esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 4 Strategies to Overcome These Barriers
### Suppression of the Gene Expression of CCDs in Microalgae  
Evidence from transgenic studies indicates that the expression of _CCDs_ is critical to the regulation of carotenoid accumulation in plants. The high transcriptional level of _CCDs_ results in white petals and low carotenoid content in liliies and chrysanthemums [96; 97]. Similarly, a negative correlation between the expression of _CCDs_ and carotenoid accumulation has been found in arabidopsis, peach fruit, and potatoes [98; 99; 100]. Furthermore, suppressing the expression of _CCDs_ by RNAi interference results in a three to six times higher carotenoid content in the petal of the mutants than in the wild-type of chrysanthemum [97]. However, the color of the petals remained yellow in transgenesis lines, while the wild type lost its color. Conflicting results were found in morning glory and citrus [102; 103]. The results indicated that the transcriptional level of _CCDs_, or the overexpression of _CCDs_, affected carotenoid accumulation.  
In microalgae, the function and the localization of CCDs enzymes and the effect of _CCDs_ expression on carotenoids accumulation are still poorly understood. Proteomics data of _C. reinhardtii_ revealed that a protein located in the eyespot was homologous to the CCDs of _Syntechocystis_ spp. [80]. Several CCDs homologous proteins in other algal strains have also been found in the NCBI database, for example, _D. salina_ (APW83743.1); _Scenedesmus_ spp. (KAF8065651.1); C. _sorokiniana_ (PRW57550.1). Therefore, investigating the correlation between _CCDs_ expression and carotenoid accumulation could elucidate carotenoid accumulation in microalgae. Findings about the effects of _CCDs_ expression on the carotenoid accumulation in plants suggest that suppressing the expression of this gene in green algae by genetic modification may increase carotenoid content in green algae.  
Investigation of the Mechanisms of \(\beta\)C-Plastoglobuli Biogenesis in Dunaliella spp. and the Carotenoid Trafficking Mechanisms in Haematococcus spp. and C. zofingiensis  
Investigating the mechanisms of biogenesis in \(\beta\)C-plastglobules in _Dunaliella_ spp. and of the trafficking of \(\beta\)-carotene through the chloroplast membrane in _Haematococcus_spp. and _C. zofingiensis_ are systematic and complex studies. Exploring the two mechanisms requires additional proteomic research, further understanding of the regulation of carotenoid accumulation in these microalgal strains, and the advanced genetic toolbox in microalgal species.
## 3 Optical Controlled Phased Sub-Array in Multi-Hop Bent-Pipe Payload  
An optically controlled phased array solution for multi-wavelength optical networks is proposed, shown in Figure 4, for multi-hop Bbnt-pipe low-orbit satellite constellations. In order to achieve beam-hopping and precise pointing, the millimeter wave phased array is directly controlled based on the multi-wavelength inter-satellite link optical signal, which simplifies the wave-controlled payloads and incorporates RF payloads into the entire optical switching satellite network.
## 3 Method
### Complexity Analysis  
In the actor network, the time complexity of the static embedding layer is \(O(|T|)\), and that of the static encoder is \(O(|T|^{2})\). At the decoding step \(n\), the time complexity of the dynamic embedding layer and that of the dynamic encoder are both \(O(1)\), and that of the decoder is \(O(|LS_{n}|)\). The time complexity of the \(N\)-step decoding process is formulated as follows:  
\[TC_{dp}=N\cdot TC_{del}+N\cdot TC_{de}+\sum_{n=1}^{N}|LS_{n}|\cdot TC_{d}=O( \sum_{n=1}^{N}|LS_{n}|), \tag{39}\]  
where \(TC_{del}\) is the computation time of the dynamical embedding layer at a step, \(TC_{de}\) is the computation time of the dynamical encoder at a step, and \(TC_{d}\) is the computation time of the decoder at a step. \(TC_{del}\), \(TC_{de}\), and \(TC_{d}\) are constants. Since \(N\leq|T|\) and \(|LS_{n}|\leq|T|\), the time complexity of the actor network is \(O(|T|^{2})\), which is formulated in Equation (40). The time complexity of generating a solution through the well-trained actor network is also \(O(|T|^{2})\).  
