esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectra Prediction for WLEDs with High TLCI
## 4 Conclusions  
The spectra of most WLEDs are synthesized using chip and phosphor emission spectra, and the quality of color rendering in films and television is poor. Dual phosphors are commonly applied in WLEDs to enhance the color rendering performance, making it difficult to accurately predict the emission spectrum. In this study, a more accurate phosphor emission spectrum was obtained by subtracting the blue light spectrum from the mixed spectrum. The spectral shift under different driving currents was studied; the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**CCT** & **wt\%g** & **wt\%r** & **Exp.** & **Exp.** & **duv** & **Sim.** & **Sim.** & **Exp.** & **Sim.** & **TLCI** \\
**(K)** & & & **CIE x** & **CIE y** & & **CIE x** & **CIE y** & **TLCI** & **TLCI** & **Error** \\ \hline
3114 & 65.36\% & 18.30\% & 0.4428 & 0.4314 & 0.0097 & 0.4335 & 0.4208 & 96.76 & 95.72 & 1.07\% \\ \hline
4178 & 68.97\% & 13.79\% & 0.3758 & 0.3844 & 0.0049 & 0.3728 & 0.3792 & 96.91 & 96.69 & 0.23\% \\ \hline
5000 & 70.18\% & 12.28\% & 0.3464 & 0.3665 & 0.0068 & 0.3391 & 0.3563 & 96.30 & 95.28 & 1.06\% \\ \hline
6990 & 74.19 & 9.68\% & 0.3058 & 0.3199 & 0.0021 & 0.3009 & 0.3094 & 95.49 & 93.65 & 1.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental and simulated results of WLEDs.  
Figure 11: Chromatic coordinates of WLEDs in measurement and simulation experiments, where dotted lines represent isotherms of corresponding color temperatures.  
functional relationship between the driving current and the spectral shift was established. Then, a spectral prediction model for the WLEDs was established, and we considered reabsorption and re-emission in order to quickly model the target spectrum. In order to verify the model, dual-phosphor WLEDs with multiple CCTs were fabricated, and the experimental results were compared with the prediction results. The results show that the prediction results match the experimental results perfectly, and WLEDs with a TLCI greater than 90 at 3114 K, 4178 K, 5000 K, 5860 K, and 6990 K were successfully obtained. The maximum error of the predicted TLCI was only 1.93%. The proposed model was established and can be easily applied.  
Conceptualization, D.X.; data curation, H.W. and Y.S.; formal analysis, Y.S.; funding acquisition, D.X.; investigation, H.W.; methodology, H.W.; project administration, D.X.; resources, D.X.; software, H.W.; supervision, Z.J.; validation, P.W. and Z.J.; visualization, H.W.; writing--original draft, H.W. and Y.S.; writing--review and editing, P.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing is not applicable. The code is available from the corresponding author upon reasonable request.  
[PERSON] thanks [PERSON] for correcting this manuscript.  
The authors declare no conflict of interest.
## 4 Discussion
### 3D-CMCC-PSM Predictability in Estimating NEE
Despite daily Reco being noisy, 3D-CMCC-PSM could reproduce respiration processes well enough, at least at monthly timescale. NPP:GPP ranged between 0.37 and 0.62, and was consistent with literature [71; 72; 73]. It was generally higher in DBF (0.49 in DEHai, 0.55 in ITCol), lower in EBF (0.38 in FRPue, 0.41 in ITCpz). These results matched with those of [71] who showed that the ratio between NPP and GPP (CUE) isgenerally 0.53, ranging from 0.23 to 0.83. CUE was relatively low in FIHyy, where Reco predictions were overestimated: therefore, poor predictability was probably ascribable to excesses in RA. Ecosystem respiration was overestimated during summer, causing NEE systematic overestimation at each simulation, especially in ITCol. Except for FRPue and ITCol, RA grew about 1 gC m\({}^{-2}\) day\({}^{-1}\) higher than RH, suggesting that it may be the principal driver of biased summer Reco (Figure 9). This misbehavior may be related to the method used to estimate maintenance respiration, an exponential relationship between respiration, moisture, and temperature [61]. Lack of data to validate the SOC dynamics reduced the spectrum of speculations, which could be statistically analyzed. SOC didn't change its quantity in ten years; this result was consistent with the theoretical stability of the SOC, an indicator which rarely changes within 10 years if no strong disturbance events (e.g., land use changes) have occurred [74]. Litter C was highly fluctuating within a year, but its quantity was stable if compared at the end of each year. This suggested that the model realistically represented litter turnover and decomposition, since residues were degraded into humus labile substances about within a year [75]. Microbial biomass was highly variable, as expected. However, the magnitude of change was too broad throughout the simulations. These results may be related to the use of 5% as the initial active microbial biomass for each site, a value that may be far from the equilibrium for different soils. Moreover, tradeoffs within microbial growth and between the environmental conditions may be scarcely represented. As a matter of fact, 3D-CMCC-PSM simulated the soil as having the same physical-chemical structure throughout the profile. This implied that microbes could find the same amount of C, O\({}_{2}\), and living space, with no depth limitation. Figure 8: Patterns in daily NEE (gC m\({}^{-2}\) day\({}^{-1}\)) per DoY (Day of Year) calculated from test-sets on a site level. Observed EC average patterns (black dotted line) and standard deviation (gray area). Simulated average patterns (red dotted line) and standard deviation (orange area). First column represents DBF sites, the second ENF, the third EBF.
