esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automated End-to-End Side Channel Analysis Based on Probabilistic Model
## 3 Proposed Approach
### Clustering Features  
In this part, we finally make clusters of the features so that we can match each segment to the operation which has been executed when generating the segment. We apply K-means clustering algorithm. Based on the pre-defined distance measure and number of clusters, the K-means algorithm repeats until convergence assigning data points to cluster based on distance and computing the mean of each cluster [20]. We assigned number of clusters K = 3 based on the number of operations (Square, Multiply and optionally idle period). For coefficients of Section 3.4.1, Euclidean distance \(Distance(\tilde{\phi}_{i},\tilde{\phi}_{j})=\sqrt{[(\tilde{\phi}_{i}-\tilde{ \phi}_{j})]^{T}[(\tilde{\phi}_{i}-\tilde{\phi}_{j})]}\) is defined. For histogram coefficients of Section 3.4.2, both Euclidean distance and symmetrized divergence \(Distance(\tilde{\phi}_{i},\tilde{\phi}_{j})=KL(\tilde{\phi}_{i}|\tilde{\phi}_{ j})+KL(\tilde{\phi}_{j}|\tilde{\phi}_{i})\) (only for normalized histogram) are defined as distance measures. The performance of K-means algorithm, however, is affected by the initial points. So we run K-means algorithm multiple times and evaluate each run [24]. Only with data we used should we evaluate each run so that any other information is not reflected and result of K-means is evaluated fairly. We adopt Davies-Bouldin index(DB index) to evaluate each run and choose best performing clusters. Desirable clusters, with high inter-cluster distance and low intra-cluster distance, produce low DB index [25]. Figure 6 shows recovering key exponent.  
Figure 5: Time series and reconstruction with polynomial coefficients. (**a**) Time series; (**b**) Polynomially reconstructed time series.  
Figure 6: Key exponent recovery process. (**a**) t-SNE visualized polynomial coefficients clustered; (**b**) Recovering key exponent with the clusters.
## 3 Principles and Modeling of an Adaptive Control Method for PMSMs Based on Super-Helical SMC
### MRAS Control System Based on Super-Helical Sliding Mode  
The super-helical sliding mode control replaces the PI adaptive control parameters in the MRAS control, which relies on stator current. The corresponding control system block diagram is depicted in Figure 4 and the variables marked with \({}^{*}\) denote reference or target values essential for motor control, predominantly determined by control algorithms or regulators.  
The permanent magnet synchronous motor receives the estimated electrical angle transmitted from the adaptive rate. Subsequently, the electrical angle is used by the adaptive controller to generate the parameters for three-phase current and three-phase voltage. These parameters are then transformed into DC voltage and current parameters on the dq-axis through a coordinate system transformation. Meanwhile, the adjustable mode calculates the estimated current of the dq-axis using Equation (5) and forwards this signal to the adaptive rate. The adaptive rate operates based on the sliding mode (Equation (11)). The output of the adaptive rate is then passed to the speed observer, which estimates the rotor speed according to Equation (13). Finally, the estimated rotor speed is integrated to produce the estimated rotor position angle. This enables the closed-loop control of the permanent magnet synchronous motor without the need for a position sensor.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 4 Proposed Architecture  
The proposed architecture is shown in Figure 1, which is based on a multi-branch architecture. There are \(N\) branches to receive the inputs from \(N\) IMUs. The number \(N\) will be equal to 3, 4, and 5, for the PAMAP2, DaLiAc, and DSAD datasets, respectively, as shown in Table 6. Each branch receives the input \(\mathbf{X}^{(n)}\) from an IMU and uses a one  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **PAMAP2** & **DaLiAc** & **DSAD** \\ \hline Sensor & 2 accelerometers, 1 gyroscope, & 1 accelerometer, 1 gyroscope & 1 accelerometer, 1 gyroscope, \\  & 1 magnetometer & & 1 magnetometer \\ Sampling Rate & 100 Hz & 200 Hz & 25 Hz \\ No. IMUs (\(N\)) & 3 & 4 & 5 \\ Positions & wrist, chest, ankle & right wrist, chest, right hip, & torso, right arm, left arm, \\  & & left ankle & right leg, left leg \\ No. Sensor Data Types & 12 & 6 & 9 \\ per IMU (\(M\)) & & & \\ No. Subjects & 9 & 19 & 8 \\ No. Activities & 12 & 13 & 19 \\ Window Size & 3 s & 3 s & 5 s \\ Segment Length (\(L\)) & 300 data points & 600 data points & 125 data points \\ No. Segments & 5764 & 7802 & 9120 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A summary of sensor data from three datasets.  