\[TC_{actor}=O(|T|)+O(|T|^{2})+O(\sum_{n=1}^{N}|LS_{n}|)=O(|T|^{2}). \tag{40}\]  
In the critic network, the time complexity of its embedding layer is \(O(|T|)\), that of its encoder is \(O(|T|^{2})\), and that of its decoder is \(O(|T|)\). Thus, the time complexity of the critic network is \(O(|T|^{2})\), which is formulated in Equation (41).  
\[TC_{critic}=O(|T|)+O(|T|^{2})+O(|T|)=O(|T|^{2}). \tag{41}\]  
In the training process, the time complexity of training once for a batch of samples is formulated in Equation (42). And solving an SAOSSP only needs the actor network to run once, so the time complexity of solving is also \(O(|T|^{2})\).  
\[TC_{training}=BS\cdot(TC_{actor}+TC_{critic})=O(BS\cdot|T|^{2}). \tag{42}\]  
From the above analysis, the training time and solving time of DRLLA rise polynomially as the task size \(|T|\) increases. In addition, the training time is also directly affected by the training times, the number of sample types, and the batch size.
## 2 Theoretical  
### Chen-Guo Model for CO\({}_{2}\)-CH\({}_{4}\) Hydrate Formation  
The Chen-Guo theoretical model is a thermodynamic model for hydrate phase equilibrium [29]. This model introduces the concepts of local stability, quasi-uniform occupation theory, and a two-step mechanism for hydrate formation. For a mixed gas system, the gas-hydrate phase equilibrium conditions can be expressed as:  
\[f_{i}=f_{i}^{H}=x_{i}f_{i}^{0}\Big{(}1-\sum_{j}\theta_{j}\Big{)}^{\alpha} \tag{1}\]  
\[f_{i}^{0}=\exp\bigg{(}\frac{-\sum_{j}A_{ij}\theta_{j}}{T}\bigg{)}\times\text{ a}_{i}\exp\bigg{(}\frac{\text{b}_{i}}{T-\text{c}_{i}}\bigg{)}\times\exp\bigg{(} \frac{\beta P}{T}\bigg{)}\times a_{w}^{\frac{1}{\lambda_{2}}} \tag{2}\]  
\[f_{i}^{0}=\exp\bigg{(}\frac{\text{D}(T-273.15)}{T}\bigg{)}\times\text{a}_{i} \exp\bigg{(}\frac{\text{b}_{i}}{T-\text{c}_{i}}\bigg{)}\times\exp\bigg{(} \frac{\beta P}{T}\bigg{)}\times a_{w}^{\frac{1}{\lambda_{2}}} \tag{3}\]  
\[\theta_{j}=\frac{Cjf_{j}}{1+\sum_{i}C_{i}f_{i}} \tag{4}\]  
\[C=X\exp\bigg{(}\frac{\text{Y}}{T-Z}\bigg{)} \tag{5}\]  
In Equation (1), \(f_{i}\) represents the fugacity of component \(i\) in the gas phase, in MPa; \(f^{H}\) represents the fugacity of component \(i\) in the hydrate phase, in MPa; \(x_{i}\) represents the mole fraction of component \(i\) in the base hydrate; \(\alpha\) is the structural parameter, which is 3/23 for CO\({}_{2}\)-CH\({}_{4}\) mixed gas hydrates; \(f_{i}^{0}\) is the fugacity of component \(i\) in the basal hydrate above the freezing point, calculated by Equation (2); by introducing a correction factor D, the degree of freedom below the freezing point is calculated by Equation (3); and \(\theta_{j}\) represents the occupancy of component \(j\) in the cages, calculated using Equation (4), where \(C_{j}\) is the Langmuir adsorption constant, calculated by Equation (5). The constants related to CO\({}_{2}\)-CH\({}_{4}\) gas hydrates in the above equations can be found in Table 1. When solving the above model, a certain temperature or pressure needs to be assumed, and a series of trial calculations are performed to satisfy Equation (1) and determine the final values.
