esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabrication and Testing of Thermoelectric CMOS-MEMS Microgenerators with CNCs Film
## 5 Conclusions  
The manufacture of a microgenerator with a CNCs film was carried out employing a CMOS-MEMS technology. Experiments showed that the TD of thermopiles in the microgenerator with CNCs exceeded that without a CNCs film. The CNCs film had a heat absorption effect, which enhanced the thermopile temperature difference, so that the microgenerator output power increased. To enhance the temperature difference of the thermopiles in the microgenerator, the hot part in the microgenerator was etched as suspension structures. The cold part in the microgenerator was anchored on silicon substrate and covered with a low thermal conductivity silicon dioxide layer. The post-process employed RIE etching to form hot part suspension structures in the microgenerator and coated the CNCs film on the microgenerator surface. The tested results depicted that the microgenerator output voltage was 3.3 mV and its output power was 125 pW at TD 3 K. Voltage factor of the microgenerator was 0.71 mV/K/mm\({}^{2}\) and its power factor was 9.04 pW/K\({}^{2}\)/mm\({}^{2}\). The CNCs film enhanced heat absorption of the hot part in the microgenerator and increased the temperature difference of the microgenerator. Comparing to the microgenerators [27; 30; 31; 32; 33] shown in Table 1, the voltage and power factors of the microgenerator in this work exhibited a little enhancement.  
\begin{table}
\begin{tabular}{c c c} \hline
**TMG** & **Voltage Factor (mV/K/mm\({}^{2}\))** & **Power Factor (pW/K\({}^{2}\)/mm\({}^{2}\))** \\ \hline [PERSON] [27] & 0.093 & 6.4 \(\times\) 10\({}^{-4}\) \\ [PERSON] [30] & 0.147 & 2.32 \\ [PERSON] [31] & 0.32 & 1.88 \(\times\) 10\({}^{-3}\) \\ [PERSON] [32] & 0.178 & 1.47\(\times\) 10\({}^{-3}\) \\ [PERSON] [33] & 0.18 & 1.14 \\ This work & 0.71 & 9.04 \\ \hline \end{tabular}
\end{table}
Table 1: Voltage factor and power factor for TMG.  
Figure 15: Measured OP of the microgenerator.  
**Author Contributions:** [PERSON] carried out the fabrication and measurement of the thermoelectric microgenerator. [PERSON] and [PERSON] analyzed the performance of the thermoelectric microgenerator. [PERSON] supervised the work of [PERSON] and wrote the paper. All authors read and approved the final manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology (MOST) of the Republic of China grant number MOST 105-2221-E-005-037-MY3 and MOST 106-2221-E-005-050-MY2.  
**Acknowledgments:** The authors would like to thank the National Center for High-performance Computing (NCHC) for chip simulation, National Chip Implementation Center (CIC) for chip fabrication and the MOST of the Republic of China for financially supporting.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
Currently, medical diagnosis and therapy can be performed using tissue mimicking materials modeling the whole human body or a part of it. In this study, we investigated several radiological properties of human tissues (adipose, cortical bone, fat, lung and muscle) and TMM candidates for a photon energy range of 0.15-15 MeV. In addition to the standard selection criteria of analyzing the attenuation coefficients, we studied the dependence of the buildup factor on photon energy and penetration depths up to 40 mfp. Therefore, we developed an in-house C++ program for BUF computation based on the GP-fitting method. Moreover, we simulated and compared the percentage depth dose distributions of a realistic 6 MV photon beam (with a \(10\times 10\) cm\({}^{2}\) field size) through tissues and TMMs using the Geant4 Monte Carlo toolkit. This procedure helped us to distinguish between good and perfect TMM candidates. Consequently, we found that MU8 and CB3 perfectly imitate muscle and cortical bone tissues, respectively. However, AD1, FA3 and LU2 can be suitable for modeling adipose, fat and lung tissues, respectively. Eventually, this study shows that TMMs are beneficial for medical research since they are capable of simulating idealized tissue and allowing the examination of medical devices, procedures and imaging in a test setting without harming animals or humans.  
