esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
A market survey was made for the state-of-the-art small satellites monopropellant propulsion systems, as well as commercial off-the-shelf green monopropellant thrusters. Specifications and performance characteristics of such propulsion components are mentioned in Tables 13 and 14. These systems are the MPS-135 4U by Aerojet Rocketdyne [54], BGT-X5 by Busek Co. [43], EPSS C1 by NanoAvionics [53], Pinot-G by IHI Aerospace Co. [57], and the Lunar Flashlight Propulsion System (LFPS) by NASA and Georgia Tech [58]; more detailed discussion about the feed and pressurization in these propulsion systems can be found in the reference [9]. All these systems are in CubeSat standard size, except for the Pinot-G, which was specified in cylindrical dimensions of \(\diameter 350\)-\(135\) mm. As shown in the table, the pump-fed systems possess the highest total impulse performance values of \(>\)7290 and \(>\)2500 N s for the MPS-135 and the LFPS, respectively. Next comes the EPSS C1 and the BGT-X5 with values of 650 and 565 N s, which is still a reasonable value for the size of a 1U propulsion system, especially when having a reasonable wet mass to dry mass fraction. On the other hand, the Pinot-G delivers a decent total impulse value, however, the dry mass of the system is surprisingly high, \(-\)9.6 kg compared to 3.5 kg for the MPS-135 4U. The results of this brief analysis along with the performance data and specifications in Table 12 may be considered as reference figures for the MIMPS-G500 mN CubeSat green monopropellant propulsion system preliminary design results, as well as to the readers of this article. The following are available online at [[https://www.mdpi.com/article/10.3390/aerospace8060169/s1](https://www.mdpi.com/article/10.3390/aerospace8060169/s1)]([https://www.mdpi.com/article/10.3390/aerospace8060169/s1](https://www.mdpi.com/article/10.3390/aerospace8060169/s1)). Video S1: MIMPS-G500 mN 3D CAD realistic render display. Conceptualization, A.E.S.N., A.C. and A.P. ; methodology, investigation, software, data curation, writing--original draft preparation, A.E.S.N. ; supervision, writing--review and editing, A.P. and A.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Any new data created or analyzed were mentioned explicitly within the article and simulation conditions were illustrated. I would like to acknowledge [PERSON] and [PERSON] from IHI Aerospace Co., Ltd. for providing us with information relating to HNP green monopropellants. The authors declare no conflict of interest. Figure 13: Feed and pressurization system side view and components.
# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 3 Methodology
### Implementation and Parameters  
A discrete-event simulator was developed to evaluate and optimize the proposed approach. The simulator handles all key events, including the arrival of the tasks, assignments, robot movements, and collision detection. Event processing is optimized for performance to enable fast evaluation over many iterations, which facilitates parameter tuning and algorithmic refinements. The simulation tracks the state of all AMRs, active tasks, and edge weights, updating them according to the mechanisms described above when events occur.  
The optimization process operates through three sequential phases, each emphasizing different aspects of the movement strategy. All edge weights are bounded between 0 and 1 and represent normalized movement costs that directly influence A* path planning. The weights are initialized at 0.5 to allow both positive preferences (weights below 0.5) and avoidance patterns (weights above 0.5) to develop. To ensure a stable and gradual evolution of the weights, the modification factors (\(r_{m}\), \(p_{c}\), \(\alpha_{s}\), \(\alpha_{d}\), and \(\lambda\)) are set several orders of magnitude smaller than the weights themselves, typically around \(10^{-3}\). This allows the system to accumulate meaningful patterns over many iterations without sudden destabilizing changes.  
The parameters for each phase are shown in Table 2.  