## 3 Experiments and Result Analysis
### Simulation Experiments
Adding fringe images with less noise can basically remove the jumps completely, but when faced with an environment with more noise, there will still be more jumping points. As shown in Figure 4, the jumps can be basically removed when the wrapping phase is unwrapped by the method in this paper, and the absolute phase rarely jumps, even in the face of varying noises, providing high noise immunity.
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
## 2 Advanced Sliding Mode Control Framework for UAV Security
### Enhanced Sliding Mode Control Design for Helicopter Attitude
#### 2.2.2 Control Law  
The control input \(u\) is designed to drive the sliding surfaces \(S_{\phi}(x)\), \(S_{\theta}(x)\), and \(S_{\psi}(x)\) to zero in finite time. It consists of an equivalent control \(u_{eq}\) and a switching control \(u_{s}\):  
\[u=u_{eq}+u_{s} \tag{12}\]  
The equivalent control \(u_{eq}\) compensates for the system's nominal dynamics:  
\[\begin{split}& u_{eq,\phi}=-B_{\phi}(x)^{-1}\big{(}f_{\phi}(x)+ \dot{\phi}_{d}-\lambda_{\phi}e_{\phi}\big{)},\\ & u_{eq,\theta}=-B_{\theta}(x)^{-1}\big{(}f_{\theta}(x)+\dot{ \theta}_{d}-\lambda_{\theta}e_{\theta}\big{)},\\ & u_{eq,\psi}=-B_{\psi}(x)^{-1}\big{(}f_{\psi}(x)+\dot{\psi}_{d}- \lambda_{\psi}e_{\psi}\big{)}\end{split} \tag{13}\]  
The switching control \(u_{s}\) handles uncertainties and disturbances:  
\[\begin{split}& u_{s,\phi}=-K_{\phi}\text{tanh}(S_{\phi}(x)),\\ & u_{s,\theta}=-K_{\theta}\text{tanh}(S_{\theta}(x)),\\ & u_{s,\psi}=-K_{\psi}\text{tanh}(S_{\psi}(x)),\end{split} \tag{14}\]  
where \(K_{\phi}\), \(K_{\theta}\), and \(K_{\psi}\) are positive gain matrices.  
By replacing the sign function \(sgn()\) with the hyperbolic tangent function \(tanh()\), the control law transitions more smoothly as the system approaches the sliding surface. While the sign function introduces a sharp switch, leading to potential chattering near the sliding surface, the hyperbolic tangent function provides a continuous and differentiable approximation, reducing chattering and ensuring that the system reaches the sliding surface more gradually. This smooth transition helps improve system stability and performance, particularly in the presence of high-frequency disturbances or model uncertainties.
# A Brief Review of Multipath TCP for Vehicular Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACCP & Adaptive Congestion Control Protocol \\ AI & Artificial Intelligence \\ AIMD & Additive Increase/Multiplicative Decrease \\ AODV & Ad Hoc On-Demand Distance Vector \\ API & Application Programming Interface \\ BBR & Bottleneck Bandwidth and Round Trip Propagation Time Congestion Control \\ BIC-TCP & Binary Increase Congestion Control-Transmission \\ CAR & Connectivity Aware Routing \\ CC & Congestion Control \\ CDN & Content Delivery Network \\ CLMR & Cross Layer Multipath Routing \\ CMT-SCTP & Concurrent Multipath Transfer-Stream Control Transmission Protocol \\ CTCP & Compound Transmission Control Protocol \\ Cwnd & Congestion Window \\ DCCP & Data Congestion Control Protocol \\ DHFT & Dual Homed FatTree \\ DoS & Denial of Service \\ DPI & Deep Packet Inspection \\ DQN & Deep Q Network \\ DRL & Deep Reinforcement Learning \\ DSDV & Dynamic Destination Sequenced Distance Vector \\ DSR & Dynamic Source Routing \\ DTN & Delay Tolerant Network \\ DWC & Dynamic Window Coupling \\ ECDP & Equal Cost Multipath Protocol \\ EWTCP & Equal Weighted Transmission Control Protocol \\ GCS & Ground Control System \\ GPS & Global Positioning System \\ HMAC & Hash Based Message Authentication Code \\ HoL & Head of Line \\ HTTP & Hypertext Transfer Protocol \\ IEEE & Institute of Electrical and Electronic Engineers \\ IETF & Internet Engineering Task Force \\ IoT & Internet of Things \\ \end{tabular}
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design  
To design a ground station system suitable for educational purposes, as discussed in Section 2, and capable of receiving signals from the sky and space, as outlined in Section 3, while maintaining a cost-effective budget, various factors need to be calculated to design a system that fits the intended usage. This section will discuss the calculations used to assess the potential of the satellite ground station to be constructed and utilized for signal reception in this research.