-dimensional (1D) CNN model to extract a set of feature maps \(\mathbf{A}^{(n)}\). Since each feature map owns different significance of information, we propose the merging-squeeze-excitation (MSE) feature fusion to combine these \(N\) sets of feature maps \(\mathbf{A}^{(n)}\) by applying the SE mechanisms [16] and produce the channel-weighted and merged feature maps \(\mathbf{Y}^{\textit{mise}}\), which will be used to predict the corresponding activity. The details are provided as follows:
## 3 Description of the Study Site and Collected Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**Data** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **X12** & **Y** \\ \hline
1.00 & 3.80 & 37.30 & 44.70 & 18.00 & 50.10 & 1.69 & 1.13 & 1.32 & 51.30 & 39.20 & 12.10 & 0.90 & 0.09 \\
2.00 & 5.80 & 45.90 & 37.00 & 17.10 & 48.30 & 1.65 & 1.11 & 1.38 & 49.50 & 37.20 & 12.30 & 0.90 & 0.08 \\
3.00 & 7.80 & 40.20 & 40.30 & 19.50 & 47.80 & 1.72 & 1.16 & 1.27 & 48.40 & 36.90 & 11.50 & 0.95 & 0.07 \\
4.00 & 9.80 & 33.30 & 45.80 & 20.90 & 52.20 & 1.63 & 1.07 & 1.43 & 53.20 & 39.10 & 14.10 & 0.93 & 0.09 \\
5.00 & 3.80 & 43.10 & 41.30 & 15.60 & 46.10 & 1.69 & 1.16 & 1.28 & 47.50 & 37.10 & 10.40 & 0.87 & 0.09 \\  &... &... &... &... &... &... &... &... &... &... &... &... \\
437.00 & 45.40 & 38.50 & 16.10 & 45.20 & 1.71 & 1.18 & 1.25 & 46.40 & 35.20 & 11.20 & 0.89 & 0.08 & 0.08 \\
438.00 & 48.40 & 35.30 & 16.30 & 42.40 & 1.68 & 1.18 & 1.25 & 43.60 & 32.70 & 10.90 & 0.89 & 0.08 & 0.04 \\
439.00 & 40.20 & 42.60 & 17.20 & 48.20 & 1.71 & 1.15 & 1.30 & 49.20 & 36.80 & 12.40 & 0.92 & 0.07 & 0.08 \\
440.00 & 35.50 & 42.30 & 22.20 & 42.10 & 1.73 & 1.22 & 1.18 & 43.50 & 31.60 & 11.90 & 0.88 & 0.08 & 0.03 \\
441.00 & 44.40 & 39.70 & 15.90 & 43.90 & 1.74 & 1.21 & 1.17 & 44.20 & 33.60 & 10.60 & 0.97 & 0.06 & 0.07 \\ \hline \end{tabular}
\end{table} 
Table 2: Demonstration of the collected dataset. Figure 3: Histograms of compression coefficient’s influencing variables. In addition, to facilitate the learning phase of ANN, the collected dataset has been normalized by means of the \(Z\)-score data transformation. The formula of the \(Z\)-score data transformation is shown as follows:  
\[\mathrm{X_{N}}=\frac{\mathrm{X_{O}-m_{X}}}{\mathrm{s_{X}}} \tag{8}\]  
where \(\mathrm{X_{N}}\) and \(\mathrm{X_{O}}\) denote the normalized and the original variables, respectively; \(\mathrm{m_{X}}\) and \(\mathrm{s_{X}}\) are the mean value and the standard deviation of the original variable, respectively.
