it is important to recognize this study's considerable success in accurately predicting landing times, particularly for long-haul flights. This achievement underscores the model's potential and sets the stage for engaging with the aviation industry and airport stakeholders to explore its practical applications. The emphasis on the en-route phase of flight, incorporating parameters such as velocity and flight speed, has been well founded, offering valuable insights into the factors contributing to delay variations along the flight trajectory.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 3 Materials and Methods
### Node Centering Degree  
Nodes move continuously and make contact with other nodes in the network as a result. The destination node is thought to be more likely to be encountered by those who have more contact with other nodes, whereas those who have more contact with the destination node are thought to be closer to it because they meet more frequently. When the current node and the destination node of the message are in the same group, the message only needs to be forwarded within the group because the nodes in the same group are in contact more frequently. This not only gets the message to the destination quickly but also saves a lot of space. In summary, we express the centrality degree \(DC\) of a node as the  
Figure 1: State Transfer Diagram.  
following Equation (16), which indicates the size of the node's ability to deliver messages in this community.  
\[DC=\partial(\sum\limits_{i=0}^{n}CN_{current}^{i}/CN_{total})+\beta(\sum \limits_{j=0}^{n}CT_{current}^{j}/\sum\limits_{k=0}^{groupsize}\sum\limits_{j=0 }^{n}CT_{k}^{j}) \tag{16}\]  
where, \(\sum\limits_{i=0}^{n}CN_{current}^{i}\) is the total number of nodes in this group that the current node has contacted, if the current node has contacted with the \(i\)th node \(CN_{current}^{i}=1\), and \(CN_{total}\) is the total number of nodes in the network. \(\sum\limits_{j=0}^{n}CT_{current}^{j}\) is the total number of contacts between the current node and other nodes in in this group, \(CT_{current}^{j}\) is the number of contacts between the current node and the \(j\)th node, \(\sum\limits_{k=0}^{groupsize}\sum\limits_{j=0}^{n}CT_{k}^{j}\) is the total number of contacts between the nodes and other nodes in this group and \(groupsize\) is the number of nodes contained in this group. \(\partial\) and \(\beta\) are the control coefficients, which are \(1/2\), respectively.
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
## 4 Application to Incompressible Navier-Stokes Equations
### Time Integration  
Time advancement is accomplished through an explicit Runge-Kutta method. Although after spatial discretization the Navier-Stokes equations reduce to a system of ordinary Differential Algebraic Equations, the introduction of a projection operator for the velocity field onto the divergence free subspace, formally conducts to a system of Ordinary Differential Equations, to which standard time integrators can be applied. In this context, the projection step has to be executed at each stage of the RK, [38]. By employing a standard notation, the time integration procedure with reference to a generic \(s\)-stage explicit Runge-Kutta method can be expressed as:  
\[\begin{cases}\mathbf{u}_{i}^{*}=\mathbf{u}^{n}+\Delta t\sum_{j=1}^{i-1}a_{ij} \mathbf{R}(\mathbf{u}_{j})\\ \Delta t\,\
abla\cdot\
abla\mathbf{p}_{i}=\
abla\cdot\mathbf{u}_{i}^{*}\\ \mathbf{u}_{i}=\mathbf{u}_{i}^{*}-\Delta t\
abla\mathbf{p}_{i},\end{cases} i=1,2,\ldots,s \tag{38}\]  
\[\begin{cases}\mathbf{u}^{*}=\mathbf{u}^{n}+\Delta t\sum_{j=1}^{s}b_{i} \mathbf{R}(\mathbf{u}_{i})\\ \Delta t\,\
abla\cdot\
abla\mathbf{p}^{n+1}=\
abla\cdot\mathbf{u}_{i}^{*}\\ \mathbf{u}^{n+1}=\mathbf{u}^{*}-\Delta t\
abla\mathbf{p}^{n+1},\end{cases} \tag{39}\]  
where \(\mathbf{u}\) and \(\mathbf{p}\) are the vectors containing the discretized velocity and pressure fields on the three-dimensional grid, \(\mathbf{R}(\mathbf{u})\) is the right hand side of momentum equation, without the pressure term, once discretized in space and \(\
abla\) is the discrete nabla operator, assuming the meaning of discrete divergence or gradient operator, depending on the context. This classical approach of projecting each intermediate field in the context of an explicit RK method (cf. Equation (38)) is quite costly with respect to multisteps methods, since each projection requires the solution of an elliptic equation. More convenient strategies which make use of an extrapolation of the pseudo-pressure and perform only the final projection (39) are possible [39; 40].
## 2 The TASIPPS Conceptual Framework
### Database Security
#### 2.1.5 The Policy Module  
The policy module within this framework is dedicated to governing and regulating various facets of system operations and data management. Its core purpose revolves around the formulation, enforcement, and administration of policies that safeguard the ethical, legal, and secure utilization of healthcare data within the framework proposed in this study; these policies will be implemented and assessed later in this research. As [52] aptly noted, the \"interoperability of EHRs is inevitably bound with data protection issues because of the processing of personal data\" and, as such, policies must be put in place to ensure compliance with guiding principles such as the GDPR or HIPAA.  
Consequently, the establishment of policies is imperative to ensure adherence to legal frameworks such as the UK/EU GDPR and the US HIPAA. The policies recommended in this work draw inspiration from the GDPR, HIPAA, and other industry best practices. The work was also inspired by the European Commission-issued Recommendation (EU) 2019/243 of February 2019, which is a comprehensive guide for achieving both technical and semantic interoperability while upholding the privacy and security of patient data. The policy module in this study aspires to follow this guidance by proposing a set of policies that will allow for the realization of privacy-preserving technical and semantic interoperability with robust security measures.  
The following policies are implemented in the framework: the Compliance Monitoring Policy; Change Management Policy; Disaster Recovery and Business Continuity Policy; Data Consent Revocation Policy; Data Retention and Deletion Policy; Audit Logging Policy; Error Handling and Reporting Policy; Interoperability Testing Policy; Identity and Access Management Policy; and Consent Management Policy. The primary objective of the policy module is to establish a robust system that ensures adherence (by all stakeholders) to the (defined) interoperability standards and regulations governing EMRs. These policies are guided by the standards presented by [53], which advocate for consistent system monitoring.