esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Result  
Figure 5 depicts the comparison of graph convolutional neural network (GCNN), CNN, ridge regression (RR), and random forest (RF). In the proposed method, we have used mean squared error (MSE) to train our model. MSE gives us the measure of how far the predictions were from actual output but we do not know whether we are under predicting the data or over predicting the data. Therefore, we have used additional metrics, also known as correlation, to evaluate the outcome. Here, the term correlation was used as how the predicted gait parameters from video inputs related to the gait parameters calculated from laboratory-based motion capture sensors. We have taken 300 gait metrics samples that were predicted by our model based on the test data and we compared these samples with the ground truth and examined how they related with each other. Thus, the correlation between the gait metrics speed from our model and the ground truth was 0.791 (0.742-0.853). For GDI the correlation was 0.792 (0.710-0.822). For cadence, knee maximum flexion our proposed method predicted a similar results with CNN [11]. During inference, our system takes 0.2 s to predict the gait parameter of the patient.  
Figure 5: Comparison prediction accuracy of the proposed graph convolutional network with the previous methods on speed and gait deviation index (GDI) gait parameters. The three methods our proposed graph convolutional neural network (GCNN) compared are: convolutional neural network (CNN), ridge regression (RR), and random forest (RF). After training our model on video inputs, we have evaluated on test data. The correlation values shows how the predicted metric values are related with the ground truth that are prepared from optical motion sensor values.  
Table 1 shows the detailed architecture of the proposed model (Figure 4). The 2D joints extracted from a video using OpenPose [12] were fed into the first part of the model. In addition to the keypoints extracted using OpenPose, we comprised a hand-engineered time serious derived from to get an optimal result. Concatenating the time-series data (computed from the relation of the skeleton joints) that is passed through 1D-CNN with GCNN increases the prediction accuracy. Table 2 shows the comparison of these two approaches in terms of correlation coefficient with the ground truth. We have the first two graph convolutional layers in part i. The graph convolutional layers used to compute 8 and 16 dimension feature maps, respectively. Part ii of the proposed method has two convolutional layers with 7 kernel size each. In part iii, i.e., after the concatenation of the joint features from graph convolutional layer and the time series features from the convolutional layer, we have used a 1D convolutional layer, followed by maxpooling and another 1D convolutional layer with size 8 kernels. All the layers are followed by batch norm and Relu activation.
## 4 Performance Evaluation and Obtained Results
### Obtained Results, Analysis and Discussion
#### 4.3.1 Energy Consumption Performance Results  
First, the average energy consumption performance of the proposed N-MANC mixed approach as the network scalability is progressively increased, is assessed with respect to the N-AFHSS, N-LEACH and N-MANNC. The obtained results for a single communication period (\(P\)) are captured in Figure 4.  
It is important to mention that the results shown in Figure 4, have been obtained by simulating each of the approaches under comparison, one at a time and for a transmission period \(T\) of one hour, with a varying cell density from 1000 to \(150,000\) nodes, and each node having an initial energy level of 7 J. As it can be observed in Figure 4, the average energy consumption within the cell is increasing for all the four approaches, as more and more nodes are added within the cell. This behavior is expected because as more and more devices join the network, the probability of successful up-link transmission reduces because of more and more interlink interference. However, as it can be noticed, despite the increase in the average energy consumption of the network, the proposed N-MANC maintains a way lower energy consumption as compared to the N-AFHSS and the N-LEACH and maintains quite a significantly lower energy consumption as compared to the N-MANNC. The N-MANC consumes an average of 41.04% less energy than the N-AFHSS, 35.01% less than the N-LEACH, and only 9.81% less than the N-MANNC. This is justified by the fact that thanks to its mixed approach strategy tapping in both network reliability of the AFHSS, the network clustering network density reduction of the clustering and the number of transmitted packets reduction as added by the network coding approach as proposed; the N-MANC saves a significant amount of energy as compared to the N-AFHSS which only enhances the network reliability and the N-LEACH which only addresses the network density reduction aspect. It is important to notice the 9.81% energy consumption reduction as contributed by the random linear network coding approach as compared to the N-MANNC which does not have any network coding. This sufficiently demonstrated that packet aggregation with enhanced reliable communication as a pre-requisite can help save a great deal of energy within the NB-IoT network, despite the increasing number of nodes within the cell. This demonstrates that the proposed N-MANC scales in a more energy-efficient manner as compared to the N-AFHSS, the N-LEACH, and the N-MANNC.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 2 System Model  
Consider a single-cell system, where the set of single-antenna users is represented as \(\{1,\ldots,U\}\). Users are served by an \(A\)-antenna BS with \(A\gg U\). The served users are divided into two equal groups, where the first group contains the cell-center users \(u_{c}\in U_{c}\) and \(U_{c}=\{1,\ldots,U/2\}\). The second group contains the cell-edge users \(u_{e}\in U_{e}\) and \(U_{e}=\{U/2+1,\ldots,U\}\).  