## 1 Introduction
However, there is a lack of systematic studies to produce high optical quality a-SiC thin films focusing on evaluating and determining their optical properties in the telecom band. Furthermore, while the a-SiCOI platform potentially offers a more straightforward fabrication process, its material optical losses, due to the inhomogeneity and disorder of the a-SiC materials (e.g., non-stoichiometric material, presence of homonuclear Si-Si, C-C bonds) and/or due to the presence of hydrogen-related bonds (e.g., Si-H or C-H), remain a substantial challenge. Furthermore, many studies on SiC films for photonics lack quantitative optical absorption data in the telecom range [24], despite its significant impact on propagation losses as seen in other materials such as silicon nitride (SiN) [32; 33], and consequently on the suitability of these films for low-loss photonic device integration. Overcoming these challenges requires control of film quality to mitigate material-related optical losses that affect the performance of photonic devices (e.g., waveguides, Kerr nonlinear devices). To this end, we present a materials-engineering-driven strategy for fabricating SiC thin films with precise thickness control, minimal surface roughness, and telecom-range optical absorption. This approach centers on the development of CVD processes for producing both amorphous (a-SiC) and crystalline (c-SiC) films, deposited using a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C\({}_{3}\)H\({}_{12}\)Si\({}_{3}\)) on a multitude of different substrates, including Si (e.g., Si (100) and Si (111)), and silicon dioxide-on-Si. This study has enabled the fabrication of fab-compatible, smooth, stoichiometric, and homogeneous SiCOI thin films (with a surface roughness of -5 A, hydrogen-free, and lacking homonuclear (e.g., Si-Si and C-C) bonds). Furthermore, optical measurements revealed a high refractive index of \(\sim\)2.7 and a low absorption coefficient (e.g., \(<\)200 cm\({}^{-1}\)) at around 1550 nm, indicating the high optical quality of the resulting SiC thin films. Our CVD-deposited amorphous thin films can be fabricated with a growth rate of \(\sim\)0.5 A/s, allowing for a thickness control on the nm scale. Therefore, directly depositing a-SiC on an insulator, using our CVD-compatible chip-scale device fabrication process, can potentially eliminate the need for expensive and time-consuming bonding transfer methods, including material waste during the thinning process, for the fabrication of next-generation chip-scale SiC PIC components [24]. In this context, our fabrication can also be applied to integrate SiC waveguides on an erbium-implanted lithium niobate (LN) platform, enabling hybrid integrated photonics [30] for applications such as modulation and enhancement of Er\({}^{3+}\) telecom emission in thin-film LN [34].
## 3 Analysis of Lightning Strike Effects on Patch-Repaired Composite
### Optimizing Aluminum Coating Applications for Repaired Laminate
#### 3.2.3 Impact of Aluminum Coating Shape on the Lightning Protection Ability  
A cross-shaped aluminum coating, 10 mm in width, was applied at the geometric center of the structure's surface. Figure 12 presents the assembly of the cross-shaped aluminum coating and the repaired structure. Upon exposure to a 100 kA peak current, the ablation damage, as depicted in Figure 13, was confined to the first ply, with an area of 1.36 mm\({}^{2}\). This damage area represents reductions of 26.49%, 18.5%, and 74.72% compared to the groups with 30 mm, 20 mm, and 10 mm aluminum coatings, respectively. Furthermore, it is a significant 79.24% reduction compared to the uncoated controls.  
