esa-sceva/satcom-chunk-collection huggingface dataset:
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 2 Related Works
### Correlation Power Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline XOR, \(\oplus\) & Exclusive-OR operation \\ \(\ll\left(\gg\right)\) & Binary left (right) shift operation \\ \(P_{D}\) & Power consumption when manipulating data \(D\) \\ \(P_{noise}\) & Noise power \\ \(\epsilon\) & Constant \\ \(\left(u{{\tau}_{4}}{{t}_{6}}\cdots{{u}_{0}}\right)_{2}\) & The binary representation of \(u\) \\ \(K\) & The secret key, \(K=\left({{K}_{n-1}}{{K}_{n-2}}\cdots{{K}_{0}}\right)_{2W}\), \(nW=\left|K\right|\) \\ \(W\) & Number of bits in a word \\ \(GK\) & Guessed key, \(GK\in\left\{0,1\right\}^{W}\) \\ \(N\) & Number of traces \\ \(\mathbb{P}\) & Plaintext set \(\left(\mathbb{P}=\left\{{{p}_{1}},\ \cdots,{{p}_{N}}\right\}\right)\) \\ \(\mathbb{T}\) & Trace set \(\left(\mathbb{T}=\left\{{{t}_{1}},\ \cdots,{{t}_{N}}\right\}\right)\) \\ \(pre\) & Preprocessing function \\ \(f\) & Arbitrary operation \\ \(f\left(\mathbb{P},K\right)\) & Set of \(f\left({{p}_{i}},K\right)\left(1\leq i\leq N\right)\) \\ \(\text{Corr}\left(X,Y\right)\) & [PERSON]’s correlation coefficient for variables \(X\) and \(Y\) \\ \(E\left[X\right]\) & The expectation for variable \(X\) \\ \(\text{HW}\left(x\right)\) & Hamming weight of \(x\) \\ \(\text{MSB}\left(x\right)\) & Most significant bit of \(x\) \\ \(\text{PC}\) & Product-combining \\ \(\text{AD}\) & Absolute-difference \\ \(C\left(S\right)\) & Collection of all possible outcomes of intermediate value \(S\) \\ \(L\left(S\right)\) & Bit length of the intermediate value \(S\) \\ \(\left|\mathbb{X}\right|\) & Cardinality of a set \(\mathbb{X}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations. mask used to conceal the output of each transformation in the Herbst schema. Note that SubBytes and Shiftrows require only one mask each, and MixColumns requires four masks.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**HD-Classifier**} & \multicolumn{2}{c}{**BIGBIOCL**} \\  & **Avg. Accuracy \(\pm\) Std.Dev (\%)** & **Avg. Accuracy \(\pm\) Std.Dev (\%)** \\ \hline BRCA & 97.7 \(\pm\) 0.54 & 98.0 \(\pm\) 0.94 \\ KIRP & 98.4 \(\pm\) 1.36 & 97.0 \(\pm\) 2.04 \\ THCA & 100 \(\pm\) 0.002 & 97.0 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of HD-classifier vs. BIGBIOCL classifier on ten different randomly generated training (80%) and test (20%) sets for each tumor dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Memory**} & \multirow{2}{*}{**Threads**} & **Regularization Method** & **Regularization Parameter** & **Iterations** & **Avg. Execution Time (h)** & **Avg. Accuracy \(\pm\) Std.Dev (\%)** \\ \hline
12 GB & 7 & L2 & 1.0 & 100 & 2.05 \(\pm\) 0.11 & 98.95 \(\pm\) 1.54 \\
12 GB & 7 & L2 & 1.0 & 200 & 3.53 \(\pm\) 0.02 & 98.97 \(\pm\) 2.04 \\
12 GB & 7 & L1 & 0.1 & 100 & 1.67 \(\pm\) 0.07 & 95.46 \(\pm\) 0.63 \\
12 GB & 7 & L1 & 0.1 & 200 & 1.32 \(\pm\) 0.04 & 99.16 \(\pm\) 1.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SVM classifier performance on the Breast Invasive Carcinoma (BRCA) dataset with ten different randomly generated training (80%) and test (20%) sets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**HD-Levels**} & **Retraining Iterations** & **Training Time (h)** & **Classification Time (s)** & **Accuracy (\%)** \\ \hline  & 1000 & 2 & 2.30 & \textless{}1 & 88.89 \\  & 900 & 3 & 2.33 & \textless{}1 & 88.89 \\  & 800 & 3 & 6.77 & \textless{}1 & 88.89 \\ BRCA & 700 & 3 & 2.73 & \textless{}1 & 88.89 \\  & 600 & 3 & 4.76 & \textless{}1 & 88.89 \\  & 500 & 65 & 11.41 & 2 & 60.00 \\ \hline  & 1000 & 3 & 0.78 & \textless{}1 & 85.94 \\ KIRP & 900 & 3 & 0.78 & \textless{}1 & 85.94 \\  & 800 & 3 & 0.77 & \textless{}1 & 85.94 \\  & 700 & 3 & 0.77 & \textless{}1 & 85.94 \\  & 600 & 3 & 0.77 & \textless{}1 & 85.94 \\  & 500 & 24 & 0.77 & \textless{}1 & 59.38 \\ \hline  & 1000 & 3 & 1.38 & \textless{}1 & 90.35 \\  & 900 & 3 & 1.47 & \textless{}1 & 90.35 \\ THCA & 800 & 3 & 1.37 & \textless{}1 & 90.35 \\  & 700 & 2 & 1.38 & \textless{}1 & 90.35 \\  & 600 & 2 & 1.35 & \textless{}1 & 90.35 \\  & 500 & 6 & 4.95 & \textless{}1 & 99.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Benchmarks of the HD classification algorithm performances with different amounts of HD-levels and HD vector dimensionality fixed to 1000 and 100 maximum retraining iterations. The best result achieved by the HD-classifier after the number of retraining iterations is shown.
