esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 8 Optimization Using the Modified GA Approach
### Crossover Operation  
Crossover is a process in which genes are exchanged between two chromosomes to create a new population. In the current generation, crossover occurs at designated points, chosen randomly, and can take place at multiple points. The probability of crossover, typically set at a high value between 80% and 95%, controls the crossover operation and aims to enhance the genes in the chromosome structure. We considered a uniform crossover operation for our optimization algorithm. The chromosomes from two parents are inherited to create the offspring chromosomes. In this operation shown in Figure 6, each gene from Parent 1 is multiplied by \(\theta\) and by \((1-\theta)\) for Parent 2, and then, results are added to generate a single gene of the offspring chromosome, where \(\theta\) is the crossover rate. The operation for crossover is explained in Algorithm 2, in which a set of Chromosomes \(C=\{C_{1},C_{2},C_{3}\ldots C_{n}\}\) and \(N\) (Population size) is taken as input. The loop executes for each population and from each population \(n\) chromosomes are chosen for crossover process. The output is then the offsprings which are stronger and better than their parents.  
Figure 5: Selection process.  
Figure 6: Crossover operation.  
```
Input:\(C=\{C_{1},C_{2},C_{3}\ldots C_{n}\}\), N i = 0 while(i < N) do \(i=i+1\) \(P_{1_{i}}=C_{i}\); \(P_{2_{i}}=C_{i+1}\); \(\theta_{i}=\) random value between \(-1\) and \(1\); \(C_{1_{i}}=P_{1_{i}}+\theta_{i}+(1-\theta_{i})*P_{2_{i}}\) endwhile
```  
**Algorithm 2** Crossover operation.
## 5 Numerical Results
The most obvious reason for this can be the heavy-tailed distribution of the primary signal strength due to the log-normally-distributed shadow fading that behave in such a way at lower SNR [27]. In Figures 6 and 7, we show the AUC plots to analyze the effect of number of antennas (_i.e._, \(L\)) for a fixed number of users \(K\). The results clearly show that the performances of the two proposed schemes are consistently better than the traditional schemes that confirm result in previous plots. However, we can see that the traditional GLRT \(\Lambda_{\text{T}}\) performance degrades by increasing L (_i.e._, \(L=2\) to \(L=8\)). It is because for fixed \(N=60\) when \(L=8\), we  
Finally, in order to further analyze the detection performance of the proposed schemes, for a fixed value of probability of false alarm (\(P_{FA}\)), in Figure 8 we compare the results by plotting probability of detection \(P_{D}\)_vs._ average SNR. Once again, we can observe that the proposed schemes \(\Lambda_{\text{MPKP}}\) and \(\Lambda_{\text{SPKP}}\) outperform the remaining detection schemes presented in this paper.
## 5 Analysis of Training Results
### Comparison of Results of Various Models on the Test Set
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model Name** & **IoU** & **Dice** & **Recall** & **Precision** & **AUC** & **MCC** & **F1-Score** & **FPS** &
\begin{tabular}{c} **Parameter** \\ **Count (M)** \\ \end{tabular} \\ \hline SE-TransUNet & 0.8318 & 0.8304 & 0.9394 & 0.8480 & 0.9733 & 0.8562 & 0.9218 & 6.53 & 223 \\ TransUNet & 0.7864 & 0.8471 & 0.9142 & 0.8481 & 0.9730 & 0.8523 & 0.8714 & 14.64 & 176 \\ UNet & 0.7614 & 0.8631 & 0.8985 & 0.8213 & 0.9712 & 0.8440 & 0.8911 & 16.64 & 23 \\ Swin-Unet & 0.8159 & 0.8184 & 0.9242 & 0.8310 & 0.9483 & 0.8472 & 0.8993 & 8.33 & 134 \\ DeepLabV3 plus & 0.7515 & 0.8430 & 0.8542 & 0.8420 & 0.9790 & 0.8314 & 0.8544 & 15.72 & 50 \\ SegNet & 0.6761 & 0.7918 & 0.7740 & 0.8473 & 0.9499 & 0.7346 & 0.8036 & 17.32 & 14 \\ BiSeNetV2 & 0.5791 & 0.6904 & 0.6961 & 0.7823 & 0.9149 & 0.6405 & 0.7279 & 19.13 & 8 \\ FPN & 0.7873 & 0.8878 & 0.8893 & 0.8968 & 0.9853 & 0.8540 & 0.8923 & 16.51 & 25 \\ DoubleUnet & 0.7500 & 0.8216 & 0.8206 & 0.8986 & 0.9735 & 0.8057 & 0.8543 & 14.47 & 40 \\ NestedUNet & 0.7320 & 0.8194 & 0.8749 & 0.8197 & 0.9639 & 0.7791 & 0.8409 & 15.33 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical table of mean values of evaluation metrics for various leakage detection models in the test set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & \
