esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology for Frequency Correction and Design for EO FP Etalon
### Design for the EO Crystal Used in This Study  
EO crystals of different crystal classes have different linear EO coefficient tensors. According to Formula (3), at least one column of the linear EO coefficient tensor \(\gamma\) meets the condition that \(\gamma_{4k,5k,6k}\) all equal zeros. This condition can preclude the principal axes of the refractive index ellipsoid from deviating from coordinate axes. The deviation will make it challenging to compute the crystal's refractive index change. On the other hand, only crystals crystals without a center of symmetry possess the linear EO effect. Therefore, only crystals of several crystal classes can be involved in EO frequency modulation, including C2v-mm2, C3v-3m, C4v-4 mm, etc.  
We chose LT crystal in this experiment. The LT crystal belongs to the C3v-3m crystal class. The Formula (8) is the LT crystal's linear EO coefficient tensor.  
\[\mathbf{\gamma}=\left(\begin{array}{ccc}0&-\gamma_{22}&\gamma_{13}\\ 0&\gamma_{22}&\gamma_{13}\\ 0&0&\gamma_{33}\\ 0&\gamma_{51}&0\\ \gamma_{51}&0&0\\ -\gamma_{22}&0&0\\ \end{array}\right) \tag{8}\]  
The tensor components \(\gamma_{43,53,63}\) are equal to zeros, fulfilling the abovementioned condition. As a result, this crystal requires an electric field along the z-axis (\(E_{3}\)) in EO frequency modulation.  
LT crystal is uniaxial, and its optic axis is also along the z-axis. Its refractive indexes without any electric field applied to it are \(n_{1}^{0}=n_{2}^{0}=n_{o}\) and \(n_{3}^{0}=n_{e}\). \(n_{o}\) and \(n_{e}\) are the LT crystal's refractive indexes for laser with polarization perpendicular to and parallel to the crystal's optic axis (z-axis), respectively. According to Formula (7), when we provide an electric field \(E_{3}\) for an LT crystal, the crystal's refractive index change is explicitly expressed as Formula (9).  
\[\left\{\begin{array}{l}\Delta n_{1}=-\frac{1}{3}{n_{o}}^{3}\gamma_{13}E_{3} \\ \Delta n_{2}=-\frac{1}{2}{n_{o}}^{3}\gamma_{13}E_{3}\\ \Delta n_{3}=-\frac{1}{2}{n_{e}}^{3}\gamma_{33}E_{3}\\ \end{array}\right. \tag{9}\]  
For the LT crystal, \({n_{e}}^{3}\gamma_{33}\) is higher than \({n_{o}}^{3}\gamma_{13}\), so we chose the laser with polarization on the z-axis. With the LT crystal, the frequency differentiation of the oscillating longitudinal mode from the EO frequency modulation is  
\[\Delta\
u=\frac{\
u_{0}l}{2L}{n_{e}}^{3}\gamma_{33}E_{3}=\frac{\
u_{0}{n_{e}}^{ 3}\gamma_{33}lU}{2 Ld}, \tag{10}\]  
where \(U\) is the voltage applied on the LT crystal and d is the distance between electrodes for voltage application.  
When the EO crystal is used as an EO etalon, the voltage applied to the EO crystal will also change the position of the etalon's transmission peaks. The shift value of one peak's central frequency is as Formula (11) presents. \(\
u_{1}\) is the central frequency of the EO etalon's transmission peak.  
\[\Delta\
u_{E}=\frac{\
u_{1}{n_{e}}^{2}\gamma_{33}U}{2d} \tag{11}\]
# Nonlinear Solid-Fluid Coupled Seismic Response Analysis of Layered Liquefiable Deposit
## 2 Numerical Model of Liquefiable Site
### Constitutive Model and Input Parameters  
The selection of a constitutive model to describe the behavior of both fully or partially liquefiable soils is critical in the numerical analysis. Many researchers have proposed various types of constitutive models to investigate the cyclic behavior of soil subjected to dynamic loading [22; 23].  