# Compact Fuzzy Systems Based on Boolean Relations
## 2 Framework  
This section presents definitions and concepts necessary for the design of compact fuzzy systems based on Boolean relations. The methodology proposes the design in a Boolean table. Through design criteria, this table is converted into a Kleene's considering an intermediate variable \(u\); then the equations that represent the true table are established to finally perform the implementation of the system.  
The concepts shown in this section are used to determine the equations to implement compact fuzzy systems based on Boolean relations. For the design, the conversion of a truth table and the simplification of the terms are carried out using [PERSON]'s algebra to establish compact structures.  
First the definitions of De Morgan, Kleene, and Boole algebra are reviewed and also the Disjunctive Normal Form (DNF) in Kleene algebra obtained from a relations table. Subsequently, the description of fuzzy inference systems based on Boolean relationships is presented. Finally, a review of the design criteria used to convert a Boolean into a Kleene table is presented.
# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 2 Materials and Methods
### Datasets
Compared to the other (public) dataset used for detection performance comparison of models, the private dataset exhibits certain differences in image quality, disease and pest symptom manifestation, and environmental factors, which enables better simulation of detection scenarios in actual agricultural production environments. The private dataset was partitioned with the same 8:1:1 ratio, used for the other dataset, into a training subset (2578 images), a validation subset (335 images), and a test subset (329 images), with fall armyworm larva representing the largest category (729 samples), followed by yellow stem borer (659 samples), gray leaf spot (512 samples), leaf rust (466 samples), yellow stem borer larva (465 samples), and maize streak disease (411 samples), ensuring balanced representation across all disease and pest categories for comprehensive model evaluation. The detailed statistics of image samples for each category in the private dataset are presented in Table 2.
## 5 Results and Discussion
### Results from Consideration Propensity Model
#### 5.1.2 Segment-Wise Variations in Key Factors Influencing Consideration  
The segment-level intercepts from the consideration model (Table 4) show that the inherent preference for contributing to consideration probability for the train is highest among the MPM segment (3.95). This suggests that travelers who have a limited mobility resource in the form of only one two-wheeler in their household have a higher potential to shift to trains than those with more vehicles. In contrast, people who own a car or multiple two-wheelers and those who do not own a car have a lower propensity to consider trains
## 5 Key Applications and Open Research Lines
In addition, it can aid in understanding and diagnosing model behaviour, facilitating debugging, troubleshooting, and identifying potential biases or ethical concerns. Lastly, explicability can be crucial for compliance with regulatory requirements in domains where transparency and accountability are essential. * Metaheuristics. Metaheuristics hold potential for various applications in RL. Firstly, they can be used for hyperparameter tuning in RL algorithms to optimize the performance of agents. Secondly, metaheuristics can be employed for policy search, where they can explore the policy space to find promising policies for RL agents. Lastly, they can aid in solving complex problems in RL with high-dimensional state and action spaces, where traditional RL algorithms may struggle, by providing effective search strategies for discovering good policies. The combination of RL with metaheuristics and simheuristics [59] is also an open challenge.