\begin{tabular}{c} **SE-TransUNet** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} &
\begin{tabular}{c} **TransUNet** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} & **Difference (d)** & **t-Value** & **\(p\)-Value** \\ \hline IoU & 0.8318 \(\pm\) 0.0224 & 0.7864 \(\pm\) 0.0213 & 0.0454 & 3.25 & 0.031 \\ Dice & 0.8304 \(\pm\) 0.0204 & 0.8471 \(\pm\) 0.0202 & \(-\)0.0167 & \(-\)2.10 & 0.095 \\ Recall & 0.9394 \(\pm\) 0.0221 & 0.9142 \(\pm\) 0.0241 & 0.0252 & 2.87 & 0.042 \\ Precision & 0.8480 \(\pm\) 0.0202 & 0.8481 \(\pm\) 0.0215 & \(-\)0.0001 & \(-\)0.01 & 0.992 \\ AUC & 0.9733 \(\pm\) 0.0204 & 0.9730 \(\pm\) 0.0201 & 0.0003 & 0.03 & 0.977 \\ MCC & 0.8562 \(\pm\) 0.0218 & 0.8523 \(\pm\) 0.0221 & 0.0039 & 0.41 & 0.698 \\ F1-score & 0.9218 \(\pm\) 0.0213 & 0.9218 \(\pm\) 0.0211 & 0.0000 & 0.00 & 1.000 \\ FPS & 6.53 \(\pm\) 0.057 & 14.64 \(\pm\) 0.031 & \(-\)8.11 & \(-\)120.5 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical table of paired t-test results for SE-TransUNet and TransUNet models.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Aes  
The Advanced Encryption Standard (AES) was standardized in 2001 and it supersedes the insecure DES-Algorithm. It is a symmetric block cipher, based on the Rijndael algorithm [49]. The AES operates on a fixed block size of 128 bits and supports three different key lengths 128, 192, and 256 bits. Our AES implementation supports a key size of 128-bit only. The encryption as well as the decryption of single 128-bit block takes 66 clock cycles only. For comparison, a software implementation of AES on TI MSP430F1611 microcontroller takes 6600 cycles for encryption and 8400 cycles for decryption [50]. That makes a speed up of two orders of magnitude in favour of our hardware implementation. The detailed overview of the implemented AES core and its advantages over a software solution is given in [51].  
Although still considered as a safe encryption standard, AES-128 is expected to become vulnerable in the near future. Therefore, the more secure AES-256 standard is to be considered as a standard option for the future releases of the TNODE chip. Our preliminary investigation on the AES-256 implementation using the same architecture shows that the encryption and decryption operations would require 90 clock cycles each to complete. Accordingly, the chip area and power consumption will increase due to the increase in the size and the number of registers required for storing the key and the data.
## Appendix A IF Neuron Model  
The behavior of the IF neuron is affected by the output \(S_{i}(t)\) of the input neurons at each time step \(t\) and their weights \(w_{i}\). Algorithm A1 presents how a spike neuron maintains its membrane potential and emits spikes. Figure A2 illustrates the behavior of a spike neuron. When the membrane potential \(V(t)\) at time step \(t\) is greater than or equal to the prespecified threshold \(V_{th}\), an IF neuron emits a spike and sets its membrane potential to the resting potential \(V_{rest}\). Figure A1 illustrates a change in the membrane potential of an IF neuron when it receives the weighted input sum of \(1.5V_{th}\), \(1.2V_{th}\), and \(0.3V_{th}\) in three successive time steps, where the resting potential is assumed to be 0 for simplicity. Figure A1b shows the spikes generated at each time step.  
