esa-sceva/satcom-chunk-collection huggingface dataset:
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
###### Abstract  
Orbital angular momentum (OAM) of optical vortex beams has been regarded as an independent physical dimension of light with predominant information-carrying potential. However, the presence of scattering environment and turbulent atmosphere scrambles the helical wavefront and destroys the orthogonality of modes in vortex beam propagation. Here, we propose and experimentally demonstrate a new basis for the recovery of the OAM mode using a holographic ghost diffraction scheme. The technique utilizes the speckle field generated from a rotating diffuser for optical vortex mode encoding, and the fourth-order correlation of the speckle field for the efficient recovery of the associated modes. Furthermore, we successfully demonstrate the complex-field recovery of OAM modes by the adoption of a holography scheme in combination with the ghost diffraction system. We evaluate the feasibility of the approach by simulation and followed by experimental demonstration for the recovery of various sequentially encoded OAM modes. Finally, the efficacy of the recovered modes was quantitatively analyzed by an OAM mode analysis utilizing orthogonal projection scheme.  
holography; ghost diffraction; orbital angular momentum; speckles
# Dynamic Behavior of an Embedded Foundation under Horizontal Vibration in a Poroelastic Half-Space
## 2 Problem Definition and Governing Equations
### General Solutions of Horizontal Vibration of Embedded Foundation
#### 2.3.2 Reaction Force at the Base of the Embedded Foundation  
The dynamic reaction at the base of the foundation is independent of the depth of embedment. The reaction force at the base of embedded foundation can be solved by considering the foundation base as a surface foundation. As it is delineated above, it is convenient to substitute the expression of stress, displacement, and pore water pressure to the solid function, and after some manipulations, the following equations can be derived. \[G_{b}{\
abla_{n+1}}^{2}U_{n}+(\lambda+G_{b})(\frac{\partial e_{n}}{\partial r}- \frac{n}{r}e_{n})+(\rho_{s}\omega^{2}+{\rho_{f}}^{2}\omega^{4}\eta)U_{n}-(\rho_ {f}\omega^{2}\eta+1)(\frac{\partial p}{\partial r}-\frac{n}{r}p)=0 \tag{32}\]  
\[G_{b}{\
abla_{n-1}}^{2}V_{n}+(\lambda+G_{b})(\frac{\partial e_{n}}{\partial r} +\frac{n}{r}e_{n})+(\rho_{s}\omega^{2}+{\rho_{f}}^{2}\omega^{4}\eta)V_{n}-(\rho _{f}\omega^{2}\eta+1)(\frac{\partial p}{\partial r}+\frac{n}{r}p)=0 \tag{33}\]  
\[G_{b}{\
abla_{n}}^{2}u_{zn}+(\lambda+G_{b})\frac{\partial e_{n}}{\partial z} +(\rho_{s}\omega^{2}+{\rho_{f}}^{2}\omega^{4}\eta)u_{zn}-(\rho_{f}\omega^{2} \eta+1)\frac{\partial p}{\partial z}=0 \tag{34}\]  
\[e_{n}=\frac{1}{b\alpha i\omega+\rho_{f}\omega^{2}\alpha}{\
abla_{n}}^{2}p_{n} +\frac{aM^{-1}\omega^{2}-Mbi\omega}{b\alpha i\omega+\rho_{f}\omega^{2}\alpha}p _{n} \tag{35}\]  
\[{\
abla_{n}}^{4}p_{n}-\beta_{1}{\
abla_{n}}^{2}p_{n}-\beta_{2}p_{n}=0 \tag{36}\]  
\[M\zeta+\alpha Me=-\dot{p} \tag{37}\]  
where \(\eta=(bi\omega-a\omega^{2})^{-1}\), \(\beta_{1}=\frac{\rho\omega^{2}M+(\lambda+2\zeta+a^{2}M)(a\omega^{2}-bi\omega)-2 aM\rho_{f}\omega^{2}}{-(\lambda+2G)M}\), \(\beta_{2}=\frac{\left(\rho_{f}\omega^{2}\right)^{2}-\rho\omega^{2}(a\omega^{2 }-bi\omega)}{-(\lambda+2G)M}\), \(\zeta=\frac{\partial i\omega}{\partial r}+\frac{i\omega}{r}+\frac{1}{r}\frac {\partial i\omega}{\partial\theta}\), \(\epsilon=\frac{\partial i\omega}{\partial r}+\frac{i\omega}{r}+\frac{1}{r} \frac{\partial i\omega}{\partial\theta}\)  
