esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Modal Volume in Nanoscale Photonic and Plasmonic Near-Infrared Resonant Cavities
## 5 Conclusions  
We have identified five expressions in the literature commonly used for calculating the effective modal volume of resonant cavities. We evaluated and compared these expressions in the context of several canonical geometries supporting photonic and plasmonic modes for near-infrared nanoscale light sources. These included a metallo-dielectric cavity and coaxial cavities with Ag and AZO as cladding material. In the metallo-dielectric cavity, the various \(V_{\mathit{eff}}\) expressions yield the same result and are robust to simulation domain changes because the electric field maximum resides in the active region. In the coaxial cavity, the \(V_{\mathit{eff}}\) expressions yield results of varying stability due to the electric field and electrical energy density maxima residing in different regions. For low Q coaxial cavities, QNMs are no longer of finite energy, so standard QNM normalization methods based on energy consideration cannot be applied because of the spatially diverging field at large distance. Therefore, only \(V_{\mathit{eff}}\) and \(V_{\mathit{eff},5}\) yield non-diverging results. Because of the relation of the effective modal volume to important quantities of theoretical and practical interest, such as the Purcell factor and spontaneous emission factor, our work conveys important results on the utility of the effective volume technique and its limitations.  
Conceptualization, J.S.T.S. and Q.G.; Methodology, J.S.T.S. and X.L.; Software, X.L. and Z.L.; Validation, X.L.; Formal Analysis, X.L. and J.S.T.S.; Investigation, X.L., J.S.T.S. and Q.G.; Writing-Original Draft Preparation, J.S.T.S. and X.L.; Writing-Review & Editing, J.S.T.S. and Q.G.; Supervision, Q.G.  
This work was supported in part by UT Dallas faculty start-up fund, in part by the Office of Naval Research Multidisciplinary Research Initiative (N00014-13-1-0678), the National Science Foundation (NSF) (ECE3972 and ECCS-1229677), the NSF Center for Integrated Access Networks (ECE-0812072, Sub 502629), the Defense Advanced Research Projects Agency (N66001-12-1-4205) and the Cymer Corporation.  
The authors declare no conflict of interest.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance  
A key challenge in the design of space systems is achieving subsystem redundancy to manage component failures. One of the core components of such redundancy architectures is the redundant switch [71], used for routing and switching RF signals between two redundant systems. In the event of a component's failure, the switch can quickly switch to a backup path or system, ensuring the continuity of communication. Traditionally, these switches have been implemented with bulky and heavy coaxial relays. However, RF MEMS switches, as a promising alternative technology, not only have excellent RF performance but also a smaller size and lighter weight. For space applications, reliability is a crucial consideration. Modern satellites typically require mission lifetimes of several decades [72], thus devices within satellites must maintain long-term stable operation. This is why redundancy designs are often used to reduce the overall system's failure risk. In aviation, equipment may face temperature ranges from \(-50\,\mathrm{\SIUnitSymbolC}\) to \(125\,\mathrm{\SIUnitSymbolC}\), and if installed outside a spacecraft, environmental conditions can be even more extreme.  
In satellite communications, RF MEMS switches and devices with excellent RF performance can provide wider bandwidth and higher data transfer rates. This means that enhancing the RF performance of RF MEMS switches can transmit more data, including high-resolution images and videos, significantly improving communication efficiency and quality.
## 4 Performance Evaluation
### Simulation Results and Discussion
#### 4.2.2 Performance Comparison and Analysis of Achievable Sum Rate between SISO-NOMA and OMA  
Figure 5 shows the achievable sum rate in \(\mathrm{bps/Hz}\) with respect to SNR values for SISO-NOMA and OMA. Considering a three-user scenario, Users 1, 2 and 3 have been allocated with fixed PA coefficients as \(\alpha_{1}=0.70\) (70%), \(\alpha_{2}=0.20\) (20%) and \(\alpha_{3}=0.10\) (10%) and path loss exponent as \(\eta=2\). It can be observed that the achievable sum rate in the OMA performs slightly better than the SISO-NOMA at low SNR values. Due to cross-user interference and simulation transmission using the same RB, the achievable sum rate in the SISO-NOMA scheme suffers when compared to the OMA scheme at low SNR values.  
