esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 3 Results
### RCS of the Rotor Blades
Figure 4: Monostatic RCS (in dB m\({}^{2}\)) of the blades made of lossless fiber-reinforced plastic (FRP) of different thickness. Continuous lines: V polarization; dashed lines: H polarization. The polarization of the incident field has noticeable effects on the RCS, especially for blade v2, where the signal return is stronger than for a pure PEC model for H polarization. (**a**) blade v1; (**b**) blade v2; and (**c**) blade v3. Figure 5: Monostatic RCS (in dB m\({}^{2}\)) of the blades made with lossy FRP (\(\tan\delta=0.05\)) of different thicknesses. Continuous lines: V polarization; dashed lines: H polarization. The introduction of material losses does not cause significant changes compared to Figure 4. (**a**) Blade v1; (**b**) blade v2; and (**c**) blade v3. Figure 3: Monostatic RCS (in dB m\({}^{2}\)) of the blades made of perfect electric conductor (PEC) material. Continuous lines: V polarization; dashed lines: H polarization. The different blades show very different characteristic RCS signatures. An increase of the physical size of the blades causes a small shift of the characteristic locations as well as a small increase of the RCS. The polarization of the incident field has no effect on the RCS. (**a**) Blades v1, v2, and v3; (**b**) blade v2, scaled; and (**c**) blade v3, scaled. Figure 6: Monstatic RCS (in \(\mathrm{dB}\,\mathrm{m}^{2}\)) of the rotor blades (left to right) composed of a layer of FRP of different thickness (downwards) and a layer of foam of different thicknesses. Figure 7: Monstatic RCS (in dB m\({}^{2}\)) of the rotor blades (left to right) composed of a sandwich setup. of FRP, foam, FRP, with variable thicknesses. (**a**) v1, FRP 5 mm, foam, FRP 5 mm; (**b**) v2, FRP 5 mm, foam, FRP 5 mm; (**c**) v3, FRP 5 mm, foam, FRP 5 mm; (**d**) v1, FRP 10 mm, foam, FRP 10 mm; (**e**) v2, FRP 10 mm, foam, FRP 10 mm; (**f**) v3, FRP 10 mm, foam, FRP 10 mm; (**g**) v1, FRP 15 mm, foam, FRP 15 mm; (**h**) v2, FRP 15 mm, foam, FRP 15 mm; (**i**) v3, FRP 15 mm, foam, FRP 15 mm; (**j**) v1, FRP 20 mm, foam, FRP 20 mm; (**k**) v2, FRP 20 mm, foam, FRP 20 mm; and (**l**) v3, FRP 20 mm, foam, FRP 20 mm.
## 2 Methods  
This study proposes a multi-layer complex network-based approach for railway station yard modeling and analysis. Figure 1 illustrates the methodological framework. The process consists of four main steps, each contributing to the comprehensive evaluation of node importance in the railway network.  
Step 1. Establishing the railway station multi-layer complex network model  
The process begins with signaling layout and interlocking tables, which provide fundamental structural and operational information about the railway station. Basic data are extracted, including node locations, adjacency matrix (AM), structural matrix (SM), connection count matrix (CCM), and transition probability matrix (TPM). Using these data, a multi-layer complex network model is constructed, consisting of different layers such as train routes network, extended routes network, and shunting routes network.  
Step 2. Calculating node importance indicators  
Five key indicators are computed to evaluate node importance within each layer: connectivity strength (CS), destination node count (DNC), source node count (SNC), node efficiency (NE), and extended PageRank (EPR).  
Step 3. Hierarchical evaluation of node importance based on a single indicator  
Each network layer (LC, YX, DC, etc.) undergoes an independent hierarchical evaluation based on the computed indicators. This step determines node rankings within individual network layers, generating layer-specific node importance rankings.  
Step 4. Comprehensive evaluation of node importance  
The layer-specific rankings are aggregated to produce a final node ranking that considers cross-layer interactions. This ranking identifies key nodes that play a significant role across multiple network layers, providing a holistic importance assessment. The final output is a comprehensive node ranking, which supports railway yard optimization, capacity planning, and operational safety analysis.
## 7 Experiments
### Test 2: From Far to Near Zone of PRN 4  
In the second test, the user moves from the far to the near zone and then returns to the far zone of PRN 4. When the user stays in the near zone, the other PL signals suffer interference from the strong signal of the near PL signal PRN 4. Figure 17 shows the test environment and user trajectory.  
Figure 16: Tracking results of PRN 4: ITL (left) and VTL (right).  