```
begin foreachtime step \(t\)do # generate the input current from incoming spikes \(I(t)=\sum_{i}w_{i}S_{i}(t)\) ; // \(S_{i}(t)\): input spike of \(i\)th synapse at time step \(t\) #input integration \(V(t)=V(t-1)+I(t)\) if\(V(t)\geq V_{th}\)then  Emit a spike: \(S(t)=1\)  Reset the potential to the resting potential: \(V(t)=V_{th}\)  end  end  end
```  
**Algorithm A1**Behavior of the IF neuron model.
## 5 Results
### Motion Estimation  
As referenced before, we tested our approach using three different sequences. All of them were placed in an outdoor environment in a ground robot context. In sequence A, we tested the mathematical approach, i.e., the omnidirectional VO system fused with the gyroscope and the laser sensor. The system was executed in a standard computer and benchmarked with the original Libviso2 approach (Figure 9). The two other sequences tested the full system, i.e., the mathematical approach with the heterogeneous computing optimizations and benchmark them with the raw Libviso2 results. Figure 10 shows the results for these two sequences. It is worth noting that we present results with and without the KF that corrects orientation for both the original version of Libviso2 and our FAST-FUSION. This is done to show the importance of this approach and to demonstrate its modularity since it can be coupled to any odometry system.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Sequence** & \multicolumn{3}{c}{**B**} & \multicolumn{3}{c}{**C**} \\ \hline
**Configuration** & \multicolumn{3}{c}{**Serial**} & \multicolumn{3}{c}{**Parallel**} & \multicolumn{3}{c}{**Serial**} & \multicolumn{3}{c}{**Parallel**} \\ \hline
**Features per Bucket** & **2** & **3** & **2** & **3** & **2** & **3** & **2** & **3** \\ \hline Random Sample & 0.032 & 0.037 & 0.003 & 0.004 & 0.031 & 0.037 & 0.003 & 0.004 \\ Get Inliers & 0.054 & 0.072 & 0.036 & 0.042 & 0.055 & 0.075 & 0.035 & 0.041 \\ Essential Matrix & 0.255 & 0.251 & 0.095 & 0.096 & 0.247 & 0.249 & 0.094 & 0.094 \\ RANSAC & 0.336 & 0.361 & 0.140 & 0.150 & 0.337 & 0.360 & 0.139 & 0.146 \\ Process & 0.501 & 0.600 & 0.305 & 0.403 & 0.468 & 0.528 & 0.267 & 0.331 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Runtime performance (sec) of the extended version of Libviso2 on Raspberry Pi using both the serial and parallel configurations.  
Figure 9: **(a)** Original version of Libviso2 and **(b)** FAST-FUSION motion estimation with and without the orientation correction running on a standard computer, and considering an approximated circle motion.
## 3. Gear Test Experimental Setup and Data Processing
Figure 7: Raw AE signals of the gear pitting fault conditions. The kernel size of all convolutional layers was set as 7 and stride as 1. Padding took 'Same padding' in order to maintain the same data size. The pool size was set as 2 for all the pooling layers. For pooling, the strides were set as none and padding as 'valid'. The AE signals used in the CNN contained 3072 features per sample. The GRU network used 6 stacked GRUs for training. The cell size of each GRU layer was set as 256, 256, 128, 128, 64, and 64, respectively. The vibrational signals used in the GRU network contained 616 features per sample. The batch size was set as 256. Both kernel initializer and recurrent initializer used the he-normal [25] method. The loss function was set as categorical cross-entropy. A stochastic gradient descent algorithm was used as the optimizer. Except for the last layer, the ReLU function was used as the activation function for the layers. In the last layer, a softmax function was used to classify gear pitting faults. An NVIDIA GeForce GTX 1080 Ti graphics card was used in the PC for training purpose. The general procedure of the data processing using the presented method is shown in Figure 8.
## 3 Computational Model  
The two-phase model is a set of equations and mathematical relations that are used to describe the phase's motion and interactions. The most commonly utilized two-phase models in the literature are homogeneous, drift flux and separated flow models [20; 21; 22; 23; 24; 25]. The homogeneous model is less complicated and can be applied to many complex two-phase flows with good accuracy. The homogeneous model is represented by the mixture two-phase model in CFD computations, as will be discussed later in this paper. In homogenous models, the average properties for the flow variables are considered in the calculations and the relative motion between phases can be accurately calculated [20; 26]. In addition, the homogenous model is easy to implement as its governing equations are close to the single-phase flow equations. In the present study, the mixture model for calculating the two-phase flow variables in the inverted U-tube is utilized.  
