esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Central Moments  
For further quantitative analysis, we calculate the central moments of the FDHs for the MMT parameters to see their statistical differences [24]. Suppose we have a random variable \(X\) whose central moments, expected value (\(I_{1}\)), variance (\(I_{2}\)), skewness (\(I_{3}\)), and kurtosis (\(I_{4}\)), are given as Equations (4)-(7), where \(\sigma\) represents the standard deviation of \(X\) and \(\mu\) represents the mean of \(X\):  
\[I_{1}=\text{E}(X) \tag{4}\]  
Figure 2: Two-dimensional (2D) backscattering 3 \(\times\) 3 Mueller matrix images of bovine tendon tissue samples: (**a**) without stretching (origin), (**b**) transverse stretching (TDO), (**c**) longitudinal stretching (MDO), and (**d**) 45 deg stretching (ODO). All the Mueller matrix elements are normalized by the M11. The color bar is from \(-1\) to \(1\) for the M11, \(-0.2\) to \(0.2\) for the M22 and M33 and from \(-0.1\) to \(0.1\) for other elements. It should be noted that the self-normalized M11 is polarization irrelevant.  
\[I_{2} =\text{Var}(X) \tag{5}\] \[I_{3} =\frac{\text{E}(X-\mu)^{3}}{\sigma^{3}}\] (6) \[I_{4} =\frac{\text{E}(X-\mu)^{4}}{\sigma^{4}} \tag{7}\]  
By observing the central moments values, we can quantitatively characterize a distribution curve. Here, \(I_{1}\) is the mean value of the distribution curve, representing the mathematical expectation of the distribution; \(I_{2}\) is the variance of the distribution, representing the degree of dispersion; \(I_{3}\) represents the skewness of the distribution curve, which is a measure of the direction and degree of the skewed distribution of the statistical data, as well as a mathematical feature of the degree of asymmetry; \(I_{4}\) represents the kurtosis of the distribution curve, which is the number of features characterizing the peak of the probability density distribution curve. Intuitively, the kurtosis reflects the thickness of the tail of the distribution curve. The detailed physical interpretations of the central moments can be found in Refs. [26; 27]. In this study, we first record the 2D backscattering Mueller matrix images of the tendon tissue samples, then transfer the pixels of Mueller matrix elements and MMT parameters to FDHs by statistical analysis. Finally, the central moments of the FDHs are obtained according to Equations (4)-(7).
## 3 CPS Resilience Quantitative Assessment Framework
### Linear (or piecewise linear) coefficient model
#### 3.2.3 Extension to Time-Varying Dynamics and Physical Interpretations  
The proposed mathematical model for performance evolution offers an abstract and flexible framework. In the previous subsection, we examined system behavior under constant, piecewise-constant, and time-linear forms of degradation and recovery rates. This subsection explores potential extensions to time-varying formulations and their corresponding physical interpretations.  
Given the realities of evolving threats, environmental fluctuations, and system aging, real-world CPSs often exhibit failure and recovery rates that are time-dependent or context-sensitive. Accordingly, \(A\) and \(R\) can be naturally extended to time-varying functions \(A(t)\) and \(R(t)\), allowing the model to capture more complex and realistic dynamics under varying operational conditions.  
In practical applications, the temporal evolution of these parameters can reflect the control laws and physical characteristics inherent to specific CPS domains. For instance, in a water-level control system, degradation and recovery rates may remain constant under nominal conditions but may gradually vary linearly with time due to valve wear or sensor drift. In contrast, in energy storage systems such as supercapacitors or batteries, performance degradation may follow a quadratic trend over time, capturing the nonlinear accumulation of charge or discharge dynamics. Furthermore, in temperature regulation within chemical reactors, degradation and recovery rates may evolve exponentially, reflecting nonlinear heat transfer behavior and delayed actuator response under thermal stress. This modeling flexibility allows the abstract formulation to approximate the underlying differential equations that govern the physical processes of diverse CPSs.  
The piecewise-constant form adopted in this work provides a tractable and intuitive approximation. From a modeling perspective, both time-invariant and time-varying representations ultimately require parameter estimation for \(A(t)\) and \(R(t)\); therefore, this assumption does not compromise the generality of the approach. By isolating stages such as degradation, stability, and recovery, this approach effectively reveals key system behaviors while preserving analytical simplicity.  
