esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 3 Proposed Work
### Illustration of the Proposed Work  
An illustration of the proposed work is presented here. For explanation, we take nine edge pixels of a block from a hybridized edge image and skip the first two steps. Let the nine edge pixels of a cover image be \(K_{1}=23\), \(K_{2}=47\), \(K_{3}=65\), \(K_{4}=91\), \(K_{c}=83\), \(K_{5}=12\), \(K_{6}=96\), \(K_{7}=64\) and \(K_{8}=34\). We obtain LBP codes using Equations (5) and (6) from the block as \(L=\{1,1,1,1,0,0,1,1\}\). Assume that the secret message bits \(M=\{0,1,0,0,0,0,1\}\). Now, using Equation (7), we get the XORed values H.  
\(H=L\oplus M\)\[H = \{1,1,1,1,0,0,1,1\}\oplus\{0,1,0,0,0,0,0,1\};\] \[H = \{0,1,1,1,0,0,0,1\}\ ;\]  
Next, we shuffle the XORed values.  
\(H=\{1,0,1,1,0,0,1,0\}\) ;  
Now using step 7, we implant the shuffle bits in edge pixels and generate stego pixels. At the same time, we also keep the trace matrix TC of this block. After implantation, stego pixels are \(SK_{1}=23\), \(SK_{2}=47\), \(SK_{3}=65\), \(SK_{4}=92\), \(SK_{c}=83\), \(SK_{5}=12\), \(SK_{6}=96\), \(SK_{7}=64\) and \(SK_{8}=33\) and the trace matrix is \(TC=\{0,0,0,1,0,0,1\}\). That stego image and trace matrices are sent to the receiver end.  
At the extraction phase, consider the stego block from the hybridized edge image and the stego pixels are \(SK_{1}=23\), \(SK_{2}=47\), \(SK_{3}=65\), \(SK_{4}=92\), \(SK_{c}=83\), \(SK_{5}=12\), \(SK_{6}=96\), \(SK_{7}=64\) and \(SK_{8}=33\). Using Equations (13) and (14), we collect LBP code \(SL=\{1,1,1,1,0,0,1,1\}\) and trace matrix is \(TC=\{0,0,0,1,0,0,1\}\). Now, we XORed the trace matrix TC with the LSB of this block SB.  
\(IS=TC\oplus LSB(SB)\);  
\(IS=\{0,0,0,0,1,0,0,1\}\oplus\{0,1,1,1,0,0,0,1\}\);  
\(IS=\{0,1,1,1,1,0,0,0\}\);  
Again, we XORed that value Is with trace matrix TC.  
\(HS=IS\oplus TC\) ;  
\(HS=\{0,1,1,1,1,0,0,0\}\oplus\{0,0,0,0,1,0,0,1\}\);  
\(HS=\{0,1,1,1,0,0,0,1\}\);  
Now, we shuffle the XORed values of HS.  
\(H^{\prime}=\{1,0,1,1,0,0,1,0\}\);  
To extract the secret message bits, we again XORed \(H^{\prime}\) with LBP code SL.  
\(M^{\prime}=H^{\prime}\ominus\)SL;  
\(M^{\prime}=\{1,0,1,1,0,0,1,0\}\oplus\{1,1,1,1,0,0,1,1\}\);  
\(M^{\prime}=\{0,1,0,0,0,0,1\}\);  
Those are our implanted secret bits which are extracted successfully.  
Using step 8 of the data extraction process, we are able to restore the original cover image pixels and those are \(K_{1}=23\), \(K_{2}=47\), \(K_{3}=65\), \(K_{4}=91\), \(K_{c}=83\), \(K_{5}=12\), \(K_{6}=96\), \(K_{7}=64\) and \(K_{8}=34\).  
