esa-sceva/satcom-chunk-collection huggingface dataset:
# Rankine Active Earth Pressure of Unsaturated Filling under Earthquake
## 3 Analyses and Results
### Verification  
#### 3.3.1 Comparison with Classic Rankine Solution  
In order to compare with the classic Rankine theory, when re-writing Equations (14), (15) and (18) with the setting of \(\alpha=\beta=\delta=\eta=\varphi^{b}=(u_{a}-u_{w})=k_{\rm v}=k_{h}=b=0\) we get  
\[\left\{\begin{array}{ll}M=\gamma z+\frac{\zeta^{\prime}}{\gamma}\sin(2\varphi ^{\prime})\\ N=\gamma z\sin\varphi^{\prime}+c^{\prime}\cos\varphi^{\prime}\end{array}\right. \tag{23}\]  
\[\begin{array}{ll}K_{\rm a}&=\frac{(1-\sin\varphi^{\prime})^{2}}{\cos^{2} \varphi^{\prime}}-\frac{2c^{\prime}(1-\sin\varphi^{\prime})}{\gamma z\cos \varphi^{\prime}}\\ &=\tan^{2}\Bigl{(}45^{\circ}-\frac{\varphi^{\prime}}{2}\Bigr{)}-\frac{2c^{ \prime}}{\gamma z}\tan\Bigl{(}45^{\circ}-\frac{\varphi^{\prime}}{2}\Bigr{)} \end{array} \tag{24}\]  
It could be seen that Equation (24) is completely consistent with the expression of the classical Rankine active earth pressure coefficient by substituting shear strength parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\((u_{a}-u_{w})/kPa\)} & \multirow{2}{*}{\(\varphi^{b}/c\)} & \multicolumn{3}{c}{\(d_{\rm aa}/\)m} & \multirow{2}{*}{\((u_{a}-u_{w})/kPa\)} & \multirow{2}{*}{\(\varphi^{b}/c\)} & \multicolumn{3}{c}{\(d_{\rm aa}/\)m} \\ \cline{3-3} \cline{5-10}  & & \(b=0\) & & \(b=0.5\) & & \(b=1\) & & \(b=0\) & & \(b=0.5\) & & \(b=1\) \\ \hline
0 & 14 & 2.69 & 2.48 & 2.23 & 30 & 10 & 2.52 & 2.31 & 2.1 \\
30 & 14 & 2.43 & 2.24 & 2.02 & 30 & 13 & 2.45 & 2.26 & 2.05 \\
60 & 14 & 2.2 & 2.03 & 1.78 & 30 & 16 & 2.38 & 2.2 & 1.98 \\
90 & 14 & 1.86 & 1.71 & 1.49 & 30 & 19 & 2.31 & 2.13 & 1.9 \\
120 & 14 & 1.57 & 1.43 & 1.22 & 30 & 22 & 2.23 & 2.02 & 1.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resultant location of Rankine active earth pressure and unsaturation parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(k_{\rm v}\)} & \multirow{2}{*}{\(k_{\rm h}\)} & \multicolumn{3}{c}{\(d_{\rm aa}/\)m} & \multirow{2}{*}{\(k_{\rm v}\)} & \multirow{2}{*}{\(k_{\rm h}\)} & \multirow{2}{*}{\(k_{\rm h}\)} & \multirow{2}{*}{\(d_{\rm aa}/\)m} \\ \cline{3-3} \cline{5-10}  & & & \(b=0\) & & \(b=0.5\) & & \(b=1\) & & \(b=0\) & \(b=0.5\) & \(b=1\) \\ \hline
0 & 0.1 & 2.43 & 2.24 & 2.2 & 0.1 & 0 & 2.34 & 2.16 & 2.08 \\
0.05 & 0.1 & 2.46 & 2.26 & 2.22 & 0.1 & 0.05 & 2.43 & 2.22 & 2.17 \\
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 1 Introduction
The right-angled power dividers provide output phases that cannot be achieved by common inline dividers but still obtain the same or similar amplitudes at the outputs. This feature is practical, for instance, when designing tracking systems since they are employed to obtain controllable phase distributions over the output ports. Moreover, a frequency-agile single- and dual-beam performance is possible which applies to frequency-dependent communications in different directions. To the best of the authors' knowledge, this is the first time that right-angled power dividers with phase control ability are used as feed system for planar antennas to achieve different radiation patterns in terms of direction, gain, and also different frequency ranges. Among other planar antenna configurations, the antipodal slot antenna (Vivaldi) is chosen as the best candidate for validating the performance of these power dividers. This paper utilizes right-angled SIW corner dividers [20] as feeding systems for Vivaldi arrays. As application examples, three such antenna systems are proposed. The first one is composed of a three-way SIW right-angled in-phase corner divider which feeds three Vivaldi antennas to provide a good performance with about 12 dB gain over the entire band of operation, reflection coefficient better than 10 dB from 21.57 GHz to 23.7 GHz, and a very directive radiation pattern. This three-way divider with high gain and directive pattern is introduced for on-axis applications. The second example includes an out-of-phase right-angled two-way divider with an on-axis minimum. This design achieves more than 8 dB gain, reflection coefficient better than 10 dB from 26.11 GHz to 28.33 GHz, and a symmetrical dual beam radiation pattern. The two-way power divider system with 180-degree phase difference is proposed for nulling applications, providing a dual beam with a deep null on axis. The last circuit is a four-way divider for a 1 \(\times\) 4 Vivaldi array with good performance at K-band frequencies and single- as well as dual-beam operation. This frequency-dependent four-waydivider system provides dual-beam and single-beam performances in the lower and higher frequency range, respectively. For measurement purposes, all three systems include at their input ports a microstrip-to-SIW transition and coaxial connector which are included in the simulation process. The simulation results comprise all losses including copper with electrical conductivity of 5.8 \(\times\) 10\({}^{7}\) S/m, RT/Duroid 6002 substrate with loss tangent of 0.0012, and metal thickness of 0.0175 mm.
# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
### Research Gap, Contributions and Paper Structure  
Despite notable advancements in PM technologies, their application in the aviation industry faces significant challenges. Existing research has often concentrated on specific technological innovations, such as machine learning models, digital twins, or IoT-based systems, and planning methods to optimize maintenance schedules. However, these approaches frequently fail to address aviation's unique and stringent safety requirements comprehensively. Current studies lack an integrated decision-making framework that systematically evaluates PM feasibility across critical safety, economic, regulatory, and operational dimensions. Furthermore, there is a critical gap in frameworks that incorporate stakeholder consensus and scalable methodologies validated for the aviation industry's highly regulated environment.  
This study addresses these gaps by developing a robust decision-making framework specifically tailored to aviation predictive maintenance, prioritizing safety as its core focus. By systematically integrating MCDA, the proposed framework evaluates aircraft components for PM suitability, emphasizing safety-critical factors such as flight risk mitigation, reliability predictability, and data sufficiency. It ensures compliance with regulatory standards while providing a structured mechanism for stakeholders to make informed, safety-focused decisions.  
The primary contributions of this paper are as follows:  
* A comprehensive decision-making framework that prioritizes aviation safety by incorporating key criteria such as flight safety impact, regulatory compliance, and reliability predictability, alongside economic and operational considerations.
* The application of the analytic hierarchy process (AHP) for criteria weighting, ensuring transparency and alignment with the aviation sector's safety and regulatory imperatives.
* Validation of the framework through expert surveys, historical maintenance data, and real-world case studies, highlighting its ability to enhance decision-making and reduce safety risks.
* Practical recommendations for integrating PM strategies into existing aviation maintenance operations, ensuring both enhanced safety and cost efficiency while aligning with global aviation standards.  
The structure of the paper is organized as follows: Section 2 details the materials and methods, focusing on the critical role of predictive maintenance in aviation and the conceptual framework for this study. Section 3 presents the results, including the selection of evaluation factors, the MCDA-based decision-making framework, and case studies showcasing its real-world application. Section 4 discusses the framework's expected safety and operational benefits, challenges, and limitations, along with a detailed roadmap for implementing PM in aviation. Section 5 concludes the study by summarizing its contributions to aviation safety and efficiency while outlining opportunities for future research.
## 3 System Model
### Three Principles for Resource Reallocation  
Note that frequent signals allow for more flexible resource allocation but also increase the load on the system. This poses a problem of how to adjust the rate of controller signals in order to balance compliance with equal resource partition, maximize resource utilization, and increase the number of signals that result in resource reallocation. Additionally, it is important to consider the extent to which the boundaries of resource slices should be adjusted.  
The controller manages resource reallocation based on three principles, each with a weight \(w_{i}\), where \(i=1,2,3\):  
1. The first principle takes into account the deviation from equal resource partition, specifically the number of requests that could be served but are waiting due to unequal resource partition. This is significant because the optimal policy should align with the initial partition outlined in the service-level agreement (SLA) between the network slice provider and network slice service providers.  
Figure 4: General scheme of cases of resource reallocation between the service providers performed by the controller.  