In Section 3.3, we will incorporate this physical domain model with a Markov-based cyber-layer model to construct a unified resilience assessment framework. Note that extending the framework to support continuously time-varying functions is a promising direction for future work. In particular, integrating such extensions with richer cyber-layer models, such as Bayesian networks or game-theoretic approaches--can further enhance the fidelity and applicability of resilience assessment in complex CPS environments.
## 5 Case Study
### Performance Analysis
#### 5.2.2 Analysis of Stop Distribution Imbalance  
Table 5 shows the stop distribution imbalance indexes of each unidirectional segment in the three stop plans. It can be seen that the imbalance indexes in the PSP are all the largest, which is caused by the imbalanced stop distributions among trains due to practical factors. Comparing the OSP and ISP, results indicate that the stop distribution imbalance indexes of 50% segments are mitigated by the proposed approach. Moreover, more stop distribution imbalance indexes of the bidirectional segments in the OSP are equal. This means that the proposed approach can further balance the stop distribution of the two directions of the same segment, which is more beneficial to train circulation.  
Figure 7 illustrates the distributions of train stop distribution imbalance indexes of each segment among the three stop plans. Comparing the OSP and the ISP, there are some slight changes in some segments that have been marked by red circles in Figure 7a,b. These changes result from the change in train stop distribution in each segment, resulting in more balanced stop distribution and a reduction in the imbalanced indexes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Stop Plan** & **SDI** & **ATR** & **TCI** & **DPK** & **DPV** & **AAT** \\ \hline OSP & 3.1074 & 349.7206 & 0.0004 & 80,437 & 163 & 14.51 \\ ISP & 3.2539 & 353.1851 & 0.0006 & 106,648 & 178 & 14.6 \\ \hline \end{tabular}  
* OSP: optimized stop plan; ISP: initial stop plan; SDI: stop distribution imbalance; ATR: additional time ratio; TCI: transit capacity insufficiency; DPK: detailed passenger kilometer/person\(\cdot\)km; DPV: detailed passenger volume; AAT: average adjustment time/min.  
\end{table}
Table 4: Comparison of overall indexes.  
In Figure 7c, the train stop distribution shows obvious imbalance in each segment compared to the OSP. As for each segment, the numbers of trains with different imbalance  
Figure 7: Illustration of the distributions of train stop distribution imbalance indexes of each segment. (The red circles represent the changed ranges between the OSP and the ISP).  
index ranges in the OSP present more uniformly. It is indicated that the proposed approach can mitigate the stop distribution imbalance by optimizing the worst index of each segment and rebalancing the distribution of stop distribution imbalance indexes.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 4 MIMT-CNN Modeling
### Network Structure
#### 4.1.3 Multi-Task Output Layer  
The output layer of the network has two branches: classification for the EMI signal identification and regression for the threat level prediction. The branch of regression output is carried out by a fully connected layer with the size of 1, whose output is the EMI threat level \(u\) to the data link. The branch of classification is composed of a fully connected layer with the size of 4 and a softmax layer, whose output is the EMI type \(v\).  
The softmax layer converts the output values of the classifications into a probability distribution in the range of [0, 1] with a sum of 1 and uses the maximum probability as the basis for category judgment. The classification result \(v\) can be calculated by  
\[v=\underset{C}{\operatorname{argmax}}\left(\frac{e^{w_{i}}}{\sum_{C=1}^{C}e^{w _{i}}}\right) \tag{8}\]  
where \(w_{i}\) is the output value of the \(i\)-th node in the previous layer of the softmax layer, and \(C\) is the number of output nodes, i.e., the number of EMI signal categories.