The damage area exhibits an equilateral diamond shape, attributed to the additional aluminum coating along the y-axis direction, which provides an alternative conduction pathway for lightning currents. Furthermore, the interconnection between coatings facilitates a more uniform dispersion of lightning currents, concentrating the damage within a relatively smaller area and effectively reducing the affected area. This design aligns with the comprehensive requirements of the modern aviation industry for lightweight and high-performance materials. While the locus of lightning strikes is random, the cross-shaped coating was specifically chosen for its ability to optimize conductivity in critical regions such as leading-edge tips, where lightning damage tends to be more significant. Moreover, the design optimizes conductivity without introducing unnecessary weight, which is crucial for maintaining the aircraft's balance. This strategy ensures that the repair structure can withstand lightning damage while minimizing the added weight, an important consideration for aircraft performance and safety. For instance, Article 25.581 of the Airworthiness Standards for Transport Category Airplanes (CCAR-25-R4 ), issued  
Figure 11: Lightning ablation damage on patch-repaired laminates under 100 kA and 200 kA. (**a**) First layer at 100 kA; (**b**) second layer at 100 kA; (**c**) first layer at 200 kA; (**d**) second layer at 200 kA.  
by CAAC, demonstrates that localized protection for specific areas is a reasonable and efficient approach.
# Characteristics of Mould Growth in Pine and Spruce Sapwood and Heartwood under Fluctuating Humidity
## 1 Introduction
Under constant conditions (approximately 90% relative humidity and 22 \({}^{\circ}\)C, on pine sapwood), initial mould growth is observed around day 7, while macroscopic mould growth is visible between days 10 and 22, which depends on surface preparation [8; 33]. The length of time required for germination is influenced by environmental conditions and the duration of unfavorable conditions. In cases where the temperature and RH fluctuate, prolonging the duration of unfavorable conditions has been associated with an increase in the time required for germination to occur. A weekly RH cycle (90/60% RH, 22 \({}^{\circ}\)C) is only suitable for the germination and formation of a limited number of hyphae. A 12 h cycle of fluctuating relative humidity (90/60% RH, 22 \({}^{\circ}\)C) ultimately results in a change in germination time over a six-week period. Following germination, however, the fungal growth curve remained identical to that observed under constant conditions. A weekly temperature cycle (90% RH, 5/22 \({}^{\circ}\)C) resulted in a prolongation of the germination period (approximately 49 days) and a slowing down of fungal growth [31]. This article examines the impact of fluctuating conditions on the growth of mould on wood. The conditions under which mould growth is most likely to occur were selected for examination rather than those that are commonly found in buildings or structures. In alignment with the findings of the literature review, this study assesses mould growth from a multifaceted perspective, extending beyond the conventional parameters of temperature and humidity fluctuations. It considers the influence of wood species and wood type (pine and spruce, heartwood and sapwood) as well as the direction of the cut used to obtain samples (tangential, radial, and transverse). The samples with different wood fiber directions were chosen to see if, even under fluctuation conditions, mould growth would be different for each direction.
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
## 2 Digital/Geometrical Hybrid MIMO Beamforming Method  
### Proposed Smart MIMO System Architecture  
Figures 1 and 2 illustrate the MIMO system architecture, which has multiple antennas at both the transmitter and the receiver. Phase shifters can perform beamsteering and beamforming very quickly with a much higher directional gain. In this MIMO system, the transmitter and the receiver are bidirectional and interchangeable.