# Inner-Loop Controllers for Grid-Forming Converters
### Grid-Forming Converters  
Grid-forming inverters, by contrast, are designed with an inner control loop to synthesize either voltage or current at the DER PoC (Figure 3). The voltage reference \(e\) is determined by a primary controller and may also incorporate virtual impedance. As a result, the inverter can be modeled as an ideal voltage or current source behind an impedance [7] or admittance [8], respectively. Controllers for grid-forming inverters can be implemented in both synchronous and stationary reference frames, allowing for versatile design and analysis. Stability assessments and controller designs are performed in either the continuous-time or discrete-time domains. With the advent of cost-effective, high-performance microcontrollers and Digital Signal Processors (DSPs), discrete-time implementation has become predominant.
## 4 Discussion
The average \(\pm\) std magnitude of error in the non-uniform approach over a similar range of working distance was 0.03 \(\pm\) 0.03 mm, which further highlights the advantage of the proposed non-uniform method. Evaluation of both methods in general measurement scenario (experiment 4) showed trends similar to radial measurements. Specifically, Figure 12 indicates that the accuracy of the uniform approach degrades with an increase in the length of the target object, whereas Figure 13 does not show any trends for the non-uniform approach. When the length of the target object increases, it spans a wider spatial location in the FOV. Considering that non-linear distortion of flexible endoscopy is spatially-dependent [14], this may translate into a larger distortion of the final image. Therefore, we may expect to see a length-dependent error for the uniform approach. It is noteworthy that this dependence did not reach the significance level, which could be attributed to the small sample size and low spatial resolution of images. Average \(\pm\) std magnitude of errors in the general measurement scenario resulted in 0.27 \(\pm\) 0.24 mm for non-uniform and 0.86 \(\pm\) 0.6 mm for uniform method, which is 318% higher than the non-uniform method. Flexible endoscopes often get very close to the target object and, in that sense, their typical working distance is very short. To compensate for this short working distance, flexible endoscopes rely on wide-angle lenses. Wide-angle lenses can significantly increase the size of the FOV at the expense of significant non-linear distortion in the acquired images. The above-mentioned 318% reduction in the error demonstrates the effectiveness of the proposed method for handling and compensating the introduced non-linear distortion of flexible endoscopy. Considering that other endoscopic procedures (e.g., colonoscopy, gastroendoscopy, etc.) also rely on flexible endoscopes, and the importance of calibrated measurements for medicine in general, the significance of the proposed method seems to go beyond laryngology and could easily be applied to other endoscopic procedures.
## 4 Experiments and Results  
The experiments were carried out with 10 scientific papers written in English, which were used to evaluate the proposed methodology. From these 10 papers, there were a total 184 paragraphs extracted. The original English paragraphs were treated as the \"reference documents\". All documents were manually translated into Indonesian using a human translator. The translated documents were used as \"test documents\". For each paper, the comparisons were conducted between all pair combinations of paragraphs. For example, paper number 1 consists of 10 paragraphs, paragraph number 1 (translated into Indonesian) was compared to all 10 paragraphs in English. Thus, there should be one pair that indicates plagiarism and nine pairs that indicate no plagiarism; this is illustrated in Figure 5. Since the papers are taken from different scientific ares, it does not make sense to perform comparison on different papers. Comparisons are performed only over paragraphs within the same paper. Thus, from the original 10 papers with 184 paragraphs, there were 4050 pairs of paragraph comparisons; 184 were marked as plagiarism, and 3856 of them were marked as not plagiarism.  
The plagiarism detection process was conducted using the proposed cross-language detection system illustrated in Figure 1. In this system, the inputs in the form of test documents and reference documents were pre-processed, and the test documents were translated using a dictionary database. Then, the LSA was performed, and the features extracted were the Frobenius norm, the angle of two vectors with slice, and the angle of two vectors with pad. The classification was conducted using LVQ.  