However, as SNR increases, the achievable sum rate in the proposed SISO-NOMA scheme outperforms the OMA scheme.
## 3 Reputation Model
### Determination of Reputation by Security Service and Global Reputation
With:  
\[\alpha_{C}=\sum N_{pos}(Con_{i});\ \alpha_{I}=\sum N_{pos}(Int_{i});\ \alpha_{A}=\sum N_{pos}(Aut_{i});\ \alpha_{D}=\sum N_{pos}(Ava_{i}) \tag{5}\]  
\[\beta_{C}=\sum N_{neg}(Con_{i});\ \beta_{I}=\sum N_{neg}(Int_{i});\ \beta_{A}=\sum N_{neg}( Aut_{i});\ \beta_{D}=\sum N_{neg}(Ava_{i}). \tag{6}\]  
The global reputation of a given application is the average of security services's reputations of the same application. \[E(p\mid\alpha,\beta)=\text{Mean}(E_{C},E_{I},E_{A},E_{D})=0.32. \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reviews**} & \multirow{2}{*}{**Polarity Index (\(\mu\)/\(-\))**} & \multicolumn{5}{c}{\(N_{pos}(\alpha)\)} & \multicolumn{5}{c}{\(N_{neg}\) (\(\beta\))} \\ \cline{3-10}  & & _Con._ & _Int._ & _Aut._ & _Ava._ & _Con._ & _Int._ & _Aut._ & _Ava._ \\ \hline C1 & – & 0.15 & 0 & 0 & 0.07 & 0.52 & 0 & 0 & 0.26 \\ C2 & – & 0 & 0.05 & 0 & 0.11 & 0 & 0.28 & 0 & 0.56 \\ C3 & – & 0 & 0.13 & 0 & 0.14 & 0 & 0.36 & 0 & 0.37 \\ C4 & – & 0 & 0 & 0 & 0. The global reputation must be within the interval [0-1] and is interpreted as follows: for an application with a good reputation in terms of security, the value of E must be greater than the value 0.5. An application with E less than 0.5 has a bad reputation and an application for a value equal to 0.5 has a neutral reputation. For instance, the Instagram application does not have a good reputation regarding the security aspect since this application has a reputation below 0.5. In a more specific context, as shown in Table 3, we can infer the reputation of the application in terms of security services, which suggests to developers to improve security aspects in the next version. Interpretation of security service reputations is the same as for the global reputation.
## 3 Results and Discussion
### Source Assessment
#### 3.5.2 Principal Component Analysis  
The source apportionment was examined through principal component analysis (PCA), using dimension reduction with Kaiser normalization and varimax rotation to maximize the variance of component coefficients. Components with Eigenvalues greater than \(1\) were considered in the assessment. Figure 5 presents the PCA results across different areas, revealing two primary components that indicate the sources of PAHs in various agricultural soils. As indicated in Figure 6, two principal components (PCs) were derived for each area. For the research area, PC 1 and PC 2 explained 78.51% and 21.49% of the total variance on loading plot, respectively; for the industrial area, 76.22% and 23.78%; for the local market area, 72.09% and 27.91%; for the coastal area, 64.51% and 36.49%; and for the rural area, 56.44% and 43.56%. Figure 6 demonstrates that two principal components (PCs) collectively explained the majority of variance across different areas, with PC 1 capturing a higher variance proportion in the research, industrial, and local market areas--likely reflecting pyrogenic sources. In contrast, the coastal and rural areas showed a more balanced contribution from both PCs, possibly indicating the mixing of pyrogenic and petrogenic sources. The first group includes Chry, Ace, Nap, BaA, Pyr, and Flu from petrogenic and pyrolytic sources, while the second group comprises Flu, Ind, Phe, BghiP, and BbF from biomass burning and vehicular emissions [18].
## 3 Results and Discussion
### Mechanical Behavior  
#### 3.2.1 Hardness Evolution at Elevated Temperatures  
The variation in the hardness of the Al-Si-(Cu)-Mg alloys as a function of temperature was investigated using Vickers microhardness testing. As summarized in Table 4, both Alloy A and Alloy B exhibited a gradual decline in hardness with an increasing temperature. At room temperature, the average hardness values were 120 \(\pm\) 6.5 HV for Alloy A and 115 \(\pm\) 5.4 HV for Alloy B, suggesting that Alloy A possessed a superior initial hardness and, potentially, enhanced wear resistance under ambient conditions.  
