esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal Transmission System
### Mach-Zehnder EO Sensor  
As shown in Figure 1, our Mach-Zehnder EO sensor was composed of a laser diode, a Mach-Zehnder EO modulator and a photodetector. The principle of the Mach-Zehnder EO sensor can be described as follows: as shown in Figure 3, supposing that the electric field of the incident input light \(E_{in}=A\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
\(E_{a}=E_{b}=(A/2)exp(j\mathrm{ot})\). Therefore, the electric field corresponding to the emergent light of the Mach-Zehnder EO modulator (\(E_{out}\)) can be expressed as [12]:  
\[\begin{split} E_{out}=&\frac{A}{2}\exp(j\omega)[\exp(j \varphi_{a})+\exp(j\varphi_{b})]\\ =&\,A\exp\{j[\omega+\frac{2\pi l}{\lambda_{0}}(n_{e} -n_{o})]\}\cdot\cos[\frac{\Gamma\pi L}{G\lambda_{0}}(n_{e}^{3}\gamma_{33}-n_{o }^{3}\gamma_{13})V_{e}+\varphi_{0}],\end{split} \tag{1}\]  
where \(\varphi_{a}\) and \(\varphi_{b}\) represent the phases of the arm \(a\) and \(b\), respectively, \(l\) is the length of the EO crystal, \(\lambda_{0}\) is the optical wavelength, \(n_{e}\) and \(n_{o}\) are the extraordinary refractive index and ordinary refractive index of EO crystal, respectively, \(\Gamma\) is the overlap integral factor representing the interaction between the electric field applied on the electrodes and the light wave field, \(L\) is the electrode length, \(G\) is the distance between the signal electrode and the ground electrode of the Mach-Zehnder EO modulator, \(\gamma_{33}\) and \(\gamma_{13}\) are the electro-optic coefficients of the EO crystal, \(V_{e}\) represents the voltage applied on the EO crystal which represents the signal voltage transmitting within the human body, and \(\varphi_{0}\) is the phase difference used for setting the operating point of the Mach-Zehnder EO modulator.  
Subsequently, the relationship between the input optical power (\(P_{in}\)) and the output optical power (\(P_{out}\)) of the Mach-Zehnder EO modulator can be expressed as:  
\[P_{out}=P_{in}\cos^{2}[\frac{\Gamma\pi L}{G\lambda_{0}}(n_{e}^{3}\gamma_{33}-n _{o}^{3}\gamma_{13})V_{e}+\varphi_{0}]. \tag{2}\]  
Finally, the output voltage of the photodetector (\(V_{out}\)), which represents the output voltage of the whole Mach-Zehnder EO sensor, can be expressed as [13]:  
\[V_{out}= 10^{-10\iota k}SR_{k}P_{out}= 10^{-10\iota k}SR_{k}P_{in}\cos^{2}[\frac{\Gamma\pi L}{G\lambda_{0}}(n_{e }^{3}\gamma_{33}-n_{o}^{3}\gamma_{13})V_{e}+\varphi_{0}]. \tag{3}\]  
where \(k\) is the insertion loss of the modulator, and \(S\) and \(R_{k}\) represent the conversion efficiency and the transimpedance of the photodetector, respectively. Therefore, the receiving signal voltage (\(V_{e}\)) transmitting within the human body can be achieved by measuring the output voltage of the photodetector (\(V_{out}\)).  
Figure 3: The principle of Mach-Zehnder EO sensor.
