esa-sceva/satcom-chunk-collection huggingface dataset:
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 5 Experiments
### Experiments with Real-Record Data  
Since the nonlinear echo may not be simulated well enough by the clipping function and memoryless sigmoidal function, we additionally performed experiments with real recordings. We used 28 far-end speech signals and a near-end speech signal recorded with a commercial mobile phone, Samsung Galaxy S8, in hand-held hands-free mode [36]. The raw microphone signals and the far-end signal are obtained by an internal development program in Samsung. Each data have a length of about 65 seconds with a sampling rate of 16 kHz. A total of 3 types of noises including Pub, Road, and Call center noises from the European Telecommunications Standards Institute (ETSI) EG 202 396-1 background noise database [37], were replayed from the loudspeakers in the recording room to simulate background noises. The total number of real-recorded data for the far-end single-talk and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multirow{2}{*}{**SNR**} & \multirow{2}{*}{**SER**} & \multirow{2}{*}{**AEC [8,30]**} & \multirow{2}{*}{**AEC [8,30]**} & \multirow{2}{*}{**AEC [8,30]**} & \multirow{2}{*}{**AEC [8,30]**} \\  & & & & **+PFRES [27]** & **+HDRES [28]** & **+Proposed RES** \\ \hline \multirow{4}{*}{clean} & \multirow{2}{*}{5 dB} & 2.902 & 2.911 & 2.918 & **2.991** \\ \cline{3-6}  & & 0 dB & 2.665 & 2.710 & 2.697 & **2.771** \\ \cline{3-6}  & &  
double-talk periods considering the same ENR, SNR, and SER conditions with simulated data were \(28\times(3\times 3+1)=280\) and \(28\times 3\times(3\times 3+1)=840\).  
Table 3 shows the average ERLE with real-recorded data for the far-end single-talk period. The average ERLE for the proposed RES was 5.23 dB higher than that of the linear AEC output, 1.27 dB higher than that of the PFRES, and 1.53 dB higher than that of the HDRES. Utilizing the correlation with the microphone signal and the previous frames of the linear echo estimate, microphone signal, and residual echo estimates were shown to be effective in the real recordings with a commercial smartphone, too. Figure 2 demonstrates an example of the ERLEs over time for the AEC system without RES, with the PFRES, with the HDRES, and with the proposed RES along with the microphone signal waveform. In Table 4, the PESQ scores for real recordings are shown. Again, we can confirm that the proposed RES could achieve better speech quality in the double-talk periods in various background noise and echo conditions for the real-recorded data.
## 5 Conclusions  
In this paper, we proposed a multi-angle VSR system in which feature extraction was conducted using angle-specific models based on view classification results, followed by feature integration and VSR. We also proposed a decision fusion-based AVSR. We employed DNNs in our system, to perform view classification, feature extraction and recognition. The advantages of our method are choosing appropriate feature extraction models based on angle classification results, reducing the negative impact of misclassification, and incorporating ASR and VSR results efficiently. Evaluation experiments were conducted using the multi-view corpus OuluVS2. Then, we found our scheme could work well compared to past works, and we clarified the effectiveness of view classification and feature extraction from pre-trained angle-specific models. Moreover, we found that our AVSR method is superior to ASR and VSR because our decision fusion method could successfully integrate ASR and VSR results.  
As our future work, we are planning to conduct experiments using different angle settings and other tasks. The implementation of this framework for real applications is also expected. In addition, because there are some research works investigating spectrograms instead of MFCCs, we will try to employ spectrograms as acoustic input. Finally, we will explore the suitable model architecture and its physical meaning for feature extraction.  
Funding acquisition, Y.G. and M.N.; Investigation, S.I.; Methodology, S.I.; Project administration, S.T.; Supervision, S.H.; Writing--original draft, S.I.; Writing--review & editing, S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The databases used in this article are OuluVS2, DEMAND and CENSREC-1-AV. For details, please refer to [22; 29; 30], respectively.  