In this study, the critical-state, bounding surface dynamic elastoplastic constitutive model, PM4 Sand model, which was developed by [PERSON] and [PERSON] [22], is used to simulate the nonlinear response of the saturated sand layers, wherein the model could capture the dilatancy, nonflow liquefaction, permanent shear strain accumulation, and continuously update the pressure dependent shear modulus. These features play an important role for modeling the undrained response of sands subjected to cyclic loading. The PM4 Sand model parameters in Table 1 demonstrate the loose sand and dense sand of two relative densities, which have been extensively calibrated to emphasize the realistic modeling of liquefaction behavior [24]. By calibrating three primary input parameters, the shear modulus coefficient, \(G_{O}\), apparent relative density, \(D_{r}\), contraction rate parameter, and \(h_{po}\), reasonable approximations of the desired dynamic behavior of soil can be obtained from the numerical analyses, including pore pressure generation and dissipation, limiting strains, and cyclic mobility, in which \(G_{O}\) is a parameter controlling the small-strain shear modulus defined through the following equation:  
\[G_{\text{max}}=G_{O}p_{A}\left(\frac{p}{p_{A}}\right)^{0.5} \tag{1}\]  
where \(G_{max}\) is the small-strain shear modulus; \(p_{A}\) is the atmospheric pressure; and \(p\) is the current mean effective stress.  
Default values are assigned to the rest of the secondary parameters. Meanwhile, the nonlinear hysteretic behavior of the clay soil under cyclic loading was simulated using the Pressure Independent Multi-Yield kinematic plasticity model [25] with a von Mises multi-yield surface, in which the plasticity appears only in the deviatoric stress-strain response. The volumetric stress-strain response in the linear-elastic response is independent of the deviatoric stress, and it is insensitive to the confining pressure variation. The critical mechanical parameters of the crust clay are listed in Table 2.  
As shown in Figure 2, the cyclic response of sand consolidated at the initial vertical stress of 100 kPa is evaluated based on the stress-controlled undrained cyclic direct simple shear (DSS) test simulations using OpenSees. The results showed that PM4 Sand can effectively capture the stress-strain response, strength degradation during cyclic loading, and the number of cycles to liquefaction for sand with a relative density of 35% and 75%.
## Appendix B Big Bang-Big Crunch Optimisation Method  
To find the optimal parameters of the controllers, we use the Big Bang-Big Crunch (BB-BC) optimisation method. The proposed optimisation method has shown a faster response than the global optimisation methods [31]. The BB-BC exists in two primary phases. The \"Big Bang phase\" involves randomly distributed candidate solutions across the search space, followed by the \"Big Crunch phase,\" which involves calculating the population's centre of mass using a contraction procedure. Like other evolutionary search methods, the initial Big Bang population is produced randomly over the search space. Similarly, all following Big Bang phases are randomly distributed around the centre of mass or the best-fitted individual.  
In [31; 52], the working concept of this evolutionary approach consists of changing a converging result into a chaotic state, which is a new set of solutions. The algorithm starts by generating a random population of candidates for calculating fitness values. Using Equation (A8), the centre of mass is calculated, and the selection is based on either the best individual (fitness value) or the centre of mass. Similar to evolutionary computing (EC) algorithms, a new set of candidates is generated based on the previously selected individual via adding or subtracting a random number whose value changes adaptively. The whole process is then repeated (without the initialisation) if the stopping criterion has not been met.  
During the Big Crunch, a contraction method is used after the Big Bang. The contraction operator computes a centre of mass using the current locations of each candidate, resulting in the population and its associated cost function value. The centre of mass \(x_{c}\) position may be calculated using the following formula:  
\[x_{c}=\frac{\sum_{i=1}^{N}\frac{1}{f^{i}}x_{i}}{\sum_{i=1}^{N}\frac{1}{f^{i}}}. \tag{10}\]  
where \(x_{i}\) is the candidate position, and \(f^{i}\) is the cost function value for each candidate \(i\) and population size \(N\). The new value \(x_{i}^{new}\) is  
\[x_{i}^{new}=x_{c}+\sigma. \tag{11}\]  
where \(x_{i}\) is the new candidate result and \(\sigma\) gives the standard deviation of a standard normal distribution.  
For the next iteration Big Bang phase, the new generation is distributed around \(x_{c}\) as follows:  
\[\sigma=\frac{r\alpha(x_{max}-x_{min})}{k}, \tag{12}\]  
In Equation (12), the standard deviation reduces as each repetition progresses. Moreover, \(r\) is a random number and \(\alpha\) is a parameter bounding the size of the search space and  
\[x_{i}^{new}=x_{c}+\frac{r\alpha(x_{max}-x_{min})}{k}. \tag{13}\]  
Here, the maximum and minimum limits are given as \(x_{max}\) and \(x_{min}\), respectively. Additionally, \(k\) is the number of iterations for the optimisation. While, typically, distributed numbers may exceed \(\pm 1\), the population must be limited to the search space boundaries. Therefore, the candidate results are constrained inside the search space boundaries due to this narrowing.
