esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Observer Useable for the Condition Monitoring of a Capacitor
## 3 Results
### The Circuit Used to Estimate the Parameters of the Output Capacitor of a DC-DC Buck Converter  
The discrete-time PO is further used to determine the equivalent parameters of the COUT electrolytic capacitor of the DC-DC buck converter in Figure 12[31]. The power supply is realized with a dedicated integrated circuit. At the input of the source, the unstabilized voltage V\({}_{\text{IN}}\) is used, and at the output, the stabilized voltage V\({}_{\text{OUT}}\) (V\({}_{\text{OUT}}\) \(<\) V\({}_{\text{IN}}\)) is applied to load R\({}_{\text{L}}\). The elements and connections represented in red were added to produce a non-invasive two-stage discharge. The signal u1, applied by an ATMEGA 328 microcontroller, on the FB pin of the integrated circuit (IC DC-DC Buck) via diode D2, stops the DC-DC power supply and triggers the first stage of the discharging process. The microcontroller also controls by u2 signal the T\({}_{\text{FET}}\) transistor to connect at the proper moment the additional resistor R\({}_{\text{a}}\). The signal applied to input A is used to monitor the voltage V\({}_{\text{OUT}}=y\), determining by its level the moment when the second stage must be initiated. For applications, the value of COUT can be adopted in a wide range. Thus, compared with the capacitor of 330 uF in Figure 12, we used two capacitors of 100 uF and 470 uF. For the values of R1, R2, C2 and CFF we used 270 \(\Omega\), 4.05 k\(\Omega\), 100 nF and 10 nF, respectively.  
Assuming that the elements connected before the COUT do not practically influence the process of discharging the COUT through the load resistor, the diagram in Figure 12 can be simplified as in Figure 13.  
Figure 11: Calculation of \(\hat{T}_{e}\): (**a**) with the bisector method; (**b**) with the extension of the bisector method.  
The signals u1 and u2 are time-delayed pulses: a high level on u1 stops the DC-DC buck converter and triggers the first stage (discharging of the COUT capacitor through the load R\({}_{L}\)), and a high level on u2, generated at \(t_{\rm s}\) seconds after u1, connects the additional resistor \(R_{a}\) in parallel with \(R_{L}\). The moment \(t_{\rm s}\) corresponds to the instant when \(y\) reaches a preset threshold level. The discharge of the COUT capacitor is represented in Figure 10b. Equation (36) resulting from Equation (32) is valid:  
\[\hat{C}_{e}=\frac{\hat{T}_{e1}-\hat{T}_{e2}}{R_{L}^{2}/(R_{L}+R_{a})}\,\ \ \ \hat{R}_{e}=R_{L}\cdot\Bigg{[}\frac{\hat{T}_{e1}\cdot R_{L}}{(\hat{T}_{e1}- \hat{T}_{e2})\cdot(R_{L}+R_{a})}-1\Bigg{]}. \tag{36}\]
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 6 Cn^2 Net Scintillometer Training with SIM Datasets: Machine Learning-Based Turbulence Profiling
Note that tunable parameters of the \(C_{n}^{2}\) profile could potentially be included into a set of DNN trainable parameters and be defined during the DNN training-validation phase. To understand how the machine learning-based \(C_{n}^{2}\) profiling described here affects the characteristic spatial features of scintillations, compare the scintillation images shown in Figure 11 resulting from different DNN training (SIM-KO-U vs. SIM-KO-P) and inference (ATM#2) datasets. Under weak-to-medium turbulence conditions the spatial structures of scintillation speckles (two left column images in Figure 11) are similar for all three datasets. At the same time, under strong turbulence (images in the right two columns) one can easily observe a noticeable difference in the characteristic size of speckles between images captured during the experimental trial (ATM#2 dataset) and computed using a uniform (SIM-KO-U) \(C_{n}^{2}\) profile (\(\kappa\) = 1.0). This difference practically vanishes in scintillation images taken from the SIM-KO-P (\(\kappa=2.5\)) dataset which were obtained during machine learning-based \(C_{n}^{2}\) profiling. The spatial structures of speckles in images belonging to the ATM#2 and SIM-KO-P datasets (two bottom rows in Figure 11) are alike under all turbulence conditions observed in experiments and mimicked in wave-optics numerical simulations. The turbulence profiling example described here illustrates the potentials for application of the deep machine learning concept to atmospheric turbulence sensing and characterization.