\[U_{n}=u_{rn}+u_{\theta n}\ \ \ V_{n}=v_{rn}-v_{\theta n} \tag{38}\]  
\[{\
abla_{a}}^{2}=\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{ \partial}{\partial r}-\frac{a^{2}}{r^{2}}+\frac{\partial^{2}}{\partial z^{2}} (a=n,n-1,n+1) \tag{39}\]  
\[e_{n}=\frac{\partial ru_{rn}}{r\partial r}+\frac{nu_{\theta n}}{r}+\frac{ \partial u_{zn}}{\partial z} \tag{40}\]  
Dimensionless variables are defined with a hat \"-\" on it. \(a_{0}\) is the dimensionless excitation frequency.
## 2 Materials and Experiment  
### Alloy Design and Preparation  
The Ti-xMo-4 Fe (x = 3.4, 5) designed in this study is presented in the phase stability map (\(\overline{Bo}-\overline{Md}\) map), Figure 1[26]. The \(\overline{Bo}-\overline{Md}\) map is a theoretical approach to the design of titanium alloys established by [PERSON] et al. [27] and consists of two parameters. In addition, it can be clearly divided into \(\alpha\) phase, \(\alpha+\beta\), and \(\beta\) phase according to the alloy composition, and when the value of Bo increases in the \(\beta\) phase and the value of Md decreases, it becomes a stable state. The Bond order (\(\overline{Bo}\)) is a value representing the covalent bonding strength between the titanium and alloying elements and it is reported that as \(\overline{Bo}\) increases, the elastic modulus tends to decrease [28]. The Metal d-orbital energy level (\(\overline{Md}\)) is correlated with the electronegativity in the element. These two parameters can be calculated using the following Equation (1).  
\[\overline{Md}=\sum_{i=1}^{n}Xi\cdot(Md)i,\ \overline{Bo}=\sum_{i=1}^{n}Xi\cdot( Bo)i \tag{1}\]  
Using Equation (1), the values of the Metal d-orbital energy level (\(\overline{Md}\)) and Bond order (\(\overline{Bo}\)) of the Ti-3.4 Mo-4 Fe (hereinafter referred to as TMF 34) alloys were 2.39 and 2.79, respectively. Further, the values of the Ti-5 Mo-4 Fe (hereinafter referred to as TMF 54) alloys were 2.38 and 2.79, respectively.  
The objective of this study is to compare the alloy designed in this study by applying the powder metallurgy (hereinafter referred to as P/M) and ingot metallurgy (hereinafter referred to as I/M) processes. In the case of the powder alloy, the HDH titanium powder was used to further reduce the cost of the titanium alloy. The powder used in P/M represented sizes of Ti (<25 \(\upmu\)m), Mo (1-2 \(\upmu\)m), and Fe (5-7 \(\upmu\)m), which were blended and bound according to Ti-xMo-4 Fe (x = 3.4, and 5 wt.%). It was blended using a mixer, pressed with a force of 200 kg/cm\({}^{2}\) for 15 s using a hydraulic press, and then the resulting bulk compacts were sintered in a sintering furnace for 5 h at 1250 \({}^{\circ}\)C. It was then furnace cooled to room temperature. Further, in order to compare it with ingots having the same alloy composition, ingots having a composition of Ti-xMo-4 Fe (x = 3.4, 5) with a diameter of 14 mm were fabricated through applying a vacuum arc remelting method using Ti with 99.9% purity, Mo with 99.95% purity, and Fe with 99.5% purity. In the case of the porosity of the alloy applied to P/M, it was derived using Equation (2) [29].  
\[P=\left[1-\left(\frac{\rho}{\rho_{0}}\right)\right]\times 100 \tag{2}\]  
where \(\rho\) and \(\rho_{0}\) represent the apparent density and theoretical density in the P/M alloy measured using the Archimedean method. The number of pores was measured using an optical microscope.