Table 1 provides an example of three feature values extracted after the LSA process, namely Fromm, \(\alpha\_\)slice, and \(\alpha\_\)pad for test documents 3001 and 3002 compared to reference documents 3001-3002.  
The evaluation of the proposed cross-language automated plagiarism detection system was conducted by a comparison with the manual/human evaluation, which was presented in three values of accuracy. These three values were precision, recall, and F-measure. They were then analyzed and used to draw conclusions.
## 2 Background and Preliminary
### Time/Frequency Domain Analysis  
Time series frequency domain analysis involves transforming a time domain signal, which is a signal that varies over time, into a frequency-domain representation. This conversion allows for a better understanding of the signal's frequency components and periodicity. Frequency domain analysis reveals the different frequency components within  
Figure 3: **(a)** Global intermetric anomalies. **(b)** The red-highlighted area on the left indicates local intermetric anomalies, while the red-highlighted area on the right indicates temporal-local intermetric anomalies.  
a time series, which helps in identifying cyclic patterns, trends, and anomalies. The most commonly used formula is the Discrete Fourier Transform (DFT):  
\[X_{k}=\sum_{t=0}^{T-1}x_{t}e^{-i2\pi kt/T},\quad k=0,\dots,T-1 \tag{11}\]  
where \(X_{k}\) is the discrete complex amplitude at frequency \(k\), \(x_{t}\) is the time series signal, and \(i\) is the imaginary unit.  
The Fast Fourier Transform (FFT) typically uses Formula (11) to convert the time domain into the frequency domain. FFT is an efficient computational algorithm for the DFT, it can convert the original time series into a spectrum and sort it by coefficients, obtaining the seasonal cycle by reversing the highest frequency. Most programming languages and mathematical libraries provide built-in FFT functions that can be directly used to compute the DFT.
## 3 Numerical Scheme
### Vertical Polarisation  
#### 3.3.1 Discrete System  
A discretisation for Equation (11) can now be written in vector-matrix form:  
\[\mathbf{L}\mathbf{w}^{n}=\mu_{t}\mathbf{J}_{\mathbf{w}}^{n}\,\mathbf{f}_{ \mathbf{w}}^{n} \tag{47}\]  
where \(\mathbf{J}_{\mathbf{w}}^{n}\) is the \((N_{\mathfrak{D}}-1)\times(N_{\mathfrak{D}}+1)\) distribution matrix, and \(\mathbf{f}_{\mathbf{w}}^{n}\) is a column vector containing all the contact force information:  
\[\mathbf{J}_{\mathbf{w}}^{n}=\left[\begin{array}{c|c}\mathbf{I}_{ \mathbf{N}-1}&-\mathbf{j}_{F}^{n}&-\mathbf{j}_{B}^{n}\end{array}\right] \qquad\qquad\qquad\qquad\mathbf{f}_{\mathbf{w}}^{n}=\left[\begin{array}{c|c} \left(\mathbf{f}_{N}^{n}\right)^{\mathsf{T}}&f_{F}^{n}&\left|\begin{array}{c |c}f_{B}^{n}\end{array}\right.\right]^{\mathsf{T}} \tag{48b}\]  
where \(\mathbf{I}_{\mathbf{N}-1}\) is the \((N_{\mathfrak{D}}-1)\times(N_{\mathfrak{D}}-1)\) identity matrix, and \(\mathbf{j}_{F}^{n}\) and \(\mathbf{j}_{B}^{n}\) are discrete spreading operators in column vector form, accounting for the continuous distributions described in Section 2.4.2. If the contact regions are sufficiently wide, these can be sampled from the true shape of the concerned object; otherwise, if contact is localised in a small region, or pointwise, interpolation is needed between grid points, as truncation to the nearest grid point would result in audible artefacts when the finger or bow move along the length of the string.  
The spreading vectors are normalised over the grid: for instance, in the simplified case of a fixed position object, located on a grid point, if \(J_{F}(x,t)\) and \(J_{B}(x,t)\) are Dirac delta functions, then \(\mathbf{j}_{F}^{n}\) and \(\mathbf{j}_{B}^{n}\) will be all-zero vectors, except for one element of value \(\frac{1}{h}\), at the position where the force is applied.  