The downlink channel vector for user \(u\) is described as  
\[\mathbf{g}_{u}=\sqrt{\beta_{u}}\mathbf{h}_{u}\quad u=1,\ldots,U. \tag{1}\]  
where \(\beta_{u}\) is the large scale fading coefficient, which represents the path-loss factor only, and the effect of shadowing is neglected. The system is in the time division duplex (TDD) mode, which provides the advantage of channel reciprocity that permits using the uplink pilots in estimating the downlink channels. For the NLoS channels, we use coherence intervals, where the channels are constant and frequency flat. The channel of user \(u\) is \(\mathbf{h}_{u}\), which follows independent Rayleigh fading distribution as the small scale fading realization \(\mathbf{h}_{u}\sim CN(\mathbf{0},\mathbf{I}_{A})\), and \(\mathbf{I}_{A}\) represents the identity matrix. The NOMA-based scheme system model is presented in Figure 1, where each NOMA group contains one cell-center user paired with a cell-edge user.
## 3 Results and Discussion
### Effect of EGR on the Dual-Fuel Engine
#### 3.3.2 Combustion Characteristics  
The effects of the EGR rate on in-cylinder combustion are depicted in Figure 8.  
Figure 8a exhibits the influence of EGR rate on the in-cylinder pressure of the engine. As shown in the figure, an increase in the EGR rate causes a slight decrease in the maximum in-cylinder pressure. This can be attributed to the partial replacement of the inlet air by EGR gas, resulting in a reduction in oxygen concentration and a decrease in combustion efficiency. The deterioration of combustion subsequently leads to a decline in peak pressure. However, even with a 20% EGR rate, the peak in-cylinder pressure remains at 18.40 MPa, which surpasses the value of 16.32 MPa observed in the original engine fueled with pure  
Figure 7: Effect of EGR rate on ITE and IMEP of the dual-fuel engine.  
diesel. This suggests that the high combustion rate and oxygenation characteristics of DMG can overcome the deterioration caused by EGR.  
Figure 8b demonstrates the effect of EGR rate on the in-cylinder average temperature of the dual-fuel engine. The in-cylinder temperature decreases with an increase in EGR rate. The decline in the in-cylinder temperature is mainly due to the fact that the recirculate gas is rich in CO\({}_{2}\) and H\({}_{2}\)O; the two components have a relative higher specific heat capacity than air, which leads to a decrease in the in-cylinder temperature.  
Figure 9 presents the relationship between HRR and crank angle. It is evident that the peak HRR diminishes and the average HRR slows down with an increase in the EGR rate. As discussed previously, the introduction of EGR results in decreases in in-cylinder pressure and temperature. This, in turn, reduces the oxidation reaction rate of the fuel, leading to a slight deceleration in the HRR. It is worth noting that the dual-fuel engine exhibits less sensitivity to EGR. This can be attributed to the presence of oxygen in the DMG, which helps to maintain the combustion process even with the introduction of EGR.  