2. The second principle considers the frequency of cases where resource reallocation does not occur when a signal arrives. It is crucial that signals from the controller result in real resource reallocation. Frequent arrival of \"non-resulting\" signals is deemed unacceptable, as it incurs unnecessary signaling messages. This aspect is also relevant in radio resource management strategies, as it can affect resource efficiency and utilization. An example is shown in Figure 5.
3. The third principle evaluates the number of free resources while users are waiting. In this case, maximizing resource utilization is important. Idle resources, representing the situation where a user is waiting for a slice that is currently occupied and another slice is available to serve the user's request, are undesirable.
## 7 Feature Extraction Approaches
### Texture-Based Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Used Datasets**} & \multirow{2}{*}{**Evaluation Protocol**} & \multirow{2}{*}{**Acc. (\%)**} \\ \cline{3-4} \cline{6-7}  & & & **Name** & **\#IDs.** & **\#Imgs.** & & 5 Imgs/sub for training, remaining \\ \hline \multirow{3}{*}{2014} & [PERSON] & \multirow{3}{*}{LBP + SFFS} & CASIA & 564 & 4512 & \begin{tabular}{c} 5 Imgs/sub for training, remaining \\ 3 Imgs/sub for testing (Rand) \\ \end{tabular} & 97.53 \\ \cline{3-4} \cline{6-7}  & [PERSON] & \multirow{3}{*}{LBP + SFFS} & PolyU & 386 & 7752 & \begin{tabular}{c} First session for training, second \\ session for testing \\ \end{tabular} & 95.35 \\ \hline \multirow{3}{*}{2015} & [PERSON] & PolyU & 352 & 7040 & \begin{tabular}{c} First 10 Imgs/sub for training, remaining \\ remaining 10 Imgs/sub for testing \\ \end{tabular} & EER = 4.06 \\ \cline{3-4} \cline{6-7}  & [PERSON] & \multirow{3}{*}{B-BSIF} & IITD & 470 & 2350 & \begin{tabular}{c} 4 Imgs/sub for training, remaining \\ 1 Img/sub for testing (Rand, \\ 10 iteration) \\ \end{tabular} & EER = 0.42 \\ \cline{3-4} \cline{6-7}  & [PERSON] & \multirow{3}{*}{BGDPPH + KDA} & PolyU-MS & 500 & 6000 & \
\begin{tabular}{c} First session for training, second \\ session for testing \\ \end{tabular} & EER = 0.00 \\ \cline{3-4} \cline{6-7}  & [PERSON] & \multirow{3}{*}{BGDPPH + KDA} & PolyU & 400 & 8000 & 4-fold cross-validation & 99.98 \\ \cline{3-4} \cline{6-7}  & [PERSON] & \multirow{3}{*}{BGDPPH + KDA} & CASIA & 624 & 5335 & \multirow{3}{*}{3-fold cross-validation} & 99.22 \\ \cline{3-4} \cline{6-7}  & and [PERSON] & & IITD & 430 & 2400 & \multirow{3}{*}{3-fold cross-validation} & 99.19 \\ \cline{3-4} \cline{6-7}  & & IIITD & 150 & 900 & & 100.00 \\ \cline{3-4} \cline{6-7}  & & PolyU-MS & 500 & 6000 & & 100.00 \\ \cline{3-4} \cline{6-7}  & & CASIA-MS & 200 & 1200 & & 98.99 \\ \hline \multirow{3}{*}{2018} & [PERSON] & \multirow{3}{*}{DGLSPH} & PolyU & 386 & 7752 & \multirow{3}{*}{99.95} \\ \cline{3-4} \cline{6-7}  & & PolyU & & & & \\ \cline{3-4} \cline{6-7}  & et al. [72] & \multirow{3}{*}{DGLSPH} & PolyU & 400 & 8000 &
\begin{tabular}{c} First 3 Imgs/sub for training, remaining for testing \\ \end{tabular} & 99.95 \\ \cline{3-4} \cline{6-7}  & & IITD & 460 & 2601 & & 99.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparative overview of texture-based approaches (#ID: number of identities, #Imgs: number of images, Acc: valid accuracy).
## 5 Cache Allocation
### Content Domain Weight  
Content domains logically separate content from different content servers. The size of contents is the most important element in a content domain and the biggest difference between different content domains. The size of the content intuitively affects the adequacy of cache space, so we first considered the content size. The weight of a content field is determined by what it contains. The more content a domain contains, the more cache size it should be allocated, so its weight should also be greater. However, different categories of content with the same size, such as VoD and Web, require different cache space to achieve the same cache hit ratio, which is represented by cache size sensitivity. In addition, more caches should be allocated to those hot spots because they are more likely to be requested.  