In Phase 1, the focus is on collision avoidance and the formation of basic traffic patterns with aggressive collision penalties. Phase 2 focuses on optimizing the timing of tasks by introducing delay penalties while reducing collision sensitivity. In the final phase, movement patterns are refined through increased movement rewards and evaporation,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Phase 1** & **Phase 2** & **Phase 3** \\ \hline Movement reward (\(r_{m}\)) & 0.001 & 0 & 0.002 \\ Collision penalty (\(p_{c}\)) & 0.007 & 0.003 & 0 \\ Side collision factor (\(\alpha_{s}\)) & 0.7 & 0.7 & - \\ Delay factor (\(\alpha_{d}\)) & 0 & 0.002 & 0 \\ Evaporation rate (\(\lambda\)) & 0.015 & 0.010 & 0.020 \\ Duration (iterations) & 100,000 & 50,000 & 50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization parameters by phase.  
allowing for smoother traffic flow. The effectiveness of the optimization depends on the balance between these mechanisms. Strong collision penalties in the early phases establish basic safety patterns, while the emphasis on movement rewards and evaporation in the later phases allows these patterns to adapt to changing conditions. The delay factor helps to maintain efficient task completion without compromising the emergent traffic patterns. An analysis of the optimization parameters as well as stability and convergence is presented next through an illustrative example.
## 5 Experiments
### Experimental Results
The error statistics are presented in Table 3.  
The results in Table 3 clearly show that our proposed algorithm outperforms the individual VIO and APDR systems across all three paths. Specifically, in terms of overall error levels, the proposed algorithm achieved an average localization error of 0.56 m, which is significantly lower than the average errors of the APDR algorithm (3.82 m) and the VIO algorithm (1.07 m). The endpoint error was 1.24 m, which is substantially lower than the endpoint errors of the APDR algorithm (7.34 m) and the VIO algorithm (1.94 m). This demonstrates that the proposed algorithm offers better localization accuracy, with endpoint errors remaining within just 1.27%. Figure 17: Localization error curves and violin plots for three different paths. Regarding extreme error levels, our proposed algorithm achieved a maximum error of 1.28 m, significantly lower than the maximum errors of the APDR algorithm (8.91 m) and the VIO algorithm (2.24 m). This shows that the proposed algorithm performs better under extreme conditions, maintaining high accuracy even in complex or unfavorable environments, such as dynamic settings and rescue scenarios. When considering error fluctuation, the proposed algorithm achieved a standard deviation of 0.24 m, which is significantly lower than the APDR algorithm (2.35 m) and the VIO algorithm (0.59 m). This indicates that the proposed algorithm offers more consistent and stable position estimates compared to the independent systems, demonstrating stronger stability and adaptability to changing external conditions. Furthermore, the proposed algorithm not only outperforms the independent systems in terms of average localization error but also shows notable advantages over the state-of-the-art VIO algorithm. For instance, compared to VIO, our algorithm reduced the average error by 45.14% and the endpoint error by 38.6%, thereby achieving higher precision. This exceptional performance demonstrates that the system can effectively meet the autonomous localization needs of rescuers in complex spatial environments.
## 4 Results and Discussion  
### Data Description  
We considered a dataset gathered from a WSN deployment at the New Generation Mobile Internet Research Laboratory (NGMI) in the University of Electronic Science and Technology of China. The results are from a 20-sensor-node testbed on the laboratory's ceiling. Figure 6 shows the sensor deployment in the laboratory. The nodes have identification numbers, _Node0_ as the root node and the other nodes follow a sequential order (_Node1_, _Node2_,..., _Node20_). The nodes in this experiment run on TinyOS and use TelosB sensor motes.  
We applied the BCM approach that uses the beacon receptions to provide historical statistics. To determine the actual packet traces results, two tunable modeling parameters were considered: window size \(W\), representing the number of packets used in each single input, and the interval-packet interval \(I\), representing the time interval between packets. The window size corresponds to the amount of historical information required by our experiment to estimate the link quality in our prediction. We programmed nodes on the meshed network topology testbed to broadcast 400,000 packets with an inter-packet interval of 50 ms using 16 channels. Each node transmitting 1000 packets to a relaying node or to the root node. Timing is an important factor for beacon packet distribution and we neededa way that could concisely describe link behavior from the packet traces. At \(I\) = 50 ms, the WSN's links stabilized to a near-perfect quality for our experiment and consequently our data collection technique also converged to a stable state. To measure the link features, we considered the bitmaps to calculate CPDFs, PRRs and retransmission count. Henceforth, the term link feature interchanges with either of these other terms, sensed data or data variable.  
