esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 3 DPA Attack and Our Proposed Countermeasure
### DPA Attack  
The DPA attack is most popular attack based on power analysis. The interesting fact about DPA is that it does not require information about the attacked device. It requires a large number of power traces along with software computation to reveal the secret key of a cryptographic device. DPA exploits power consumption at a specified instant of time, as then it used power traces as a function of processed data. To apply the DPA attack, the initial step is to collect the power samples during the computation of the operations involved in the selected algorithm or protocol (AES in our case). The method to accumulate  
Figure 1: Structure of the AES algorithm.  
the power traces is described later in Section 4.1. Therefore, the subsequent steps are described in the text given below.  
_Step 1: Selection of the intermediate values inside the algorithm._ The first step of a DPA attack is to choose an intermediate result from the cryptographic algorithm running on a cryptographic device. Then, the intermediate results can be used to reveal the part of a secret key. The function \(f(d,k)\) is used as the intermediate result. The value of \(d\) determines either the plain text or ciphered text, while \(k\) is a selected part of the key.  
_Step 2: Measuring the power consumption._ In the second stage, we monitor the power traces of the cryptographic device during the execution of the operations associated with it. When performing encryption or decryption operations, the attacker must know the relevant data value \(d\) (this is required in the calculation of intermediate results, as described in step 1). For DPA attacks, it is crucial that the measured traces are aligned appropriately.  
_Step 3: Calculating intermediate values of hypothetical model._ The next stage in the attack is to compute a hypothetical intermediate value for each conceivable \(k\) value. Using Equation (1), an attacker may simply compute the hypothetical intermediate  
In Equation (3), the values for \(\mu_{\rm X}\) and \(\mu_{\rm Y}\) determine the mean of variables \(X\) and \(Y\), respectively. Furthermore, \(\mathbf{E}\) shows the expectation or expected value. It is important to provide that the Pearson correlation is not much efficient (as it performs correlation in a sequence of rows one after the another) and is challenging to extract the correlation coefficient between two sets of stochastic variables (i.e., \(X\), and \(Y\)), due to the presence of excessive noise. Additionally, this results in an increase in the correlation calculation time. Therefore, we have described our custom Pearson correlation function (in the succeeding Section 3.1.2) that optimizes the computation based on the statistical implementation. Moreover, it also reduces the calculation time.
## 4 Results and Discussion
### Clustering of Spectral Polarization Data
Then, the classification results were projected to the first row of each data in the reorganization matrix above the first dimension and the eighth dimension, forming a two-dimensional clustering scatter diagram as shown in Figure 5. In the figure, data points of the same color belong to the same cluster, which is identified as a material. In the parallel pre-experiment of the six small data sets, the four types of materials were correctly identified and distinguished, and the correct rate of division for the sampling points of each material reached 100%. These experimental results prove the correctness of the algorithm theory and the practicality of implementing it. Finally, following the Kosko subset measure test, the flag value established to determine whether there is a fuzzy cluster classification mark is 0 for each set of parallel tests, which is displayed as false. Therefore, the precision of the boundary delineation demonstrated by the laboratory experiments is credible. Based on the experimental results and theoretical verification, the proposed algorithm is reasonable and can be used for clustering research in actual scenarios. Figure 4: The data contour port at incident angle is 40\({}^{\circ}\). Figure 5: FCM algorithm in a 2D scatter plot.
## 3 Materials and Methodology
### Preprocessing the Categorized Signals
#### 3.3.5 Steps in Preprocessing Implicit Signals  
Preprocessing implicit signals is a critical step in ensuring that the raw behavioural data collected from learners is structured, meaningful, and ready for analysis or machine learning models. This process begins with the data cleaning and is performed to handle missing values and remove irrelevant or redundant interactions. Transformation is performed to convert unstructured behavioural data into structured data. Implicit signals such as time spent on tasks, click count, and revisit frequency often exist in raw inconsistent forms. Feature engineering is used to create higher order metrics such as time spent, click  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Learner ID**} & \multirow{2}{*}{\begin{tabular}{c} **Pre-Test** \\ **Score (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Post-Test** \\ **Score (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Satisfaction** \\ **Rating (1–5)** \\ \end{tabular} } & \multicolumn{2}{c}{**Module**} & \multirow{2}{*}{\
\begin{tabular}{c} **Score** \\ **(1 = Completed,** \\ **0 = Not** \\ **Completed)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Improvement** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**Initial Module**} \\  & & & & & & \\ \hline
1 & 70 & 85 & 5 & 1 & 15 & Data Structures \\
2 & 60 & 80 & 4 & 1 & 20 & Data Structures \\
3 & 55 & 65 & 5 & 1 & 10 & Algorithms \\
4 & 72 & 90 & 3 & 0 & 18 & Binary Trees \\
5 & 65 & 77 & 4 & 1 & 12 & Graph \\
6 & 50 & 58 & 3 & 0 & 8 & SQL Basics \\
7 & 60 & 75 & 5 & 1 & 15 & Testing \\
8 & 55 & 65 & 4 & 1 & 10 & Networking \\
9 & 70 & 95 & 5 & 1 & 25 & Machine \\  & 10 & 62 & 80 & 4 & 1 & 18 & Data Analytics \\ \hline \hline \end{tabular}
\end{table}
Table 9: Sample data after preprocessing explicit signals.  