## 2 Preliminary Design of SOEs
### Comparison of Three Types of SOE  
Table 2 summarizes the optical performances of the three types of SOE based on the simulated results presented in the preceding sections. The focal distance of the POE is the same for the three-types of SOE. The optical performance of the CPV unit without any SOE is also listed in Table 2 for comparison purpose. According to Table 2, the following can be observed: (1) the CPV unit with the KOD-type SOE exhibits a highest acceptance angle of 1.2\({}^{\circ}\) among the three preliminary types of SOE, while the SP-type SOE has a relatively limited improvement on the acceptance angle (only 0.3\({}^{\circ}\)); (2) The CPV unit with the SP-type SOE yields a highest optical efficiency at normal incidence (88.7%), while the KOD- and KFTS-types of SOE lead to similar optical efficiency under normal incidence (84.6% and 85.3%, respectively); (3) The irradiance distribution on the solar cell is most uniform with the KFTS-type SOE, and the SP-type of SOE has insignificant improvement on the irradiance uniformity on the solar cell; (4) The SP-type SOE is believed to have the easiest manufacturing processes and the lowest cost among the three-types of SOEs.  
Figure 10: Irradiance distribution on the receiver under normal incidence for the Fresnel lens with SP-type SOE.  
Figure 9: Optical efficiency of the Fresnel lens with SP-type SOE.
# Numerical Investigation of the Relationship between
## Materials and Methods
### Determination of the Optimal Anastomosis Angle  
Steady-state CFD analysis was performed to evaluate the effects of the anastomosis angle on the hemodynamics in the distal region of the ridged-graft-to-artery anastomosis. The fluid material was blood. It was set to have a constant density of 1050 kg/m\({}^{3}\), and its shear-thinning properties were considered by describing the viscosity using the Carreau model (time constant = 3.313 s; power-law index = 0.3568; zero-shear viscosity = 0.056 kg m\({}^{-1}\) s\({}^{-1}\); infinite shear viscosity = 0.0035 kg m\({}^{-1}\) s\({}^{-1}\)). The flow was set to be laminar, and heat transfer effects were ignored. A constant, uniform velocity of 0.317 m/s and zero pressure were imposed on the inlet and outlet, respectively. Rigid wall and non-slip assumptions were used for the walls [24].  
ANSYS-Fluent 2022 R1 Student Version was used to solve the CFD problem. Convergence was ensured by reducing the continuity and velocity residuals to \(10^{-6}\), monitoring the overall mass balance, and waiting for the outlet velocity value to approach a constant value with further iterations.  
The best anastomosis angle was decided by the comparison of pressure drop values, secondary velocity magnitudes, and areas affected by recirculation and abnormal wall shear stress (WSS) values. An increase in the secondary velocity magnitude and a decrease in all other variables would indicate improved graft performance [24].
## 4 Challenges and Future Directions
Complementary approaches, such as Multiple Hypothesis Testing (MHT), can further refine anomaly detection in deformation time series. AI, encompassing ML, enables systems to autonomously analyze data and identify patterns. ML models excel in classifying images, detecting anomalies, and predicting events. For example, U-Net-based DL systems [27] have demonstrated high accuracy in sinkhole detection using high-resolution drone and satellite imagery, showcasing adaptability to varying resolutions and scalability for broader geohazard applications. Integrating these methodologies with remote sensing advances provides a robust framework for addressing geological and environmental challenges. Combining AI, 3D/4D ground-based techniques, and new satellite missions enhances our ability to monitor, predict, and mitigate hazards, improving public safety and infrastructure resilience.