Conceptualization, methodology, investigation, writing--original draft preparation, writing--review and editing, O.K. and A.A. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Relative percentage deviation of BUF values of substitute materials for muscle tissue as a function of: (**left side**) photon energy for fixed depths of 0.5, 15 and 40 mfp and (**right side**) depth for fixed photon energies of 0.5, 5 and 15 MeV.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors extend their appreciation to the College of Applied Medical Sciences Research Center and the Deanship of Scientific Research at King Saud University, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion  
As shown in Table 7, LSTM combined with the Hawkes process can effectively capture the cascading correlations among historical records for the inherent temporal self-exciting characteristic of anomalies. Hence, the output of the LSTM corresponding to self influence  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Dataset**} & \multicolumn{1}{c}{**KDD99**} & **SWAT** & **WADI** & **J10031** \\ \hline \multirow{3}{*}{LSTM-GAN} & P & 0.720 & 0.712 & 0.765 & 0.775 \\  & R & 0.796 & 0.720 & 0.711 & 0.784 \\  & F1 & 0.844 & 0.722 & 0.624 & 0.753 \\ \hline \multirow{3}{*}{K-GAN} & P & 0.780 & 0.745 & 0.782 & 0.842 \\  & R & 0.842 & 0.775 & 0.738 & 0.826 \\  & F1 & 0.882 & 0.754 & 0.652 & 0.826 \\ \hline \multirow{3}{*}{H-GAN} & P & 0.820 & 0.740 & 0.794 & 0.817 \\  & R & 0.944 & 0.842 & 0.810 & 0.827 \\  & F1 & 0.864 & 0.833 & 0.645 & 0.792 \\ \hline \multirow{3}{*}{MTAD-GAN} & P & 0.945 & 0.932 & 0.956 & 0.972 \\  & R & 0.960 & 0.955 & 0.931 & 0.912 \\ \cline{1-1}  & F1 & 0.965 & 0.890 & 0.796 & 0.904 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of the P, R, F1 of ablation experiment.  
Figure 9: Comparison plot of the loss function ablation experiment AUC-ROC.  
of the Hawkes process demonstrates the accumulated results which decay exponentially with time for anomalies during historical intervals for current detection. The knowledge graph of oil and gas station for multivariate time series data, which enhance the semantic information of detecting anomalies, is constructed to capture the latent inter-dependencies between entities and relations. Then, the anomaly information related to the occurrence of sensors is transformed into the feature vector to mine the potential connections among multi-sensors. The feature vectors are used for training LSTM model to improves the ability of high-level feature expression and the accuracy of anomaly detection.  
The novel loss functions including reconstruction loss function, discrimination loss function and score loss function is designed to constrain the hidden variable and accelerate the gradient descent speed with few iterations during the training process. The highest results of AUCROC shown in Table 6 demonstrate the efficiency of our MTAD-GAN method with ADGS to improve the accuracy of detecting anomalies for the imbalanced and high-dimensional data in oil and gas stations.
## 4 Methodology of the Survey
### Limitations  
The literature survey concentrates on the publishers of IEEE, ACM, and Springer Link. It excludes others, where further literature could be published, and cannot be complete. As we choose major publishers, the most known publications should be included. The categorization is based on the title and the content of the paper. Depending on the actual paper, other categories might be involved. We chose the most prominent one, appearing in the title. Additionally, we exclude publications before 2020, since the field is rapidly evolving. Lastly, we focus on threats and vulnerabilities in this survey. We note countermeasures and summarize them per layer, but these might not be all the measures that can be taken.
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 4 Results and Discussion
### Comparison of EE-DL, GPR, and RF
Although, the GPR detected the least amount of false positives, this was at the expense of the total number of anomalies. Therefore, the EE-DL approach detected the most true positives compared to the other approaches, but its true positives were not at the expense of false positives, like the RF model.