The message bits can be successfully extracted and the cover image can be restored. Thus, the proposed work is reversible.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
###### Abstract  
In OFDM wireless communication systems, low-resolution channel characteristics and noise interference pose significant challenges to accurate channel estimation. To solve these problems, we propose a super-resolution denoising residual network (SDRNet), which combines the advantages of the super-resolution convolutional neural network (SRCNN) and the denoising convolutional neural network (DnCNN) to construct a pilot-based OFDM signal model, train SDRNet using OFDM pilot data containing Gaussian noise, and optimize its feature enhancement ability in frequency-selective fading channels. To further explore the role of channel estimation in communication security, we propose a frequency-domain adversarial attack method based on SDRNet output. This method first converts the time-domain signal to the frequency domain by using the Fourier transform and then applies Gaussian noise and selective masking. By integrating the channel gradient information, the adversarial perturbation we generated significantly improves the attack success rate compared with the non-channel awareness method. The experimental results show that SDRNet is superior to traditional algorithms (such as the least square method, minimum mean square error estimation, etc.) in both mean square error and bit error rate. Furthermore, the adversarial samples optimized through channel awareness frequency-domain masking exhibit stronger attack performance, confirming that accurate channel estimation can not only enhance communication reliability but also provide key guidance for adversarial perturbation. The experimental results show that under the same noise conditions, the MSE of SDRNet is significantly lower than that of LS and MMSE. The bit error rate is lower than 0.01 when the signal-to-noise ratio is 10 dB, which is significantly better than the traditional algorithm. The attack success rate of the proposed adversarial attack method reached 79.9%, which was 16.3% higher than that of the non-channel aware method, verifying the key role of accurate channel estimation in enhancing the effectiveness of the attack.  
channel estimation; deep learning; frequency-domain transformation; adversarial attack; wireless communication system +
Footnote †: journal: Sensors
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 4 Simulation Results
### ABER Performance
the quantization errors (i.e., one-bit ADC is utilized) and in the absence of the quantization errors (i.e., infinite-bit ADC is utilized) for the uncoded and coded scenarios (e.g., soft Viterbi decoder is utilized at the RX side). It should be noted that when the infinite-bit ADC is utilized with the OSP-MSK modulation, the use of a MLSE equalizer is mandated at the RX side in order to efficiently recover the input binary data. As shown in Figure 8, for the uncoded scenario, about an eight-dB difference in the SNR value is observed between the one-bit and the infinite-bit cases. While, for the coded scenario, the difference is about three dB. The prior results without a doubt manifest the effect of the harsh quantization error caused by the one-bit ADC, which severely degrades the performance of the proposed scheme as the conventional massive MIMO systems [11; 12]. Furthermore, the results emphasize the importance of using channel coding techniques in both the MIMO-CEM and the proposed OSP-MSK modulation. Figure 6: The ABER performance of the proposed single-input single-output orthogonal shaping pulses minimum shift keying (SISO-OSP-MSK) compared to the ABER of the conventional SISO-CEM system with Gaussian minimum shift keying (GMSK) and quadrature multiplexed Gaussian minimum shift keying (QMGMSK) modulation schemes [32; 33]. Figure 7: The ABER performance of the proposed MIMO-OSP-MSK compared to the ABER of the conventional multiple-input multiple-output constant envelope modulation (MIMO-CEM) system with GMSK and QMGMSK modulation schemes [32; 33].
## 4 Discussion
### Prediction of Oxide Layer Thickness in High-Temperature LBE  
The thicknesses of oxide layers can be predicted by the parabolic oxidation law [53]. The experimental values obtained for T91, HT9, and SS316L in this study at 1000 h, together with abundant literature values [15; 18; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67], are plotted against temperature in Figures 15-17. The data points plotted in the figures can be found in Tables A1-A3 in Appendix A.  
Figure 14: Stable oxygen control range in LBE as a function of LBE temperature [50; 52].  
Figure 16: Prediction of the corrosion depths on HT9 [18; 53; 56; 67].  
Figure 15: Prediction of the corrosion depths on T91 [15; 53; 54; 55; 60; 65; 66].  
For T91, the predicted values showed good agreement with previous experimental results at temperatures below 500 \({}^{\circ}\)C, while large deviations were observed above 535 \({}^{\circ}\)C. For HT9, the parabolic law was valid below 500 \({}^{\circ}\)C, with the corresponding rate constant being almost equal to that of T91. For SS316L, a thin Fe-Cr spinel layer was formed at temperatures below -450 \({}^{\circ}\)C, while duplex layers were observed at higher temperature. Dissolution started at -500-550 \({}^{\circ}\)C, and experimental results were consistent with predictions for T91 and HT9 at temperatures below the dissolution onset.  
As the corrosion tests in this study were carried out in static cells, the results cannot be correlated or extrapolated to the corrosion behavior under LBE-flowing conditions as there is an additional stress given as shear by the flow at the interface between the LBE and oxide layer, in addition to the stress given by oxide growth. Additionally, a system with a non-uniform temperature distribution along the flow path also differs from the static condition, since temperature dependence in solubility influences the spalling of an oxide layer. Once the dissolution of alloying elements occurs at a hotter region, it leads to a continuous mass transfer to a colder zone since no local equilibrium can be met. Moreover, the colder zone may undergo a flow area blockage from the reduction in solubility, which results in the precipitation of the transported material.