## 2 Methodology
### Determination of SuperDove Attributes

Table 2: Spectral and spatial attributes of the SuperDove data selected to assess their variations with different levels of pasture degradation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Vis** & **Equation** & **Reference** \\ \hline NDVI & \((\text{pNIR}_{\text{bg}}-\text{pRed}_{\text{bg}})/(\text{pNIR}_{\text{bg}}+ \text{pRed}_{\text{bg}})\) & [PERSON] et al. ([PERSON] et al., 2019) \\ \hline EVI & \(2.5\times(\text{pNIR}_{\text{bg}}-\text{pRed}_{\text{bg}})/(\text{pNIR}_{\text{bg }}+6\times\text{pRed}_{\text{bg}}-7.5\times\text{pBlue}_{\text{bg}})+1\) [PERSON] et al. ([PERSON] et al., 2019) \\ \hline MPRI & \((\text{pGreen}_{\text{bg}}-\text{pGreen}_{\text{bg}})/(\text{pGreen}_{\text{bg }}+\text{pGreen}_{\text{bg}})\) & [PERSON] et al. (2019) \\ \hline REND & \((\text{pNIR}_{\text{bg}}-\text{pRe}_{\text{bg}})/(\text{pNIR}_{\text{bg}}+ \text{pRe}_{\text{bg}})\) & [PERSON] et al. (2019) \\ \hline GRND & \((\text{pGreen}_{\text{bg}}-\text{pRed}_{\text{bg}})/(\text{pGreen}_{\text{bg }}+\text{pRed}_{\text{bg}})\) & [PERSON] et al. (2019) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Suite of vegetation indices (Vis) selected for the data analysis. The original formulations were adjusted to the band positioning of the SuperDove instrument. Bands 2 (Blue band II centered at 490 nm), 3 (Green band I at 531 nm), 4 (Green band II at 565 nm), 6 (Red band I at 665 nm), 7 (Red-Edge band at 705 nm), and 8 (NIR band at 850 nm) were employed in the equations. The reflectance is denoted by ’p’ in the equations, and the abbreviations for the VIS are defined in the text. the SMA estimated the relative proportions of these four components on a per-pixel basis. The model assumes that the reflectance of a given pixel is a linear combination of these endmembers [45]. Finally, spatial attributes encompassed texture metric signals derived from the GLCM methodology [46]. In our analysis using the GLCM approach, the focus was on SuperDove's NIR band 8. We computed eight GLCM metrics, employing small window sizes of 3 \(\times\) 3 pixels and an offset of one pixel, as recommended by previous studies [47; 48]. These metrics include texture mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. The definitions and interpretations of these texture metrics are detailed by [PERSON] et al. [46] and [PERSON] et al. [48].
## 5 Numerical Results and Discussion
We observe that the BER graphs decrease exponentially with the increasing value of OSNR from 0 to 30 dB and cross the set FEC threshold limit of \(3\times 10^{-3}\) at 20 dB and 24.1 dB for both of these systems, respectively. The OAM-SMM based MIMO FSO system clearly demonstrates the superior performance over the OAM-OFDM-SMM based FSO system. This also validates the concept that both systems are influenced by atmospheric turbulence due to intensity fluctuations; however, this effect on the OAM-SMM based MIMO FSO system is less than that on the OAM-OFDM based MIMO FSO system. For the chosen threshold value of \(3.8\times 10^{-3}\), the OAM-SMM based system requires a 4.1 dB lesser amount of power compared to the OAM-OFDM based FSO system. At OSNR value of 25 dB, the performance of the OAM based system is approximately 100 times better than OFDM based SMM MIMO FSO system. Both the FSO systems can perform excellently below the set BER threshold for higher OSNR values beyond 23.5 dB. The performance of the proposed system is also compared for log BER vs. OSNR ranging from 2 to 35 dB for GG turbulence conditions and different numerical values of the Rytov constant as shown in Figure 8. It can be seen from the plots that as the numerical value of the Rytov constant \(\sigma^{2}\) decreases from 3.98 to 0.83, the atmospheric turbulence becomes weaker, resulting in an excellent proposed system log BER performance. For the fixed value of OSNR at 20 dB, the log BER are \(-3.28\), \(-2.91\), and \(-2.55\), corresponding to \(\sigma^{2}\) = 0.83, 1.81, and 3.98, respectively. Figure 6: BER vs. OAM states for different optical link distances. Figure 9 demonstrates the increase in channel capacity with respect to the function of different OSNRs (dB) for the three FSO systems, OFDM-MIMO, OAM-MIMO, and OAM-OFDM-SMM-MIMO, respectively, under turbulence conditions (\(2\times 10^{-12}\)) m\({}^{-2/3}\). It is observed from the graph that the OAM-OFDM-MIMO FSO system for OAM state \(l=+1\) and the 8 OFDM subcarrier based on SMM outperforms compares to the other two conventional MIMO-FSO systems. At 18 dB OSNR, the channel capacity offered by the proposed OAM-OFDM-SMM-MIMO system is nearly 3.3 bits/s/Hz better than a simple OFDM-MIMO system and 1.6 bits/s/Hz better than an OAM-OFDM-SMM based MIMO FSO transmission system. When the channel capacity was set to 14 bits/s/Hz, the proposed OAM-OFDM-MIMO FSO system based on SMM required nearly 3.2 dB less power than OFDM-MIMO and 1.1 dB less power than the OAM-MIMO FSO system. The result clearly validates that by combining OAM and OFDM along with the spatial mode diversity scheme, one can enhance the capacity of the FSO transmission system. We present a novel approach to increasing channel capacity while also increasing diversity gain. Figure 8: BER vs. OSNR for different Rytov constants under GG turbulence. Figure 7: BER vs. OSNR for \(l=+1\) OAM mode and OFDM SMM FSO system.