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 5 Simulation Results
The proposed method did not require a large amount of data for training, and also there was no need to reset the parameters of the method if there existed new kinds of an anomaly. At the same time, what needed to be calculated was just the cosine similarity when dealing with the task of anomaly identification. Besides, the time complexity of the proposed method was lower than the MSVM method; therefore, our method could satisfy the requirement of rapid satellite anomaly detection and identification, and the method presented in this paper had clear advantages in practical application. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Number**} & \multirow{2}{*}{**Verification Method**} & **Pseudo-Period Extraction** & \multicolumn{2}{c}{**Abnormal Identification**} \\ \cline{2-5}  & & **Pseudo-Period** & **Partition Error of Pseudo-Periodic** & **Statistical Value of Each Interval of Phase Trajectory** & **Identification Result** \\ \hline
1 & 4 & [+1 +2 +3] & [0.134 0.152 0.139 0.094 0.296 0.185] & Normal \\ \hline
2 & 5 & [+2 0 +2 +1] & [0.117 0.120 0.109 0.158 0.266 0.231] & Normal \\ \hline
3 & 5 & [+1 +1 -2 +2] & [0.105 0.068 0.102 0.183 0.330 0.211] & Normal \\ \hline
4 & 5 & [0 +2 +3 -1] & [0.149 0.096 0.192 0.181 0.215 0.161] & Type 3 fault \\ \hline
5 & 5 & [+1 +3 0 +2] & [0.036 0.101 0.087 0.099 0.325 0.352] & Type 2 fault \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test data pseudo-period partition and anomaly identification results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Abnormal Type**} & \multirow{2}{*}{**Method**} & **MSVM** & \multicolumn{2}{c}{**The Proposed Method**} \\ \cline{2-5}  & **Accuracy** & **Time** & **Accuracy** & **Time** \\ \hline
1 & 0.78 & 0.0025 s & 0.88 & 0.0020 s \\
2 & 0.76 & 0.0022 s & 0.81 & 0.0032 s \\
3 & 0.73 & 0.0037 s & 0.78 & 0.0021 s \\
4 & 0.91 & 0.0019 s & 0.93 & 0.0016 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Test results obtained with 200 groups used as the training sample. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Abnormal Type**} & \multirow{2}{*}{**Method**} & **MSVM** & \multicolumn{2}{c}{**The Proposed Method**} \\ \cline{2-5}  & & **Accuracy** & **Time** & **Accuracy** & **Time** \\ \hline
1 & 0.9033 & 0.1700 s & 0.8900 & 0.0022 s \\
2 & 0.8525 & 0.1660 s & 0.8267 & 0.0046 s \\
3 & 0.8250 & 0.1790 s & 0.8045 & 0.0025 s \\
4 & 0.9564 & 0.1560 s & 0.93 & 0.0023 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test results obtained with 500 groups used as the training sample.
## 4 Experimental Simulation
### Detection Simulation of Communication Signal Waveform by FUBSR and SUUBSR
#### 4.1.1 Detection Simulation of Single-Carrier Signal Waveform by FUBSR and SUUBSR
After reaching the steady state, the upper envelope waveform is regular, and the burrs on the time-domain waveform have basically disappeared. This indicates that the SUUBSR system has better anti-noise performance and faster system response speed compared to the FUBSR system. The envelope can better reflect the changing trend of signal amplitude. By observing the change in the envelope, the response speed of the system can be analyzed. Figure 9 shows the changing trend of the output signal envelope of the SUUBSR system under different system parameters. In Figure 8, as the system parameter \(a\) increases, the envelope amplitude value increases, and the oscillation slope increases, indicating that the system response speed becomes faster and the signal enhancement effect becomes better. In Figure 8, the parameter \(r\) only affects the oscillation slope of the envelope and has little effect on the envelope amplitude. The smaller the parameter \(r\), the faster the system response speed is. In Figure 8, as \(v\) increases, the envelope amplitude and oscillation slope both decrease, and the system response speed slows down. In Figure 8, when the damping coefficient is too small, the envelope oscillation slope becomes smaller, and the  
Figure 7: Time–frequency domain diagram of the FUBSR system output signal. (**a**) Time-domain diagram of the FUBSR system output signal. (**b**) Frequency-domain diagram of the FUBSR system output signal. envelope also oscillates due to the system oscillation. As the damping coefficient increases, the envelope slope increases, and the envelope waveform is more regular, indicating that increasing the damping coefficient within a certain range can speed up the system response speed and increase the system stability. Figure 8: Time–frequency domain diagram of the SUUBSR system output signal. (**a**) Time-domain diagram of the SUUBSR system output signal. (**b**) Frequency-domain diagram of the SUUBSR system output signal. Figure 9: The impact of different system parameters on the envelope of the system output signal. (**a**) The influence of parameter \(a\) on the system’s response speed. (**b**) The influence of parameter \(r\) on the system’s response speed. (**c**) The influence of parameter \(v\) on the system’s response speed. (**d**) The influence of the damping coefficient on the response speed of the system.