## 2 Mathematical Modeling of EV/DC Fast Charging Station
### Modelling of Energy Storage Stacks  
Charging stations with stationary energy storage stacks are intrinsically capable of providing services to distribution utilities. It plays a fundamental role in the integration of EV fast electric stations as they act as backup systems and ease fast charging under adverse generation conditions. The underneath equation is used to model the state charge (SOC) of the battery banks at each time step \(t\)[43]  
\[E_{b}=E_{b}(t-1)+P_{ch}(t)\times\eta_{ch}\times\Delta t-\left(\frac{P_{dch}(t)} {\eta_{dch}}\right)\times\Delta t \tag{6}\]  
where \(P_{ch}(t)\) is the delivered electricity from the renewable energy sources to the battery packs, \(P_{dch}(t)\) is the energy delivered from the battery modules to the inverter, \(\eta_{ch}\) and \(\eta_{dch}\) are the charging and discharging efficiencies of the battery bank respectively.  
Initially, the ESS was reflected to be 30% charged with the rated capacity. When \(P_{bat}(t)<0\), it shows a power generation deficit. \(P_{bat}(t)>0\) shows the indicator of battery power generation having surpassed energy demand. Charging of battery occurs when \(SOC>SOC_{min}\) or \(P_{pv}>P_{d}\) and therefore the charge state of the battery is represented below;  
\[SOC(t)=SOC(t-1)*(\;1-\tau)+\left(P_{pv}-\frac{P_{d}}{\eta_{inv}}\right)\times \eta_{bat} \tag{7}\]  
Likewise, if there is a power demand and the battery storage system can discharge (i.e., \(SOC>SOC_{min}\)), to meet the demand of energy than it can be represented by the equation:  
\[SOC(t)=SOC(t-1)*(\;1-\tau)+\left(\frac{P_{d}}{\eta_{inv}}-P_{pv}\right)\times \eta_{bat} \tag{8}\]  
where,  
\[\tau=Battery\;self\;discharge\;rate;\;\eta_{bat}=Battery\;efficiency;\;P_{d}=Power\;demand\]  
Figure 6: Irradiance horizon of a photovoltaic cell for a year.  
The power required by the ESS (i.e., \(Red\_ESS\_Pwr\)) is outlined as a least power value that rises its level of charge from the initial to maximum value of SOC (i.e., SOCM) in definite time step \((\Delta t)\) as defined by;  
\[Red\_ESS\_Pwr(t)=\frac{(SOCM-SOC(t)\times N_{bat}\times Q_{bat}}{\Delta t} \tag{9}\]  
Similarly, the power obtainable with ESS (i.e., \(Avl\_ESS\_Pwr\)) is stated as an extreme power that can be provided seamlessly by ESS for the given time step \((\Delta t)\) before the state of charge reaches its lower bound (called \(SOCL\)) defined by:  
\[Avl\_ESS\_Pwr(t)=\frac{(SOC(t)-SOCL)\times N_{bat}\times Q_{bat}}{\Delta t} \tag{10}\]
# A Flexible Localization Method of Multi-Constrained Free-Form Surface Based on the Profile Curves
## 2 Literature Review
Subsequently, [PERSON] and [PERSON] [11] formulated the general 3D localization problem using three different optimization algorithms to solve this problem. Nevertheless, the presented method is just for simple geometric models that do not involve a free-form surface. Accordingly, [PERSON] et al. [12] proposed a workpiece localization optimization algorithm for a free-form surface and a mathematical model was established to ensure that the workpiece had sufficient machining allowance. Combined with the simplex method, it was concluded that the objective function of the logarithmic form had a higher efficiency. Furthermore, [PERSON] et al. [13] proposed sequential quadratic programming (SQP) with a mathematical model of unified weight and made a quality evaluation of the registration. This algorithm is more efficient than the direct search algorithm, but it has low computational efficiency because of the Jacoby matrix and the Hessian matrix calculations in each iteration. [PERSON] et al. [14] modified the simplex method that is used to evaluate the shape error of the machined model. In summary, previous literature focuses on the simple to-be-cut feature but rarely on the free-form surface feature. The above works focus on the optimization method, and the constraints are only used to ensure sufficient material allowance. Nevertheless, there are few works considering the profile tolerance in the free-form surface localization. Therefore, it is desirable to find a localization method to obtain an optimal to-be-cut blade surface. This surface is used to generate the toolpath that satisfies the material allowance and tolerance requirements. Contrary to previous studies, in this paper, a more flexible localization method is investigated to find available to-be-cut surfaces.