# Influence of Multiple Factors on the Wettability and Surface Free Energy of Leaf Surface
## 3 Results and Discussion
### The Comparison of Surface Free Energy on Rice and Rape Leaf
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Crop**} & \multirow{2}{*}{**Leaf Surface**} & \multirow{2}{*}{**Deionized Water (\({}^{\circ}\))**} & \multirow{2}{*}{**Formamide (\({}^{\circ}\))**} & \multirow{2}{*}{**Ethylene Glycol (\({}^{\circ}\))**} \\ \hline rice & adaxial & 134.01 \(\pm\) 3.22 & 126.53 \(\pm\) 2.26 & 122.98 \(\pm\) 2.79 \\  & abaxial & 128.88 \(\pm\) 2.08 & 126.30 \(\pm\) 1.39 & 123.67 \(\pm\) 3.15 \\ sixth leaf of rape & adaxial & 121.97 \(\pm\) 3.59 & 110.74 \(\pm\) 3.73 & 103.90 \(\pm\) 4.59 \\ (new leaf) & abaxial & 115.57 \(\pm\) 3.66 & 105.04 \(\pm\) 2.28 & 96.78 \(\pm\) 3.79 \\ first leaf of rape & adaxial & 113.55 \(\pm\) 2.79 & 104.73 \(\pm\) 1.29 & 86.95 \(\pm\) 2.19 \\ (old leaf) & abaxial & 122.30 \(\pm\) 3.71 & 117.21 \(\pm\) 1.30 & 104.69 \(\pm\) 3.74 \\ \hline \hline \end{tabular} Note: values are means \(\pm\) SD.  
\end{table} 
Table 3: Contact angles of probe liquids on rice and rape leaves. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Type III Sum**} & \multirow{2}{*}{**DF**} & \multirow{2}{*}{**MS**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**P-Value**} \\  & **of Squares** & & & & & \\ \hline crop type & 4893.393 & 1 & 4893.393 & 390.432 & \textless{}0.001 \\ leaf surface 1  & 238.052 & 1 & 238.052 & 18.994 & \textless{}0.001 \\ probe liquid & 1784.951 & 2 & 892.476 & 71.209 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of crop type, leaf surface, and probe liquid on contact angles. surface of rice leaves is slightly higher than that of the abaxial surface, and the dispersive component accounts for the larger proportion in the adaxial surface, which suggests that the wettability difference of the adaxial and abaxial surfaces of rice leaves is smaller comparing to rape leaves, which could also be confirmed in Figure 4.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
###### Abstract  
The potential short-circuit current in active distribution network features time-variance with the increasing distributed generations. This feature makes the online estimation of fault level necessary. In this paper, a novel online estimation method is proposed to be implemented by either phasor measurement unit (PMU) or the measurements from protection relays. The equivalent circuit of the radial distribution network with distributed generators (DGs), e.g., wind turbines and photovoltaic cells, is derived with necessary simplifications. The natural disturbances downstream are used to evaluate the parameters of the equivalent circuit so that the potential fault level can be estimated in advance of the actual fault occurrence. A fuzzy logic identifier is presented to rank the confidence of the measurements incurred by the disturbance and to distinguish the qualified disturbance to launch the estimation. The mechanism based on multi-measurements and confidence indices was applied, to improve the accuracy. A typical distribution network in the United Kingdom (UK) with DGs was taken, as an example, to validate the proposed method under various load fluctuation. The results confirm the effectiveness of the proposed method, which is suitable for online estimation of short-circuit fault level in active distribution networks.  