As the temperature increased to 150 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, both alloys showed a marked decrease in hardness, with reductions of nearly 50% relative to their room temperature values. This degradation is primarily attributed to the thermally activated coarsening of intermetallic phases and the softening of the Al matrix. Two primary mechanisms are considered responsible for the observed thermal softening:  
First, elevated temperatures promote increased atomic diffusion within the Al matrix, thereby weakening the solid solution strengthening effects of Cu and Mg. At 250 \({}^{\circ}\)C in particular, the diffusion of these solute atoms out of the Al lattice reduces their contribution to the lattice distortion and dislocation resistance, leading to a pronounced drop in hardness. Second, intermetallic phases such as Fe-Mn-Si and Mg\({}_{2}\)Si may undergo thermal instability, including phase decomposition or coarsening. As a result, fine precipitates that initially contributed to strengthening lose their effectiveness, leading to a further reduction in hardness.  
Due to the microstructural heterogeneity and relatively poor mechanical performance of the as-cast alloys, all subsequent tensile and wear evaluations were conducted on specimens in the T6 heat-treated condition. This choice reflects the improved microstructural  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Alloy A**} & \multicolumn{4}{c}{**Alloy B**} \\ \hline
**Condition** & **Temperature (\({}^{\circ}\)C)** & **Hardness (HV)** & **Condition** & **Temperature (\({}^{\circ}\)C)** & **Hardness (HV)** \\ \hline As-cast & RT & 97.63 \(\pm\) 2.81 & As cast & RT & 86.88 \(\pm\) 4.91 \\ \hline \multirow{4}{*}{T6 heat treatment} & RT & 139.97 \(\pm\) 5.96 & \multirow{4}{*}{T6 heat treatment} & RT & 128.54 \(\pm\) 6.54 \\ \cline{2-6}  & 150 \({}^{\circ}\)C & 120.51 \(\pm\) 5.09 & & 150 \({}^{\circ}\)C & 104.29 \(\pm\) 6.40 \\ \cline{1-1} \cline{2-6}  & 250 \({}^{\circ}\)C & 93.88 \(\pm\) 7.91 & & 250 \({}^{\circ}\)C & 82.26 \(\pm\) 5.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vickers hardness of Alloy A and Alloy B in as-cast and T6-treated conditions at various temperatures.  
uniformity and precipitate stability achieved through the heat treatment, offering a more representative assessment of the materials' performance under service-like conditions.
# Edge-AI-Based Real-Time Automated License Plate Recognition System
###### Abstract
However, even with an enhanced image function, a single threshold is still difficult to obtain. To solve this problem, [PERSON] [14] proposed the application of an adaptive binarization technology. Most ALPR systems still use network connection servers or cloud infrastructure for image recognition. Edge AI involves the installation of embedded systems directly on the edge to avoid the use of complex centralized AI systems [15], such as Raspberry Pi Pico (RPI), Jetson AGX Xavier (AGX), and JETSON TX2. Compared with methods that involve the sending of images back to a server or the cloud, Edge AI saves many images that occupy bandwidth during transmission, reduces image transmission delays [16], and increases capability in terms of frames-per-second (FPS) during execution. When a computer is installed on the edge, it can also directly connect to a camera without using a network. An embedded system is cost-efficient, easy to disassemble and assemble, and small in size. For real-time images, dozens of frames are combined every second. The key task is to determine the correlation between a frame and subsequent frames. [PERSON] et al. [17] proposed a simple, online, and real-time tracking (SORT) algorithm that uses the Hungarian algorithm to perform Kalman filtering frame by frame in image space. In the SORT algorithm, Intersection over Union (IoU) is used as the basis for association. Objects are numbered and labeled to enable the tracking of objects. Different solutions are applied based on the given situation. In a study on road real-time image license plate recognition, the reduction of recognition time and increase of model recognition rate are highly crucial points that designers aim to improve on [18; 19]. In the present study, an edge-AI-based real-time automated license plate recognition (ER-ALPR) system is proposed. This system incorporates an AGX XAVIER embedded system that is installed on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. The major contributions of this study are as follows:1. An embedded system is embedded on the edge of a camera to enable real-time automatic license plate character recognition. 2. You Only Look Once v4-Tiny (YOLOv4-Tiny) is used to detect the license plate frame of a car. 3. A virtual judgment line is used to determine whether a license plate frame has passed the judgment line. 4. A modified YOLOv4 (M-YOLOv4) for recognizing license plate characters is proposed. 5. A logic auxiliary judgment system for improving the accuracy of license plate recognition is proposed. The present study is organized as follows. Section 2 describes the proposed approach, Section 3 discusses the experimental results, and Section 4 provides the conclusions and future research directions.