## 2 Algorithm  
Let \(\textbf{x}=[x_{1},x_{2},\ldots,x_{i},\ldots,x_{N}]\) be a given univariate uniformly sampled signal containing a component of periodic or quasi-periodic peaks. The first step of the automatic multiscale-based peak detection (AMPD) algorithm consists of calculating the _local maxima scalogram_ (LMS). To this end, the signal **x** is first linearly detrended, _i.e._, the least-squares fit of a straight line to **x** is calculated and subtracted from **x**. Then, the local maxima of the signal **x** are determined using a moving window approach whereby the window length \(w_{k}\) is varied \(\{w_{k}=2k\mid k=1,2,\ldots,L\}\) with \(k\) the \(k\)-th scale of the signal and \(L=\left\lceil N/2\right\rceil-1\) where \(\left\lceil z\right\rceil\) is the ceiling function that gives the smallest integer not less than \(z\). This is realized for every scale \(k\) and for \(i=k+2,\ldots,N-k+1\) according to  
\[m_{k,i}=\left\{\begin{array}{ll}0,&x_{i-1}>x_{i-k-1}\wedge x_{i-1}>x_{i+k-1} \\ r+\alpha,&\text{otherwise}\end{array}\right. \tag{1}\]  
where \(r\) is a uniformly distributed random number in the range [0, 1] and \(\alpha\) a constant factor (\(\alpha=1\)). For \(i=1,\ldots,k+1\), and for \(i=N-k+2,\ldots,N\) the value \(r+\alpha\) is assigned to \(m_{k,i}\). These operations of Equation 1 result in the matrix \[\mathbf{M}=\begin{pmatrix}m_{1,1}&m_{1,2}&\ldots&m_{1,N}\\ m_{2,1}&m_{2,2}&\ldots&m_{2,N}\\ \vdots&\vdots&\ddots&\vdots\\ m_{L,1}&m_{L,2}&\ldots&m_{L,N}\end{pmatrix}=(m_{k,i}) \tag{2}\]  
where the \(k\)-th row contains the value for the window length \(w_{k}\).  
Thus, all elements of the \(L\times N\) matrix \(\mathbf{M}\) are in the range of [0, 1 + \(\alpha\)]. The matrix \(\mathbf{M}\) is called the LMS of the signal \(\mathbf{x}\).  
The second step of the algorithm comprises a row-wise summation of the LMS matrix \(\mathbf{M}\)  
\[\gamma_{k}=\sum_{i=1}^{N}m_{k,i}\text{, for }k\in\{1,2,\ldots,L\} \tag{3}\]  
The vector \(\gamma=[\gamma_{1},\gamma_{2},\ldots,\gamma_{i},\ldots,\gamma_{L}]\) contains the information about the scale-dependent distribution of zeros (and thus local maxima). The global minimum of \(\gamma\), \(\lambda=\arg\min(\gamma_{k})\), represents the scale with the most local maxima. This value \(\lambda\) is used in the third step of the algorithm to reshape the LMS matrix \(\mathbf{M}\) by removing all elements \(m_{k,i}\) for which \(k>\lambda\) holds, leading to the new \(\lambda\times N\)-matrix \(\mathbf{M}_{\mathbf{r}}=(m_{k,i})\), for \(i\in\{1,2,\ldots,N\}\) and \(k\in\{1,2,\ldots,\lambda\}\).  
In the last step of the algorithm, the peaks are detected by (
## 6 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **D** & **P** & **SaNSDE-1-6** & **DE-1** & **DE-3** & **NSDE** & **PSO** \\ \hline
12 & 30 & 30 & 14,772.423 & 35,531.47 & 25,760.51 & 118,419.7 & 57,773.8 \\
13 & 40 & 40 & 2,016,957.3 & 83,749.26 & 104,356 & 131,989.4 & 108,469.1 \\
14 & 50 & 50 & 2,890,556.2 & 171,886.2 & 233,575.7 & 201,288.2 & 145,752.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._for finding the solutions for most cases. Although our proposed algorithm outperforms all of the other four algorithms in terms of performance and convergence rate for most cases, the computation time of the proposed algorithm is much longer for several big cases due to the additional computation in each iteration. The results of this study have two implications. First, the performance in solving the DGRP with two strategies and a self-adaptation mechanism is better than with one strategy. Second, although the performance in solving the DGRP can be improved and the average number of generations required for finding the solution is reduced, the computation time of the proposed algorithm is much longer than all of the other four algorithms for bigger instances. This implies that either a more powerful computer or a proper divide-and-conquer strategy to divide a big instance of the DGRP into small ones must be used before applying the proposed algorithm. The computational experience showing that the proposed self-adaptive algorithm outperforms the other four algorithms for the test cases in this paper sparks an interesting research question: does the proposed self-adaptive algorithm outperform the other four algorithms? This research question requires further study in the comparative analysis of the proposed algorithm. A comparative analysis of the algorithms studied in this paper for specific performance indicators is a challenging future research direction. Studies of other performance evaluation indicators for the proposed algorithm are another interesting future research directions. The other interesting future research direction is to extend the success rate-based self-adaptive scheme proposed in this study to other evolutionary approaches. This research was supported in part by the National Science and Technology Council, Taiwan, under Grant NSTC 111-2410-H-324-003. **Data Availability Statement:** Data available in a publicly accessible repository described in the article. **Conflicts of Interest:** The author declares no conflicts of interest.