## 3 Proposed Scheme: MC-TIRP
### Trusted Route Discovery Component  
In this component, a route discovery method works similarly to the basic AOMDV. However, there are differences in the method processes. Nodes perform direct observations, indirect observations, and witness observations to find a trusted route. Trust discovery works closely with trust evaluation to share the necessary trust and learning values. The source node broadcasts the RREQ (Route Request) to find trusted paths to the gateway node.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(B\) & The bandwidth (raw data rate) of the link \\ \hline \(EInit_{i}\) & The initial energy of node \(i\) \\ \hline \(EQTV\) & Energy-based QoS and Trust Value \\ \hline \(ERes_{i}\) & The residual energy of node \(i\) \\ \hline \(ETT\) & Expected Transmission Time \\ \hline \(ETX\) & Expected Transmission Count \\ \hline \(F_{i,j}\) & The number forwarded by node \(j\) \\ \hline MC-TIRP & Mission-Critical Trust based Intelligent Routing Protocol \\ \hline \(PFR\) & Packet Forwarding Ratio \\ \hline \(PTV\) & Path Trust Value \\ \hline \(S_{i,j}\) & The number of packets sent by node \(i\) to node \(j\) \\ \hline \(\omega\) & Weight factor \\ \hline \(X\) & The size of the packet \\ \hline \(\alpha\) & Learning rate \\ \hline \(\gamma\) & Blacklist threshold \\ \hline \(\mu\) & Decay factor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation related to the proposed scheme.  
Intermediate nodes receiving the request message reply to the source node with trust information. The nodes check the blacklist, except the malicious node. The intermediate node considers the trust value and multiple loop-free route discovery processes and broadcasts the RREQ. When the destination receives the RREQ message, it responds with the RREP (Route Reply) and reward. In a wireless environment, more routes may exist according to various situations, and this can be set according to the operator's judgment. In the paper, we set up to maintain a maximum of five valid trust paths. The reason is that we assumed five types of mission-critical data to be distributed over a trusted path. The source node can transmit data over trusted paths formed on the basis of Q-learning. Mission-critical data can be transmitted over a path with the maximum Q-value. Data of relatively low mission importance can be transmitted randomly over trustworthy paths. We presumed that data packets A and B had high priority, and packets C, D, and E had a relatively low priority; however, the definition could be obtained according to the preferences and intentions of the operator.
## 3 Results
### Comparison with the Single-Tier Classification Pipeline  
In this study, we used the two-tier pipeline to make the classifiers focus on the fuzzy phenotypes which are difficult to recognize. To evaluate the effectiveness of the two-tier pipeline, we also conducted phenotype classification based on a single-tier pipeline, in which 10 separated binary classifiers were trained for each of the 10 phenotypes. By doing so, each classifier has to recognize one phenotype from 10 phenotypes, which might be more difficult than recognizing one from eight as in the two-tier pipeline. Figure 6 compares the accuracies of the two-tier vs. single-tier pipelines. It can be observed that although the two-tier pipeline does not outperform the single-tier pipeline for all the phenotypes, and it achieved much better accuracy for those difficult phenotypes including edema, hemostasis and down curved tail whose accuracies were improved by 0.06, 0.03 and 0.09, respectively.  
