esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Comparisons
### Malimg Dataset  
The performance of the proposed FT CNN-based TL models was evaluated using the Malimg dataset [12]. Malimg consists of 9341 malware samples. Each sample corresponds to one of the 25 malware families as illustrated in Table 5. The malware samples of the Malimg dataset are visualized as gray-scale images within the range (0: black--255: white). Different fragments of the malware binary file demonstrate various image textures as shown in Figure 4. This figure illustrates the first fragment of the malware image which has a fine-grained texture since its corresponding.text section contains the malware executable code [6]. However, the remaining part of the.text section is visualized as a black block indicating zero paddings. Furthermore, the.data section, also, consists of a fine-grained texture that visualizes the initialized and uninitialized variables. Finally, the generated resources of the malware by the compiler are visualized in the.rsrc fragment. The Malimg dataset was deployed to train the CNN layers on the main characteristics of these families to identify and classify them correctly.  
Figure 3: Multi-classification confusion matrix.  
Figure 4: A sample of malware binary file sections and their corresponding fragments in the visualized version.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Malware Type** & **Family Class Name** & **Class ID** & **No. of Images** & **Percentage** & **Total** \\ \hline \multirow{4}{*}{Worm} & VB\_AT & 23 & 408 & 4.37\% & \multirow{4}{*}{5748} \\ \cline{2-5}  & Yuner\_A & 25 & 800 & 8.56\% & \\ \cline{2-5}  & Allaple\_A & 3 & 2949 & 31.57\% & \\ \cline{2-5}  & Allaple\_L & 4 & 1591 & 17.03\% & \\ \hline \multirow{4}{*}{Trojan} & Alueron\_gen!J & 5 & 198 & 2.12\% & \multirow{4}{*}{760} \\ \cline{2-5}  & C2 LOP\_gen!g & 7 & 200 & 1.56\% & \\ \cline{2-5}  & C2 LOP\_P & 8 & 146 & 2.14\% & \\ \cline{2-5}  & Male\_gen!J & 17 & 136 & 1.46\% & \\ \cline{2-5}  & Skintrim\_N & 20 & 80 & 0.86\% & \\ \hline \multirow{4}{*}{Dialer} & Adialer\_C & 1 & 122 & 1.31\% & \multirow{4}{*}{730} \\ \cline{2-5}  & Dialplatform\_B & 9 & 177 & 1.89\% & \\ \cline{2-5}  & Instantaccess & 12 & 431 & 4.61\% & \\ \hline \multirow{4}{*}{PWS} & Lolyda\_AA1 & 13 & 213 & 2.28\% & \multirow{4}{*}{679} \\ \cline{2-5}  & Lolyda\_AA2 & 14 & 184 & 1.97\% & \\ \cline{2-5}  & Lolyda\_AA3 & 15 & 123 & 1.32\% & \\ \cline{2-5}  & Lolyda\_AT & 16 & 159 & 1.70\% & \\ \hline \multirow{4}{*}{Trojan} & Dontovo\_A & 10 & 162 & 1.73\% & \multirow{4}{*}{661} \\ \cline{2-5}  & Obfuscator\_AD & 18 & 142 & 1.52\% & \\ \cline{2-5}  & Swizzor\_gen!E & 21 & 128 & 1.37\% & \\ \cline{2-5}  & Swizzor\_gen!I & 22 & 132 & 1.41\% & \\ \cline{2-5}  & Wintrim\_BX & 24 & 97 & 1.04\% & \\ \hline \multirow{2}{*}{Rogue} & Fakerean & 11 & 381 & 4.08\% & 381 \\ \cline{2-5}  & Agent\_FYI & 2 & 116 & 1.24\% & 274 \\ \cline{2-5}  & Rottgen & 19 & 158 & 1.69\% & \\ \hline Worm:AutoIT & Autorun\_K & 6 & 108 & 1.16\% & 108 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The distribution of malware families among Malimg samples.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 3 Analysis Method of Structural Time-Variant Reliability Model with Random and Interval Variables
### Formulation of Stochastic Process Discretization with Mixed Variables
\begin{array}{l}\text{Outer Optimization:}\\ \text{s.t.}\ \underset{\mathbf{Y}}{\text{min}}\ G(\mathbf{U},\mathbf{Y}^{*},t_{i})=0 \end{array}  \tag{27}\]  
Inner layer optimization:  
\[ \begin{array}{l}\text{$G(\mathbf{U},\mathbf{Y}^{*},t_{i})=\underset{\mathbf{ Y}}{\text{min}}\ G(\mathbf{U},\mathbf{Y},t_{i})$}\\ \text{s.t.}\ Y_{i,j}^{L}\leq Y_{i,j}\leq Y_{i,j}^{U}\ j=1,2,\ldots,n\end{array}  \tag{28}\]  
Equation (27) is a two-level nested optimization problem with complicated calculations and high computational expenses. Therefore, the optimization efficiency is difficult to meet the needs of engineering. Although decoupling strategies have been successfully applied to static hybrid reliability analysis problems, such as sequential single-loop strategies. It is a difficult problem to solve the time-variant reliability problem, because of time discretization, and inner and outer layer iteration. Also, a rigorous mathematical proof for decoupling strategy is lacking. Therefore, it is challenging to develop time-variant hybrid reliability decoupling strategies with good robustness and convergence.