Figures 18 and 19 show the tracking results of the pseudorange and range rate, respectively. The arrangement of plots follows the distance from the near zone of PRN 4. As the user approaches PRN 4, the tracking performance of the far signals PRN 1, 5, 6 and 7 is disrupted in ITL, and a divergence of pseudorange measurement is observed as shown in the right plot of Figure 18 for the case of PRN 7.  
The middle plot of Figure 18 shows the tracking performance of PRN 2 signal at mid distance from the near zone. It is disturbed for a while when the user is in the near zone. As the user gets out of the zone, it resumes tracking successfully, whereas VTL continuously track the signal. The pseudorange estimation of ITL only works successfully on the near signals, PRN 3 and 4. And the tracking results of PRN 4 are shown in the left plot of Figure 18. The performance of frequency tracking and the results of range-rate measurements are similar: independent FLL could not track the far signals. The near-far problem causes frequency offset in the tracking results. Compared with ITL, VTL can track all the signals properly, even when the user stays in the near zone of PRN 4. No estimation divergence was observed in VTL for either pseudo orange or range rate. We can therefore say that VTL can track the majority of signals that are attenuated from the near-far problem for long periods Figure 20 shows the navigation results. As stated at the beginning of this section, positioning results are filtered to monitor the result. Multipath interference, caused by the small indoor environment, is assumed to be the reason for position offset from the true trajectory. Figure 20 represents only the trend of user movement. As ITL failed to track far signals when the user stayed in the near zone, the navigation results also diverge. However, the VTL results show the movement of the user, back and forth to PRN 4. This is why the coverage of the conventional PL navigation system is restricted to the area that is roughly equidistant from each PL. Our test results show that VTL can improve positioning availability and widen the coverage of a PL navigation system.
## Appendix A
This distinctive attribute of a 4\(f\) optical setup is not valid, e.g., for the 2\(f\) system. The wavelength-independent beam pattern is again transformed to the wavelength-independent waist at the THz detector. Given Equation (13), it is possible to find the formula describing the Rayleigh lengths near the focus by substituting \(\omega_{0}\) with \(\omega_{focus}\) in Equation (11) to finally obtain the following equation:  
\[Z_{R}\approx\frac{\pi}{\lambda}\bigg{(}\frac{f_{focus}}{f_{trans}}\omega_{trans} \bigg{)}^{2}. \tag{14}\]  
The estimation of Rayleigh lengths for the 2\(f\) quasi-optical system (Figure 1b) can be made in a similar way. From the Gaussian beam theory it is known that a radiation source of a constant output aperture, \(2\omega_{trans}\), situated at a focal plane in front of a focusing mirror or lens of the focal length, \(f_{trans}\), will form a wavelength-dependent profile of concentric collimated beams after the mirror. Within this profile, long-wavelength components lay concentrically outside the short-wavelength components. In this case, the wavelength-dependent beam waists at the coupling mirror can be calculated by means of Equation (10) by putting \(\omega_{0}=\omega_{trans}\) and \(z=f_{trans}\). The calculation of \(Z_{R}\), which is present in this equation, again requires that \(\omega_{0}=\omega_{trans}\). Knowing the beam waists, the wavelength-dependent Rayleigh lengths can be calculated according to Equation (11). Note, however, that since the beam waists of the wavelength components at the coupling mirror are unequal in this case, the beam pattern in the Fourier plane consists of concentric circles with the radii of the longer wavelengths laying outside the radii of the shorter wavelengths. This means that the samples must be homogeneous over large areas in order to be investigated with the \(2f\) quasi-optical beam delivery systems. The estimations of Rayleigh lengths given in Section 2.2.2. were made using the methodology described above. It is important to understand that, contrary to optical measurements, the THz-TDS measurements require careful designing of beam delivery setups and their parameters, including all optical elements and cuvette lengths. The paraxial Gaussian beam approximation, which in a strict mathematical formulation requires that \(\omega_{0}\gg\lambda\), may not be fulfilled due to the sub-millimeter and millimeter wavelength components of the pulse. Still, it is a common practice to use Gaussian beam approximation in THz-TDS spectroscopy [49; 50].