# Model-Free Method for Damage Localization of Grid Structure
## 4 Cases
As seen in Figures 8 and 9, in the case of no noise interference, the length change rate of the element obtained from the dynamic data can also determine the damage position of the element in a relatively clear manner. When there was 5% noise interference, as seen in Figures 10 and 11, there was a significant peak at the position of the damaged element, which also indicated that the element with the larger length change rate was most likely to sustain damage. Note that the results obtained from the dynamic data were less accurate than those obtained from the static data. This is because the flexibility matrix obtained by the dynamic flexibility method was an approximation, and the displacement obtained by applying the virtual force based on this was naturally less accurate than that obtained from the direct static test. Therefore, in engineering practice, static conditions are preferred to obtain displacement parameters in order to obtain damage localization results with higher accuracy, when the conditions permit. In order to study the sensitivity of the proposed method, Figures 12 and 13 present the results of the elemental length change rates when element 44 (between nodes 12 and 22) was damaged with an added 5% noise. Specifically, Figure 12 gives the results when element 44 has 20%, 15%, and 10% stiffness reductions, and Figure 13 gives the results of 7.5% and 5% stiffness damages. From Figure 12, one can see that there was a significant peak at the damaged element 44, which indicated that the damage position can be determined in a relatively clear manner when the damage degree is more than 10%. The results in Figure 13 are confused, and it was impossible to accurately identify the true damage location, which indicated that the proposed method is ineffective when the damage degree is less than 10%. Obviously, the cause of misjudgment lies in the adverse effect of data noise, because the displacement changes caused by the damage are often submerged by the data noise when the damage degree is very small. For this case, 10% stiffness reduction is the minimum detectable damage. \[\Delta u^{*}_{(j)}=\Delta u_{(j)}\times(1+nl\times unifrnd[-1,1]) \tag{23}\]0 Length change rate of element obtained from the dynamic data (elements 35 and 40 were damaged adding 5% noise). \[\Delta u^{*}_{(j)}=\Delta u_{(j)}\times(1+nl\times unifrnd[-1,1]) \tag{23}\]1 Length change rate of element obtained from the static data with an added 5% noise (element 44 has 20%, 15%, and 10% stiffness reductions).