Combining the impact of size, popularity, and sensitivity to cache space on cache performance, we express the weight \(CDW_{i}\) of content domain \(i\) as:  
\[CDW_{i}=\varphi_{1}*N_{i}*(\varphi_{2}*P_{i}+\varphi_{3}*S_{i})\hskip 36.135 pti\in CD \tag{1}\]  
In Expression (1), \(N_{i}\) represents the content size contained in content domain \(i\), \(P_{i}\) represents the average popularity of content in content domain \(i\), \(S_{i}\) is the average sensitivity of content in content domain \(i\) to the size of the cache, and \(CD\) is a collection of content domains in the network. \(\varphi_{1}\),\(\varphi_{2}\), etc. are the weights of each factor, which can be adjusted according to the actual situation or analysis requirements. The size of the content in the content server can be easily obtained from the content provider. Nodes in ICN can handle transport layer protocols, so we switch from different application types to preset values of corresponding content size sensitivity by predefined lookups and bring them to the transport layer along with content popularity. Sensitivity values for different types of applications were determined from previous studies [36; 37]. Content popularity can be collected and saved by individual nodes, then analyzed and calculated offline on additional servers. In many scenarios, content sensitivity to cache space and popularity can be difficult to obtain. In order to make our scheme more general and easy to implement, we simplify the weight \(CDW_{i}\) of content domain \(i\) as:  
\[CDW_{i}=N_{i}\hskip 36.135 pti\in CD \tag{2}\]  
In this case, the calculation of content domain weights only considers the size differences of the content contained within the content domain, and the popularity and sensitivity to the cache size of the content within each content domain are the same by default. This will increase the evaluation error of node importance and reduce the system performance. At the same time, it can make the algorithm easier to implement.
## 3 Results  
### Saturated Hydraulic Conductivity of Soil Columns  
As presented in Figure 2, saturated hydraulic conductivity (Ks) was measured under two initial soil conditions. In comparison to the control group without macropore, AMSF method increased _K_s by 268% (_p_ = 0.05) in mild salinization condition, and by 309% (_p_ = 0.05) in severe salinization condition. The results indicated an improvement for AMSF method on drainage capacity in soils. This could be explained by the replacement of cohesive particle, so that continuous macropores were generated between sand particles. Thus, water could be delivered preferentially into deeper soil layers through pores.
## 2 A Mathematical Model for the Treatment of CML with BCR-ABL1-Targeted and Immunomodulatory Drugs
### A Brief Review of the Mathematical Model
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Symbol** & **Interpretation** & **Units** & \
\begin{tabular}{c} **Values Used** \\ **in Figure** 2 \\ \end{tabular} &
\begin{tabular}{c} **Values Used** \\ **in Figures** 3 **and** 4 \\ \end{tabular} \\ \hline \(Q\) & concentration of quiescent leukemic cells & \(10^{2}\) cells/mL & & \\ \hline \(P\) & concentration of proliferating leukemic cells & \(10^{7}\) cells/mL & & \\ \hline \(P_{\text{ss}}\) & carrying capacity of proliferating & \(10^{7}\) cells/mL & 10 & 15 \\ \hline \(E\) & effector T cells & \(2\times 10^{3}\) cells/mL & & \\ \hline \(E_{\text{ss}}\) & carrying capacity of effector T cells & \(2\times 10^{3}\) cells/mL & 1.75 & 2.25 \\ \hline \(r_{Q}\) & replication rate constant of quiescent cells & \(1\)/day & & 0.02 \\ \hline \(\delta_{Q}\) & natural death rate constant of quiescent cells & \(1\)/day & & 0.005 \\ \hline \(k_{P}\) & rate constant for quiescent cells \(Q\) & \(1\)/day & & 0.10 \\  & differentiating into proliferating cells \(P\) & & & \\ \hline \(r_{P}\) & replication rate constant of proliferating & \(1\)/day & 8 & 0.30 \\  & leukemic cells & & & \\ \hline \(\delta_{P}\) & natural death rate constant of proliferating & \(1\)/day & 0.75 & 0.02 \\  & leukemic cells & & & \\ \hline \(s_{E}\) & growth rate constant for effector T cells & \(1\)/day & 0.25 & 0.01 \\ \hline \(\delta_{E}\) & natural death rate constant of effector T cells & \(1\)/day & 0.25 & 0.005 \\ \hline \(P_{\text{max},1}\) & maximum stimulation effect of proliferating & & & \\  & leukemic cells \(P\) on effector T cells \(E\) & & 2 & 0.50 \\ \hline \(P_{\text{max},2}\) & maximum inhibition effect of proliferating & & & \\  & leukemic cells \(P\) on effector T cells \(E\) & & 5 & 0.20 \\ \hline \(PC_{50}\) & size of \(P\) with half the maximum effect & \(1\)/mL & \(10^{7}\) & \(10^{7}\) \\ \hline \(E_{\text{max},1}\) & maximum effect of effector T cells \(E\) on & & & \\  & quiescent leukemic cells \(Q\) & & & \\ \hline \(E_{\text{max},2}\) & maximum effect of effector T cells \(E\) on & & & \\  & proliferating leukemic cells \(P\) & & 1 & 5 \\ \hline \(EC_{50}\) & size of \(E\) with half the maximum effect & \(1\)/mL & \(2000\) & \(2000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: States and parameters for the dynamical system.