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 5 Results and Analysis  
### Scheduling Performance  
In order to analyze and evaluate the performance of the proposed dynamic task scheduling (DTS) algorithm (Algorithm 1), this algorithm together with the OI-EleAttG-GRU is compared with LC (local computing) and OLC (online computing) in terms of energy consumption and accuracy for the task of identifying different numbers of human activity. Figure 8 shows the accuracy versus HAR task number and energy cost versus HAR task number, respectively.  
Figure 8 shows the optimization comparison of the DTS algorithm with the LC and OLC algorithms in terms of accuracy and energy cost, respectively, with the increase of human activity recognition task numbers. From the figure, the optimization effect of accuracy and energy consumption of the DTS algorithm is much better than that of the LC and OLC algorithms. This improvement is ascribed to dynamic scheduling strategy employed by the DTS algorithm, which enables the rational allocation of computing burden while effectively mitigating the impact of changes in the network environment on task scheduling.
## 4 Numerical Simulations
However, unlike the noise-free case where DI fluctuates around 0.9, the median value for the normalized DI is around 0.3 for the noisy data. Nevertheless, it still leads to correct localization by identifying the damaged member with the highest DI, while the index for the remaining members remain clustered around 0.1. The only exception to this clear distinction between two clusters is D.Case 6, for which a few misclassifications occurred in the previous stage of damage detection. DI for the segment between sensors 7-8 has deviated from those of the remaining undamaged segments, albeit still lower than the truly damaged segment.The sensor locations in this model-free approach serve as discrete measurement points, analogous to finite elements that represent structural behavior in a model. The choice of sensor placement significantly influences the resolution of damage localization. By employing denser sensor arrangements, resolution can be enhanced, leading to more refined and accurate identification of damage. However, denser sensors may complicate data analysis due to the larger volume of data to process, potentially increasing computational demands. Additionally, while more sensors can enhance resolution, they may also amplify the effects of measurement noise, making it more challenging to distinguish between true damage and noise. Therefore, a careful balance must be achieved between sensor density and the overall complexity of the analysis.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 3 Methodology
### Sensors
#### 3.1.3 Tachometer  
A tachometer is also employed to monitor the location and speed of the rail cart along the track. This device facilitates the transformation from the time domain to the spatial domain. The tachometer comprises an 18-tooth gear and a Hall effect probe. Along with the cart's wheel diameter of 7.5 inches, a linear resolution of 1.66 cm can be achieved, as derived from Equation (1). The tachometer is attached to the front shaft of the cart, which is a passive (idler) shaft. This configuration was chosen because the active (driver) shaft is directly connected to the DC motor, causing it to experience slippage (rotation without corresponding linear movement) more often, which could result in false information. Thus, using the passive shaft minimizes the occurrence of erroneous readings.  
\[\textit{Linear resolution}=\frac{1}{\textit{number of tooth}}\times\pi \times\frac{\textit{wheel diameter}}{2} \tag{1}\]
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 2 Methods
### Multi-Space Discriminators
#### 2.2.1 Discriminator on Feature Space  
The well-trained segmentation without the domain adaptation tends to produce inaccurate predictions on target domain images. To obtain the semantic segmentation information from the global features extensively, we chose higher-level features, extracted by the segmentation network as inputs of the feature discriminator. As high-level features contain lots of contexts and semantic information, their alignment could force the encoded features of the source domain images and the target domain to be semantically consistent.  