## 5 Estimation and Simulations
### Estimation  
Various methods of estimation have been developed for estimating the model parameters; however, a well-known method, MLE, is used commonly. Here, from a complete sample only, we define the MLE method for the estimation of the unknown parameters of the NGLog-X family distributions. Suppose that \(x_{1},x_{2},\ldots,x_{k}\) are the observed values of the size \"\(n\)\" selected from the proposed NGLog-X family of distributions with the parameters \((\delta_{MLE},\Theta_{MLE})\) randomly. Then, the expression of the LF (likelihood function) \(\varphi(x;\delta,\Theta)\) is acquired as  
\[\varphi(x;\delta,\Theta)=\prod_{i=1}^{n}\left(\frac{f(x;\Theta)e^{\delta} \Big{\{}1+\delta(e-\log F(x;\Theta))^{-1}\Big{\}}}{\left(e-\log F(x;\Theta) \right)^{\delta}}\right) \tag{32}\]  
The corresponding log-LF, \(\ell(\Phi)\), is given by  
\[\begin{array}{rl}\ell(\Phi)=&n\delta+2\sum_{i=1}^{n}\log f(x_{i};\Theta)+ \sum_{i=1}^{n}\log\Big{(}1+\delta(e-\log F(x_{i};\Theta))^{-1}\Big{)}\\ &-n\delta\sum_{i=1}^{n}\log(e-\log F(x;\Theta))\end{array} \tag{33}\]  
Using \(\ell(\Phi)\), the partial derivatives are the following equations:  
\[\begin{array}{rl}\frac{d}{d\delta}\ell(\Phi)=&n+\sum_{i=1}^{n}\frac{\left(e -\log F(x_{i};\Theta)\right)^{-1}}{\left(1+\delta(e-\log F(x_{i};\Theta))^{-1 }\right)}\\ &-n\sum_{i=1}^{n}\log(e-\log F(x;\Theta)),\end{array} \tag{34}\]  
\[\begin{array}{rl}\frac{d}{d\Theta}\ell(\Phi)=&+2\sum_{i=1}^{n}\frac{d/d \Theta f(x;\Theta)}{f(x_{i};\Theta)}+n\delta\sum_{i=1}^{n}\frac{[F(x;\Theta)]^ {-1}d/d\Theta f(x;\Theta)}{\left(e-\log F(x;\Theta)\right)}\\ &-\sum_{i=1}^{n}\frac{\left(e-\log F(x_{i};\Theta)\right)^{-2}[F(x_{i};\Theta) ]^{-1}d/d\Theta f(x_{i};\Theta)}{\left(1+\delta(e-\log F(x_{i};\Theta))^{-1} \right)},\end{array} \tag{35}\]  
where \(\Phi=(\delta,\Theta)\).  
Setting \(\frac{d}{d\delta}\ell(\Phi)=0\) and \(\frac{d}{d\Theta}\ell(\Phi)=0\) and solving these equations simultaneously yielded the MLEs of \(\delta_{MLE}\), \(\Theta_{MLE}\). Clearly, the resulting equations cannot be solved analytically. Therefore, statistical software can be applied to solve these equations numerically through iterative Newton-Raphson-type algorithms.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 4 Discussion
In the second-order system, the elimination of the three mentioned poles can be effectively carried out if \(N=1\), which was the case with the analyzed example of a CT system. The modified (\"non-ringing\") control signal from the Dahlin controller, matched the control signal from the Dahlin PID controller as shown in Figure 23, and the desired aperiodic response was achieved as presented in Figure 22. For systems when \(N\) is greater than 1, the authors propose the application of only the elimination of one negative pole by including \(z=1\) in the factor \((C_{3}+C_{4}z^{-1})\).
## 2 The Proposed SAR ADC
### The Proposed Switching Scheme
 \right]\times\frac{CV_{\rm ref}^{2}}{2^{i-1}} \tag{5}\]  
N\({}^{\rm th}\) comparison: If D\({}_{\rm N-1}\) = 1, in the unit array of the positive-conversion capacitor array, the reference voltage of the unit capacitor is switched from V\({}_{\rm ref}\) to V\({}_{\rm cm}\), while the reference voltage of the other capacitors remains constant. If D\({}_{\rm N-1}\) = 0, the reference voltage of the capacitor in the unit array of the negative-conversion capacitor array is switched from V\({}_{\text{ref}}\) to V\({}_{\text{cm}}\), and the reference voltage of the other capacitors remains unchanged. At the N\({}^{\text{th}}\) comparison, the capacitor array switching energy is  
\[\text{E}_{\text{N}}=\left\{2\times\left[\sum_{\text{i=4}}^{\text{N}-2}((1- \text{D}_{\text{i}-1})\text{D}_{\text{N}-1}+\text{D}_{\text{i}-1}(1-\text{D}_{ \text{N}-1}))\times 2^{\text{N}-1-\text{j}}\right]-2^{\text{N}-2}+1\right\} \times\frac{\text{CV}_{\text{ref}}^{2}}{2^{\text{N}}} \tag{6}\]  
For N-bit resolution, the average switching energy of the capacitor array is  
\[\text{E}_{\text{average}}=\left(2^{\text{N}-6}+\sum_{\text{i=5}}^{\text{N}}2^{ \text{N}-\text{i}-1}\right)\text{CV}_{\text{ref}}^{2} \tag{7}\]  
Starting from the 3 rd comparison, the voltages can all be summarized as  
\[\left\{ \begin{array}{l}\text{V}_{\text{P}}(\text{i})=\text{V}_{\text{P}}+[1- \text{D}_{1}]\times\frac{\text{V}_{\text{ref}}}{2}-\sum_{\text{i=3}}^{\text{ N}}\text{D}_{\text{i}-1}\times\frac{\text{V}_{\text{ref}}}{2^{\text{i}-1}}\\ \text{V}_{\text{N}}(\text{i})=\text{V}_{\text{IN}}+\text{D}_{1}\times\frac{ \text{V}_{\text{ref}}}{2}-\sum_{\text{i=3}}^{\text{N}}[1-\text{D}_{\text{i}- 1}]\times\frac{\text{V}_{\text{ref}}}{2^{\text{i}-1}}\end{array} \right. \tag{8}\]  
Figure 4a displays the successive approximation waveform, where it can be seen that the common-mode shift of the proposed switching scheme is only V\({}_{\text{ref}}\)/4. The voltage conversion flow chart of the 10-bit SAR ADC is shown in Figure 4b. Only after the first comparison does the voltage on the low-voltage side increase (V\({}_{\text{ref}}\)/2), and each subsequent comparison is a voltage drop (V\({}_{\text{ref}}\)/2\({}^{\text{i}}\)) on the high-voltage side.