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 5 Experiment and Results
### Stop Frequency and CO\({}_{2}\) Trends over Time  
Figure 10 highlights the reduction in stop frequency from seven stops (100% HV) to three (50% AV). Figure 11 shows cumulative emissions over the simulation period, confirming that higher AV penetration yields slower accumulation of emissions. The figure details hourly accumulation of emissions; notably, peak hours (7-9 a.m. and 4-6 p.m.) show the strongest benefit of AV smoothing effects, with the 50% AV scenario emitting 15.5% less CO\({}_{2}\) than baseline.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Scenario** & **Travel Time (s)** & **Stops per Trip** & **Speed (km/h)** & **CO\({}_{2}\) (g)** \\ \hline
100\% HV & 785 & 7 & 38.9 & 2450 \\
10\% AV & 705 & 5 & 42.8 & 2240 \\
30\% AV & 640 & 4 & 46.5 & 2135 \\
50\% AV & 585 & 3 & 49.7 & 2092 \\ \hline \end{tabular}
\end{table}
Table 6: Traffic and emissions metrics for each AV penetration scenario.  
Figure 9: Impact of AV penetration on travel time, stops per trip, speed, and emissions.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ACER  
Agency for the Cooperation of Energy Regulators  
AI  
Artificial Intelligence  
API  
Application Programming Interface  
BRIDGE  
Bringing Research & Innovation in the Digital Energy field to Growth  
CAPEX  
Capital Expenditure  
OPEX  
Operational Expenditure  
CBA  
Cost-Benefit Analysis  
CEEDS  
Common European Energy Data Space  
CEER  
Council of European Energy Regulators  
CEF  
Connecting Europe Facility  
CEN  
European Committee for Standardization  
CENELEC  
European Committee for Electrotechnical Standardization  
CGMES  
Common Grid Model Exchange Standard Library  
CIM  
Common Information Model  
CORDIS  
Community Research and Development Information Service  
DGA  
Data Governance Act  
DMS  
Distribution Management System  
DSO  
Distribution System Operator  
EMS  
Energy Management System  
ENISA  
European Union Agency for Cybersecurity  
ENTSO-E  
European Network of Transmission System Operators for Electricity  
ETIP  
European Technology and Innovation Platform  
ETSI  
European Telecommunications Standards Institute  
EU  
European Union  
EV  
Electric Vehicle  
GDPR  
General Data Protection Regulation  
GIS  
Geographic Information System  
GW  
Gigawatt  
ICT  
Information and Communication Technology  
IEC  
International Electrotechnical Commission  
IEEE  
Institute of Electrical and Electronics Engineers  
IT  
Information Technology  
MQTT  
Message Queuing Telemetry Transport  
OPC UA  
Open Platform Communications Unified Architecture  
OPEX  
Operational Expenditure  
PRISMA  
Preferred Reporting Items for Systematic Reviews and Meta-Analyses  
RAB  
Regulated Asset Base  
SAIDI  
System Average Interruption Duration Index  
SAIFI  
System Average Interruption Frequency Index
## 1 Introduction
Due to the nonconvex characteristics of the optimization problem, it is difficult to obtain the explicit expressions and value of global optimal parameters [59]. To overcome the above-mentioned challenges, this paper introduces a time-varying degradation model based on MCS and PSO to optimize LFC systems in multi-area settings and find the optimal solution for LFC controllers, such as PI and PID controllers. This approach copies the inherent complexity of real-world scenarios in which systems comprise multiple interconnected units. MCS is used to generate various possible operating environments and analyze the impact of different degradation levels on the system performance, thus evaluating the reliability of multi-area LFC systems with weakened components. PSO is utilized to design an optimal PI controller that strikes a balance between the reliability and the performance quality parameters, ensuring that the control strategies can adapt to component degradation and maintain system stability and reliability. This paper thoroughly analyzes key performance parameters, such as peak time, percentage overshoot, and settle time, to guarantee that the LFC system satisfies the operational requirements. This comprehensive assessment highlights the impact of degraded components on system performance and the effectiveness of the proposed control strategies. A case study illustrates that by incorporating stochastic degradation and cyber-attacks, and by extending the solution to multi-area power systems, the proposed methodology effectively addresses the coupling effects between Automatic Voltage Regulation and LFC loops across multiple areas. The contributions can be summarized as follows:  
* A time-varying degradation model considering FDIA is introduced for multi-area LFC systems, which extends from single-area to multi-area applications, effectively capturing the dynamic degradation characteristics and addressing the coupling effects between LFC loops and Automatic Voltage Regulation across different areas. * The model incorporates physical constraints such as GRC and speed governor dead bands, providing a comprehensive representation of industrial conditions that are often neglected in the existing models. An optimal controller is designed using PSO, tailored to trade off reliability and performance quality metrics, which enhances both system stability and reliability. * The proposed MCS-PSO method is validated through practical applicability to real-world multi-area LFC systems, demonstrating substantial improvements in reliability and performance over traditional control methods through simulation results. This paper is organized as follows: Section 2 presents the model of multi-area LFC systems incorporating degradation and FDIA described by time-varying functions. Section 3 discusses the event-based MCS reliability evaluation and the performance enhancement of LFC systems through the application of the PSO method. Section 4 provides a case study of a three-area LFC system along with the corresponding results. Finally, the conclusions are drawn in Section 5, while future work is discussed in Section 6.