short-circuit current; fault level estimation; active distribution network; disturbance; fuzzy logic identifier; DGs +
Footnote †: journal: Applied  
Article Online Estimation of Short-Circuit Fault Level in Active Distribution Network
###### Abstract  
We investigated the improvement of cabbage growth through the interaction of biochar, which can promote microbial activity, with the microbes in the soil. An increase in cabbage growth could be detected in soil with biochar or soil microbes, but the fresh weight of cabbage in soil supplemented with both biochar and microbes was 8.8% and 5% higher, respectively, than that with either microbes or biochar alone. The phosphorus content in Chinese cabbage was also increased by 36.3% when compared with the control without the addition of biochar and microbes. Such an improvement on cabbage growth is closely related to the soil amelioration. The application of biochar in soil significantly stimulated the growth of soil microbes and further altered the microbial community structure in soil. When 2% biochar and microbes were simultaneously applied in soil, the content of the organic matter and available phosphorus content in soil was 36.7% and 45.5% higher, respectively, than that in soil with or without biochar. The maximal increment in the available potassium content was observed in the soil supplemented with both 5% biochar and soil microbes, which was 46.4% higher than that in soil without the addition of biochar and microbes. Both phosphatase and urease activity in soil were also increased by 61.2% and 49.4%, respectively, by applying 2% biochar in soil together with microbes, as the interaction of biochar with the microbes could promote the activity of soil microbes and enzymes which resulted in an improvement in soil fertility. The interaction of biochar with microbes in soil could promote the plant growth of Chinese cabbage by increasing the soil fertility.  
biochar; cabbage growth; improvement; interaction; soil microbes +
Footnote †: journal: Applied sciences
## 4 Performance of KF Used for Parameter Estimation in RTHS  
As described in the last section, only the parameter estimation method is convergent, and the time delay can be well compensated by the proposed KF-ADC. Moreover, it can be seen from Equations (8)-(10) that the performance of the KF is affected by the measurement noise covariance \(R\) and the initial values of \(\mathbf{x}_{0}\) and \(\mathbf{P}_{0}\). Hence, the performance of the KF in RTHS is investigated in this section.
## 6 Algorithms and Frameworks in Federated Learning
### Federated Averaging (FedAvg)
#### 6.1.1 Potential Benefits and Use Cases of FedAvg  
FedAvg combines theoretical elegance with practical use by allowing decentralized devices to learn locally and contribute to a global model. This method reduces the need for large data transfers to a central server, saving the bandwidth and speeding up model training. It keeps raw data on devices, enhancing users' privacy compared to centralized models. FedAvg is ideal for scenarios with many IoT devices, smartphones, and medical devices, where it supports local learning while maintaining data privacy.
# A Simple and Compact Laser Scattering Setup for Characterization of a Pulsed Low-Current Discharge
## 4 Results  
To test the proposed setup, the relevant plasma parameters of a pulsed low-current plasma jet were determined. Before the study was started, the radial position of the plasma generator was adjusted so that the incident laser beam passes exactly through the generator axis. Thereafter, the generator was positioned so that the incident laser beam passed under the nozzle outlet at a downstream axial distance \(d\) of 1 mm, as marked in Figure 5. A total of 8 measurements at that axial distance were made and subsequently evaluated.  