## 3 Experimental Validation
### Results  
The predicted RCF behavior, using the methodology described in this paper, is similar to the measurements for both assessed case studies. Figure 12 shows a qualitative comparison between measured and predicted locations of cracks in Sections 1a and 2b, respectively. The three main conclusions are also:  
* No cracks have been predicted for any straight track.
* Different crack depths at the whole circular curve and some cracks at clothoids are predicted for Section 1.
* Punctual cracks are predicted for Section 2.  
The comparison between the predicted and measured values shows when a high density of cracks or no cracks are expected. According to Innotrack report [44], the curve radii threshold for RCF is between 700 m and 1000 m. On the one hand, Section 1 is within this threshold (curve radio of 720 m) and has a lot of cracks on its circular curve. Between the distance of 400 m and 700 m, long cracks appear and shorter cracks between the distance of 700 m and 850 m. On the other hand, Section 2 with a curve radius out of the threshold (1100 m) has very few cracks to consider an RCF behavior. For both Sections 1 and 2, no cracks appear at straight tracks.  
Figure 11: Number of cracks measured as a function of their depth, grouped by curvature type: (**a**) Section 1; (**b**) Section 2.  
For further validation, the trend-lines of measurements and predictions have been compared for circular curves of both sections and the entry clothoid in Section 1. The number of cracks has been normalized (crack quantity per depth has been divided by a track section of 100 m), to compare each curvature type independently of its length. As crack depth assessments are around every meter, much more cracks are taking place in rails. However, this quantification is enough in terms of comparison. Figure 13 shows that predictions replicate the behavior change of different sections in the same order of magnitude. According to the results of the calculation, it can be seen that for initial cracks the values are similar. Specifically, the relative error at small circular curve (Section 1) is 4.1% and the relative error at clothoid is 18.5%; the cracks at large circular curve are far too punctual to obtain a relative error.  
Figure 12: Location comparison between predicted and measured cracks for 4.752 MTn: (**a**) Section 1; (**b**) Section 2.
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
## 2 A Twice TWSTT NavCom Signal Authentication Scheme
### Signal and Detection Model
\begin{array}{ll}R_{\text{outh}}&=\Delta T_{1}-\Delta T_{2}\\ f_{\text{detect}}&=\begin{cases}\text{H}_{0},&R_{\text{outh}}&>R_{\text{th}}(P _{FA})\\ \text{H}_{1},&R_{\text{outh}}&\leq R_{\text{th}}(P_{FA})\end{cases}\end{array}  \tag{23}\]  
Specifically, after the calculation of the clock difference measurements from twice TWSTT, the magnitude of the difference is compared against the detection threshold to determine whether the difference exceeds this threshold.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
###### Abstract  
The separation mechanism is a critical device that transports and releases satellites during launch and on-orbit. However, as satellites become smaller and more compact, the traditional bell-locking device or pyrotechnic release and separation device cannot meet the micro-nano satellite's separation requirements. A novel separation mechanism kinematic system was designed, analyzed, and experimentally verified to achieve non-interference and non-pyrotechnic separation of the satellites from the launch vehicle while maintaining the initial separation attitude. First, an overall structural strategy for the kinematics system was proposed based on the MF (product code of the satellite) satellite's structural properties. The structural characteristics of the separation mechanism were also confirmed by the principle of energy conservation. Then, a finite element model and a dynamic model of the kinematic coupling system between the MF satellite and the separation mechanism during launch and deployment were constructed, along with an analysis of the mechanical characteristics and a kinematic simulation. Finally, the mechanical characteristics and dependability of the separation mechanism were verified using a ground vibration test and a separation test. The prototype test results demonstrate that the separation process is essentially identical to the numerical simulation results and that the MF satellite can be deployed successfully without interference, with the MF satellite's initial separation attitude fully satisfying the designed technical indexes.  