## 5 Conclusions  
This paper proposed an iterative sensor positioning (ISP) algorithm that uses the neighboring-positioned node to assist the mobile anchor in locating the unknown nodes and a mobile anchor route scheduling method, named MARS, using the ISP algorithm. MARS uses the projection mechanism to reduce the traveling distance of the mobile anchor. The simulation results show that the mobile anchor in MARS is able to reduce the traveling distance by 21-49% compared to when using the SCAN method. When the communication range of the mobile anchor is 150 m, which corresponds to the unfavorable scenario for the projection mechanism, the traveling distance using the MARS method is still better than that using the SCAN method. The traveling distance is 34% less than that when using the SCAN method. In the dense deployment scenario that is favorable for the ISP algorithm, applying the ISP algorithm to the mobile anchor using the MARS method made it possible to locate all unknown nodes in only 68% to 84% of the traveling distance required when using the SCAN method. If the MARS method is applied directly to the mobile anchor, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Error** & **SCAN** & **SLMAT** & **NHexCurve** & **SCAN-ET** & **i-SCAN** & **MARS** \\
**Ratio** & [26] & [30] & [34] & [31] & & \\ \hline
0–3\% & 17 & 14.72 & 15.35 & 14.45 & 5.14 & 4.31 \\
0–4\% & 19 & 16.64 & 16.68 & 16.59 & 6.26 & 5.32 \\
0–5\% & 20 & 19.42 & 19.38 & 19.4 & 7.9 & 7.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The size of the positioning area for different non-ideal signal error ratios (m\({}^{2}\)).  
traveling distance is 45% to 70%. In the sparse deployment scenario, which is unfavorable for the ISP algorithm, the mobile anchor using the MARS method was still able to achieve a 15% to 21% reduction in traveling distance compared to when using the SCAN method. In the non-ideal signal scenario, CAPPS was applied in MARS to improve positioning accuracy. The size of the positioning area was about 53% to 74% smaller than when using the Triangulation algorithm.  
The proposed MARS method uses the trajectory of the SCAN method to improve the scheduled route. In future work, we will extend the MARS to random waypoints. Without the limitation of the straight-line movement strategy, MARS should be able to provide a shorter route.  
Conceptualization, S.-C.H. and Y.-K.W.; methodology, S.-C.H.; software, Y.-K.W.; validation, S.-C.H. and Y.-K.W.; formal analysis, S.-C.H.; investigation, S.-C.H. and Y.-K.W.; resources, S.-C.H.; data curation, S.-C.H. and [PERSON]-[PERSON]; writing--original draft preparation, S.-C.H.; writing--review and editing, S.-C.H.; visualization, S.-C.H. and [PERSON]-[PERSON]; supervision, S.-C.H.; project administration, S.-C.H.; funding acquisition, S.-C.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Science and Technology Council project of Taiwan, Grant Nos. MOST 111-2221-E-150-022.  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 2 Materials and Methods
### Network Compression  
Depthwise separable convolution is an effective solution for model speeding up, which has been explored for efficient network design, e.g., Xception [23] and MobileNet [24; 25]. The depthwise separable strategy changes the standard convolution with two layers, which could reduce the number of parameters and network complexity without hurting the learning capacity. The structure of depthwise separable convolution is shown in Figure 3. Depthwise separable convolution comprises of two layers: depthwise convolutions and pointwise convolutions. The kxk depthwise convolution is used to apply a single filter per each input channel (input depth is M). Pointwise convolution, a simple \(1\times 1\) convolution, is then used to create a linear combination of the output of the depthwise layer.  
In this paper, \(3\times 3\) depthwise separable convolution is used, and the computational cost is \(8\times\) less than the standard convolutions. To ensure the detection accuracy of the original network while using depthwise separable convolution, the number of channels are expanded by \(1\times 1\) convolution in each stage, so that the depthwise separable convolution can capture useful features in a higher spatial dimension. In addition, inspired by ESPNet [26; 27], the original image at the first two stages is used for strengthening information flow. The detailed structure and parameter are displayed in in Figure 4. Conv means the standard convolution, Dwconv represents the depthwise separable convolution  
Figure 3: (**a**) The standard convolution. (**b**) Depthwise separable convolution comprises of two layers: depthwise convolutions and pointwise convolutions.  