## 2 Materials and Methods
### Comparison of ANN and MLR Models  
Six environmental variables that included \(T_{s}\) at the 5-cm depth, \(T_{a}\), downwelling PAR (\(Q\)), vapor pressure deficit (\(D\)), \(R_{n}\), and \(\theta\) in the 0-30 cm layer, and stand age were selected to train the ANN model using pre-fertilization flux measurements. The particular environmental variables that worked best for GPP, \(R\) and ET were chosen as inputs after trying several ANN networks. GPP, \(R\) and ET were simulated independently using different input variables for the ANN model. Specifically, three different variable groups, (\(T_{a}\), \(\theta\), \(Q\), \(D\), stand age), (\(T_{a}\), \(\theta\), \(T_{s}\), stand age) and (\(T_{a}\), \(\theta\), \(R_{n}\), \(D\), stand age), were then used to train the ANN model and predict monthly GPP, \(R\) and ET, respectively, for the three sites assuming the stands had not been fertilized. To ensure high precision in the period of model prediction, we used multi-year monthly values of climate variables, stand age and EC-measured C fluxes and ET before 2005 to train the ANN model and then verified the trained model with measurements in 2005 and 2006. When we were convinced that the optimized ANN model successfully simulated the multiyear seasonal variations in C fluxes and ET, the input values for the post-fertilization period were transferred into the trained model to predict the GPP, \(R\) and ET for 2007 to 2010. The resulting differences between the measurements and predictions were used to discern the impact of fertilization.  
At the beginning of the training, inputs and outputs were normalized between 0 and 1, which is a common preprocessing method for variables with different values. Moreover, we also used an MLR model to simulate C fluxes and ET with the same variables to assess the advantages of the ANN modeling approach over traditional regression modeling techniques. Similar to the ANN model, we used a variable group (e.g., \(T_{a},\theta,Q,D\), stand age) as the independent variables to establish an MLR equation and predict the dependent variable (e.g., GPP). Because of the differences in age among these three stands, it is important to note that both ANN and MLR models were run separately for each stand. The performances of the models were evaluated by comparing predicted with measured values in the calibration and verification periods. In this study, computer programming and data analysis were performed using MATLAB (version 7.13, R2011b).
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 14 Cost and Economic Feasibility
### Commercialization Challenges  
The commercialization of nanomaterial-based photocatalytic systems faces several challenges, including market competition, consumer acceptance, and regulatory hurdles. Although the demand for clean indoor air is high, many consumers are unaware of the benefits of photocatalytic air purifiers or are hesitant to adopt new technologies due to perceived high costs. In addition, regulatory standards for the safety and efficacy of nanomaterials in consumer products are still evolving. Manufacturers must comply with regulations concerning the use of nanomaterials, which may increase both the time and cost required to bring products to market.  
The high initial investment required for developing commercial-grade photocatalytic air purifiers and building materials could also deter potential investors. To overcome this, manufacturers may need to partner with government agencies or research institutions to secure funding or subsidies, particularly in regions where air quality is a significant concern. This can help offset the high upfront costs and make the technology more accessible to a wider audience.
## 3 Methodology  
This section describes the research design along with the method of implementation of a GA-based hybrid encryption policy in SDN. The experimental and quantitative research techniques used here implement and evaluate a GA-based hybrid encryption solution implemented in SDN. The experiments were conducted in a Mininet-based SDN environment. Mininet offers a robust platform for testing and evaluating SDN security policies by creating a network of virtual switches, hosts, and controllers. This allows for controlled experimentation and systematic assessment of encryption efficiency and performance. The implementation of encryption as a policy process begins with formulating the encryption algorithm. This algorithm is implemented with the POX controller in a way that it enforces the algorithm as a policy over the network, resulting in policy establishment. When the topology designed in Mininet is connected to the controller, and messages are transmitted between hosts, the controller enforces the designed policy. We evaluate the algorithm's effectiveness and network performance based on the metrics illustrated in Figure 1.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 4 A Real Case Study
### ATM Routing Network
The reinforcement learning model successfully optimized the vehicle's route, ensuring efficient service to these locations. The final learned route consistently followed across 100 test episodes was as follows (Figure 9). Final route: [911, 882, 239, 110, 127, 482, 175, 228] with an average total travel distance of 9.38 km (\(\pm 0\) standard deviation). To mitigate the traffic congestion commonly experienced along Dogan Arash Boulevard, the current route was designed to incorporate an alternative path. While it remains feasible to traverse Dogan Arash Boulevard, its propensity for congestion introduces significant delays, rendering it less desirable. Consequently, despite a slight increase in distance, this alternative route was selected for its smoother traffic flow, ensuring uninterrupted and timely travel for the vehicle. The performance of the proposed DDQN-RL model was benchmarked against [PERSON]'s solver with its built-in heuristic strategies (e.g., Improved RINS, Feasibility Pump, Relaxation Induced Neighborhood Search, and Heuristic Solutions from Relaxed Problems). The comparison results, summarized in Table 11, show that the DDQN-RL model consistently achieved shorter total distances across all scenarios. For example, in the baseline case (Scenario 1), the DDQN model found a route with a total distance of 9.38 km, while [PERSON]'s Heuristic Solution resulted in 16.31 km. Even in the most complex scenario involving 30 ATM nodes (Scenario 7), DDQN produced a stable solution of 43.84 km, significantly  
Figure 8: Training curve of the DDQN model. Figure 9: Final route in selected region. outperforming [PERSON]'s 53.10 km result. These findings highlight the model's ability to adaptively generate high-quality routes in dynamically changing environments. These results indicate that the model converged to a stable and optimal routing policy, producing highly consistent routing decisions without deviation across all test runs. This stability underscores the effectiveness of the proposed approach in delivering reliable and efficient ATM servicing routes under dynamic urban conditions.