The authors declare no conflict of interest.
# A Methodological Approach to Securing Cyber-Physical Systems for Critical Infrastructures
## 5 Proposed Approaches and Solutions
### Instantiating the Detection Component  
#### 5.2.1 Machine Learning-Based Detection  
Machine Learning (ML) enables the efficient and rapid detection of complex trends and patterns, surpassing human capabilities, even when training models with vast datasets to identify security risks might not be suitable for unreliable, continuous, and real-time architectures. Our approach proposes to classify unusual patterns in the behavior of smart devices as potential cybersecurity threats within the IoT framework [63]. An explainable AI (XAI) strategy is designed to address these behavioral anomalies. Predictive association modeling is developed to enhance its effectiveness in identifying behavioral patterns and providing transparent explanations for them and their classifications. This approach stems from combining association-rule mining with classification techniques. The proposed methodology utilizes the representation of network users' (devices') activities through sequences of events that outline their behavior patterns.
## 1 Introduction
Moreover, how to balance the accuracy and robust performance of the continuous-discrete filtering algorithms is of great significance. Therefore, this paper proposes the square-root continuous-discrete cubature Kalman filter (SRCD-CKF) with Huber's method, maximum correntropy criterion, and variational Bayesian criterion, and analyzes their filtering performance in the measurement of uncertainty problems. The CD-CKF is chosen because it is derivative-free and generally more accurate than the CD-EKF. Additionally, compared with the continuous-discrete unscented Kalman filter (CD-UKF), CD-CKF has the smaller computational burden. The square-root technique can also improve the stability of the filter. Compared with the proposed robust algorithms, the robust square-root continuous-discrete maximum correntropy cubature Kalman filter (RSRCD-MCCKF) algorithm and the variational Bayesian square-root continuous-discrete cubature Kalman filter (VBSRCD-CKF) algorithm have higher accuracy and stronger robustness. To sum up, in this paper, the robust estimation methods based on a continuous-discrete system are proposed to solve the problem of sufficient evaluation of continuous-discrete robust algorithms. Compared with the discrete-discrete filtering system, considering the continuous-discrete filtering system can effectively ensure the accuracy of bearings-only tracking, while the robust methods are used to improve the robustness of the tracking. From the perspective of improving accuracy and robustness, the proposed algorithms have excellent performance in continuous-discrete systems. Another contribution is that the article compares the performance of the algorithms in different continuous-discrete measurement environments, fully considering the environmental adaptability of the algorithms. The structure of the rest of the article is as follows: Section 2 reviews the target tracking model of the continuous-discrete system and the algorithm of SRCD-CKF, Section 3 proposes the robust SRCD-CKF algorithms with [PERSON]'s method, the RSRCD-MCCKF algorithm, and the VBSRCD-CKF algorithm, Section 4 shows the results of the simulation, and Section 5 summarizes the main work of this paper. Continuous-Discrete System Model and Square-Root Continuous-Discrete Cubature Kalman Filter Algorithm for Target Tracking
## 2 Variable Model for Mechanical Parameter of Phyllite with Confining Pressure Influence  
### Variable Model for Elasticity Modulus of Phyllite  
According to triaxial experimental results from previous references (the triaxial experimental information and reference source is listed in Table 1), the elasticity modulus variation of phyllite with variable confining pressure is shown in Figure 1.  