The average radial distribution of the estimated parameters is presented in Figure 7. It became apparent that for the chosen parameter set, electron parameters can be reliably estimated for a radial distance \(r\) perpendicular to the jet axis of 0.7 mm. As depicted in Figure 7, the estimated electron number density varies by about 0.7 \(\times\) 10\({}^{21}\) m\({}^{-3}\) between the jet axis and the outer fringes at radii above 0.6 mm, an increase of slightly more than 30%. It should be noted that the maximum value of 2.3 \(\times\) 10\({}^{21}\) m\({}^{-3}\) is reached about 0.1 mm outside the axis. Looking at the error bars, a deviation of 25% from the average \(n_{e}\) value is to be expected throughout the analyzed diameter. In contrast, no significant increase in the electron temperature with varying radius is observed. A mean \(T_{e}\) value of 41,000 K is estimated with a similar deviation of about 25%. The temperature of heavy particles in turn shows a significant, Gaussian-like radial distribution with a maximum value of 5800 K lying at the jet axis and a negative gradient of \(\sim\)3000 K/mm toward larger distances from the axis. Additionally, an approximately 0.2 mm wide temperature plateau can be observed centered around the axis, as marked in Figure 7. Analogous to other plasma parameters,  
Figure 5: The effluent plasma jet of a PG31 generator equipped with an A450 nozzle, showing the axial distance \(d=1\) mm at which the measurements were performed.  
Figure 6: Typical voltage and current traces recorded for a frequency of 60 kHz and a power setting of 100%. The trigger timeframe at which the experimental setup was triggered is marked in the current plot.  
the standard deviation of \(T_{h}\) also ranges about 25%, although a decrease with increasing radius can be observed. The increased standard deviation of \(T_{h}\) about the jet axis, as well as the asymmetric behavior (different error values for positive and negative \(r\)-values), appear to be caused by a slight lateral movement of the discharge channel from one measurement to the other and are amplified by the averaging. The latter effect was also observed to some extent in the raw data for the electron parameters, in particular, \(n_{e}\), but is much less pronounced than for \(T_{h}\).
# Birth Allocation and Query Crane Assignment Considering the Uncertain Maintenance Requirements
## 3 Problem Description  
The occurrence of emergencies involving quay cranes can significantly impact the execution of terminal operation plans, particularly within container terminals that are actively functioning. In cases where the disruption is brief, minor adjustments to the operational plan or optimizations of the loading and unloading activities for the affected vessel may suffice. However, if the incident duration exceeds a critical threshold, more substantial changes to the operational schedule may be required.  
To further demonstrate the impact of quay crane (QC) maintenance activities, we consider an example involving four vessels. As shown in Figure 1, QC3 is under maintenance, rendering both the crane and its associated berth segment unavailable for use. During this maintenance period, Vessels 1, 2, and 3 are scheduled for processing, but their service times overlap with the duration of QC3's maintenance. As a result, Vessel 1, located to the left of QC3's berth segment, can only be serviced by QCs 1 and 2 due to the non-crossover constraint, which prohibits QCs from crossing over the maintenance zone. On the other hand, Vessels 2 and 3, positioned to the right of QC3's berth, must rely on QCs 4, 5, and 6 for their operations.  
Figure 2 presents a vessel berth allocation schedule that incorporates quay crane (QC) maintenance over time. The vertical axis represents time in hours, while the horizontal axis indicates berth positions in meters. The plot is divided into two distinct zones, labeled V2 and V3, each representing different berthing areas for vessels. Notably, the blue-shaded area between hours 3 and 5 highlights the maintenance period for QC3, during which both the crane and its associated berth are unavailable. Furthermore, circles within each zone represent the quay cranes assigned to vessels at specific times and berth locations. For example, in zone V2, QC1 is assigned to service a vessel at berth position 100 m during hour 1. Thus, this diagram effectively illustrates the scheduling of quay cranes for vessel operations while accounting for the constraints posed by maintenance activities.