Figure 2: Illustration of our multi-scale feature fusion network for conveyor deviation detection. The resized original images are imposed at the first two stages for strengthening information flow. The side outputs of the multi-scale feature fusion network are upsampled to the original image and then fused to get the final prediction.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 5 Results
### Particle Swarm Optimization  
Particle swarm optimization is a stochastic optimization method that is inspired from the social behavior of fish schooling or birds flocking. Just like EA, PSO is also initialized with a population (swarm) of individuals (particles). However, unlike EA, PSO does not use evolutionary operations to search for optima; instead, each particle is guided through the search space in every iteration by updating their positions and velocities using better positions found by other particles. In our CubeSat design problem, the fitness is defined by Equation (21) as \(\mathcal{F}\big{(}\mathcal{G}^{(i)}\big{)}\), where \(\mathcal{G}^{(i)}\) is the position of each particle, and the velocity of each particle is denoted by \(\mathcal{V}^{(i)}\). The algorithm is initialized by  
Figure 14: Variation of number of batteries and solar panels over generations where the color denotes the number.  
Figure 13: Variation of the design variables in the subsystem specifier over generations where the color denotes the component selected from the inventory.  
generating \(N\) particles with their positions and velocities initialized randomly. Next, at every iteration, \(t\), the velocity and position of each particle are updated by Equations (22) and (23), respectively.  
\[\mathcal{V}_{t+1}^{(i)}=\omega\mathcal{V}_{t+1}^{(i)}+\beta r_{1}\Big{(}p_{t}^{( i)}-\mathcal{G}_{t}^{(i)}\Big{)}+\gamma r_{2}\Big{(}p_{t}^{(s)}-\mathcal{G}_{t}^{(i) }\Big{)} \tag{22}\]  
\[\mathcal{G}_{t+1}^{(i)}=\mathcal{G}_{t}^{(i)}+\mathcal{V}_{t+1}^{(i)} \tag{23}\]  
where \(\omega\), \(\beta\), and \(\gamma\) are the tuning parameters, \(r_{1}\) and \(r_{2}\) are random numbers generated between 0 and 1, \(p_{t}^{(i)}\) is the best remembered individual particle position, and \(p_{t}^{(s)}\) is the best remembered swarm position. This process of updating the position and velocity of each particle continues until the desired fitness is achieved or maximum number of iterations reached.
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 3 Numerical Simulations
### Analysis of the Impact of Pilot Density  
Figure 7 presents the MSE performance of the LS, PO, and MMSE channel estimation algorithms under different pilot densities (1/3, 1/6, 1/9, and 1/12) with 16 QAM modulation. Overall, the trends of the three algorithms are consistent across different pilot densities. Under 1/3 pilot density, the MSE of the PO algorithm is slightly higher than that of the MMSE under low SNR conditions but significantly lower than that of the LS algorithm. Under high SNR conditions, the MSE of the PO algorithm approaches that of the MMSE, with both significantly outperforming the LS algorithm. This indicates that under 1/3 pilot density, the PO algorithm can maintain high channel estimation accuracy while keeping computational complexity low, making it suitable for practical applications.  
In summary, the PO algorithm demonstrates high channel estimation accuracy under different pilot densities, especially under high SNR conditions, where its MSE approaches that of the MMSE and significantly outperforms the LS algorithm. The data in Figure 7 further validate the superiority of the PO algorithm under different pilot densities and SNR conditions.  
Figure 8 presents the BER performance of the LS, PO, and MMSE channel estimation algorithms under different pilot densities (1/3, 1/6, 1/9, and 1/12) with 16 QAM modulation. Overall, the BER trends in Figure 8 show a higher similarity and consistency compared to Figure 7.  
Figure 7: MSE performance comparison of LS, PO, and MMSE under different pilot densities. X-axis: SNR [dB]. The curves are the results of simulations.  
For instance, under \(1/3\) pilot density, the BER of all three algorithms significantly decreases as SNR increases. Under low SNR conditions, the BER of the PO algorithm is significantly lower than that of the LS algorithm but slightly higher than that of the MMSE algorithm. Under high SNR conditions, the BER of the PO algorithm approaches that of the MMSE algorithm, with both outperforming the LS algorithm. This indicates that under \(1/3\) pilot density, the PO algorithm can maintain high channel estimation accuracy while keeping computational complexity low.  
In summary, the PO algorithm demonstrates high channel estimation accuracy under different pilot densities, especially under high SNR conditions, where its BER approaches that of the MMSE algorithm and it significantly outperforms the LS algorithm. This indicates that the PO algorithm achieves a good balance between performance and complexity, making it suitable for various practical application scenarios. The data in Figure 8 further validate the superiority of the PO algorithm under different pilot densities and SNR conditions.