## 3 Communication System Design
### Backward Communication Based on S-Bar
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Periods** & **MOSFETs** & **Diodes** & **Power Transmission** \\ \hline \(t_{0}\)-\(t_{1}\) & \(Q_{5}\) is ON, \(Q_{6}\) is ON & \(D_{1}\) is OFF, \(D_{2}\) is OFF & Within resonant tank \\ \(t_{1}\)-\(t_{2}\) & \(Q_{5}\) is OFF, \(Q_{6}\) is ON & \(D_{1}\) is ON, \(D_{2}\) is OFF & To load \\ \(t_{2}\)-\(t_{3}\) & \(Q_{5}\) is ON, \(Q_{6}\) is ON & \(D_{1}\) is OFF, \(D_{2}\) is OFF & Within resonant tank \\ \(t_{3}\)-\(t_{4}\) & \(Q_{5}\) is ON, \(Q_{6}\) is ON & \(D_{1}\) is OFF, \(D_{2}\) is OFF & Within resonant tank \\ \(t_{4}\)-\(t_{5}\) & \(Q_{5}\) is ON, \(Q_{6}\) is OFF & \(D_{1}\) is OFF, \(D_{2}\) is OFF & To load \\ \(t_{5}\)-\(t_{6}\) & \(Q_{5}\) is ON, \(Q_{6}\) is ON & \(D_{1}\) is OFF, \(D_{2}\) is OFF & Within resonant tank \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Control strategy for power devices during one switching cycle. Figure 8: (a) Circuit of semi-bridgeless active rectifier (S-BAR). (b) Block diagram of DPSK based modulation. In the capacitive mode, the real and imaginary parts of \(Z_{\rm e}\) are given by  
\[\left\{ \begin{array}{l}R_{\rm e}=\Re(Z_{\rm eq})=\frac{8}{\pi^{2}}R_{\rm L}{\rm sin }^{4}\left(\frac{\varphi_{\rm s}}{2}\right)\\ X_{\rm e}=\Im(Z_{\rm eq})=\frac{8}{\pi^{2}}R_{\rm L}{\rm sin}^{3}\left(\frac{ \varphi_{\rm s}}{2}\right)\cos\left(\frac{\varphi_{\rm s}}{2}\right).\end{array} \right. \tag{20}\]  
In the resistive mode, the imaginary part is zero and the real part is given by  
\[R_{\rm e}=\Re(Z_{\rm eq})=\frac{8}{\pi^{2}}R_{\rm L}{\rm sin}^{2}\left(\frac{ \varphi_{\rm s}}{2}\right). \tag{21}\]  
Whether the secondary side is capacitive or resistive mode, the current in secondary compensated circuit has constant characteristic. Substituting (20) or (21) into (5), the relation of output power during the modulation of bit \"1\" and bit \"0\" is derived as  
\[P_{\rm out}(\varphi_{\rm s}-\Delta\varphi_{\rm s})+P_{\rm out}(\varphi_{\rm s }+\Delta\varphi_{\rm s})\approx 2P_{\rm out}(\varphi_{\rm s}). \tag{22}\]  
As evident from (22), the output power in the modulation of bit \"1\" is almost equal to that in the modulation of bit \"0\" during every two switching cycles. Therefore, the backward communication with DPSK modulation has little impact on power transmission.
## 4 System Integration with Smart Contract
### Sensing Data Logging  
Sensors typically generate a large volume of sensing data with high sampling rates, as such, it is not practical to create one transaction per sample for data storage at public blockchains. As we pointed out in our previous publications [10; 33], it is better to first aggregate sensing data and only place the aggregated data on the blockchains for immutability protection.  
Even with aggregation, the data for each sample could be 500 bytes or larger. Considering that Ethereum charges 20,000 gas for each 32-byte of data, this means that the minimum cost for each data entry of 500 bytes would be 312,500 gas. Assuming that the gas price is 14.3 gwei (1 gwei = 10\({}^{-9}\) ETH) and the ETH price is USD 1640, this transaction would cost at least USD 7.33. While this amount does not appear to be large, the total transaction fees would add up in the long run. For example, if one transaction is issued every 10 minutes, 144 transactions will be issued every 24 h and the total cost in transaction fees would be over USD 1000 per day! Only large institutions could afford this amount of transaction fees.  