rate, retries, and engagement rate, which are calculated using Equations (3)-(6). All of these steps are performed as described in Table 10.
## 3 Optimization of the Boundary of Energy Supply Area Based on Relative-Load-Distance
### Optimization Formula  
This paper takes the minimum comprehensive annual cost as the optimal scheme, that is, the sum of annual converted cost and annual maintenance cost of DES station network as the objective function. The objective function is expressed as follows:  
\[\begin{array}{ll}\min C_{\text{total}}&=\min\Big{(}C_{\text{station}}+C_{ \text{pipeline}}\Big{)}\\ &=\sum\limits_{j=1}^{N_{e}}\Big{(}\chi_{t}^{ES}C_{oj}^{ES}+C_{mj}^{ES}\Big{)}+ \sum\limits_{i=1}^{N_{p}}\sum\limits_{j=1}^{N_{g}}\Big{(}\chi_{t}^{PL}C_{oj_{ j}}^{PL}+C_{\Delta Qij}^{PL}+C_{a_{ij}}^{PL}\Big{)}\end{array} \tag{6}\]  
In the formula, \(C_{\text{total}}\) represents the total annual cost of energy stations and pipelines. \(C_{\text{station}}\) represents the total annual cost of energy stations. \(C_{\text{pipeline}}\) represents the total annual cost of pipelines. \(C_{oj_{j}}^{ES}\) represents the initial total investment cost of energy station \(j\). \(C_{oj_{j}}^{PL}\) represents the initial investment cost of pipe network from the energy station \(j\) to the load point \(i\). \(C_{\Delta Qij}^{PL}\) represents the annual cost of heat loss. \(C_{a_{ij}}^{PL}\) represents the depreciation and maintenance costs from the energy station \(j\) to the load point \(i\). \(\chi_{t}^{ES}\) represents standard investment effect coefficient of energy station and \(\chi_{t}^{PL}\) represents standard investment effect coefficient of pipelines.
# Classification of Malaria Using Object Detection Models
## 3 Results
### Final Results for Stage Classification  
The models were first trained for 300 epochs and achieved mean average precisions of 41% and 44.5% for YOLOv5 and scaled YOLOv4, respectively. These results were unsatisfactory. As mentioned before, changes to the pre-processing and augmentations were made to see if the mAP could be improved. The dataset was even edited to have only images similar in color and shape of the cells, but this also did not affect the improvement of the mAP. Therefore, a fresh start was taken, and an alternate dataset was employed to train the algorithms. The results for the same are presented in Table 6  
From Table 6, it is observed that the scaled YOLOv4 algorithm was able to predict the classes of the objects with a 68.4% accuracy. The YOLOv5 algorithm, on the other side did not perform as well. The results from the scaled YOLOv4 were considered as the relevant results for this part of the research. Figure 10 provides graphical information about the metrics: mean average precision, precision and recall for the superior model. Scaled YOLOv4 is trained with the second dataset for stage classification, and is shown in Figure 11 as an example output as to how the classification model identifies each stage of the cells.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **YOLOv5** & **Scaled YOLOv4** \\ \hline Precision & 0.447 & 0.369 \\ Recall & 0.556 & 0.859 \\ mAP & 0.399 & 0.684 \\ Loss & 0.103 & 0.039 \\ Time & 1 h 22 min & 2 h 2 min \\ Epochs & 200 & 200 \\ Image resolution & 416 & 416 \\ Batch size & 16 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the results for stage classification with the revised dataset.  
Figure 10: Metrics for stage classification model: scaled YOLOv4.  