## 2 MODIS-Compatible VIIRS EVI  
The MODIS-compatible VIIRS EVI was derived using the vegetation isoline equations [17]. The equations analytically approximate and describe the vegetation isoline, which is defined as the line formed between the reflectances at two different wavelengths for an optically and structurally constant canopy and a constant atmospheric condition over varying canopy background brightness [29]. A horizontally infinite homogeneous atmospheric layer was assumed and a portion of the target area was assumed to be covered with a homogeneous canopy. It was further assumed that the radiative transfer problem in both the covered and uncovered areas could be simulated independently by modeling a horizontally homogeneous canopy and Lambertian soil surface, respectively. The first-order photon interactions between soil and canopy and between canopy and atmosphere were considered (higher order interaction terms were truncated) for deriving the vegetation isoline equations.  
In deriving the MODIS-compatible VIIRS EVI, we first obtained equations that related the VIIRS blue, red, and NIR (M3, I1, and I2 bands) to the MODIS respective counterparts (band 3, band 1, and band 2) [17] using the vegetation isoline equations  
\[\rho_{b,m} =A_{b}\rho_{b,p}+D_{b} \tag{2a}\] \[\rho_{r,m} =A_{r}\rho_{r,p}+D_{r}\] (2b) \[\rho_{n,m} =A_{n}\rho_{n,p}+D_{n} \tag{2c}\]  
where \(\rho_{b,m}\), \(\rho_{r,m}\), and \(\rho_{n,m}\) are MODIS blue, red, and NIR band reflectances which are TOC or partial (uncorrected for aerosol)-atmosphere corrected reflectances that are modeled by adding aerosol layer over the canopy; \(\rho_{b,p}\), \(\rho_{r,p}\), and \(\rho_{n,p}\) are VIIRS counterparts. \(A_{b}\), \(A_{r}\), and \(A_{n}\) (slopes of the isoline equation for blue, red, and NIR bands) and \(D_{b}\), \(D_{r}\), and \(D_{n}\) (offsets of the isoline equation for blue, red, and NIR bands) are dependent on the reflectance and transmittance of the canopy and atmospheric layers, and the soil line slope and offset for MODIS blue and VIIRS blue, MODIS red and VIIRS red, and MODIS NIR and VIIRS NIR band pairs, respectively.  
We then substituted Equations (2a-c) for the MODIS reflectances in Equation (1) to express the MODIS EVI equation as a function of VIIRS reflectances [17],  
\[\hat{\sigma}_{m}=G\frac{\rho_{v,n}-K_{1}\rho_{v,r}+K_{2}}{\rho_{v,n}+K_{1}C_{ 1}\rho_{v,r}-K_{3}C_{2}\rho_{v,b}+K_{4}} \tag{3}\]and  
\[K_{1}=\frac{A_{r}}{A_{n}} \tag{4a}\] \[K_{2}=\frac{D_{n}-D_{r}}{A_{n}}\] (4b) \[K_{3}=\frac{A_{b}}{A_{n}}\] (4c) \[K_{4}=\frac{C_{1}D_{r}+D_{n}-C_{2}D_{b}+L}{A_{n}} \tag{4d}\]  
where \(\vartheta_{m}\) is the MODIS-compatible VIIRS EVI. The coefficients in Equation (3), \(K_{1}\), \(K_{2}\), \(K_{3}\), and \(K_{4}\) vary with the soil, vegetation, and aerosol conditions [17] and that the exact translation is possible only when the exact conditions of soil, vegetation, and atmosphere are known.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 5 Experimental Setup  
### Fixed Equipment Scene Experimental Setup  
As depicted in Figure 1, the mine's grab crane unloads ore from the cargo hold into the loading basket and subsequently transfers it to the storage yard or loading platform via the conveyor belt. This operation is analogous to manipulating a robotic arm to grasp materials and deposit them into the material box on the production line. In the loading task, failure of the robotic arm to securely grasp the material and accurately place it into the material box can result in scattered materials, disrupting the production line and hindering its regular operation. In such instances, immediate intervention is crucial to prevent more severe risks. To ensure the smooth operation of the production line, it is imperative to deploy equipment with sufficient computing and storage resources to process real-time data. This reflects the edge node deployment challenges encountered in mining scenarios. Hence, this study focuses on validating the algorithm's efficacy in tasks related to material grabbing and loading within a laboratory production line platform.  