Figure 8: Effects of different EGR rates on in-cylinder combustion: (**a**) Pressure; (**b**) temperature.
###### Abstract  
The rapid advancement in deep forgery technology in recent years has created highly deceptive face video content, posing significant security risks. Detecting these fakes is increasingly urgent and challenging. To improve the accuracy of deepfake face detection models and strengthen their resistance to adversarial attacks, this manuscript introduces a method for detecting forged faces and defending against adversarial attacks based on a multi-feature decision fusion. This approach allows for rapid detection of fake faces while effectively countering adversarial attacks. Firstly, an improved IMTCCN network was employed to precisely extract facial features, complemented by a diffusion model for noise reduction and artifact removal. Subsequently, the FG-TEFusionNet (Facial-geometry and Texture enhancement fusion-Net) model was developed for deepfake face detection and assessment. This model comprises two key modules: one for extracting temporal features between video frames and another for spatial features within frames. Initially, a facial geometry landmark calibration module based on the LRNet baseline framework ensured an accurate representation of facial geometry. A SENet attention mechanism was then integrated into the dual-stream RNN to enhance the model's capability to extract inter-frame information and derive preliminary assessment results based on inter-frame relationships. Additionally, a Gram image texture feature module was designed and integrated into EfficientNet and the attention maps of WSDAN (Weakly Supervised Data Augmentation Network). This module aims to extract deep-level feature information from the texture structure of image frames, addressing the limitations of purely geometric features. The final decisions from both modules were integrated using a voting method, completing the deepfake face detection process. Ultimately, the model's robustness was validated by generating adversarial samples using the I-FGSM algorithm and optimizing model performance through adversarial training. Extensive experiments demonstrated the superior performance and effectiveness of the proposed method across four subsets of FaceForensics++ and the Celeb-DF dataset.  
Keywords:diffusion model; facial geometry; texture feature enhancement; deepfake face detection; multi-feature decision fusion; adversarial attack and defense
## 5 Conclusions  
In the present work, we investigated the compensation scheme of phase mismatch in an optical 90\({}^{\circ}\) hybrid FSO system with a QPSK modulation. The effects of log-normal amplitude fluctuations and Gauss phase fluctuations are also considered. The ASOP scheme is proposed, and the system performance improvement due to this scheme is investigated by simulation. All the simulation results reveal that the ASOP is more powerful than the traditional single GSOP or SYOP schemes. The SER could achieve 10 \(\times\) 10\({}^{-6}\) at the rate of 20 Gbps, and the phase mismatch tolerance can reach 25\({}^{\circ}\). Hence, the ASOP scheme we proposed can contribute to the performance improvement of the FSO system and its practical application.  
It should be noted that only the phase mismatch is considered in this paper. Issues such as the development of further optimized algorithms that are suitable for IQ gain mismatch and IQ skew at the same time require further study. In addition, limited by the space and the focus of our team's research, we only simulate the QPSK modulation scheme here; in fact, this algorithm can be used for higher-order QAM modulation, which we do not discuss here.  
Writing--original draft preparation, X.L.; writing--review and editing, X.L.; visualization, Y.G. and R.T.; supervision, T.G.; project administration, S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The study did not report any data.  
This work is supported by the Research Project of Scientific Research Equipment of Chinese Academy of Sciences. The authors also gratefully acknowledge the Optical Communication Laboratory of CIOMP for the use of their equipment.  
Figure 11: SER versus \(C_{n}{}^{2}\) with phase estimation under different turbulence conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(C_{n}^{2}\) & \(L\) & \(\sigma_{R}^{2}\) & \(\sigma_{\phi}^{2}\) & **Regime** \\ \hline \(3\times 10^{-15}\) & 1 km & 0.05 & 0.15 & Weak \\ \(8\times 10^{-16}\) & 10 km & 1 & 0.39 & Moderate \\ \(6.2\times 10^{-16}\) & 20 km & 3 & 0.6 & Strong \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in the numerical simulation, as shown in Figure 11.