Figure 2 shows the CFD model with its boundary conditions. In the proposed work, the siphon flow generated by water flowing up in an inverted U-tube creates a vacuum in the tube upper region. This vacuum region can be connected by a side pipe to extract a mixture of air and water vapor from the equipment cavity, the equipment cavity is not shown in this figure. The equipment cavity is considered the steam condenser in the present case. This results in mixing the water with the induced  
Figure 1: Experimental test rig and its components.  
side mixture of air and water vapor. This mixing will cause the water vapor condensation while all the flow mixture will continue flowing down due to flow inertia and gravity. At the lower outlet of the inverted U-tube, the air is released to the environment while water is re-circulated through the inverted U-tube. Furthermore, a variable speed circulating pump is used to transfer the water between the two tanks at the inlet and outlet of the inverted U-tube (the pump is not shown in the figure). Additionally, the hydrostatic head difference between the water surfaces in both tanks can be altered to control the water mass flow rate. The considered tube has low maintenance and operational cost, which makes it more favorable compared to the other energy consuming systems, i.e., water ring pump and steam ejector [27].
# Preparation of Piezo-Resistive Materials by Combination of PP, SEBS and Graphene
## 3 Results and Discussion
### Thermal Properties  
TGA analysis was conducted to evaluate the homogeneity of the sample, to calculate the filler content and to monitor the stability of the materials throughout the compounding and processing steps (mixing, extrusion, and pressing). TGA curves of the materials prepared by both routes display only one degradation process (Figure 4). The observed degradation temperatures and char residue percentages are presented in Table 1. The influence of the formulation route was primarily observed for the iPP/SEBS blend, which shows improved thermal stability when all components are coprecipitated from solution (R1). The addition of G and GPP fillers improves the thermal properties, resulting in materials with very similar thermal stability. This confirms a good filler dispersion, providing optimum inhibition in the emission of pyrolytic gases a and delay in the matrix degradation. The results suggest little effect of the processing method and the type of filler used, as in all cases the ternary mixtures are more stable than the binary iPP/SEBS ones. The corrected residual weights from the TGA analysis verified the comparable filler loading in all nanocomposites, with values ranging between 4.8-5 wt.% G. No significant thermal degradation throughout the extrusion and melt-pressing steps was observed for all the materials studied.  
Figure 3: SEM images of cryo-fractured samples of iPP/SEBS (**A,B**); iPP/SEBS/G (**C,D**); iPP/SEBS/GPP (**E,F**) prepared via the SoM/SSM route. Scale bars in (**A,C,E**) correspond to 50 \(\upmu\)m) while those in (**B,D,F**) correspond to 10 \(\upmu\)m.
_Applied_  
_sciences_  
_Article_  
**Structural Dynamic Improvement for Petal-Type**  
**Deployable Solid-Surface Reflector Based on**  
**Numerical Parameter Study**  
**[PERSON] \({}^{\text{\textregistered}}\), [PERSON] and [PERSON]**  
School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an 710072, China; [EMAIL_ADDRESS] (Q.C.); [EMAIL_ADDRESS] (L.Z.)  
\({}^{\text{\textregistered}}\) Correspondence: [EMAIL_ADDRESS], Tel.: +86-13-6566-76823  
Received: 21 August 2020; Accepted: 18 September 2020; Published: 20 September 2020  
**Featured Application: This article can be applied to deployable reflector antennas in aerospace engineering.**  
**Abstract:** Petal-type Deployable Solid-surface Reflector (PDSR) is a kind of important structure widely applied in deployable reflector antennas in aerospace engineering. The dynamic properties of this reflector structure in deployed state are significant to the reflector accuracy for antennas. However, the study of the dynamic evaluation of deployable structure with revolute joints is difficult and seldom concerned by researchers. In order to study dynamic properties of the PDSR, the Cable Replacement Method (CRM) was utilized to equivalently simulate the nonlinear structural stiffness of the revolute joint for numerical analyses. The Finite Element Model (FEM) of this reflector structure was established by commercial software ANSYS (ANSYS Inc., Canonsburg, PA, USA) and verified by the theoretical analysis and dynamic test of actual prototype model. The natural frequencies and mode shapes of deployed reflector were computed to study the influence of drag spring design parameters as stiffness, pre-tensioned force, and distance of two adjacent linkage butts. Finally, the analysis results were concluded that the drag springs between two adjacent petals can essentially improve the dynamic performance of reflector structure in deployed state. It can be a useful technical system for future engineering applications of PDSR antennas.  