The small production line test bench is shown in Figure 7. The comprehensive system consists of modules such as conveyor belts, loading, unloading, packaging, robotic arms, and other components. Upon receiving a remote order, the production line system undergoes the process of order confirmation, independent execution of product customization and assembly, and visual display of operational status to facilitate intelligent production. Simulation parameters for the experiments are specified in Table 2.
## 2 Results
### Gene Clustering Analysis  
The 7721 DEGs were clustered into nine co-expression modules using the \(k\)-means clustering method based on their expression patterns, and the results showed that the expression patterns were different among different groups (Figure 3 and Table S4). The L3 and L4 groups showed a relatively high transcript level on cluster 4 and 5, which mainly included genes encoding for proteins responsible for biosynthesis of unsaturated fatty acids (DN526726_c0_g1, DN565207_c1_g1, and DN568337_c0_g1), peroxisome (DN563687_c0_g4, DN569779_c3_g1, DN547142_c0_g1, DN570126_c7_g1) and nitrogen metabolism (DN550397_c0_g1, DN565349_c1_g2, DN566965_c2_g2, DN556336_c1_g1) while the expression of these genes was inhibited in LNC and L1 groups. The result indicated that these genes may be related to ammonium assimilation under high concentrations of ammonium. However, some genes in cluster 2 and cluster 8 were expressed at a higher level in the LNC and L1 groups compared with the other groups. For example, the expression of genes responsible for translation (DN491679_c0_g1, DN516345_c0_g2, DN523402_c0_g2, and DN558204_c4_g4), glyoxylate and dicarboxylate metabolism (DN532260_c1_g2, DN532412_c0_g1, DN543963_c1_g2, and DN554457_c1_g6) and starch and sucrose metabolism (DN554130_c1_g2, DN556040_c0_g1, DN564501_c1_g1, and DN567789_c1_g2) was highly enhanced in LNC or the L1 group. In addition, catabolic-related genes including DN516345_c0_g2, DN523744_c0_g1, DN524943_c0_g1, and DN544492_c2_g1 were also highly regulated at low concentrations of ammonium. The rest of DEGs were clustered into cluster 1, cluster 3, cluster 6, cluster 7, and cluster 9.  
Figure 2: Differentially expressed genes (DEGs) among different groups in _Myriophyllum aquaticum_. **(A)** The total number of upregulated and down-regulated DEGs. **(B–D)** Venn diagram of all DEGs, down-regulated and up-regulated genes, respectively. LNC: group treated with 0 mM ammonium. L1: group treated with 0.1 mM ammonium. L2: group treated with 1 mM ammonium. L3: group treated with 12 mM ammonium. L4: group treated with 36 mM ammonium.
## 3 System Model
### User Random Movement Model  
The VLC indoor localization technique provides a means to determine the specific dynamics of users' random motions within real indoor VLC heterogeneous network environments. High positioning accuracy can be achieved by using LEDs as transmitters [16]. By recording the user's position and velocity at each moment, the localization technique enables the identification of whether the MT is situated in a CCI area [17]. To address the scenario of MTs moving randomly in VLC heterogeneous networking environments, we enhance the random waypoint (RWP) model to establish a specific model for simulating the random movement of a single MT in a 6 m \(\times\) 6 m indoor environment. The RWP model is a commonly used mobility model in network simulations [18]. It is used to describe the mobility of humans in a two-dimensional region. In the RWP model, a node randomly chooses to stay in place or move according to a set probability at every moment. It will randomly choose the direction, duration, and speed of movement. A schematic diagram illustrating the stochastic movement model for an MT entering an indoor environment is presented as Figure 4.  
Figure 3: **(a)** Receiving power distribution of 9 VLC APs in a room measuring 6 m by 6 m by 3 m; **(b)** The coverage area of the VLC APs.  
The MT engages in stochastic movement characterized by random displacements within a specified time interval T, governed by maximum velocities V\({}_{\max}\). For V\({}_{\max}\) equal to 1.4 m/s, the MT is classified as engaging in fast walking movements, while V\({}_{\max}\) equal to 0.6 m/s corresponds to slow walking movements [3]. The direction of movement spans a range from 0 to 2\(\pi\) radians. Following a duration of t for active motion, a pause of t\({}_{w}\) seconds is introduced before resuming the subsequent sequence of motion patterns.