As shown in Figure 1, the elasticity modulus (\(E\)) gradually increases with increasing confining pressure (\(\sigma_{3}\)) and does not remain constant. In order to propose the nondimensional variable model to describe the elasticity modulus variation, the dimensionless process is carried out with the result data. In the nondimensional variable model, the stress-strength ratio (\(\sigma_{3}/\sigma_{\text{UCS}}\)) is regarded as the \(X\)-axis, and \(\sigma_{\text{UCS}}\) is the uniaxial compression strength of rock; \(E/E_{0}\) is regarded as the \(Y\)-axis, and \(E_{0}\) is the elasticity modulus under the 0 MPa confining pressure condition. By the mathematical statistics method, the nondimensional variable model for the elasticity modulus is proposed in Figure 2, whose form is a power function. Additionally, in the variable model for the elasticity modulus, \(A_{E}\) and \(B_{E}\) are undetermined parameters, which can be obtained by experimental test.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Lithology** & **Rock Sample Source** & **Confining Pressure/MPa** & **Reference** \\ \hline
1 & quartz phyllite & Liutongzhai tunnel in China & 0, 5, 15, 20 & [PERSON], et al. [21] \\
2 & chlorite phyllite & Yangjiaping tunnel in China & 0, 5, 15, 20 & [PERSON], et al. [21] \\
3 & sercite phyllite & Maoxian tunnel in China & 0, 5, 15, 20 & [PERSON], et al. [21] \\
4 & quartz phyllite & Liutongzhai tunnel in China & 0, 5, 15, 20 & [PERSON], et al. [21] \\
5 & carbon phyllite & Li county, Sichuan province in China & 0, 2, 4, 6, 8 & [PERSON], et al. [22] \\
6 & carbon phyllite & Zheng mountain tunnel in China & 0, 10, 20, 30 & [PERSON], et al. [12] \\
7 & sercite phyllite & Tugongling tunnel in China & 0, 5, 10, 15 & [PERSON] [23] \\
8 & chlorite phyllite & Tiancheng tunnel in China & 0, 5, 10, 15, 20 & [PERSON], et al. [11] \\ \hline \end{tabular} Note: (1) Reference [21] of group 1–4 and 6 provides the stress–strain curve results, and the corresponding elasticity modulus variations are obtained with the recommended method from the “standard for test methods of engineering rock mass in China (GB/ T 50266-2013)” according to experimental results. (2) References [11; 22; 23] of Group 5, 7 and 8 provide directly elasticity modulus variation result. (3) Although groups 1 and 4 are both from the same rock sample source, the angle between loading direction and bedding angle of the rock sample is different, so the two groups are regarded as different groups.  
\end{table}
Table 1: Triaxial experimental result sources for elasticity modulus of phyllite.  
Figure 2: The nondimensional variable model for elasticity modulus.
## 6 Results and Analysis
### Verification of the Landslide Susceptibility Map  
Validation of the efficiency of the machine learning models on producing landslide susceptibility maps was done using statistical metrics. Performance of the models' accuracy was checked and evaluated by the ROC curve and AUC methods. Training (goodness-of-fit/performance) and validation (prediction accuracy) datasets were analyzed based on the ROC curve method (Figure 11). The results of all four studied models showed high goodness-of-fit (AUC \(>\) 0.814); however, the new proposed model (MBNBT) had the best prediction accuracy (AUC = 0.825) for spatial prediction of landslides. The results also indicated that the MBNBT (AUC = 0.825) ensemble model enhanced and improved the prediction accuracy of NBT (AUC = 0.800) as a base classifier. Thus, it can be concluded that the MBNBT model outperformed and outclassed the MLPN (AUC = 0.804), SVM (0.804), and NBT (AUC = 0.800) models.  
Figure 9: Landslide Susceptibility Map (LSM) of the study area based on the SVM model.  
Figure 11: The ROC curve of the different models using the training dataset (**a**) and validating dataset (**b**).  
Figure 10: Frequency ratio result of the four machine learning methods (MBNBT, MLPN, NBT, and SVM).