The feature discriminator \(\text{D}^{f}\) aligns the high-level feature distributions by classifying whether the feature is produced from the source domain or the target domain. When the discriminator cannot judge from which domain features are extracted, the source domain and target domain are successfully aligned on the feature space. Otherwise, gradients of the discriminator \(\text{D}^{f}\) are delivered backward to encourage the features of the target domain to be more similar to the source domain.  
The adversarial loss of the feature discriminator \(\text{D}^{f}\) is:  
\[\mathcal{L}_{D^{f}}=\frac{1}{M}\sum_{x^{s}\in X^{S}}\mathcal{L}_{D}\left(P_{x^ {s}}^{f},1\right)+\ \frac{1}{N}\sum_{x^{t}\in X^{T}}\mathcal{L}_{D}\left(P_{x^{t}}^{f},0\right), \tag{2}\]  
where \(\mathcal{L}_{D}\) is the binary cross-entropy loss, and \(M\) and \(N\) are the number of source domain images and target domain images, respectively; \(X^{S}\) is the source domain dataset and \(X^{T}\) is the target domain dataset; \(P_{x}^{f}\) is the predicted fused feature of the image \(x\).
## 2 Materials and Methods
### Echocardiographic Acquisitions and Image Processing  
The echocardiographic functional and morphological assessment was carried out according to the current guidelines [17] using a commercially available echocardiographic system EPIQ 7 equipped with an X5-1 xMATRIX-array transducer (Philips Healthcare, Eindhoven, The Netherlands). Participants were scanned at the pre-inter-seasonal medical assessment and within one week of completing the inter-seasonal training program. Cardiac 3D volume acquisitions were performed during end-expiratory apnea (4 to 6 cardiac cycles) in two separate sequences for the LV and RV. All data were stored digitally on a computer system. Offline data analysis was processed, semi-automatically by a single operator blinded to study time points, using commercially available clinically validated software (4D RV-Function and 4D LV-Function, TOTEC-Arena TTA2, TOTEC Imaging Systems GMBH, Unterschleissheim, Germany), from which left and right ventricular volumes and RV free wall global longitudinal strain were obtained. RV 3D volume segmentation sequences corresponded to a full cardiac cycle and resulted in a mesh for each time step consisting of 938 nodes and 1872 triangles of the endocardi, and the coordinates of each node were exported for our in-house regional analysis. Two-dimensional linear dimensions for both ventricles and atria were measured manually following the recommendations [17]. To assess the intra-and inter-observer reproducibility, quantifications were blindly repeated in 10 randomly selected players by the same operator who originally quantified the whole dataset and a second independent operator.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 4 Simulation and Numerical Results
### Time Delay Estimation  
According to the requirements of modulation parameter setting, the modulation parameters selected in this paper are \(K=5,N=60,f_{c}=455\) KHZ and\(\Delta\tau=1\) ms, and there are only two paths of main path and sub-path. The delay range is calculated to get m value (\(m=7\)), and the \(\Delta\tau-mT\) conforms to the second case of parameter setting in Section 3, that is, the delay waveform of the previous seventh symbol is included in the current symbol period, and the delay waveform of the current symbol is included in the seventh symbol period later. When the main path and the sub-path can be separated, the signal waveform of the received signal in two symbol periods after demodulation is shown in Figure 7.  
Figure 7: Waveform of two symbol periods after the received signal is demodulated. It can be seen from Figure 7 that when the MPPSK modulated signal passes through the multipath channel, two peaks appear in one symbol period, one of which is the decision peak of the current symbol, and the other is the multipath peak of the previous seventh symbol delay to the current symbol period. When the signal-to-noise ratio takes different values, the signal modulation parameter multipath gain is modified, and the multipath delay is estimated using the LMSTDE method. The delay value of the sub-path relative to the main path is given in Table 1.  