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 3 Methodology
### Categorical Associations  
The chi-squared test of independence is a statistical approach to determine if there is a significant relationship between two categorical variables. The test is based on comparing observed frequencies in each category of the variable with the expected frequencies if the two variables have no association or dependencies. Conducting the test requires a contingency table of the categories where \(E_{ij}\) is the expected frequency for the cell at row \(i\) and column \(j\) such that  
\[E_{ij}=\frac{\sum_{j}O_{ij}\times\sum_{i}O_{ij}}{\sum_{ij}O_{ij}} \tag{2}\]  
where \(O_{ij}\) is the observed frequency at the intersection of category \(i\) for one variable and category \(j\) for the other variable. A chi-squared statistic then measures how much the observed frequencies deviate from the expected frequencies such that  
\[\chi^{2}=\sum_{i,j}\frac{\left(E_{i,j}-O_{i,j}\right)^{2}}{E_{i,j}} \tag{3}\]  
which is based on the sum of the squared differences between the entries of the expected and observed values. The null hypothesis is that there is no association between the two variables. Evaluating a \(p\)-value determines if one can reject the null hypothesis of no association. The \(p\)-value is the probability of observing a chi-squared statistic at least as large as the one observed when the null hypothesis is true. One can reject the null hypothesis at a low chance (typically a \(p\)-value \(\leq\) 0.05) of observing such a large deviation from the expected frequencies, suggesting that there is an association between the two variables. The \(p\)-value is the area under the chi-squared distribution curve to the right of the chi-squared statistic. The general form of the probability density function (pdf) for the chi-squared distribution is  
\[f(x;k)=\frac{2^{-k/2}e^{(-x/2)}x^{(k/2-1)}}{\Gamma(k/2)} \tag{4}\]  
where \(x\) is a positive chi-squared statistic, \(k\) is the degree of freedom (DOF), and \(\Gamma\) is the Gamma function. The DOF is \((r-1)\times(c-1)\) where \(r\) and \(c\) are the number of rows and columns, respectively, of the contingency table. The Gamma function is  
\[\Gamma(z)=\int_{0}^{1}\left[\ln\left(\frac{1}{t}\right)\right]^{z-1}dt. \tag{5}\]  
In practice, statistical software packages use numerical methods to compute the integral for finding the area under the pdf.
## 3 Results and Discussion
### Oil Displacement Results
Fewer surfactant molecules adsorbed on the rock surface in presence of NPs, leading to a reduction in surfactant loss. Hence, a greater volume of oil was contacted by the surfactant molecules and a higher residual oil saturation was mobilized during NP-surfactant stage. Several possible mechanisms explain the better performance of surfactant in the presence of NPs. Nanoparticles can augment the ability of surfactants to lower the oil-water IFT. Their high surface area-to-volume ratio enables them to adhere to the interface between oil and water. Hence the contact area between oil and water increases, leading to a higher IFT reduction, enhancing the detachment of oil from reservoir rock, and facilitating the mobilization of oil with greater ease. Alkali, on the other hand, seems to not work here in lowering the IFT, which may be due to a very low acid number of the target field crude oil. However, alkali does work in minimizing surfactant adsorption, which is evident from the relatively lower pressure drop during AS flooding compared to standalone surfactant flooding. Based on these results, the surfactant, alkali, and NPs screened in this study can be tested with the previously selected polymer to design the most efficient chemical flood recipe for Field A.