## 1 Introduction
Here, plasma actuators are meant to reproduce qualitatively the effect of the mechanical L-tab investigated in Refs. [16; 17; 18; 19; 20] for rotorcraft vibration reduction. Specifically, plasma actuation is meant to develop vortical structures and recirculating flow areas similar to those generated by mechanical control surfaces (see, for instance, Ref. [21]). Plasma actuators are thought to be located on the pressure and on the suction side (PS and SS, respectively) of the three central blades in the targeted cascade. The plasma induced flow is conceived to be opposite relative to the freestream direction, with the specific aim to generate the aforementioned separated flow areas. Numerical computations performed on the clean blade configuration are validated with experimental data achieved on the wind tunnel facility of the Chair for Aero Engines at Technische Universitat (TU) Berlin and with literature results [22; 23; 24]. Constant angle of attack configurations for the blade cascade are assessed first. It is shown that plasma affects significantly both local and resulting loads on the blade, including the pitching moment. At the same time, no remarkable drag rises are encountered. Traveling wave mode simulations are then performed with alternate PS/SS actuation. Within a traveling wave mode, the blades oscillate at the same frequency but at a certain--constant and uniform--phase lag referred to as Interblade Phase Angle (IBPA), see [25]. In this work, it is found that appropriate phasing the switch between PS and SS actuation allows for effectively manipulating the peaks of unsteady airloads, especially the pitching moment. As a result, mean and oscillating loads can be controlled, and fatigue phenomena can be alleviated. Additionally, the blade aeroelastic stability could also be improved. The manuscript is structured as follows. Section 2 presents the computational geometry, the set up of the simulations, as well as the numerical modeling for plasma. The comparison between numerical results achieved on the clean cascade and experimental measurements on the same geometry is also reported. Section 3 discusses the simulations carried out on the blades at constant angle of attack. The results of time-dependent computations performed at constant angle of attack are shown to prove the steadiness in the mean of the flow, both for the clean and for the plasma equipped configuration. The effects of PS and SS actuation on the airloads and on the flow field are reported. Section 4 describes the results obtained with traveling mode simulations. The triggering of alternate PS/SS actuation is presented first. The affection of plasma on the pitching moment oscillations is discussed as a second instance. Concluding remarks are provided in Section 5.
# Enhancement of Mechanical Properties of PCL/PLA/DMSO2 Composites for Bone Tissue Engineering
## 2 Materials and Methods
### Hydrophilicity, Bending Test, Fractography, and Spectroscopy Analysis  
Hydrophilicity was assessed through contact angle measurements using the sessile drop method, following the ASTM D7334 standard [38]. Distilled (DI) water was a common test liquid for this evaluation. In the test, a 10 \(\mu\)L droplet of DI water was placed on the specimen, and the angle between the droplet and the specimen was measured at the endpoints on the left and right sides. The test was repeated five times, and the contact angle was calculated as the average of these measurements.  
Yield strength (0.2% offset) was determined using a three-point bending test. The tests were conducted using a bench-mounted universal testing machine, model 5 ST ([PERSON], Redhill, UK), which has a maximum force capacity of 5000 N and a load measuring accuracy of 0.2% within the range of 0.2% to 100% of the load cell capacity. Yield strength measurements were taken at a strain limit of 5%, with the deflection at the mid-span of the materials reaching 6.8 mm. Nine specimens per composition were tested to obtain the yield strengths.  
A SEM analysis of specimen surfaces was conducted to examine the bonding mechanism. Additionally, SEM was employed to inspect the surfaces of the composites and analyze interface adhesion, both in their as-molded state and in cross-sections/surfaces after the bending test. The blade-cut surfaces of specimens were examined at the maximum deflection point following the bending tests. The surfaces of the composites were coated with Au for 4 min using an ion-sputter coater. The SEM analysis was performed using an SNE-4500M Plus system (SEC Co., Ltd., Suwon-si, Republic of Korea), while the surface coating was conducted with an MCM-100P device (SEC Co., Ltd., Suwon-si, Republic of Korea).  
A spectroscopy analysis of specimen was performed to investigate chemical structure and composition after SEM analysis. The tests were conducted using FTIR equipment, specifically an iS10 Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a deuterated triglycine sulfate (DTGS) detector and a standard IR light source. Spectra were recorded over the range of 4000-400 cm\({}^{-1}\) with a spectral resolution of 4 cm\({}^{-1}\). An attenuated total reflectance (ATR) accessory with a diamond crystal was used for all measurements.