As can be seen from Table 1, when the multipath gain is modified, the accuracy of time delay estimation using LMSTDE method is not significantly affected. Therefore, the LMSTDE method can be used to estimate the time delay of MPPSK multipath signals. The traditional multipath separation technique spreads the signal through a Pseudo-Noise Sequences of a specific design, and the receiver performs correlation operations to separate the multipath. Since the Pseudo-Noise Sequences has a sharp autocorrelation property, the receiver sends the received multipath signal to a plurality of correlators, and each path signal can be separated. The premise of multipath separation is multipath delay estimation. Therefore, after spreading the MPPSK signal spectrum, a m-sequence known to the receiver is inserted in front of the symbol to be transmitted. When the receiver correlates the received multipath signal with the known m-sequence, the multipath delay can be determined by the time interval between the peaks of the correlation function graph. With the same parameters as above, the multipath delay is estimated by using autocorrelation method under different signal-to-noise ratios, different multipath gains and different m-sequences. When using the autocorrelation method, the delay of the sub-path relative to the main path is shown in Table 2.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 5 Conclusions  
High-resolution satellite images show relatively narrow swath widths such that users often face obstacles to orthorectify and mosaic a bundle of delivered images to create a complete image map. Therefore, the proposed sensor-level mosaicing can generate a seamless image product with improved geometric accuracy. The experimental result with KOMPSAT-3 data showed that the irregular discrepancy between the input images due to the differences in acquisition angles could be minimized for geometrical continuity in the resulted mosaic image. In addition, single sensor modeling information of the mosaic image could be generated for the later photogrammetric processes. The accuracy improvement of the sensor modeling ranged from 18% to 47.4%. Therefore, we believe that the proposed sensor-level mosaic method enables users to take advantage of more efficient and convenient photogrammetric data processing.  
Conceptualization, C.L.; data curation, C.L.; formal analysis, C.L. and J.O.; methodology, C.L. and J.O.; validation, C.L. and J.O.; writing--original draft, J.O.; writing--review and editing, C.L. and J.O. All authors have read and agreed to the published version of the manuscript.  
This study was supported by National Research Foundation of Korea, grant number 2019R1I1A3A01062109.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods  
### Experiment and Data  
Laboratory-recorded data provide a controlled benchmark for assessing ML and TL models under minimal external disturbances, allowing for the validation of baseline trends and effectiveness of the proposed methodology. Once validated, these models can be applied in real industrial environments. A typical testbed setup for identifying unbalance consists of a rotating machinery system equipped with a motor, shaft, bearings, and controlled mechanism to introduce unbalance at different levels. This study utilized a publicly available vibration-based unbalance detection dataset from the Fraunhofer Fordatis database [21].
## 5 Conclusions  
In this work, we propose a PS scheme based on a single-layer LUT that requires only one LUT for mapping and achieves favorable system performance. Simulation results show that the proposed method achieves a performance improvement comparable to that of CCDM-PS, reducing the bit error rate (BER) from 1.2376 \(\times\) 10\({}^{-4}\) to 6.3256 \(\times\) 10\({}^{-5}\), a difference that corresponds to a 48.89% enhancement. Furthermore, the Q-factor is enhanced by approximately 0.93 dB. To further mitigate nonlinear distortion while simultaneously reducing the average signal power, we integrate the LUT-PS scheme with RBFNN-based nonlinear equalization. We conducted a simulation experiment in an OFDM photonic terahertz communication system at 400 GHz, successfully achieving the transmission of a 5 GBaud signal over a 10 km optical fiber. The results show that this combined scheme reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB.  
Figure 8: Simulation results of different schemes for 400 GHz photonic terahertz OFDM signal transmission over 10 km of optical fiber. (**a**) BER of different schemes; (**b**) EVM of different schemes; (**c**–**e**) recovered constellation diagram under the woPSNN scheme, LUT-PS-RBFNN scheme, and LUT-PS-DFE scheme. The red points represent the constellations after nonlinear equalization.  