CPDF is the probability the next packet will be transmitted successfully given a number of consecutive packet successes or failures. CPDFs can give a good visual intuition of link behavior. We wanted to present link behavior between nodes as a single scalar value. To do so, we used Earth Mover's Distance (EMD) function [62] to compute the retransmission cost. EMD computes the distance between successful packet trace distribution and the failed packet trace distribution with a window size of \(W=1\). While distance is informative, as shown in Table 1, it is not sufficient to measure link quality. Thus, we computed PRR to distill a measure of packet delivery successes and failures within a link. Measuring link behavior and its effect on the network performance, we computed the retransmission count showing the distribution of the number of resumed transmission upon packet failures.
## 3 Materials and Methods
### Anomaly Detection and Classification  
3.6.1. Dynamic Anomaly Thresholding  
The Transformer-based Autoencoder was trained to reconstruct normal sensor behavior. The reconstruction error was used to detect anomalies using an adaptive threshold. Data points exceeding the threshold were classified as anomalies and further processed using the Isolation Forest and XGBoost classifiers.  
To further refine anomaly detection, a dynamic thresholding [23] approach was applied:  
\[T_{\text{adaptive}}=\mu_{\text{rec}}+k\sigma_{\text{rec}} \tag{4}\]  
where \(\mu_{\text{rec}}\) and \(\sigma_{\text{rec}}\) represent the rolling mean and standard deviation of the reconstruction errors, respectively, and \(k\) is an adjustable sensitivity factor. This method improves robustness against fluctuating sensor readings.  
3.6.2. Isolation Forest for Initial Anomaly Classification  
To distinguish between intrusions and malfunctions, we applied the Isolation Forest algorithm, which identifies anomalies based on how quickly they can be isolated in the feature space [24]. The model classified the following:  
* Class 1 (intrusions): anomalies caused by unauthorized access or external interference.
* Class 2 (malfunctions): sensor faults or hardware failures.  
3.6.3. Oversampling with SMOTE  
Since the dataset was imbalanced, we applied the Synthetic Minority Oversampling Technique (SMOTE) to balance the classes. This method generated synthetic samples for anomalies (intrusions and malfunctions) to improve model training [25].  
3.6.4. XGBoost for Final Classification  
To refine anomaly classification, we trained an XGBoost model on labeled anomaly data [26]. The model was optimized with the following:  
* Learning rate: 0.05.
* Max depth: 8.
* Estimators: 200.  
The classifier was trained and evaluated using an 80:20 train-test split.
###### Abstract  
A compact multifrequency measurement system based on frequency-to-time mapping technology is proposed and experimentally demonstrated using an integrated frequency scanning signal generator. The relationship between the input microwave frequency and the time difference of a pair of pulses is established to realize the frequency information mapping to the time information. As a main part in the proposed frequency measurement system, the frequency-scanning signal is generated by heterodyning of two lasers with the monolithic integrated laser array, of which one is modulated on a saw-tooth signal. In the proposed frequency measurement system, it can measure single/multiple frequency microwave signals with a large bandwidth for high resolution and flexible tunable measurement range for multifrequency band. In the experimental demonstration, the single frequency measurement errors are less than 90 MHz within the measurement range from 4 to 12 GHz. For two-tone signal, the measurement resolution reaches about 150 MHz.  