# Problem-Driven Scenario Generation for Stochastic Programming Problems: A Survey
## 2 Problem Definition
### A Multi-Stage Stochastic Programming Problem  
Let us consider a set of time stages \(t=\{1,\ldots,T\}\) and a random vector \(\xi\in\mathbb{R}^{d}\) that represents the uncertain parameters of a problem, whose value is revealed gradually over time \(t\). The realization of the random vector is denoted as \(\{\xi_{t}\}_{t=1}^{T}\). In practice, \(\xi_{1}\) is a fixed value and \(\xi_{t}\) is the observation value of the stochastic process following a specified probability distribution when \(t>1\). At each stage, a decision \(x_{t}\in\mathbb{R}^{n}\) must be taken with knowledge available up to stage \(t\), and the future realizations \(\xi_{t+1},\ldots,\xi_{T}\) are still to be observed. This concept is known as non-anticipativity. With these notations, a generic multi-stage Stochastic Programming problem that aims at maximizing the profit can be represented as follows:  
\[\max_{x_{1},\ldots,x_{T}}f_{1}(x_{1})+\mathbb{E}_{\xi}[\sum_{t=2}^{T}f_{t}(x_{ t},\xi_{t})] \tag{3}\]  
s.t.  
\[x_{1}\in X_{1} \tag{4}\]  
\[x_{t}\in X_{t}(x_{t-1},\xi_{t}),\quad\forall t\in\{2,\ldots,T\}, \tag{5}\]  
where \(f_{t}\) is a continuous function, \(X_{t}\subseteq\mathbb{R}^{n}\) is the feasibility set for the decision \(x_{t}\) that depends on the decision \(x_{t-1}\) at the previous stage and the observation \(\xi_{t}\).  
Different from the formulation represented by Equations (1) and (2), which exclusively involve two decisions, the initial decision \(x\) and the recourse decision \(y\), the multi-stage problem model (3)-(5) accommodates sequential decisions \(x_{t}\) over multiple time periods. This introduces greater complexity and computational demands. In multi-stage optimization problems, the optimal decision is often hard to compute, especially when the underlying uncertain parameters are modeled by continuous random vectors. Therefore, a variety of approximation methods have been developed in which the stochastic process is approximated over a finite number of scenarios, commonly organized in a tree structure, i.e., a scenario-tree (see Figure 1). In the following section, we will provide a comprehensive discussion concerning the application of a scenario-tree and the implementation of solving techniques in SP problems.
## 2 Project Overview  
Railway bridge stations are divided into two types: \"building-bridge integrated\" and \"building-bridge separated\". The \"building-bridge integrated\" type further splits into arrival-departure line and mainline versions, depending on structural joints. The arrival-departure version has joints between the mainline and arrival-departure line, while the mainline version has no joints. The \"building-bridge separated\" type has continuous joints, keeping the bridge and station structures independent. This type reduces vibrations but underutilizes space beneath the bridge. In contrast, the \"building-bridge integrated\" type is more widely used for better space utilization. A detailed comparison is given in Table 1.  
For railway bridge-type stations, the utilization of the space under the bridge often needs to be considered. Although the \"building-bridge separated\" scheme makes the station structure and the bridge structure entirely independent, which can mitigate  
vibrations generated when trains pass at high speeds, the large size of the beams and columns results in inefficient use of the space under the bridge. Therefore, compared with the \"building-bridge separated\" scheme, the \"bridge-building integrated\" scheme is increasingly widely applied.  