## 3 Results
### Determination of Oxidation States of Mn and Co Ions  
The high-resolution Mn 2p and Co 2p XPS spectra of La\({}_{0.9}\)A\({}_{0.1}\)Mn\({}_{0.9}\)Co\({}_{0.1}\)O\({}_{3}\) (A: Li, K, Na) ceramics are shown in Figure 4. It can be seen that the Mn 2p spectrum consists of two asymmetric peaks located at 641.8 (Mn 2p\({}_{3/2}\)) and 653.3 eV (Mn 2p\({}_{1/2}\)). Both of these peaks can be deconvoluted into two peaks centered at 641.5 and 643.1 eV, and at 653.1 and 654.3 eV, respectively. The peaks located at 641.5 and 653.1 eV correspond to Mn\({}^{3+}\) ions, while the peaks located at 643.1 and 654.3 eV are assigned to Mn\({}^{4+}\) ions [37; 38; 39]. The absence of a satellite peak at +5 eV from Mn 2p\({}_{3/2}\) indicates that no Mn\({}^{2+}\) is present [40]. The Co 2p XPS spectrum exhibited two main peaks, corresponding to the 2p\({}_{3/2}\) and 2p\({}_{1/2}\) levels, and shake-up satellite peaks centered at around 790.0 and 796.9 eV. Similarly as in the case of Mn, the asymmetric peaks of Co 2p\({}_{1/2}\) and Co 2p\({}_{3/2}\) can be resolved into components. The results indicate that Co ions exist in the Co\({}^{2+}\) and Co\({}^{3+}\) oxidation state. The peaks at lower BE were attributed to Co\({}^{3+}\) oxidation state, whereas the peaks at higher BE to Co\({}^{2+}\)[41]. The atomic ratio of Mn\({}^{3+}\)/Mn\({}^{4+}\) and Co\({}^{2+}\)/Co\({}^{3+}\) is summarized in Table 2.
## 3 Proposed Anomaly-Based IDS for IoT Ecosystem
### Dimensionality Reduction  
This step represents phase 2 (A and B) of Figure 1. Our hybrid feature selection approach utilizes a filter-based approach that includes two selection modules. The first uses the intersection rule, while the second uses the union rule. As mentioned above, both the IG and GR attribute selectors are used to reduce feature vectors' high dimensionality and accordingly select the best features (Note, IG is called \"info gain attribute evaluator\" and GR is called \"gain ratio attribute evaluator\" in Weka). We choose IG and GR because they are widely used in many different domains. Moreover, they present a similar knowledge bias for selecting features to match our mathematical set theory requirements. Both are considered as one of the best and most popular feature selection approaches. In contrast, different ranking approaches require more memory to load the computational result. Feature selection plays a significate role in improving a classifier/predictive system because it reduces time complexity and enhances model performance measures [11; 60]. To design our hybrid feature selection approach (IMF and UMF as introduced in the definition in Section 1.2 including the details in Section 3.3), we utilize the concept of mathematical set theory to \"smooth\" the selection of relevant features in a reliable way for building an Anomaly-based IDS model (Note, features with scores close to \"1\" are deemed to positively affect model performance (more relevant), while \"0\" indicates a negative impact (meaning irrelevant and/or redundant).  
Using IG and GR to produce two groups of relevant features, is the first attempt to extract the best most relevant features in our process per Figure 1. Moreover, in our experiments, the first group consisted of 60 features, and the second consisted of 20 derived from the whole IoTID20 dataset. Furthermore, there is zero correlation between the two groups because all group productions were run independently. In other words, starting with the same full set of features, both IG and GR approaches were used to extract these features one at a time in an iterative fashion resulting in the two groups of 20 and 60. Thus, achieving the first step of phase 2 in Figure 1 (dimensionality reduction) discovering relevant features and eliminating irrelevant features.