s +
Footnote †: journal: selected topics  
**Citation:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON].  
Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites. _Appl. Sci._**2022**, _12_, 5997. [[https://doi.org/10.3390/app12125997](https://doi.org/10.3390/app12125997)]([https://doi.org/10.3390/app12125997](https://doi.org/10.3390/app12125997))  
Academic Editor: [PERSON]
# A Performance Evaluation for Software Defined Networks with P4
## 5 Results and Analysis of Tier 1--Single Type of Traffic Run
### Case Study 2--TCP  
TCP traffic was generated via a file download of 1 GB using iPerf for multi-path and grid topologies. For the Internet topology, a transmission of TCP for 1200 s was employed. See Figure 1 and Table 1 for respective topologies and traffic configuration. The results for the experiments are shown in Figure 5 with a summary of the results given in Table 2.  
In _SDN+P4_ (red line) TCP has achieved a higher and more constant throughput in comparison to _SDN+OvS_ (blue line). In Figure 5a,b, the fixed sized TCP download completed earlier for SDN+P4 in approximately 750 s as opposed to approximately 820 s for _SDN+OvS_. In Figure 5b, (the blue line) experienced fluctuations in throughput. TCP traffic, by design, requires acknowledgements. As the data packets and the acknowledgements traverse in the same route (as observed by accessing the ONOS GUI), the throughput fluctuates due to the saturation of links. It is also noteworthy that for both _SDN+P4_ and _SDN+OvS_, TCP traffic traversed in the same route for the grid topology. However, the _SDN+P4_ didn't record fluctuations in throughput due to its faster processing capability. Figure 5: Throughput of TCP data transfer in case study 2. (**a**) TCP transfer of 1 GB data using iPerf in multi-path topology. (**b**) TCP transfer of 1 GB data using iPerf in grid topology. (**c**) TCP transfer for 1200 s using iPerf in the Internet topology. For multi-path topology (Figure 5a), a fluctuation of throughput was not recorded in the _SDN+OvS_ due to the small size of the network and fewer nodes with processing requirements. For the Internet topology (Figure 5c), similar results were observed as per multi-path topology. This is because the links in the Internet topology were not saturated with network traffic. Figure 5c presents the transmission of TCP traffic in the Internet topology. In contrast to a fixed download size (Figure 5a,b), the TCP traffic file downloading from iPerf was run with a time constraint of 1200 s. Again, _SDN+P4_ (red) achieves a higher throughput than that achieved with _SDN+OvS_ (blue), demonstrating that P4 switches process packet forwarding in _SDN+P4_ faster than OvS switches in _SDN+OvS_. Table 2 summarises the performance metrics in terms of throughput, packet loss, Syn packet delay, delay, amount of data transmitted and transmission time for this group of experiments, averaged over the connections for each of the topologies given in Table 1. In all counts, _SDN+P4_ has shown significant improvement over _SDN+OvS_. Throughput
## 2 Materials and Methods
### Model Reduction and Optimization
#### 2.2.1 Advanced Model Reduction Strategy  
The proposed hybrid model reduction method combines three complementary techniques to achieve optimal performance. The reduction process consists of three sequential stages:Initial Model Reduction  
The process begins with numerical methods applied to the higher-order system. This initial reduction provides a baseline SOPTD (Second Order Plus Time Delay) model of the form  
\[\text{Gm(s)}=e^{-sL}/\left(\text{as}^{2}+\text{bs}+\text{c}\right) \tag{9}\]  
where  
- a, b, c are system parameters  
- L represents the time delay  
- s is the Laplace operator  
Following initial reduction, Genetic Algorithm optimization refines the model parameters to minimize the error between the reduced and original models. The optimization process incorporates multiple performance criteria to ensure accurate representation of system dynamics.  
LSTM-Based Fine-Tuning  
The final stage employs LSTM networks to perform real-time fine-tuning of the reduced model parameters, enabling adaptive response to system changes.