# Multi-Input Logic-in-Memory for Ultra-Low Power Non-Von Neumann Computing
## 2 Materials and Methods
### The \"SIMPLY\" Architecture
**(b)** Pulses applied to the top electrodes of P (V\({}_{\text{P}}\)) and Q (V\({}_{\text{Q}}\)) to execute the IMPLY operation in SIMPLY architecture when the comparator detects P = Q = 0 (blue lines) and in all other cases (dashed red lines). **(c)** Pulse applied at the top electrode of P to perform the sFALSE P operation in SIMPLY architecture when P = 0 (dashed blue lines) and P = 1 (red lines). **(d)** Comparator implementation from [30] that is used in this work (45 nm technology [34]—MOSFETs W/L are shown). **(e)** Distribution of V\({}_{\text{N}}\) (technology 3 [29]) resulting from circuit simulations including variability and RTN (50 trials) when P = Q = 0 (violet bands) and P \(\
eq\) Q (grey bands) for a the 2-SIMPLY operation at different V\({}_{\text{READ}}\). The read margins (RM—blue arrows) and associated threshold voltages (V\({}_{\text{TH}}\)—orange stars) for the comparator are evidenced. Black whiskers indicate the extreme points of the distributions. Red crosses indicate outliers due to RTN. V\({}_{\text{N}}\) when P = Q = 1 is always much higher than in all other cases (thus is not reported in these box plots). In addition, both the traditional IMPLY and the SIMPLY architectures can be implemented in a crossbar array [12; 30]. An example of the SIMPLY crossbar implementation is shown in Figure 6. FET devices in the array periphery are used to implement \(\mathrm{R_{G}}\), to connect adjacent columns on-demand to realize COPY between any two devices, and to enable specific columns of the array. The parallel computation of SIMPLY or sFALSE operations can be implemented by increasing the number of comparators in the architecture as discussed in [30] and shown in Figure 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Energy 2-IMPLY** & **Energy 2-SIMPLY** & **2-SIMPLY Average** \\
**Combination** & **(Min-Avg-Max)** & **(Min-Avg-Max)** & **Energy Improvement** \\ \hline P = 0 Q = 0 & 533–626–669 fJ & 498–532–557 fJ & 1.2 \\ P = 0 Q = 1 & 657–672–691 fJ & 11.7–11.8–11.9 fJ & 57 \\ P = 1 Q = 0 & 251–266–287 fJ & 11.7–11.8–11.9 fJ & 23 \\ P = 1 Q = 1 & 660–678–699 fJ & 12.6–12.6–12.6 fJ & 54 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy consumption comparison of the 2-IMPLY operation in IMPLY and SIMPLY architectures with technology 3. The use of SIMPLY enables the use of a lower \(\mathrm{V_{SET}}\) voltage (\(\mathrm{V_{SET,\ IMPLY}}\) = 3.2 V, \(\mathrm{V_{SET,\ SIMPLY}}\) = 3 V). In all cases, 1 ns voltage pulses are considered (t2-IMPLY = 2-t2-t2-t2-t2-t2-simply = 4-t2-t2-t2-simply = 4-t2-t2-
## 3 Experimental Results
### The Results of Reconstruction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Algorithms** & **PCC** & **MSE** & **SSIM** \\ \hline \multirow{6}{*}{Dataset 1} & [PERSON] et al. [13] & 0.767 & 0.042 & 0.466 \\  & BCCA [18] & 0.411 & 0.119 & 0.192 \\  & DCCAE-A [20] & 0.548 & 0.074 & 0.358 \\  & DCMM [25] & 0.803 & 0.037 & 0.645 \\  & FCN & 0.594 & 0.065 & 0.601 \\  & SRN & 0.736 & 0.045 & **0.725** \\ \hline \multirow{6}{*}{Dataset 2} & [PERSON] et al. & 0.481 & 0.067 & 0.191 \\  & BCCA & 0.348 & 0.128 & 0.058 \\ \cline{1-1}  & DCCAE-A & 0.354 & 0.073 & 0.186 \\ \cline{1-1}  & DGMM & 0.498 & 0.058 & 0.340 \\ \cline{1-1}  & FCN & 0.386 & 0.071 & 0.310 \\ \cline{1-1}  & SRN & 0.461 & 0.068 & **0.446** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of several image reconstruction methods on the test sets. Results were averaged over 10-fold cross validation. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline SRN & 0.739 & 0.747 & 0.735 & 0.737 & 0.720 & 0.717 & 0.731 & 0.675 & 0.684 & 0.761 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The main corresponding quantitative evaluation of structural similarity index (SSIM) reconstructed by Siamese reconstruction network (SRN) for each cross validation on Dataset 1. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline SRN & 0.422 & 0.430 & 0.483 & 0.457 & 0.442 & 0.465 & 0.444 & 0.433 & 0.459 & 0.427 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The main corresponding quantitative evaluation of SSIM reconstructed by SRN for each cross validation on Dataset 2. We set up the comparative experiment of single fully connect network (FCN) in order to further illustrate the effect of Siamese reconstruction net, as shown in the Table 4. The Siamese reconstruction net provided about 12% higher accuracy on the most important SSIM metric in Dataset 1 and 13% higher accuracy in Dataset 2 when compared with the FCN methods. The images that were reconstructed by the Siamese reconstruction net on each dataset performed poorer or better than the existing methods. We attribute this phenomenon to the fact that, because of the handwritten digits having simpler characteristics and less variety, the image similarity and its quantitative evaluation are significantly superior to those of characters'. Therefore, the images reconstructed by the Siamese reconstruction net can hardly be improved in Dataset 1. In addition, there are several complex characteristics and a variety of the obtained reconstructions of handwritten characters, which increase the difficulty in reconstruction, but improve the accuracy of distinction by Siamese reconstruction net.
## 7 Discussion of Experimental Results  
1. In terms of the rationality of the experimental design, the experiment simulated the growing environment of sand fruit based on the real picking scene. Five and three sand fruit types were set at different locations on the sand fruit tree, and the number of sand fruit was different for each cluster, covering diverse scenes such as different cluster distributions, fruit density, and branches and leaves occlusion degree. Compared with the traditional AOC algorithm and the traditional RRT algorithm, the path planning and path length are set up in the experiment. Through the comparison of simulation data, the superiority of the algorithm is demonstrated.
2. As for the scientific evaluation of the results, the experimental results show the success rate of identification, positioning, picking, and collision with the robotic arm of different groups, which are all important evaluation indicators to measure the obstacle avoidance algorithm, and the experimental results are scientific, rigorous, and convincing.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Group**} & \multirow{3}{*}{**Number of** **Picking** **/**Clusters**} & \multirow{3}{*}{**Number of** **Successful** **Recognizing**/**Clusters**} & \multirow{3}{*}{**Number of** **Successful** **Position-** **Picking** **/**Clusters**} & \multirow{3}{*}{**Recognizing** **Successful** **Picking** **Picking**/**Clusters**} & \multirow{3}{*}{**Positioning** **Success** **Picking** **Rate/\%**} & \multirow{3}{*}{**Picking** **Success** **Picking**/**Clusters**} \\  & & & & & & & \\ \hline
1 & 3 & 3 & 3 & 3 & 100 & 100 & 100 & 0 \\
2 & 4 & 4 & 4 & 4 & 100 & 100 & 100 & 0 \\
3 & 5 & 5 & 5 & 5 & 100 & 100 & 100 & 0 \\
4 & 6 & 6 & 6 & 5 & 100 & 100 & 83.33 & 0 \\
5 & 7 & 6 & 6 & 6 & 85.71 & 85.71 & 85.71 & 0 \\
6 & 7 & 7 & 7 & 6 & 100 & 100 & 85.71 & 0 \\
8 & 8 & 8 & 7 & 7 & 100 & 87.50 & 87.50 & 0 \\ sum & 48 & 46 & 45 & 43 & 95.83 & 93.75 & 89.58 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of clustered crabapple picking tests.  
3. The experimental results also have an impact on the actual picking operation. The experimental results show that the algorithm can improve the picking efficiency of the picking robot arm in the actual scene, which reflects the adaptability to the clustered sand fruit environment and meets the requirements of continuous picking. 4. In terms of improvement and applicability, the algorithm planning effect can be further improved by combining deep learning, reinforcement learning, and other methods. The algorithm can be applied not only to sand fruit trees, but also to grapes, cherries, and other clustered fruits.