## Nomenclature  
\begin{tabular}{l l} A\(a\) & Solar collector area (m\({}^{2}\)) \\ C & Cost rate, USD/h \\ DNI & Direct normal irradiation (kWh/m\({}^{2}\cdot\)day) \\ Ex & Exergy (kW) \\ Ex\({}_{\rm dest}\) & Rate of exergy destruction (kW) \\ f\({}_{\rm k}\) & Thermoeconomics factor (\%) \\ F\({}_{\rm R}\) & Collector heat gain factor \\ h & Enthalpy (kJ/kg) \\ i & Interest rate \\ k & Conductivity heat transfer (W/m\(\cdot\)K) \\ \(l\) & Insulation thickness (m) \\ \(\dot{\rm m}\) & Mass flow rate (kg/s) \\ n & Predicted life of the system’s (years) \\ \(\dot{\rm Q}\) & Heat rate (kW) \\ \(\dot{\rm Q}_{\rm solar}\) & Solar energy input (kW) \\ \(s\) & Specific entropy (kJ/kg\(\cdot\)K) \\ T & Temperature (K) \\ \(\
u\) & Specific volume (m\({}^{3}\)/kg) \\ \(\dot{\rm W}\) & Work by the control volume per unit time (kW) \\ \(\dot{Z}_{\rm k}\) & Entire cost rate \\
**Greek Symbols** \\ \(\eta_{\rm Ex}\) & Exergy efficiency (\%) \\ \(\eta_{\rm th}\) & Thermal efficiency (\%) \\ \(\alpha\) & Receiver's absorption coefficient \\ \(\gamma\) & Reflection coefficient \\ \(\tau\) & Coefficient of the glass cover \\ \(\varphi\) & Total operating and maintenance cost \\
**Abbreviations** \\ AC & Air compressor \\ BC & Brayton cycle \\ CC & Combustion chamber \\ CCPP & Combined cycle power plant \\ Cond & Condenser \\ CPVT & Concentrating photovoltaic thermal \\ CRF & Capital Recovery Factor \\ CSP & Concentrated solar power \\ cv & Control volume \\ DSG & Direct steam generation \\ EES & Engineering Equation Solver \\ EVAP & Evaporator \\ GHGs & Greenhouse gases \\ GT & Gas turbine \\ GTIT & Gas turbine inlet temperature \\ HE & Heat exchanger \\ HPST & High-pressure steam turbine \\ HRSG & Heat recovery steam generation \\ HTF & Heat transfer fluid \\ IPST & Intermediate-pressure steam turbine \\ ISCC & Integrated solar combined cycle \\ LCA & Life Cycle Assessment \\ LHV & Fuel’s lower heating value \\ LPST & Low-pressure steam turbine \\ NG & Natural gas \\ \end{tabular}
## 3 Results and Discussion
### Model Performance Parameters
These included precision, recall, specificity, F1 score, Matthews Correlation Coefficient (MCC), and Cohen's Kappa, as summarized in Table 4. Among the models, Random Forest achieved the highest overall performance, with a recall of 0.909 and an F1 score of 0.885, followed closely by XGBoost. Although Logistic Regression had slightly lower performance, it remains interpretable and helpful in understanding the influence of each variable. Notably, recall plays a critical role in landslide susceptibility mapping. It quantifies the model's ability to identify actual landslide occurrences (true positives) correctly. In the context of disaster risk management, high recall is essential to minimize false negatives, that is, locations prone to landslides being misclassified as safe. Such errors could lead to serious safety and infrastructure consequences. Hence, the high recall values obtained by the ensemble models (especially RF and XGBoost) underline their suitability for landslide prediction tasks. This comprehensive metric evaluation ensures robust and balanced interpretation of the models' classification capabilities, with an emphasis on minimizing the risk of overlooking hazardous zones.
## 4 Experimental Results
### Experimental Setup
#### 4.1.3 Implementation Details  
In this study, the OSTrack-384 [19] is selected as the main component of the preliminary object tracking module. The input sizes of templates and search regions are 192 \(\times\) 192 pixels and 384 \(\times\) 384 pixels, the initial learning rate was set to 4 \(\times\) 10\({}^{-5}\), the batch size was set to 128, and the training epoch was defined as 300.  
The bipartite matching model in TRTrack based on NeighborTrack [30] is used to optimize the tracking results. For SoftNMS in it, the IoU threshold was set to 0.25, and the time period \(\tau\) of backtracking tracklets was set to 9.  