To address this issue, we propose to store the full data entry in IPFS and record the IPFS hash returned by the IPFS in the smart contract. The IPFS hash is a 46-byte-long self-describing multihash that can be used to identity both the content of the data entry as well as the peer and key to access the entry in the IPFS network ([[https://richardscheider.github.io/net-ipfs-core/articles/multihash.html](https://richardscheider.github.io/net-ipfs-core/articles/multihash.html)]([https://richardscheider.github.io/net-ipfs-core/articles/multihash.html](https://richardscheider.github.io/net-ipfs-core/articles/multihash.html)) (accessed on 1 March 2023)). The mechanism to do so is illustrated in Figure 9. With this design, only 46 bytes are stored in the smart contract instead of 500 bytes. For each transaction, this would saves 272,500 gas, which is equivalent to USD 6.39, and the daily savings would be USD 920.26.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 3 Integrity Risk Probability Distribution
### Design Values of \(P_{\mathrm{sat}}\) and \(P_{\mathrm{c}onst}\)  
Table 5 shows the bottom event risk probability analysis results of the BDS satellite SIS integrity. The design value of \(P_{\mathrm{sat}}\) is the sum of the design integrity risk probabilities of  
all the bottom events in Table 3. For the B1I signal, the design Psat is 0.9 \(\times\) 10\({}^{-5}\)/h; for the B1C and B2a signals, the design Psat is 0.8 \(\times\) 10\({}^{-5}\)/h.  
For the BDS constellation service failure, the bottom events include the Earth orientation parameters (EOPs) determination or prediction abnormality, the MS antenna phase center deviation, the MS hardware/software failure, the MCS hardware/software failure, the satellite orbit and clock calculation parameter failure, and the satellite design defects, etc.  
Investigating the operation status of BDS-3 in 2019 revealed that no constellation service failure event occurred. In this paper, we make assumptions and conservative estimates. Assuming that the system has 0.5 constellation service failure events since 27 December 2018, then Pconst can be estimated according to Equation (8) as following [27; 28]:  
\[\text{P}_{\text{const}}=\frac{0.5\text{-}1\text{ h}}{365\text{-}24\text{ h}}=5.7\times 10^{-5}\approx 6\times 10^{-5} \tag{15}\]  
where 1 h is the assumed MTTN.
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
###### Abstract  
Temperature variations of flue gas have an effect on carbon dioxide (CO\({}_{2}\)) emissions monitoring. This paper demonstrates accurate CO\({}_{2}\) concentration measurement using tunable diode laser absorption spectroscopy (TDLAS) with temperature compensation methods. A distributed feedback diode laser at 1579 nm was chosen as the laser source for CO\({}_{2}\) measurements. A modeled flue gas was made referring to CO\({}_{2}\) concentrations of 10-20% and temperatures of 298-338 K in the exhaust of a power plant. Two temperature compensation methods based on direct absorption (DA) and wavelength modulation (WMS) are presented to improve the accuracy of the concentration measurement. The relative standard deviations of DA and WMS measurements of concentration were reduced from 0.84% and 0.35% to 0.42% and 0.31%, respectively. Our experimental results have validated the rationality of temperature compensations and can be further applied for high-precision measurement of gas concentrations in industrial emission monitoring.  
carbon dioxide monitoring; absorption spectroscopy; temperature compensation; wavelength modulation +
Footnote †: journal: Applied Physics Letters
## Materials and Methods
### Methods
#### 2.2.2 HNT-DAS-FA/FITC Characterization  
The nanoparticles were characterized via FTIR, 13C[H1]CPMAS, and UV-Vis. FTIR spectra were obtained using a Thermo Scientific (Waltham, MA, USA) IR100 and KBr IR spectra were acquired with a Mattson Genesis II FTIR. A Shimadzu 2401 UV-Vis spectrometer (Tokyo, Japan) operating with the UVProbe software package, supplied as standard with Shimadzu UV-VIS Spectrophotometers. The UVProbe software was used to identify the presence of FA and FITC in solution. Additionally, a Thermo-Fisher NanoDrop 2000 was used to acquire UV-Vis data. All UV-Vis samples were placed in standard 1 cm quartz cuvettes. Solution state \({}^{1}\)H NMR was obtained using a Bruker AVIII HD 500 MHz spectrometer (Bruker, Milton, ON, Canada) with a Prodigy probe. All experiments were done at ambient temperature (300 K). Spectra were obtained for the starting material (HNT), intermediates (HNT-DAS and HNT-DAS-FA), and final product (HNT-DAS-FA/FITC). The 13C[H1]CPMAS analyses were run at 4 kHz for 156,000 scans.