**Author Contributions:** Conceptualization, K.L. and J.Y.; Data curation, J.Y.; Formal analysis, Y.H.; Funding acquisition, J.Y.; Investigation, Y.H. and F.Z.; Methodology, Y.H.; Project administration, J.Y.; Resources, J.Y.; Software, Y.H.; Supervision, K.L.; Validation, Y.H. and F.Z.; Visualization, Y.H.; Writing--original draft, Y.H.; Writing--review & editing, K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (NO.62127802, NSFC) and National Key Research and Development Program of China (Grant No.2023 YFB905603).  
**Data Availability Statement:** The data supporting reported results can be found from the author on reasonable request.  
**Acknowledgments:** We sincerely appreciate [PERSON] for his careful guidance and assistance. We truly appreciate our colleagues at Beijing University of Posts and Telecommunications for their support and encouragement. This article is a revised and expanded version of a paper entitled [PS-16 QAM-OFDM with Nonlinear Equalization Based on RBF Neural Network by 350 GHz Photonic Terahertz System], which was presented at [2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC), Beijing, 2-5 November 2024].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Numerical Simulation
### Simulation Setup
In this case, the nominal system parameters, which are slightly different from the actual system parameters, are employed to design the backstepping control law, as illustrated in Table 2.
4. Proportional-Integral-Derivative (PID) controller with controller gains manually tuned to make the tracking performance as good as possible without chattering in the control input as \(K_{P}=750\), \(K_{I}=500\), and \(K_{D}=0.02\). The time-varying sinusoidal-like mismatched and matched disturbances \(d_{1}(t)\) and \(d_{2}(t)\) are intentionally injected into the control system for performance examination as follows:  
\[ \begin{array}{ll}d_{1}(t)=1.5\times 10^{3}\sin(\pi t)&\left(\mathrm{rad}\cdot \mathrm{s}^{-2}\right)\\ d_{2}(t)=10^{6}\sin(\pi t)&\left(\mathrm{rad}\cdot\mathrm{s}^{-3}\right)\end{array}  \tag{61}\]  
where the magnitudes of these disturbances are chosen based on the specifications of the EHSS. Performance indexes--i.e., the maximum, average, and standard deviation of the tracking errors [38]--are used to measure the efficiency of the aforementioned controllers. These criteria are defined as follows:  
1. Maximal absolute tracking error: \[M_{e}=\underset{i=1,\ldots,N}{\max}\{|e_{1}(i)|\}\] (62)
2. Average tracking error: \[\mu_{e}=\frac{1}{N}\sum_{i=1}^{N}|e_{1}(i)|\] (63)
3. Standard deviation of the tracking errors: \[\sigma_{e}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(|e_{1}(i)|-\mu_{e}\right)^{2}}\] (64)
## 2 Problem Formulation
Below, we give an exact formal definition of the underlying criteria and demonstrate that adequate a priori sensor weighting is quantitatively ensured by a set of meta-parameters strongly influencing the character of the result (see (3) and (15) below). We construct recursive estimators of the calibration parameters \(b^{i}\) and \(a^{i}\) by assuming that the sensor outputs, \(y^{i}_{t}\), are locally available and the nodes interchange messages according to a given adjacency matrix, \(A_{ad}\), in such a way that each message between the selected nodes, \(j\) and \(i\), is corrupted by _additive communication noise_, \(\eta^{ij}_{t}\). **Remark 3**. : _It is to be noted at this point that all the sensors measure the same signal, \(x_{t}\) (observed through sensor outputs \(y^{i}_{t}\)). In large sensor networks spreading over large 2D or 3D spaces, this assumption may not be justified. Then, a straightforward approach would be to build a spacial model, connecting signals at different spacial coordinates according to spacial correlation functions, and to incorporate such a model in the calibration procedure (see, e.g., [23]). Notice that, in some cases, there exist special working regimes of sensor network environments within large networked control systems, which justify the assumption that all sensors observe the same signal; one practically important example is described in Section 8 in relation to complex experiments dealing with aerodynamic pressure sensing._