This paper investigates two types of \"bridge-building integrated\" station structures: the arrival and departure line \"building-bridge integrated\" structure and the mainline \"building-bridge integrated\" structure. The typical cross-sectional views of the station structures are illustrated in Figure 1. The former features a structural joint in the mainline, while the latter does not. Both station structures have a layout of two platforms and six tracks, comprising six operational lines. 3\(\#\), 4\(\#\) are mainlines, and the remaining tracks are arrival and departure lines. The structural joint in the mainline is located between the mainlines (3\(\#\), 4\(\#\)) and the arrival and departure lines (2\(\#\), 5\(\#\)). The station buildings of both types have a length of 49 m in the direction perpendicular to the tracks and a length of 98.8 m in the direction parallel to the tracks. The structures have three spans in the direction perpendicular to the tracks: the arrival and departure line \"building-bridge integrated\" type has spans of 17.95 m + 9.9 m + 17.95 m, while the mainline \"building-bridge integrated\" type has spans of 18.75 m + 11.5 m + 18.75 m. In the direction parallel to the tracks, there are four spans with a length of 4 \(\times\) 24.7 m. The platform level is at an elevation of 14.43 m, and the track-bearing level is at an elevation of 11.93 m. The waiting room is located at the ground level. To enhance the clarity and precision of the discussion, Station Type A and Station Type B are respectively used to denote the arrival-departure line \"building-bridge integrated\" Structure and mainline \"building-bridge integrated\" Structure.  
Figure 1: Schematic diagram of “building-bridge integrated”. (**a**) section of Station Type A. (**b**) section of Station Type B.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 4 Establishment of Fluid-Solid Coupling Model  
### Simulation Method  
The simulation of grouting in dam construction is divided into two main stages. The first stage involves simulating the rockfill body at the dam's crest. Utilizing the theory of porous media, the grouting process is conceptualized as diffusion within porous materials. These materials are defined as solids containing numerous pores, which may be interconnected, partially closed, or entirely isolated. In the rockfill body, the initial aggregation of stones or particles creates spaces that form the structure of the porous medium, thus classifying the rockfill as such. The second stage focuses on simulating the injection process of polymer foam grouting. This stage emphasizes the expansion and filling of pores by polymer foam post-foaming. The numerical model primarily replicates the flow and filling behavior of polymer foam within the rockfill's pores post-expansion. For simplification in numerical simulation, the foam is assumed to be a homogeneous fluid, discounting the heterogeneity arising from the incomplete blending of different material components. There is no prefabricated model of the rockfill bodies and the high polymer model in MatDEM, so the model-building process requires secondary coding based on the operational functions in MatDEM.
# Improving a Process-Based Model to Simulate Forest Carbon Allocation under Varied Stand Density
## 2 Materials and Methods  
### The Model  
#### 2.1.1 Model Description  
TRIPLEX, originally designed by [PERSON] et al. [19], integrates three well-established models: the model of tree biomass production, 3-PG [20]; the model of forest stand dynamics, TREEDYN3.0 [21]; and the model of soil organic matter dynamics, CENTURY4.0 [22]. [PERSON] et al. [18] modified the original TRIPLEX model and developed a new version, TRIPLEX-Management, that can simulate the effects of thinning on forest growth, carbon, and nitrogen cycling at the stand level.  
The climate driving forces included in the model are the monthly average temperature, precipitation, and relative humidity. Moreover, the gross primary productivity (GPP) in the model is calculated by photosynthetically active radiation, leaf area index, and conversion constant, as well as modifiers that are limited by forest age, temperature, soil water, nitrogen, and frost duration. Respiration includes both autotrophic (including growing and maintaining respiration) and heterotrophic respiration. The maintenance of respiration is the product the carbon content of each plant component (foliage, roots, and wood) and the temperature sensitivity of respiration (\(Q_{10}\)) [23]. Growth respiration is assumed to be one-fifth of the difference of maintaining respiration and GPP [19]. Net primary productivity (NPP) is allocated to the stems, leaves, and roots according to the allocation parameters. The stand structure is simulated using an empirical Weibull diameter distribution model. Forest thinning is driven by thinning time, thinning type, and thinning intensity [18]. Previously, [PERSON] et al. [19] and [PERSON] et al. [18] described the characteristics, structure, mathematical algorithms, sensitivity analysis, and development strategies of this model in detail.