## 2 Materials and Methods
### Mathematical Model
For the standard \(k\)-\(\varepsilon\) model, the transport equations have the following forms:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho u_{ i}k)=\frac{\partial}{\partial x_{j}}\Bigg{(}\bigg{(}\bigg{(}\mu+\frac{\mu_{t}}{ \sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\bigg{)}+G_{k}+G_{b}- \rho\varepsilon-Y_{M}-S_{k}, \tag{2}\]  
Equation of turbulence dissipation rate:  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} (\rho u_{i}\varepsilon)=\frac{\partial}{\partial x_{j}}\Bigg{(}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{j}}\bigg{)}+C_{\varepsilon 1}\frac{\varepsilon}{k}(G_{k}+C_{\varepsilon 3}G_{b})-C_{ \varepsilon 2}\rho\frac{\varepsilon^{2}}{k}+S_{\varepsilon}, \tag{3}\]  
where \(k\) is the turbulent kinetic energy; \(\varepsilon\) is the rate of dissipation of turbulent kinetic energy; \(\mu\) is the dynamic viscosity; \(\mu_{t}\) is the turbulent viscosity; \(\sigma_{k}\), \(\sigma_{\varepsilon}\) are the turbulent Prandtl numbers for \(k\) and \(\varepsilon\), respectively; \(G_{k}\) is the generation of turbulence kinetic energy due to the mean velocity gradient; \(G_{b}\) is the generation of turbulence kinetic energy due to buoyancy; \(Y_{m}\) is the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate; \(S_{k}\), \(S_{\varepsilon}\) are the additional source terms; \(C_{\varepsilon 1}\), \(C_{\varepsilon 2}\), \(C_{\varepsilon 3}\) are the constants. For an ideal gas, the density is calculated using the ideal gas law, and \(C_{p}\) is a function of temperature:  
\[p=\frac{wp_{abs}}{R_{0}T}, \tag{4}\]  
\[\partial h=C_{p}\partial T, \tag{5}\]  
\[C_{p}=C_{p}(T), \tag{6}\]  
where \(w\) is the molecular weight; \(p_{abs}\) is the absolute pressure; \(R_{0}\) is the universal gas constant.
# An Admission Control Mechanism for 5G LWA
## 3 System Model
### Problem Formulation  
For the cellular network, downlink transmission is based on orthogonal frequency-division multiple access (OFDMA), which enables a base station (BS) to transmit to multiple UEs simultaneously. The system throughput models for LTE UEs can be enhanced through spectrum efficiency. The spectrum efficiency of UE m without mutual interference in LTE networks, \(C_{m}\), can be written as  
\[C_{m}=log_{2}\bigg{(}1+\frac{P_{m}H_{m}}{B_{m}N_{0}}\bigg{)} \tag{1}\]  
where \(B_{m}\) is the bandwidth allocated to UE \(m\), \(P_{m}\) is the transmission power allocated on the licensed band for UE \(m\), \(H_{m}\) is the channel power gain between transmitter and receiver \(m\), and \(N_{0}\) is the noise power. Hence, achievable throughput \(R_{l}\) from the LTE network can be formulated as  
\[R_{l}=\sum\
olimits_{m=1}^{M}B_{m}C_{m} \tag{2}\]  
When multiple UEs are connected through WLAN networks, the UEs have to compete for the unlicensed channel opportunities by using the carrier sensing mechanism popularly known as the distributed coordination function (DCF) protocol. With DCF, the system throughput of the WLAN network is related to the number of competing WLAN UEs. Let \(P_{tr}\) be the probability that there is at least one transmission signal in a time slot, and let \(P_{s}\) be the probability that there is no collision on a channel. Then, they can be expressed as  
\[P_{tr}=1-(1-\tau)^{N} \tag{3}\]  
\[P_{s}\ =\ \frac{N\tau(1-\tau)^{N-1}}{P_{tr}} \tag{4}\]  
where \(\tau\) is the transmission probability for each of the UEs, and \(N\) is the number of competing WLAN UEs. According to [PERSON] [25], the throughput of the WLAN network, \(R_{w}\), can be expressed as  
\[R_{w}(N)=\frac{P_{tr}P_{s}E[P]}{(1-P_{tr})T_{\sigma}\ +\ P_{tr}P_{s}T_{s}+\ P_{tr}(1-P_{s})T_{c}} \tag{5}\]  
Figure 1: System model of LTE and WLAN coexistence.  
where \(T_{s}\) is the average time that the channel is sensed as busy caused by a successful transmission, \(T_{c}\) is the average time that the channel is sensed as busy by each station during a collision, \(E[P]\) is the average packet size, and \(T_{\sigma}\) is the duration of an empty time slot.  