# Modeling and Analysis of Dynamics of Rigid-Flexible Coupled Parallel Robots
## 3 Modeling of Spatial Delta-Type Robot Dynamics Considering Linkage Flexibility
### Rigid-Flexible Coupling Bond Graph Model of Parallel Mechanism  
Since the structure of each branch of this robot mechanism is the same, one of the branches is selected for analysis, and the coordinate system, as in Figure 9, is established. The inertial coordinate system \(\Psi_{o}\) (\(o\)-\(xyz\)) is established with the center of the motion platform as the origin, the centers of mass of the slider and the two connecting rods \(N_{i}\), \(U_{i}\), and \(U_{i}^{\prime}\), and the constructed center-of-mass coordinate system \(\Psi_{Ni}\), \(\Psi_{Ui}\), and \(\Psi_{U_{i}^{\prime}}\) (\(i=1,2,3\)) are parallel to the inertial coordinate system \(\Psi_{o}\). The coordinate origins of the coordinate systems \(\Psi_{Ei}\), \(\Psi_{Hi}\), \(\Psi_{H_{i}^{\prime}}\), \(\Psi_{Ki}\), and \(\Psi_{K_{i}^{\prime}}\) are the contact points of the kinematic joints \(E_{i}\) and the centers of rotation of the spherical joints \(H_{i}\), \(H_{i}^{\prime}\), \(K_{i}\), and \(K_{i}^{\prime}\), respectively, and the coordinate systems are parallel to the branched-chain coordinate system \(\Psi_{i}\). The direction of the \(z_{i}\) axis of the branched coordinate system \(\Psi_{i}\) is along the up and down sliding direction, and the direction cosine of the \(y_{i}\) axis concerning the inertial coordinate system \(\Psi_{o}\) can be expressed as follows  
\[\begin{split}& y_{1}=\left[\begin{array}{cc}0&-1&0\end{array} \right]^{\mathrm{T}},\mathbf{y}_{2}=\left[\begin{array}{cc}-\sin 60^{ \circ}&\cos 60^{\circ}&0\end{array}\right]^{\mathrm{T}},\\ & y_{3}=\left[\begin{array}{cc}\sin 60^{\circ}&\cos 60^{\circ}&0\end{array} \right]^{\mathrm{T}},\end{split}\]  
\(x_{i}\) axes perpendicular to the plane formed by the \(y_{i}\) and \(z_{i}\) axes, that is, \(x_{i}=y_{i}\times z_{i}\).  
Figure 8: Mechanism structure of Delta-type rigid–flexible coupling parallel robot.  
Figure 9: Branch chain structure graph.  
Considering the linkage flexibility, the bond graph model of the Delta-type rigid-flexible coupled parallel robot strut chain is established by combining the modal vector bond graph and screw bond graph in Figure 10.