For YOLOv5-DeepSORT in the boundary object detection module of TRTrack, the YOLOv5n network with balanced performance is chosen as the basic module of object detection, which is introduced in the alpha-IoU loss function. Following the universal approach, the training batch size was valued at 32, the decay rate was set to 5 \(\times\) 10\({}^{-4}\), and the learning rate was defined as 1 \(\times\) 10\({}^{-2}\). In particular, the value of parameter \(\alpha\) of the alpha-IoU loss function is 3.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries
### Arithmetic Coding  
AC [38; 39; 40] is a sophisticated method of entropy encoding used in lossless data compression. It was first introduced by [PERSON] in the late 1970s and has since been refined and adopted in various applications, particularly where high compression efficiency is required. Unlike traditional Huffman coding [43], which assigns fixed-length codes to symbols, arithmetic coding represents an entire message as a single number, a fraction between 0 and 1. This technique allows for more efficient data representation, especially in cases where symbol probabilities vary significantly. The primary idea behind arithmetic coding is to represent a sequence of symbols by a subinterval within the range [0, 1). This interval narrows as more symbols are processed, with the width of each subinterval proportional to the probability of the corresponding symbol. The processes of encoding and decoding for data compression are described in Algorithms 1 and 2, and they are explained step by step as follows:  
**Step 1:**: The initial interval is set to \([0,1)\). The variables low and high are initialized to 0 and 1, respectively. **Step 2:**: **Encoding**  
```
1:a: For each symbol in the input data, the interval is updated based on the cumulative distribution function (CDF) of the symbol. 2:b: The range is calculated as the difference between high and low. 3:c: The high value is updated to low + range \(\times\) CDF(symbol). 4:d: The low value is updated to low + range \(\times\) CDF(previous_symbol). 5:**: **Final Encoded Value**
```
1:a: After processing all symbols, the midpoint of the final interval is selected as the encoded value. 2:Step 4:**: **Decoding**  
```
1:a: Decoding is performed by reversing the encoding process. Starting from the encoded value, the interval is iteratively narrowed down. 2:b: For each symbol, the range is calculated, and the value is adjusted to locate the corresponding symbol index using the CDF. 3:c: The decoded symbol is appended to the output sequence, and the interval is updated accordingly.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 4 Experiments  
To verify the superiority of our HSI super-resolution method, we compared the proposed method with the following state-of-the-art works: (1) FUSE [35]; (2) Bayesian Sparse Representation (BSR); (3) Coupled Spectral Unmixing Hyperspectral Super-Resolution (CSU) [23]; (4) Clustering Manifold Structure (CMS) [26]; and (5) Learning a Low Tensor-Train Rank Representation (LTR) [31]. The experiments were carried out using two open-source HSI data sets. All experiments in this paper were implemented in MATLAB R2018b on a server with 24 2.20 GHz Intel Xeon CPUs and 60.0 GB RAM. In this section, we introduce the data sets used in the experiments, followed by the results and discussions.
## 2 Materials and Methods
### Instrumental Analysis with HS-SPME-GC-Ms  
In addition to the human evaluation of the ground coffee samples, volatile profiles of each sample were collected in analytical triplicate by headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Then, 2.00 \(\pm\) 0.05 g of freshly ground coffee was weighed into a 20 mL amber HS vial (Restek, Bellefonte, PA, USA) and capped with a bi-metal magnetic crimp cap (Supelco, Bellefonte, PA, USA). Method parameters were optimized based on prior work and included a 5 min equilibration at 37 \({}^{\circ}\)C, followed by 30 min extraction with a 2 cm DVB/Car/PDMS SPME fiber (Supelco) and an MPS robotic autosampler (Gerstel U.S., Linthicum Heights, MD, USA). Using a 7890B-5977B GC-MS (Agilent Technologies, Wilmington, DE, USA), extracted volatiles were injected into the 250 \({}^{\circ}\)C hot inlet equipped with a SPME inlet liner (Supelco) in splitless mode (1.2 min) and separated on a Rtx-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Restek) operated at a constant helium carrier gas flow of 1 mL/min, using an oven program as follows: 30 \({}^{\circ}\)C for 3 min, ramp of 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, a second ramp of 30 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C with a final hold time of 14 min. The MS transfer line, quadrupole, and detector temperatures were set to 250 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively. EI spectra were collected in scan mode from 32 to 350 amu, with 5.14 scans per second. From the collected GC-MS data, 48 previously reported coffee aroma compounds were identified based on their spectral and retention index match with libraries. Peak areas (selected ion chromatograms; Table S1) were used for further data analysis.
## 4 Experimental Results  
### Implementation Details and Datasets  
The proposed self-supervised learning method was implemented with PyTorch and based on the VISSL library [51]. The main task considered in our study was the discrimination between CG images and NIs. For the experimental evaluation of CG forensics performance, we used the four recent datasets collected by [PERSON] et al. [27] (some example images are shown in Figure 1), in which the CG images were generated, respectively, by four advanced rendering engines (i.e., Artlantis [52], Autodesk [53], Corona [11] and VRay [9; 10]), while the NIs were from the popular RAISE [13] and VISION [12] datasets. Following [PERSON] et al. [27], hereafter we used, respectively, Artlantis, Autodesk, Corona and VRay to denote the four datasets. We adopted the same dataset splits as those of the original paper [27]: For each of the four datasets, the training set comprised 5040 CG images and 5040 NIs, and the testing set had 360 CG images and 360 NIs. We made use of the well-known ImageNet dataset [49] for the self-supervised pretext task training, and this also ensured that there was no overlapping between NIs used for pretext task training (from ImageNet) and those used for main task evaluation (from RAISE and VISION). ImageNet is a huge dataset, comprising more than 1 million images in its training set. We randomly drew 10% ImageNet training images and obtained around 128 K images which were then used for the training of our pretext task, i.e., the 9-class classification problem presented in Section 3.2. We used the well-known and popular ResNet50 [54] as the backbone neural network. The pretext task training was carried out by using the SGD (Stochastic Gradient Descent) optimizer for 105 epochs, and we set an initial learning rate of 0.1, which was successively divided by 10 at 30, 60, 90 and 100 epochs. This training was conducted on two NVIDIA Quadro GPUs (Graphics Processing Units) and, typically, lasted for about 14 h. Although the performance of the pretext task was not the focus in self-supervised learning [8], in practice we found that our pretext task (a 9-class classification problem) could be solved reasonably well with an accuracy of about 85%. As presented later in this section, in our study we conducted experimental comparisons with several popular and representative self-supervised methods. In order to ensure fair comparisons, the self-supervised training of these existing methods was carried out on the same data, for the same number of epochs and with the same backbone neural network as our pretext task, by using the training scripts provided in the VISSL library [51].