# A Data Processing and Distribution System Based on Apache NiFi
## 5 Research and Results
### Five Nodes  
Running a cluster of five nodes resulted in a drop in the average CPU usage by user processes of around 10 and 4 percentage points compared with the three-node scenario for the optimized and default configurations, respectively. It should be noted that the variability in resource usage was so high that the statistical differences in the user CPU usage between the configurations were blurred. Detailed results are also presented in Table 3. The memory usage for both configurations decreased by approximately 300 MB relative to that in the single-node scenario.  
The cluster consisting of five optimized processing nodes achieved values of the load average parameter that were smaller by approximately one compared to that obtained with the default settings (Figure 14). However, the confidence intervals broadened. Most likely, this was caused by the fact that the FlowFile Processors were not fully utilized during the processing. We believe that this relatively small amount of available RAM started to limit the influx of FlowFiles, which led to idling, and this was further observed as a broad confidence interval.  
Running a cluster of five processing nodes allowed a significant reduction in the value of the average time required to obtain one resulting entry (Figure 15). The reductions were approximately 1 and 14 min compared to the single-node instance for the optimized and default configurations, respectively. Nevertheless, the proposed configuration still had a significant advantage. On average, the fine-tuned five-node cluster needed about 40 s, and the default five-node cluster needed up to 534 s to extract a single result entry.  
Figure 14: Load average parameter as a function of the processing time of the input set (five nodes). Panel (**a**) shows the result of the optimized configuration, whereas (**b**) depicts the default configuration.  
To sum up, our optimization reduced the potential operational expenses of a monitoring system for a 5G (and future 6G) network operator. Instead of adding more nodes, it is possible to utilize the existing infrastructure more effectively and to successfully collect monitoring results for the underlying optical network. Furthermore, the improvement in the performance as a function of the number of nodes is less significant for larger clusters if Apache NiFi is not properly configured. In 5G networks and beyond, this may be a limitation, as adding sites will entail adding a significant number of additional optical devices with the large number of high-speed optical transceivers reporting their parameters.
## 3 Results and Discussion
### Quantitative Analysis of Biospeckle Activity
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Activity** & \multicolumn{3}{c}{**F0**} & \multicolumn{3}{c}{**F24**} & \multicolumn{3}{c}{**F72**} \\ \cline{2-9}
**Indicator** & **Mean** & **Median** & **Mean Rank** & **Mean** & **Median** & **Mean Rank** & **Mean** & **Median** & **Mean Rank** \\ \hline AVD & 0.994 & 0.917 & 14.000 \({}^{\text{a}}\) & 1.445 & 1.165 & 18.733 \({}^{\text{a}}\) & 4.122 & 2.720 & 36.267 \({}^{\text{b}}\) \\ IM & 3.848 & 2.943 & 12.667 \({}^{\text{a}}\) & 13.263 & 4.614 & 20.067 \({}^{\text{a}}\) & 98.974 & 37.922 & 36.267 \({}^{\text{b}}\) \\ RVD & \(-\)0.002 & \(-\)0.002 & 24.733 \({}^{\text{a}}\) & \(-\)0.001 & \(-\)0.002 & 24.000 \({}^{\text{a}}\) & \(-\)0.004 & \(-\)0.007 & 20.267 \({}^{\text{a}}\) \\ NAD & 0.009 & 0.009 & 13.133 \({}^{\text{a}}\) & 0.012 & 0.010 & 18.600 \({}^{\text{a}}\) & 0.033 & 0.027 & 37.267 \({}^{\text{b}}\) \\ BAI & 0.003 & 0.003 & 9.133 \({}^{\text{a}}\) & 0.014 & 0.013 & 22.333 \({}^{\text{b}}\) & 0.075 & 0.070 & 37.533 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} \({}^{\text{F0--uninfested samples}}\) (control); F24--infested samples (24 h after exposure); F72--infested samples (72 h after exposure); IM--inertian moment; AVD--average value of differences; RVD--regular value of differences; NAD--average difference; BAI--biospeckle activity indicator. \({}^{\text{a}\text{c}}\) different lower-case letters in the same row indicate significantly different values of mean ranks (\(p\leq 0.05\)).  