Figure 5: Comparison of classification accuracy between our method and the state-of-the-art baseline method.  
Figure 6: Comparison of classification accuracy between the two-tier and the single-tier pipelines.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 2 Method  
Figure 2 shows the framework of the proposed method. The clean image is fed into the pipeline to obtain noisy latent space. Next, we embed modulated spread spectrum watermark into the noisy latent vectors. Finally, the denoising network and the decoder for the latent vector are used to generate watermarked images. Unlike the existing deep learning-based watermarking schemes, which typically require a decoder network to restore the watermark, blind detection is achieved in this work by calculating the correlation between the watermark patterns and the feature representation vector in noisy latent space.
# Optimal Energy Integration and Off-Design Analysis of an Amine-Based Natural Gas Sweetening Unit
## 3 Heat Integration Model
### Second Loop: ORC Integration  
The application of the organic Rankine cycle for secondary waste heat recovery has been proven to be a reliable method for electricity production from mid- to low-temperature heat sources, such as geothermal, solar, or even waste heat from industrial plants [6]. The ORC system considered in this study uses high-temperature waste heat remaining from direct integration, if available, as well as low-temperature waste heat from the background process.  
A basic ORC layout was adopted in this study consisting of an evaporator, a turbine, an air-cooled condenser, and a pump. Table 3 summarizes the assumptions of the ORC loop. The model was built using AspenHysys V11 in order to harness its large thermodynamic properties databank; it was then connected with Matlab (r2020b) through the actsserver command to facilitate the automation of scenarios. The evaporator and condenser were modeled using the weighted heat transfer calculation mode available on AspenHysys, which takes into account the phase change occurring within these units [20]. A saturated vapor was assumed at the inlet of the ORC turbine, and a saturated liquid was assumed at the inlet of the ORC pump. The evaporator pressure was directly optimized by the particle swarm optimization algorithm (PSO), whereas its temperature was indirectly optimized by  
Figure 3: Grand composite curve of the integrated process at 30 bar and 10 mol-% CO\({}_{2}\).  
manipulating the amount of energy that is absorbed by each heat exchanger of the heat recovery network.  
Figure 4: The proposed bi-level heat integration algorithm.  
The selection of appropriate working fluid is crucial for efficient energy conversion in ORC systems. Additionally, environmental considerations need to be taken into account as many fluids are phased out due to their high ozone depletion potential. In this study, the temperature difference between the heat sources and sink allows many fluids to be used. Yet, as the background process is a natural gas production facility, linear hydrocarbons present a double advantage: an abundant presence in the processed gas and a low environmental impact. This study was limited, however, to the fluids operating above atmospheric pressure. Three candidates found to satisfy these conditions, namely propane (R290), i-butane (R600a), and i-pentane (R601a), were screened all over the investigated domain.
# Joint Communication-Motion Planning in Networked Robotic Systems
## 3 Cognitive-Relay-Assisted Robot System with Interference Constraints
### Performance Assessment: Orient  
We aim at minimizing the total energy consumption, including both the communication energy consumption and the motion energy consumption. The communication energy depends on the transmit powers of the sensing robot and the relay robot, which should satisfy both the PER constraint and the interference constraint. The PER formulation is given by Equation (2). During the \(k\)-th time step, the transmit power \(P_{i,k}\) emitted by the sensing robot and the relay robot should satisfy the interference constraints. Specifically, the average received signal-to-noise power ratio at the PU is \(\overline{\gamma}_{ip,k}(P_{i,k})=\frac{P_{i,k}d_{ip,k}^{-\beta}}{\mathrm{N} \mathrm{B}}\) should be smaller than the pre-defined threshold \(\gamma_{p,th}\), namely, \(\overline{\gamma}_{sp,k}<\gamma_{p,th}\) and \(\overline{\gamma}_{rp,k}<\gamma_{p,th}\). Finally, the motion energy consumption is quantified by Equation (5).