In LWA access mode, the selected LTE UE traffic will be transmitted through the unlicensed band over an MNO-operated AP network. Therefore, with LWA activation, there will be an additional \(m\) UEs in a WLAN AP to the \(N\) WLAN UEs already being served in the WLAN network. For this condition, throughput for each of the LWA UEs or WLAN UEs can be expressed by \(R_{w}(N+m)/(N+m)\), where \(R_{w}(N+m)\) is the system throughput of the WLAN network with an additional m LWA UEs from the cellular network defined in (5).
## 2 Digital Twin
### Architecture
#### 2.2.1 DT Variants  
Based on different types of data sharing between physical and digital systems, we can categorize DT into three variants: Digital Model, Digital Shadow, and Digital Twin (DT). Figure 2 represents the architecture of these DT variants.  
Generally speaking, digital modeling refers to a digital representation of one existing physical system or its theoretical model. The digital representation of the system may  
Figure 1: DT Architecture.  
Figure 2: DT Variants.  
include a detailed description of the physical components. A distinct difference is that a true DT system would utilize the data collected from the physical system to build a digital system for simulation and control of the physical system based on the simulation results. Nonetheless, for digital models, digital systems cannot automatically manipulate physical systems based on digital system simulation results [30; 41]. Changes to the physical representation will not affect the digital representation and vice versa.  
To expand on the modeling capabilities of the digital model, the Digital Shadow further takes the simulation and integrates a one-way data flow from the physical object to the digital representation [42]. Changes to the state of the physical systems can dynamically alter the representation of the digital model based on changing states in the physical system. This type of digital modeling can be used as a virtual representation of the physical system. In this way, the system administrator can intuitively observe the operation of the physical system and can respond in time according to the existing problems of the system. Changes to the physical system can have impacts on the virtual model but not vice versa.  
The DT is an extension of the digital model and digital shadow. The distinct difference between the two previous examples and a DT system is the two-way link between the physical system and the digital model. One benefit of having the two-way link is the ability to affect the physical system based on the digital representation. Changes in the physical system can be reflected over the digital model so that possible outcomes based on system variables can be output. The link back to the physical system allows the control system to interact with the physical system so that a desired outcome can be achieved.
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Capabilities
#### 4.4.2 Processing  
The sensors and devices used for data collection also vary in their processing capabilities [130]. The study of [PERSON] et al. [127] classifies sensors as high-end or low-end devices, depending on resources and computational capabilities. Low-end devices are resource-constrained with regard to energy, processing power, and communication capacities. The processing capability refers to the sensors' ability to process aggregated data locally [55].  
For IoE systems, data are automatically processed to deduce knowledge and generate actionable insights. In general, data processing techniques are either historical or proactive. Historical data processing is related to knowledge discovery, whereas proactive data processing provides predictive and actionable insights [24]. A broad category of applications participates in the continuous generation and analysis of high-volume heterogeneous stream data. Next-generation applications will be developed to handle the data in streaming mode and on-the-fly as the value of data resides in its real-time processing [131].  
Analytics technology refers to the systematic computational analysis of transforming a variety of data from different sources into information [105] and applying data fusion and mining techniques [94] to make intelligent decisions at the following distribution levels: (1) the device level, where devices are responsible for storage and computing process; (2) the network level, which demands remote communication to fog computing nodes (hubs, base stations, gateways, routers, and servers); and (3) cloud level, which demands remote communication within a group of interconnected servers [24; 38; 73; 114; 118].  
Cloud, edge and fog computing are critical aspects of the centralized and decentralized IoE environment, considering that devices that have restricted compute and memory capacity need to delegate these functions [25; 26; 29]. Integrated with cloud computing, edge computing can efficiently address the processing problems related to edge big data. Since in the edge computing paradigm, the data are at the edge of the network [28; 56].  
A variety of cloud computing and edge computing paradigms are mobile cloud computing, mobile edge computing, and fog computing [32; 60; 126]. Cloudlets, mobile edge computing, and fog computing are edge computing technologies and rely on virtualization, while mobile cloud computing processes the data of mobile applications at a remote cloud data center.