\end{table} 
Table 1: Basic statistics calculated for THSP obtained from single column of ROI of 51 \(\times\) 51 pixels with the global maximum at the central point. Comparing results of the Kruskal-Wallis multiple test for mean ranks, identical effects were obtained in each ROI size. RVD did not prove successful as a measure of activity differentiation of infested and uninfested fruits. AVD, IM and NAD showed the potential to distinguish only the late stage of infestation. As previously described, BAI turned out to be the best discriminant of healthy, early and late stage of infestation. As for results obtained for IM in different ROI, its values in groups were considerably different as observed earlier, which proves that BA changed drastically with infestation development (from F0 to F72).
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 5 Swarm Based Solution
### Cluster Head Selection Problem
#### 5.2.2 The Heuristic Information Calculation  
Since data transmission reliability is a critical concern in resource-constrained WSNs, member nodes must send data packets to cluster heads via a reliable wireless link. However, WSN is typically used in hostile environments. Therefore, packet loss is expected. Packet loss is one of the most critical yet difficult challenges in WSNs since retransmitting missed packets takes more time and energy, reducing in-time delivery and network lifetime. The  
Figure 4: The proposed clustering algorithm flowchart.  
suggested link quality indicator is utilized as heuristic information to increase reliability, preserve node energy, and promote in-time data delivery as follows:  
\[\eta_{x}^{2}(t)=\frac{\sum\limits_{l\in NM_{x}}^{1/\left(1+\ QM_{x}(t)\right)}}{ \sum\limits_{l\in NM_{x}}^{\left(1/\left(1+\ QM_{l}(t)\right)\right)}}. \tag{97}\]  
The node with the highest value has a better chance than the other candidates of being the next cluster head as it has a higher link quality rating.  
Due to the data traffic generated by collecting, aggregating, and transferring member data to sink nodes, cluster heads use their remaining energy more quickly than distant nodes. In order to balance node energy consumption and optimize network lifetime, we employ an energy metric, represented by:  
\[\lambda_{x}^{2}(t)=\frac{EM_{x}(t)}{\sum\limits_{l\in NM_{x}}^{\sum\limits_{l \in NM_{x}}}EM_{l}(t)}. \tag{98}\]  
The node with greater \(\lambda_{x}^{2}\) has more residual energy and is more likely to be the next cluster head.
## 4 Simulation Results
### The Localization Error of ACFL and PCFL under Different Parameters
#### 4.1.4 The Estimated and Original Location  
In the simulation space, 45 mobile nodes are randomly distributed to localize themselves, the distances between each pair of them are 120 m and 160 m. As we can see in Figure 8, black circles represent the original position, red and blue signs are the estimated coordinates computed by ACFL and PCFL, respectively, so it is quite clear that calculations of PCFL are more close to actual locations.  
We can find that localization error of PCFL (or ACFL) reduces slightly when the number of samples is more than 400. When \(D_{\mathit{anchor}}\) is 4 or larger, the localization error varies smoothly. Based on the running time of simulation experiments and localization error, we set the threshold for 0.0142, \(D_{\mathit{anchor}}\) for 4, and the number of samples for 400 in the following simulation experiments.  
Figure 7: The density of anchors.