As observable in the figure, the model can recognize cells and classify them according to stage. This is highlighted by the bounding boxes drawn over the infected red blood cells with the stage of progression.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 4 Implementation
### SoC FPGA  
Figure 3 illustrates the inference phase block diagram on the Zynq-7000 SoC architecture, which includes two main domains: namely, the Processing System (PS) and the Programmable Logic (PL/FPGA). The PS has basic modules for a computer system: Multiplexer Input/Output (MIO), I/O Peripherals, Memory (MEM), Interconnects and an Application Processor Unit (APU). The SoC FPGA board is booted from an SD card, and the programmed application is loaded into the APU. Here, network packets will be sent to the APU for extracting input features in the _Feature Extraction_ module. Then, the extracted features are grouped together in the _Buffer Initialization_ module, before being sent to the PL. This buffer helps reduce overhead in data exchanges between the PS and the PL. Processed results from the PL are delivered back to the _Monitoring_ module for analysis. The PL domain has five main blocks, which are:  
\begin{table}
\begin{tabular}{c l l c l} \hline \hline \# & **IoT-23** & **UNSW-NB15** & **Inputs** & **Description** \\ \hline
1 & id\_orig.h & srcip & 4 & Source IP address \\
2 & id\_orig.p & sport & 1 & Source port number \\
3 & id\_resp.h & dsltip & 4 & Destination IP address \\
4 & id\_resp.p & dsport & 1 & Destination port number \\
5 & proto & proto & 1 & Transaction protocol \\ \hline \hline \end{tabular}
\end{table}
Table 5: Flow Features from the IoT-23 and UNSW-NB15 datasets.  
* _The PS Reset_ block: receives the reset control signals from the PS to reset the PL to the initial state.
* _The AXI Interconnect_ block: assigns user's input values to registers in PL through General-Purpose Ports (GP0), which is configured as a 32-bit AXI-lite interface. There are two registers to be configured in the _NN_ block: namely, the _RESET_ register for resetting the module and the _NUM_OF_INPUT_ register to indicate the number of records to be sent from PS each time.
* _AXI Smart Connect_ block: receives grouped input features, which are buffered in the PS, using High-Performance Ports (HP0). Then, data are sent to the _AXI DMA_ block by a Memory-Mapped to Stream (MM2S) channel. In addition, the scanned data are transferred back from _AXI DMA_ to this block by a Stream to Memory-Mapped (S2 MM) channel. These data are intrusion detection results that are ready to be sent back to PS for analysis.
* _The AXI DMA_ block: this is connected to the _NN_ block through an AXI Stream interface for sending/receiving data to/from the _NN_ block.
* _The Neuron Network Architecture_ block: this contains the NN model on FPGA. This block is implemented in two approaches: namely, high-level synthesis (HLS) and Verilog implementation.
## 2 Simulation Model
### Fuel Cell  
The output power of the fuel cell, \(P_{\text{FC}}\), is proportional to the rate of hydrogen consumption (\(\text{m}_{\text{H}_{2}}\)) [30]:  
\[P_{\text{FC}}=\text{N}\cdot\text{I}_{\text{FC}}\cdot\text{E}_{\text{FC}} \tag{10}\]  
where N is the number of cells, and \(\text{I}_{\text{FC}}\) is the current flow of cells [A]. \(\text{E}_{\text{FC}}\) refers to the electromotive energy of fuel cell [V], which is calculated as follows:  
\[\text{I}_{\text{FC}}=\frac{2\text{F}}{\text{N}\cdot\text{U}\cdot\text{M}}\text {m}_{\text{H}_{2}} \tag{11}\]  
\[\text{E}_{\text{FC}}[\text{V}]=\text{E}_{0}-\text{b}(\text{log}(\text{i})+3)- \text{R}_{\text{ohmic}}\text{i}-0.000014\text{e}^{8\text{i}} \tag{12}\]  
where F is the Faraday constant \(\left[\frac{\text{s}\text{A}}{\text{mol}}\right]\); \(v\) is the stoichiometry of the reaction; M is the molecular mass of hydrogen \(\left[\frac{\text{g}}{\text{mol}}\right]\); \(\text{m}_{\text{H}_{2}}\) is the rate of hydrogen consumption \(\left[\frac{\text{g}}{\text{s}}\right]\); and i is the current flow density, which is a function of the reaction area (A) as follows:  
\[\text{i}=\text{I}/\text{A} \tag{13}\]  
\[\text{b}=\frac{1}{2}\frac{\text{nF}}{\text{RT}} \tag{14}\]  
where n is the number of exchanged electrons in the reaction, T is the working temperature of fuel cells (\({}^{\circ}\)C), and R is the gas constant (J/mol-C). Generally, the range of working temperature of PEM (polymer electrode membrane) fuel cells is between 353.15 K (80 \({}^{\circ}\)C) to 393.15 K (120 \({}^{\circ}\)C). \(E_{0}\) is the open-circuit voltage (OCV). This value is dependent on the working cell temperature. The ohmic loss in Equation (13) can be calculated by using the following equation:  
\[\text{R}_{\text{ohmic}}=\text{r}_{\text{m}}\text{L}_{\text{mem}} \tag{15}\]  
Figure 3: Calculation flow in solar power simulation.  