**Keywords:** solid surface; deployable reflector; reflector antenna; dynamic property; revolute joint; natural frequency
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 3 Proposed Method
* IWT scheme permits fast implementation of wavelet transforms with half the calculation time as compared to standard hiding techniques. * IWT works on integer numbers so takes very less time to compute the coefficients. * It analyzes the input signal at different frequencies with varying resolutions known as multi-resolution analysis. * It also allows reversible IWT as compared to conventional DWT which presents errors because of floating-point processes. * It can efficiently reconstruct the original signal because of lossless compression and its integer nature. * It analyzes discontinuity and sharp spikes in the signal. * It works equally in the time and frequency domains, and in the time domain discontinuities, can be analyzed easily. Despite various traditional compression techniques such as LZW, PNG, Run-length encoding, and many more. JPEG compression is chosen because of its intrinsic features. It is the very first international standard for the compression of images. It is used widely these days and provides lossy compression. It can store and transmit information efficiently. It decomposes the image into blocks and represents it in the form of a matrix. The pixel intensity varies from 0 to 255 [23]. First of all, it converts the image into the YIQ or YUV color space. Y represents the illumination value and UV represents the color of the image. The second step is the sampling in the form of blocks of 8 \(\times\) 8 or 64 \(\times\) 64 and so on. Blocks are subjected to DCT transform. With DCT transform, the value of coefficients is calculated. The next step is quantization and in its actual compression takes place. The unwanted values are ignored at this step. AC and DC coefficients are calculated during the quantization process. DC coefficients carry no useful information, and these coefficients can be ignored. The process of image compression is known as encoding. For getting the original image, back process of decoding takes place. JPEG is the lossy compression so during decoding, the quality of the image may degrade to some extent. Some of the important features of JPEG compression are as follow [24; 25]:  
* It is an interactive visual compression process. * It provides a multi-document interface for time enabling users to view and compare compression parameters. * It carries a compression slider for both illumination and color value. * It can crop unnecessary parts of the image. * It has graphic detail quality equalizer to provide flexibility in compression. * JPEG format is compatible with almost all image processing applications. * It is a portable and widely used format. * It finds the heterogeneous areas in the image and compresses those parts of the image as human eyes are extra sensitive to the variations in homogenous areas as equated to the heterogeneous area. * It works on chrominance information in the image as the human eyes perceive changes in brightness more sharply.
# Evaluating the Utility of UV Lamps to Mitigate the Spread of Pathogens in the ICU
## 2 Materials and Methods
### UV Light Installation and UV Treatment Protocol  
The manufacturer (UV Partners, Inc., Grand Haven, MI, USA) installed the UV lights (UV Angel) on 140 high-touch devices in the ICU. These devices included: IV pumps (Alaris Pump, BD Medical, Franklin Lakes, NJ, USA), stationary computer keyboards, portable computer workstation surfaces and keyboards, touchscreen vitals monitors, ventilators, and Pyxis drug dispensers (Pyxis Medstation, BD Medical, Franklin Lakes, NJ, USA). The UV lights used were small, 3.2 cm deep by 30.5 cm wide, and were placed above high-touch items with the goal of providing fully automated decontamination cycles after each use. Detailed descriptions of the UV device have been published previously [18]. The UV lights were programmed to turn on 90 s after user input was no longer detected. The UV light remained on for an 18 min cycle. The cycle length was determined based on a previous analysis in which UV-C light was used to inactivate C. difficile spores [19]. The UV lights reported the number of cleaning cycles initiated and completed via their cloud connectivity. No other HAI reduction strategy, outside of the standard of care, was implemented during this analysis.