## 2 The RDT-SSI Method Under Non-Stationary Excitation
### Hankel Matrix  
The Hankel matrix \(Y_{0|2i-1}\) is a matrix with a size of 2i blocks of rows and j columns [19; 20; 21]. Each element consists of \(l\) rows, and \(l\) is the number of measurement points. The Hankel matrix can be divided into two parts, the past and the future, as shown in Equation (5).  
\[\mathbf{Y_{0|2-1}}=\frac{1}{\sqrt{j}}\left(\begin{array}{cccc}y_{0}&y_{1}& \cdots&y_{j-1}\\ y_{1}&y_{2}&\cdots&y_{j}\\ \vdots&\vdots&\ddots&\vdots\\ y_{i-1}&y_{i}&\cdots&y_{i+j-2}\\ \hline y_{i}&y_{i+1}&\cdots&y_{i+j-1}\\ y_{i+1}&y_{i+2}&\cdots&y_{i+j}\\ \vdots&\vdots&\ddots&\vdots\\ y_{2i-1}&y_{2i}&\cdots&y_{2i+j-2}\end{array}\right)=\left(\begin{array}{c}Y_{0 |i-1}\\ \overline{Y}_{0|2i-1}\end{array}\right)=\left(\begin{array}{c}Y_{p}\\ \overline{Y}_{f}\end{array}\right)\frac{\\[T_{1|i}=Y_{f}(Y_{p})^{T}=\begin{pmatrix}R_{i}&R_{i-1}&\cdots&R_{1}\\ R_{i+1}&R_{i}&\cdots&R_{2}\\ \vdots&\vdots&\ddots&\vdots\\ R_{2i-1}&R_{2i-2}&\cdots&R_{i}\end{pmatrix} \tag{6}\]  
The covariance matrix is decomposed  
\[\begin{split}& T_{1|i}=\begin{pmatrix}R_{i}&R_{i-1}&\cdots&R_{1} \\ R_{i+1}&R_{i}&\cdots&R_{2}\\ \vdots&\vdots&\ddots&\vdots\\ R_{2i-1}&R_{2i-2}&\cdots&R_{i}\end{pmatrix}=\begin{pmatrix}CA^{i-1}G&CA^{i-2}G& \cdots&CG\\ CA^{i}G&CA^{i-1}G&\cdots&CAG\\ \vdots&\vdots&\ddots&\vdots\\ CA^{i-2}G&CA^{i-3}G&\cdots&CA^{i-1}G\end{pmatrix}\\ \\ &=\begin{pmatrix}CA\\ CA\\...\\ CA^{i-1}\end{pmatrix}\begin{pmatrix}A^{i-1}G&...&AG&G\end{pmatrix}=O_{i}\Gamma_{i} \end{split} \tag{7}\]  
thereinto  
\[O_{i}=\begin{pmatrix}C&CA&\cdots&CA^{i-1}\end{pmatrix}^{T} \tag{8}\]  
\[\Gamma_{i}=\begin{pmatrix}A^{i-1}G&\cdots&AG&G\end{pmatrix} \tag{9}\]
## 6 Key Challenges and Mitigation Strategies
### Communication Overhead  
The high communication overhead associated with FL poses another significant challenge, particularly in IoT networks characterized by potentially unreliable or low-bandwidth connections [10]. Transmitting large model updates frequently between numerous devices and a central server can saturate the network and consume considerable energy. Several approaches aim to mitigate this communication burden. As mentioned, PEFT methods are highly effective, as only the small adapter updates need to be transmitted [101]. Update compression techniques can further reduce the size of transmitted data, but carry a risk of information loss [111]. Reducing the frequency of communication rounds can save bandwidth, but typically slows down the convergence of the global model [9]. Additionally, asynchronous protocols allow devices to communicate more flexibly based on their availability, alleviating delays caused by stragglers, but they introduce challenges related to model staleness and potential inconsistencies [112].