where \(\mathbf{r}_{\mathrm{m}}\) refers to the specific resistivity for the flow of hydrated protons (\(\Omega\) cm), and L\({}_{\mathrm{mem}}\) is the thickness of the polymer membrane ( cm) [31]. The input values of the technical parameters used in the above equations are reported in Table 3.  
The calculation flow used in the simulation of the fuel cell is shown in Figure 4.
## 3 Method
### Abnormal Behavior Recognition
#### 3.3.1 Judgment of Fall Events  
The probability of a fall event is small and transient in nature. Therefore, when the above similarity match D is smaller than the similarity threshold \(T_{si}\), the differentiation between the two behaviors for falling and lying on the ground is increased by introducing motion acceleration.  
Taking the acceleration in the y-direction as an example, assume that the centers of mass of the target in the two consecutive images of the current frame are \((i_{c}{}^{n},j_{c}{}^{n})\) and  
Figure 8: Abnormal behavior identification flowchart.  
(\(i_{c}{}^{n+1},j_{c}{}^{n+1}\)), respectively, and define the velocity of the moving target in the y-direction of the video in frame \(n+1\) as  
\[v^{n+1}=\frac{j_{c}{}^{n+1}-j_{c}{}^{n}}{t}, \tag{10}\]  
where \(t\) is the time between the two images, the acceleration of the elderly along the y-direction in the \(n+1\) image \(a^{n+1}\) is  
\[{a_{y}}^{n+1}=\frac{v^{n+1}-v^{n}}{t}. \tag{11}\]  
Through the calculation of Equation (11), the acceleration of the moving object in the y-direction \(a_{y}\) can be found, and similarly, the acceleration in the x-direction \(a_{x}\) can be found.  
For the fall event, if the above similarity matching D is less than the similarity threshold \(T_{si}\), the acceleration in both directions is calculated separately for the moving target, and if \(a_{x}\) is greater than the judgment threshold \(T_{ax}\) or \(a_{y}\) is greater than the judgment threshold \(T_{ay}\), then the fall event is considered to occur and Fall is set to true. The calculation formula is as  
\[Fall=\left\{\begin{array}{ll}true&if\left(a_{x}>T_{ax}\;\big{|}\big{|}a_{y}>T _{ay}\right),\\ false&otherwise.\end{array}\right. \tag{12}\]
## 5 Thermomechanical Composite Properties
### Scratch resistance, wear and creep properties  
[PERSON] and coworkers [123] compared the influence of micron-sized and nanosized TiO\({}_{2}\) (10 wt %, average particle size 0.24 um and 32 nm, respectively) on the scratch resistance of an epoxy. They found an improvement of the scratch resistance when using the nanosized in comparison to the micron-sized filled polymer and the neat polymer. Same trend was found for the strain to failure behavior; interestingly at other concentrations no impact relative to the pure polymer of the nanosizedTiO\({}_{2}\) was found. The grafting of nanosized SiO\({}_{2}\) (average particle size 9 nm) with acrylamide and the dispersion in an epoxy matrix via stirring and sonification yielded composites with improved wear properties and reduced friction coefficient relative to the pure polymer and a composite with the untreated SiO\({}_{2}\) even at low nanofiller content (\(\sim\)2 vol %) [124]. A slight increase of the glass transition temperature due to an attractive filler-matrix interaction and the resulting reduced polymer chain mobility was observed also. Surprisingly no difference between the uncoated and grafted nanosilica was observed. Highly transparent composites, consisting of surface modified SiO\({}_{2}\) (Aerosil\({}^{\circledR}\) 600, average primary particle size 40 nm) and Al\({}_{2}\)O\({}_{3}\) (average primary particle size: 20 nm), dispersed in an acrylate-based polymer, with improved scratch resistance were synthesized by [PERSON] _et al._[125, 126]. Melt mixing of polyethyleneterephthalate and nanosized Al\({}_{2}\)O\({}_{3}\) yielded a composite with slightly increased friction coefficient and reduced wear rate at low filler contents up to 2 wt % [127]. In contrast, a further Al\({}_{2}\)O\({}_{3}\) addition caused an increase of the wear rate to values significantly higher than the pristine polymer. This optimized filler concentration of 2 wt % correlates with a reduction of the polymers crystallinity [127]. [PERSON] and coworkers published quite recently a comprehensive overview covering all aspects of wear and scratch resistance in polymer-based nanocomposites [128]. Co-extrusion of nanosized TiO\({}_{2}\) (primary particle size 21 nm) with polyamide 6,6 yielded composites, which were examined with respect to their creep resistance under ambient conditions and at elevated temperature (50 \({}^{\circ}\)C) [129]. Test specimens were fabricated by injection molding. Composites containing 1 vol % TiO\({}_{2}\) possesses a significant improvement of the creep resistance and a reduction of the creep strain in comparison to the unfilled polyamide.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### The Macro State of City Clusters in the Greater Bay Area  