###### Abstract  
This paper proposes a reliability-based design optimization (RBDO) approach that adopts the second-order reliability method (SORM) and complex-step (CS) derivative approximation. The failure probabilities are estimated using the SORM, with [PERSON]'s formula and the technique established by [PERSON] and [PERSON], and their sensitivities are analytically derived. The CS derivative approximation is used to perform the sensitivity analysis based on derivations. Given that an imaginary number is used as a step size to compute the first derivative in the CS derivative method, the calculation stability and accuracy are enhanced with elimination of the subtractive cancellation error, which is commonly encountered when using the traditional finite difference method. The proposed approach unifies the CS approximation and SORM to enhance the estimation of the probability and its sensitivity. The sensitivity analysis facilitates the use of gradient-based optimization algorithms in the RBDO framework. The proposed RBDO/CS-SORM method is tested on structural optimization problems with a range of statistical variations. The results demonstrate that the performance can be enhanced while satisfying precisely probabilistic constraints, thereby increasing the efficiency and efficacy of the optimal design identification. The numerical optimization results obtained using different optimization approaches are compared to validate this enhancement.  
reliability-based design optimization; sensitivity analysis; second-order reliability method; complex-step derivative approximation 2021115312[[https://doi.org/10.3390/app1115312](https://doi.org/10.3390/app1115312)]([https://doi.org/10.3390/app1115312](https://doi.org/10.3390/app1115312))
# Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile
## 6 Conclusions  
This paper illustrated that the matrix profile algorithm could be a very efficient tool for detecting malicious attacks via discovering respective symptoms rather than monitoring traffic directly. The findings showed that the precision of the matrix profile algorithm is 93.33-100% when using time-series data, whereas the accuracy of the algorithm is 80-100% when using Z-normalised time-series data, see Table 1. Regarding computation time, the matrix profile algorithm managed to detect DDoS in 5 s in a time series with 100K  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Time-Series Data**} & \multicolumn{3}{c}{**Z-Normalised Time-Series Data**} \\ \hline
**Window Size** & **No. of TP** & **No. of FP** & **Precision in \%** & **No. of TP** & **No. of FP** & **Precision in \%** \\ \hline
3 & 14 & 1 & 93.33 & 13 & 2 & 86.67 \\ \hline
5 & 14 & 1 & 93.33 & 12 & 3 & 80.00 \\ \hline
10 & 14 & 1 & 93.33 & 12 & 3 & 80.00 \\ \hline
25 & 15 & 0 & 100.0 & 13 & 2 & 86.67 \\ \hline
50 & 15 & 0 & 100.0 & 15 & 0 & 100.0 \\ \hline
100 & 15 & 0 & 100.0 & 15 & 0 & 100.0 \\ \hline
500 & 15 & 0 & 100.0 & 15 & 0 & 100.0 \\ \hline
1000 & 15 & 0 & 100.0 & 13 & 2 & 86.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precision of matrix profile. FP= False Positive; TP= True Positive  
Figure 16: Matrix profile algorithm runtime with time-series data and Z-normalised time-series data.  
reading, and 25 s for the same number of readings for time-series data. Furthermore, this paper showed that the runtime of the algorithm could be improved using Z-normalised time-series data, as the runtime can be 20% of the computation time of the matrix profile algorithm using time-series data, see Figure 16. Finally, the findings demonstrated that the matrix profile algorithm is scalable, as shown in Figure 16, and is a data-agnostic tool that does not require specific experience to utilise.  
This research received no funding.  
Not applicable.  
The authors declare no conflict of interest.