## 4 Conclusions  
(1) As a result of comparing the cement paste specimen containing MWCNTs and GNPs that were noncovalently functionalized with melamine using the ball-milling process with the specimen containing MWCNTs and GNPs that were only ultrasonication-treated, the electrical conductivity and dispersibility of the CBNs were improved within the cement matrix using noncovalent functionalization; in particular, the decrease in electrical resistance was larger with a high content of CBNs.  
(2) As the MWCNT and GNP content increased, the electrical resistance decreased. However, at an incorporation ratio \(\geq\) 0.3%, the range of decrease in the electrical resistance was small, and there was almost no change at ratios \(\geq\) 0.6%. The compressive strength  
Figure 5: Effect of MWCNT or GNP content on compressive strength.  
Figure 6: Effect of the hybridization of MWCNTs and GNPs on the compressive strength: (**a**) non-functionalization and (**b**) functionalization.  
increased up to approximately 0.30-0.60% of the CBNs content; however, for CBN content higher than 0.60%, the compressive strength decreased. Nonetheless, the variation in compressive strength was not large.  
(3) The electrical resistance of the hybrid mixture involving the differently shaped MWCNTs and GNPs was actually larger or similar to that of independently mixed specimens. The effect on the compressive strength of the MWCNT and GNP hybrid was also difficult to determine. The results of the hybrid CBNs on the electrical and mechanical properties of cementitious composites are considered to be different according to the type and content of CBNs; therefore, further research is necessary.  
Conceptualization, J.H.L.; data curation, K.M.L.; writing--original draft preparation, J.H.L.; writing--review and editing, K.M.L. and J.H.L.; supervision, J.H.L.; project administration, J.H.L.; funding acquisition, J.H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant (21 CTAP-C157478-02) from the Technology Advancement Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government and by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (no. NRF-2021R1F1A1063528).  
The authors declare no conflict of interest.
# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
## 4 Case Test  
This paper uses a microphone as the sound pickup device, and it converts the analog audio signals into digital signals through an analog/digital adapter card. The sampling period is 16 ms, there are 256 sampling points, and the sampling rate is 22,050 (data/sec). The experimental motor is a four-pole, three-phase, 2.4 kW, 220 V, 60 Hz, and 1800 rpm induction motor. Figure 5 show the diagram of the sampled sound stored. The sampled sound is digitally stored in the memory card according to the sample period, sample points, and sample rate through the A/D adapter card.  
This paper plans four operating conditions for simulation, which are described below. Type 1 = normal: The normal condition is important data in the database, and the monitoring results of the trouble-free operation of the motor can be used as a reference for other fault conditions. Type 2 = out-of-phase fault: The out-of-phase fault is simulated under the original operating condition of the three-phase voltage power supply; one phase of the power supply is cut off, and the remaining two phases supply power, resulting in the motor voltage out of phase. Type 3 = unbalanced fault: The unbalanced fault is simulated, and the single-phase auto-transformer will be adjusted so that the voltage of phase 1 is less than the voltage of the other 2 phases, resulting in the unbalanced voltage of the motor. Type 4 = axis-misalignment fault: half of the screws of the coupling are loosened, and the other half are tightened, leading to a coupling misalignment in the operation of the motor.  
In the above four conditions, this paper measures the operational sound of the motor. The load is measured from no load to a full load and divided into 16 segments of operational sound; No. 1 is no load, No. 8 is a half-load, and No. 16 is a full load. The operational sound of the rotary motor is recorded under each load. Four states, Type 1 = normal,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Exp.** & **1** & **2** & **3** \\ \hline
6 & 2 & 3 & 1 \\ \hline
7 & 3 & 1 & 3 \\ \hline
8 & 3 & 2 & 1 \\ \hline
9 & 3 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: The diagram of the sampled sound stored.  
Type 2 = out-of-phase fault, Type 3 = unbalanced fault, and Type 4 = axis-misalignment fault, are recorded. The data set is divided into two parts, the training data and testing data. The training data are used for training and updating the biases and learning rates. The test data are used to test the proposed methods after training. The simulation is implemented with Matlab on an Intel Core i5-7300 HQ 2.5 GHZ computer with 16 GB of RAM.