#### 4.3.1 Regional Logistics Advantage Linkages  
The flow backbone structure of the logistics and capital flow networks emerges from the initial connections between cities, illustrating the dynamics and spatial patterns of factor flows within the Greater Bay Area. Both networks display a core-edge structure, primarily dominated by Hong Kong, Shenzhen, and Guangzhou. These core cities exert substantial influence over the flow of resources and capital. As integration policies within the Greater Bay Area advance, the inter-city linkages in both logistics and capital flow  
Figure 3: Distribution of logistics and capital flow centrality within the Greater Bay Area, 2016–2021.  
networks are significantly strengthened. This enhancement of intra-regional connections facilitates more efficient movement of cargo and capital, reinforcing the dominance of core cities while promoting collaboration among secondary cities. This evolving structure reflects the ongoing efforts to optimize regional integration and economic cooperation.  
In the logistics network, the link between Hong Kong and Shenzhen consistently occupies a prominent dominant position, leading other cities by a significant margin and maintaining a stable core flow direction. This central connection underscores the importance of these cities in regional logistics dynamics. Non-core cities, such as Zhaoqing, Jiangmen, and Zhuhai, exhibit weaker logistics flow direction and intensity, highlighting their limited role in the overall logistics framework. The distribution of logistics flow intensity reveals distinct characteristics of central city dominance: Hong Kong, Shenzhen, and Guangzhou form a core hub with strong interconnections. In contrast, secondary cities like Dongguan demonstrate limited connections, primarily oriented towards local directions (e.g., Dongguan \(\rightarrow\) Guangzhou, Dongguan \(\rightarrow\) Zhuhai). This pattern indicates that while core cities drive the logistics network, secondary cities are still developing their connectivity and influence within the regional logistics landscape, as illustrated in Table 3.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Knowledge  
#### 2.3.1 Support Vector Machine  
The method based on support vector machine (SVM) for leak detection and localization is to monitor the parameters at a number of sensor nodes in the pipeline networks under consideration and to feed these parameters' values into a SVM trained to predict leak size and leak location [29, 30]. The SVM is trained on a number of samples representing the presence of leaks of various sizes and locations in the pipeline monitoring networks.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### Benefiting from Both Spectral and Temporal Dimensions
When using at least the spectral bands in the feature vector (SB and SB-SF columns), our TempCNN model outperforms all other algorithms with variations in OA between 1 and 3%. We have also performed a paired \(t\)-test to compare the mean accuracy obtained over the different folds of TempCNNs against the three other competitors. The \(p\)-values are lower than 0.001 for TempCNN vs both FC and RF, and \(\approx\) 0.0116 for TempCNN vs RNN; TempCNN thus significantly outperforms the three models at the traditional 5% significance threshold. Interestingly, models based on only spectral convolutions with spectral features (fifth row, second column) slightly outperform models that used only temporal guidance (fourth row, second column). This result confirms the importance of the spectral domain for land cover mapping application. In addition, the use of convolutions in both temporal and spectral domains leads to slightly better OA compared to the other three levels of guidance. Finally, Table 3 shows that the use of spectral indices in addition of the available spectral bands does not help to improve the accuracy of traditional and deep learning algorithms. To explore this result further, Table 4 gives the producer's accuracy (PA), the user's accuracy (UA) and the F-Score per class for the three main classification algorithms: RFs, RNNs and TempCNNs. Table 4 shows that TempCNNs outperform other algorithms with the highest F-Scores for all but two classes, while obtaining very close results for the two remaining classes. TempCNNs obtain very good results on the most frequent classes (wheat, sunflower, and grassland), but also on the rarer ones (peas, sorghum, and conifer).