Keywords:photonic microwave frequency measurement; frequency-to-time mapping; heterodyning +
Footnote †: journal: Applied Physics Letters
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## 3 Numerical Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scheme 1**} & \multicolumn{2}{c}{**Scheme 2**} & \multicolumn{2}{c}{**Scheme 3**} \\ \hline \multirow{3}{*}{Frame} & Interval & flatness & Interval & flatness & Interval & flatness \\  & number & interval & number & interval & number & interval \\ \cline{2-6}  & 3 & \([0.22,0.31]\lambda\) & 5 & \([0.41,0.5]\lambda\) & 4 & \([0.31,0.41]\lambda\) \\ \hline \multirow{3}{*}{Base plate} & Interval & flatness & Interval & flatness & Interval & flatness \\  & number & interval & number & interval & number & interval \\ \cline{2-6}  & 1 & \([0.03,0.11]\lambda\) & 2 & \([0.11,0.18]\lambda\) & 2 & \([0.11,0.18]\lambda\) \\ \hline \multirow{3}{*}{1 st reflection} & Interval & flatness & Interval & flatness & Interval & flatness \\  & number & interval & number & interval & number & interval \\ \cline{2-6}  & 5 & \([0.27,0.33]\lambda\) & 3 & \([0.15,0.21]\lambda\) & 3 & \([0.15,0.21]\lambda\) \\ \hline \multirow{3}{*}{2 nd reflection} & Interval & flatness & Interval & flatness & Interval & flatness \\  & number & interval & number & interval & number & interval \\ \cline{2-6}  & 2 & \([0.09,0.15]\lambda\) & 4 & \([0.21,0.27]\lambda\) & 4 & \([0.21,0.27]\lambda\) \\ \hline \multirow{3}{*}{3 rd reflection} & Interval & flatness & Interval & flatness & Interval & flatness \\  & number & interval & number & interval & number & interval \\ \cline{2-6}  & 3 & \([0.15,0.21]\lambda\) & 2 & \([0.09,0.15]\lambda\) & 5 & \([0.27,0.33]\lambda\) \\ \hline \multirow{3}{*}{4 th reflection} & Interval & flatness & Interval & flatness & Interval & flatness \\  & number & interval & number & interval & number & interval \\ \cline{2-6}  & 2 & \([0.09,0.15]\lambda\) & 2 & \([0.09,0.15]\lambda\) & 4 & \([0.21,0.27]\lambda\) \\ \hline \multirow{3}{*}{failure probability} & \multirow{3}{*}{0\%} & \multirow{3}{*}{0\%} & \multirow{3}{*}{2.12\%} \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Three kinds of combinations for the array antenna. Figure 11: The parallel coordinates plot of all sets of samples passing the interval [3,1,5,2,3,2]. Tolerance analysis can only help designers to judge whether a specific tolerance design scheme is feasible, it cannot determine the optimal scheme. Further works are needed to achieve the optimal tolerances of the spaceborne array antenna.
## 4 Channel Models
### Relationship between the Transmitter Power and SNR
Let \(I_{SS}\) be the signal intensity of spherical spreading, and we have the source signal intensity \(I\). By spherical spreading, the intensity decreases as the inverse square of the transmission distance \(d\). Accordingly, the spherical spreading \(SS\) is usually expressed in decibels (dB) as follows:  
\[SS=-10\log\Big{(}\frac{I_{SS}}{I}\Big{)}=-10\log\Big{(}\frac{1}{d^{2}}\Big{)} =20\log d \tag{9}\]  
Third, the noise level \(N_{level}\) is affected by a number of sources, such as turbulence, shipping, and waves. In the real world, it is difficult to compute the exact value of the noise level. Fortunately, the author in [48] provides a practical approximation of the noise level in underwater environments. The approximation of \(N_{level}\) is given by the following equation:  
\[N_{level}=50-18\log f. \tag{10}\]  
Fourth, the directivity index \(D_{index}\) in underwater environments can be set as 0. This is because the underwater hydrophones are often omnidirectional. \[D_{index}=0 \tag{11}\]Combining Equations (2), (6), (7), (10) and (11), we can model the relationship between the transmitter power \(P\) and SNR \(\gamma\), as follows:  
\[ \begin{array}{ll}\gamma=&10\left[\log(P)-\log\bigl{(}4\pi r^{2}\bigr{)}-\log \bigl{(}0.67\times 10^{-18}\bigr{)}\right]\\ &-20\log d-\alpha(f)\times d\times 10^{-3}-50+18\log f,\end{array}  \tag{12}\]  
where \(r\), \(d\), \(\alpha(f)\), and \(f\) are transmission range, transmission distance, absorption coefficient, and frequency (of signal), respectively. Based on the relationship between the transmitter power \(P\) and SNR \(\gamma\) in Equation (12), we can actually compute the SNR by combining Equations (8) and (12), as follows:  
\[ \begin{array}{ll}\gamma=&10\left[\log(P)-\log\bigl{(}4\pi r^{2}\bigr{)}- \log\bigl{(}0.67\times 10^{-18}\bigr{)}\right]-20\log d\\ &-\Bigl{[}\frac{0.11}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 10^{-4}f^{2 }+0.003\Bigr{)}\times d\times 10^{-3}\Bigr{]}\\ &-50+18\log f.\end{array}  \tag{13}\]
# Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping
## 6 Experimental Tests  
In order to measure the amount of time reduction by moving from one-to-one to subtree mappings, we conducted an experimental test and recorded the time expended in both approaches. In our test, we used the circuit files that come with the Revkit package (40 files) as testing data. These files contain data regarding the set of BDDs generated by the Revkit. The files contain enough information with respect to the nodes and links of sample BDDs to construct the entire BDD in computer memory. Our algorithm takes these files as an input, works on them to construct the BDD for each file, then converts these BDDs to their corresponding reversible circuits once by considering the one-to-one approach and by considering the subtree. In each approach, the running time (in microseconds) is recorded for the comparison process. The result of the run is a table containing the file name and the running times of both approaches. In order to get rid of the machine dependency and to be sure that results are accurate, we repeated the process for all files 100 times and computed the average time for each file and for each approach.  
With respect to benchmark 30, the reduction time is 100% due to the huge size of the related BDD, so the difference between the one-to-one synthesis and subtree-based times is significant. Table 4 contains the collected data (the bold ones represent the most significant results), where the Quantum Cost (QC) has been found to be the same for both one-to-one and subtree-based approaches. This confirms the correctness of the proposed algorithm. Figure 9 depicts the reduction of time betweenthe two mappings (the time values that appear in Table 4 are normalized here to achieve a better graph visualization).  
Figure 10 illustrates the BDD for one of the benchmarks (Benchmark 9) and its resultant reversible circuit (Figure 11) after applying our proposed algorithm. It is very important to mention that the deeper the BDD, the more Toffoli gates are needed to realize that circuit. It is also important to mention that the source code of our algorithm, along with the benchmarks, is available in [29].  
Figure 10: BDD of Benchmark 9.  
Figure 9: One-to-one vs. subtree time mapping comparison.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Benchmark \#** & **\# of Variables** & **\# of Nodes** & **Quantum Cost (QC)** & **One-to-One Average Time** & **Subtree Average Time** & **Time Reduction Rate** \\ \hline  
197** & 127,494.95 & 487.206 & **99.61\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: One-to-one vs. subtree mapping time comparison.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Benchmark \#** & **\# of Variables** & **\# of Nodes** & **Quantum Cost (QC)** & **One-to-One Average Time** & **Subtree Average Time** & **Time Reduction Rate** \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: _Cont._
###### Abstract  
The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation. Although computing devices continue to increase in power, the complexity of evaluating a solution continues to keep pace. Ordinal optimization (OO) is developed as an efficient framework for solving CESOP. In this work, a heuristic algorithm integrating ordinal optimization with ant lion optimization (OALO) is proposed to solve the CESOP within a short period of time. The OALO algorithm comprises three parts: approximation model, global exploration, and local exploitation. Firstly, the multivariate adaptive regression splines (MARS) is adopted as a fitness estimation of a design. Next, a reformed ant lion optimization (RALO) is proposed to find \(N\) exceptional designs from the solution space. Finally, a ranking and selection procedure is used to decide a quasi-optimal design from the \(N\) exceptional designs. The OALO algorithm is applied to optimal queuing design in a communication system, which is formulated as a CESOP. The OALO algorithm is compared with three competing approaches. Test results reveal that the OALO algorithm identifies solutions with better solution quality and better computing efficiency than three competing algorithms.  
expensive simulation optimization; ordinal optimization; ant lion optimization; multi-variate adaptive regression splines; ranking and selection; queuing design; communication system Article  
1  
1 Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan  
2 Department of Electronic Engineering, National Quemoy University, Kinnen 892009, Taiwan; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +886-4-2332300 (ext. 7801)