## 2 Dynamic Modeling of the Proposed QVIP
### Dynamic Model of the QVIP  
The Lagrange principle is employed to establish the dynamic model of the QVIP. The kinetic energy \(T\) of expressed as  
\[T=\frac{1}{2}M\Delta\dot{y}^{2} \tag{10}\]  
where \(\dot{(\cdot)}=d(\cdot)/dt\).  
The relative velocity \(\Delta\dot{x}\) in the horizontal direction of the QVIP, as well as the generalized relative velocities in other directions, can be expressed as  
\[\Delta\dot{x}=(\partial\Delta x/\partial\Delta y)(\partial\Delta y/\partial t) \tag{11}\]  
\[\dot{\Delta\dot{l}_{p}}=\left(\partial\Delta l_{p}/\partial\Delta y\right)( \partial\Delta y/\partial t) \tag{12}\]\[\Delta\dot{l}_{n}=(\partial\Delta l_{n}/\partial xy)(\partial\Delta y/\partial t) \tag{13}\]  
The potential energy \(V\) is obtained as  
\[V=\frac{1}{2}k_{h}\Delta x^{2}+4\times\frac{1}{2}k_{p}\Delta{l_{p}}^{2}+4\times \frac{1}{2}k_{n}\Delta{l_{n}}^{2} \tag{14}\]  
Considering the damping effect, the generalized force \(Q\) of the QVIP containing the non-constraint force \(F\) along the virtual displacement \(r\) can be expressed as  
\[Q=\sum F\frac{\partial r}{\partial\Delta y}=-c\Delta\dot{x}\frac{\partial \Delta x}{\partial\Delta y}-c_{0}\Delta\dot{y}=-c\Delta\dot{y}\left(\frac{ \partial\Delta x}{\partial\Delta y}\right)^{2}-c_{0}\Delta\dot{y} \tag{15}\]  
where \(c\) is the viscous damping coefficient of the horizontally placed damper, and \(c_{0}\) is the linear air damping coefficient.  