## 5 Analysis of HWMP Security Protocols
### Ibc-hwmp, ECDSA-hwmp and Watchdog-hwmp
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c} \hline \multirow{2}{*}{**Frame**} & \multirow{2}{*}{**Field**} & **Authentication** & \multirow{2}{*}{**Encryption*} & \multirow{2}{*}{**Authentication**} & \multirow{2}{*}{**Anti-**} & \multirow{2}{*}{**Root**} & \multirow{2}{*}{**Foregery**} \\  & & **And Integrity** & (Point-To-Point) & & & & **Replay** & **Permission** & **Detection** \\  & & **Point** & & **Point** & (End-To-End) & & & **Scheme** & **Level** & **Method** \\ \hline \multirow{4}{*}{PREQ} & HDR & No & - & - & - & - & - \\ \cline{2-8}  & MF & Yes & No & - & - & - & Yes \\ \cline{2-8}  & NMF & No & No & No & No & No & - \\ \hline \multirow{4}{*}{RANN} & HDR & No & - & - & - & - & - \\ \cline{2-8}  & MF & No & No & - & - & - & No \\ \cline{2-8}  & NMF & No & No & No & - & No & - \\ \hline \multirow{4}{*}{PERR} & HDR & No & - & - & - & - & - \\ \cline{2-8}  & MF & No & No & - & - & - & - \\ \cline{1-1} \cline{2-8}  & NMF & No & No & No & - & - & - \\ \cline{1-1} \cline{2-8}  & HDR & No & - & - & - & - & - \\ \cline{1-1} \cline{2-8}  & MF & Yes & No & - & - & - & Yes \\ \cline{1-1} \cline{2-8}  & NMF & No & No & No & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 12: Security services provided by IBC-HWMP and ECDSA-HWMP. Figure 13: IBC-HWMP detection method. integrity for NMFs. We highlight the security services that are not addressed. This protocol does not provide integrity assurance of NMF from the source node for the intermediate nodes on the transmission path to protect NMF from modification attacks. In addition, no anti-replay scheme exists in this protocol.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 1 Introduction
### Task Planning for Satellite Earth Observation
the task planning problem is expressed as a mixed integer programming model, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed in Section 5. Section 6 introduces the experimental setting, compares the proposed algorithm with several advanced heuristic algorithms, and concludes that the proposed algorithm outperforms several other algorithms. Finally, Section 7 presents a discussion and analysis, and the application of this method to iSAT satellites is explained.
## 2 Materials and Methods
### Multiparameter Station
#### 2.5.2 Acquisition Hardware  
The digitizer is based on a Raspberry Pi 3 CPU with 1.2 GHz processing and 2 GB of RAM, with an independent video chip. Different peripherals are integrated into it via GPIO or USB. The 24-bit Analog-Digital Converter (ADC) is connected via serial communication protocol, having eight single channels to sample up to 500 sps that can be expanded to 16 channels. To achieve time synchronization with an error of fewer than 10 us, we use a GPS connected via asynchronous serial communication, with the possibility of an external high-gain antenna to improve the view of the sky. Data transmission is via 3G modem. The power supply is obtained using a 5 V DC source at 2.5 A that is provided using a solar panel. The maximum consumption is around 10 W. The entire system is installed in a hard box.
# MIMO Radio Frequency Identification: A Brief Survey
## 6 Conclusions  
The state-of-art in MIMO RFID systems was briefly explored and some of the recently proposed algorithms were detailed. We summarized some of the significant improvements achieved through promising new techniques in estimating both the number of tags and fading channel. We have provided a brief survey in some of the recent developments related to MIMO RFID systems, the protocols and algorithms used, and improvements achieved. While doing so, we looked at several recent works that explored MIMO techniques for RFID receivers. Various passive UHF RFID implementations were considered that employed multiple antennas at the reader and single or multiple antennas at each tag. As shown in Tables 1-3, a number of recent algorithms have been designed to tackle the issues associated with MIMO RFID systems. One of the key challenges is to keep the design of the RFID tag simple, cutting cost and power requirement when employing anti-collision  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Algorithm** & **Layer** & **Attack** & **Technique** & **Rx Antennas** \\ \hline [PERSON] et al. [60] & Projected Gradient & Physical & Passive Eavesdropper & Backscatter & 2 \\ \hline [PERSON] et al. [65] & Modified HB & Physical & Relay Attack & Challenge Response & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of various security schemes for MIMO RFID.  
schemes. The domain of MIMO based communication systems is already quite complex but the computational and power limitations as a unique case of RFID systems makes it more complex. There is a strong need for exploring low cost, low power algorithms for passive UHF RFID systems that are able to enhance various performance metrics without overburdening the system and also stay within the boundaries of the prevalent standards such as EPCglobal Class 1 Gen 2.  
Conceptualization, M.A. and M.M.; methodology, S.A.H.A.; investigation, F.R.A.-O.; resources, S.A.H.A.; data curation, S.A.H.A.; writing--original draft preparation, M.M.; writing--review and editing, M.M.; visualization, F.R.A.-O.; supervision, F.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by National Science, Technology and Innovation Plan (MAARIFAH), King Abdulaziz City for Science and Technology (KACST), Kingdom of Saudi Arabia, grant number (14-ELE1448-10).  
Not applicable.  
We thank Department of Computer Engineering, College of Computer Information Systems, Umm Al Qura University, Makkah, Saudi Arabia for their logistic support.  
The authors declare no conflict of interest.