###### Abstract  
The current paper addresses the challenges in manufacturing a critical component of a centrifugal pump for space active thermal control systems of satellites, namely, the closed centrifugal impeller. Compared to the classical technologies, there is an obvious advantage of additive manufacturing of closed impellers, due to the possibility of creating complex geometries, which boost the hydraulic performances of the part and, implicitly, of the pump. In this regard, the authors performed a comparative analysis between a closed impeller obtained by classical machining and three manufactured by additive technology (selective laser melting) by means of dimensional inspection, non-destructive testing, and experimental evaluation. The study performed here showed that the additively manufactured closed impellers exhibited similar hydraulic performances to the classical one, without the need for performing post-processing of internal surfaces. Also, in terms of dimensional and geometrical stability, the additively manufactured closed impellers were within the imposed tolerances, demonstrating the feasibility of obtaining such complex parts by using additive manufacturing. Subsequently, the allocated time for manufacturing decreased by 75% for the closed impellers manufactured by additive technologies, and the need for using four technological processes was decreased to only two, printing and post-processing.  
additive manufacturing; centrifugal pump; closed impeller; space application; thermal control system +
Footnote †: journal: Applied  
Article  
Hydraulic Performance Comparison of Centrifugal Closed Impellers Fabricated by Means of Additive Manufacturing and Classical Machining for Space Active Thermal Control Systems of Satellites
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Routing Protocols
#### 3.2.3 Reactive-Greedy-Reactive (RGR)  
The reactive-greedy-reactive (RGR) [10; 11; 12; 13] routing protocol is obtained by merging AODV with the geographic greedy forwarding (GGF) [13; 39] protocol. The very high mobility of the nodes and the limited transmission range lead the routes to break quite often in AODV. When that happens, AODV offers a route repair mode. In RGR, this route repair is replaced by GGF for an improved performance. GGF works as follows. When a link in a route breaks while a packet is trying to be transmitted to the next hop, the packet is sent to the neighbor that is closer to the destination. In RGR, as inherited from AODV, nodes periodically broadcast HELLO messages so that neighbors can update their respective routing tables. This association of AODV and GGF yielded the RGR protocol. A reliability criterion [10] is made use of during the route discovery phase, and a recovery strategy [11] deals with GGF failure. The goal of the reliability criterion is to select the most robust and reliable route from the route discovery. This is achieved by making use of the concept of reliable distance [40]. As far as GGF is concerned, the idea is to forward the data packet to the neighbor whose location is closer to the destination than the forwarding node (the node currently having the packet). If there is no such neighbor, GGF is said to have failed, and the packet is dropped. In order to overcome this failure, a low-complexity and low-overhead recovery strategy consists of forwarding the packet to the best-moving node (BMN) when GGF fails. The BMN is the node, within the transmission range (the forwarding node included), that is deemed to move faster toward the destination. The decision on which node is moving faster toward the destination is based on predictions that are made using the current speed and direction of the nodes.
# ADS-B Like UTM Surveillance Using APRS Infrastructure
###### Abstract  
Automatic packet reporting system (APRS) is selected as a candidate for automatic dependent surveillance-broadcast (ADS-B) like solution for unmanned aircraft system traffic management (UTM). The APRS on-board unit (OBU) at 0.5 W radio power and a proper ground transceiver station (GTS) deployment together makes up the infrastructure for unmanned aerial vehicle (UAV) surveillance. The airborne APRS OBU, designed and built using an available LightAPRS module, and the GTS to relay received data into the UTM Cloud is developed in this study. By system integration, the APRS OBU reports position data and flight data periodically to UTM Cloud. This paper presents the development of the ADS-B like operation for UTM using APRS. To avoid communication jamming by HAMs, the adopted APRS shifts its carrying frequency from 144.64 MHz to 144.61 MHz. In addition, the data period is tuned to around 10 s to test its functional capability. The APRS OBU downlinks 90 bytes of surveillance data onto the UTM cloud using the Internet, including position and flight data from Pixhawk flight controller (FC). A series of system performance verifications are conducted to examine APRS ADS-B like reliability and continuity with coverage limit. Through 19 flight tests, the GTS collected 1330 packets of data out of 1331 transmitted from the APRS OBU. Each data packet has the complete 90 bytes for surveillance with position and six degree-of-freedom (DoF) flight data on the UTM cloud. The APRS GTS deployment, with a low rate of missing data, covers a 40 km radius at the specific frequency of 144.61 MHz. The test results verify the functional capability of APRS to support an ADS-B like for UTM in Taiwan.  
U  
Aerospace 2020  
100; 3390/aerospace/070/0100