## 5 Discussions
### Contribution to Coastal Management  
The digital twin allows for the creation of dynamic simulations, enabling coastal managers to explore various scenarios. Decision makers can assess the potential impact of different factors, such as extreme weather events, sea-level rise, or human activities, on the coastal environment. The digital twin framework also significantly enhances data-driven decision-making in coastal management by harnessing comprehensive information, thus ensuring that management decisions are not only grounded but also optimized through accurate and current data. Leveraging advanced Artificial Intelligence (AI) techniques, the digital twin goes beyond static data representation, introducing predictive capabilities. For instance, AI algorithms can simulate scenarios on the tidal flat by virtually placing numerous individuals, allowing for the prediction of potential risks. This dynamic approach provides decision makers with foresight into various situations, enabling them to proactively address emerging challenges and implement adaptive strategies for enhanced coastal safety and resilience. Through the integration of AI, the digital twin becomes a proactive tool that not only analyzes historical and real-time data but also anticipates and models future scenarios, contributing to a more forward-thinking and responsive approach to coastal risk management. This concept is our ongoing work to quantify the risk coefficient rapidly.
## 3 Results and Discussion  
### Numerical Result Validation  
We conducted a comparison between experimental and numerical data, as shown in Figure 6. The experimental data are consistent with the calculated results. Under all discharge conditions, the maximum errors of head and efficiency were 4.2% and 4.7%, respectively. The pressure fluctuation under the low-discharge condition is shown in Figure 6b. The measuring point is in the inlet pipe near the impeller inlet. The frequencies of 2.2\(f_{n}\) and 6\(f_{n}\) appeared both in the numerical simulation and experimental test. 3\(f_{n}\) appeared in the experiment but not in the numerical simulation, possibly as a result of the mechanical vibration caused by impeller rotation. The results show that the numerical simulation is accurate.  
Figure 5: Computational domain grids of the impeller. (**a**) Impeller; (**b**) Blade inlet; (**c**) Blade outlet.  
Figure 6: Comparison between experimental and numerical data: (**a**) external characteristic curves; (**b**) pressure fluctuation under low-discharge condition.
## 5 Discussion
### Influence of Mass of FPTLIR on System Dynamics  
The aim of the simulation experiment was to study the dynamic response of FPTLIRs with different masses, ranging from 10 kg to 100 kg, when landing at the center of the PTL. The simulation results of initial peak values and rise times for FPTLIR of different masses are shown in Figure 20a. The peak Z-displacements and roll angle of different mass were also all in the safe workplace. The peak value of the Z-displacement was found to be significantly influenced by the mass of the FPTLIR. For an FPTLIR mass of 10 kg, the peak value of the relative Z-displacement was \(-0.06\) m, while for an FPTLIR mass of 100 kg the peak value of the relative Z-displacement was \(-0.65\) m. The peak value of the relative Z-displacement increased gradually, with the peak value for a 10 kg FPTLIR being 9.2% of a 100 kg FPTLIR. The roll angle peaks increased proportionally with the FPTLIR's mass rising from 1.0\({}^{\circ}\) at 10 kg to 7.8\({}^{\circ}\) at 100 kg. Notably, the peak for 10 kg was only 12.8% of the peak value for 100 kg, highlighting the significant effect of increased mass on roll angle amplitude.  
Figure 20b reveals that the peak time for the Z-displacement varied between 0.52 s and 0.91 s, depending on the FPTLIR's mass, while the peak time for the roll angle ranged from 1.05 s to 1.40 s. Larger masses led to greater Z-displacement and roll angles due to higher inertia, which in turn resulted in a slower response time for the FPTLIR to reach its peak values.