The Lagrange equation incorporating the generalized force \(Q\) can be expressed as  
\[\frac{d}{dt}\left(\frac{\partial T}{\partial\Delta\dot{y}}\right)-\frac{ \partial T}{\partial\Delta y}+\frac{\partial V}{\partial\Delta y}=Q \tag{16}\]  
The term \(\frac{\partial V}{\partial\Delta y}\) can be obtained as  
\[\frac{\partial V}{\partial\Delta y}=\frac{\partial V}{\partial\Delta x} \frac{\partial\Delta x}{\partial\Delta y}+4\frac{\partial V}{\partial\Delta l _{p}}\frac{\partial\Delta l_{p}}{\partial\Delta y}+4\frac{\partial V}{ \partial\Delta l_{n}}\frac{\partial\Delta l_{n}}{\partial\Delta y} \tag{17}\]  
Therefore, based on the Lagrange principle, the dynamic equation under the base excitation (\(z_{0}=A_{0}cos\omega t\)) can be obtained as  
\[M\Delta\ddot{y}+c_{0}\Delta\dot{y}+c\left(\frac{\partial\Delta x}{\partial \Delta y}\right)^{2}\Delta\dot{y}+k_{h}\frac{\partial\Delta x}{\partial\Delta y }\Delta x+4k_{p}\frac{\partial\Delta l_{p}}{\partial\Delta y}\Delta l_{p}+4k _{n}\frac{\partial\Delta l_{n}}{\partial\Delta y}\Delta l_{n}=-M\tilde{z}_{0} \tag{18}\]
## 3 Materials and Methods
### Inference with BRB Using ER Approach  
Inference with BRB using ER approach mainly includes four steps namely input transformation, calculation of individual matching degree, calculation of of activation weights, belief update, and rule aggregation as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ref. No.** & **Method** & **Limitations** \\ \hline \multirow{3}{*}{[4]} & Logistic Regression, K Nearest Neighbor, & Accuracy is low, Cannot integrate \\  & Random Forest, Support Vector Machine & expert knowledge in the prediction process, \\  & Decision Tree & Dataset is small and imbalance \\ \hline \multirow{3}{*}{[26]} & Logistic Regression, Gradient Boosting Tree, & Accuracy is low, Cannot integrate \\  & Random Forest, Support Vector Machine & expert knowledge in the prediction process \\  & Neural Network & Dataset is small and imbalance \\ \hline \multirow{3}{*}{[27]} & Logistic Regression, e\%treme Gradient Boosting, & Accuracy is low, Cannot integrate \\  & Random Forest, Support Vector Machine & expert knowledge in the prediction process \\  & Neural Network & Dataset is small and imbalance \\ \hline \multirow{3}{*}{[28]} & Deep Neural Network, Convolutional Neural Network, & Can not integrate \\  & Long Short-Term Memory, Recurrent Neural Networks & expert knowledge in the prediction process \\  & CNNLSTM, and CNNRNN & Dataset is small and imbalance \\ \hline \multirow{3}{*}{[29]} & Machine Learning based radiomic & Sensitive to inter-observer classifications, \\  & model using logistic regression & Can not be used without any caution \\ \hline \multirow{3}{*}{[30]} & Gini impurity for feature selection & Based on many biochemistry features \\  & Random forest for classification & which are costly to collect from lab test \\ \hline \multirow{3}{*}{[31]} & Logistic Regression, Adaptive Boosting, & Accuracy is low, Require around 52 features \\  & Random Forest, Support Vector Machine & for prediction. Therefore, not suitable \\ \cline{1-1}  & K nearest neighbor & for practical application \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the related works.
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
###### Abstract  
The air-booster vacuum preloading method has been applied to slurry ground improvement. It is based on the conventional vacuum preloading method but with an additional injection of pressurised air into the soil via pre-installed conductors. The drainage effect of air-booster vacuum preloading has been demonstrated by past studies; however, direct observations of the real-time behaviour of slurries subjected to boosted air remain lacking. This study used a combined monitoring technique that included particle image velocimetry, pore water/air pressure gauges, a vortex flowmeter and an electronic balance to conduct a laboratory test of air-booster vacuum consolidation of dredged slurry. The tests allowed analyses of (1) the real-time displacement field of the slurry, (2) the pressure-flux relationship of the pressurised air, and (3) the pore water pressure responses during air boosting. The first aspect allowed direct observation of small-crack initialisation and propagation during pressurisation; while the latter two confirmed the crack initiation based on drops in air and pore water pressures. The measured crack initiation pressure was verified by comparison with theoretical predictions. The results demonstrate that pressurised air induces cracks in soil, which promote the drainage consolidation of dredged slurry.  
pressurised air; vacuum consolidation; dredged slurry; cracks +
Footnote †: journal: Medical Physics
## 5 Discussion
### Future Research Directions and Possible Applications  
The findings of this study indicate that customized surgical guides, digitally planned and fabricated through 3D printing, allow for the effective clinical transfer of the virtually defined 3D position of palatal miniscrews. Nonetheless, the angular and linear discrepancies observed--although limited--highlight the need for the further refinement of each phase within the digital workflow. The greater deviations noted at the screw tip, likely due to the cumulative effect of mechanical tolerances and inherent limitations of printing and component coupling, underscore the importance of standardizing clinical-digital protocols. Looking ahead, future developments may include the integration of artificial intelligence algorithms to predict deviation risk and the adoption of dynamic navigation or robotic systems to enhance placement accuracy. Clinically, personalized guides may prove especially valuable in patients with unfavorable palatal anatomy because they improve insertion safety and the biomechanical reliability of skeletal anchorage. Long-term studies will be necessary to determine whether the detected deviations have a significant impact on primary stability or the functional longevity of miniscrews in vivo.