Note the use of the averaging operator \(\mu_{t}\). in Equation (47), necessary to show an energy balance for this scheme in the case of moving finger or bow (see Section 3.5).  
\(\mathbf{f}_{N}^{n}\), \(f_{F}^{n}\) and \(f_{B}^{n}\) are the discrete counterparts of those defined in Sections 2.4.2 and 2.4.3.
## 2 Materials and Methods
### OCT Setup
If the process speed is to be increased by at least one order of magnitude, the integration time must be adjusted. The theoretical resolution of the image information in the axial direction, which is reconstructed from the interference signal, is determined by the round-trip coherence length \(l_{c}\) of the light source. The coherence length is defined by the central wavelength \(\lambda_{0}\) and the spectral bandwidth \(\Delta\lambda\) of the SLD [18]. Where the constant term \(\gamma\) is 0.44 for a source for a Gaussian spectrum. However this term can differ depending on the windowing function used to sample the interferograms in the spectrometer [19]. \[\delta\text{z}=l_{c}=\gamma\frac{\lambda_{0}{}^{2}}{\Delta\lambda} \tag{3}\]  
For the given case, this results in a theoretical axial resolution of 13.3 \(\mu\)m. The experimentally determined value of the actual axial resolution deviates from this value due to dispersion mismatch between the sample and reference arm and the use of different windowing functions. The FWHM of the depth scan was calculated to be 30.6 \(\mu\)m. Numerical dispersion compensation methods could be used to reduce peak broadening, which affects the measured FWHM of the depth, and thus the axial resolution.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 4 Function-Based Methodology
### How It Is Related to Historical Blackouts  
The world has faced several large-scale blackouts in recent times [35; 36; 37]. The blackouts were originated either because of malicious or non-malicious sources, but they had a profound impact on the economy. The Ukraine power grid attack [5] led to power outages in the thousands of homes, and it is a perfect example of a cyber-attack on the grid, where the attackers compromised the cyber system (such as the electricity control center) that controls the functionality of various substations. Although malicious events did not cause some of the blackouts, blackouts could have easily originated from cyber-attacks on the grid; for example, the Arizona and Southern California blackout, which affected approximately 2.7 million people [37]. During a hot day (hours of peak demand), a high voltage line failed, which redistributed the power towards San Diego. This redistribution of power resulted in additional line failures and led to the separation of San Diego from the rest of the Western Interconnection. Because of the huge imbalance of the power demand and supply, the frequency diverged beyond thresholds, and generators started tripping, resulting in the blackout.  
How is this related to Attack Scenarios I and II? An adversary has compromised a cyber system in the electricity control center from where he/she controls DER in a large area (Attack II). He/she sends shutdown signals to a large number of DERs to reduce power generation and dispatch. Therefore, the initial line failure (in the Arizona blackout) could have resulted due to the reduced generation in San Diego, causing the imbalance between demand and supply. Since it was a hot day with peak demand, the reduced generation did not meet the power demand. Furthermore, an adversary could have shut down the peaker power plant (Attack I) by attacking the gas distribution pipeline to prevent gas reaching plants and generating power. The attack on GPP further reduced the generation, and generators started tripping resulting in the blackout in San Diego.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Processing and Evaluation of the Acquired Data
#### 2.6.5 Local ICP Transformation for Evaluation of Profiles  
If the basic transformation to the spherical targets is used, the highest accuracies can be expected at the beginning and end of the trajectory (the clouds were georeferenced using points placed at the extreme ends of the floor) and the worst accuracy in the middle of the route. Therefore, continuous profiles located in approx. 25%, 50%, and 75% along the route were selected to evaluate the local accuracy of the individual clouds (see Figure 7, highlighted in blue).  
Subsequently, we performed an additional local ICP transformation of the profiles (ICP-local) to the reference cloud (this allowed us to determine the displacement between the profile points transformed to accurately fit the reference cloud and the profile points after the overall transformation).  
The ICP transformation procedure for individual profiles was identical to that for the whole cloud. Here, we aimed to determine the local characteristic displacements in individual axes (i.e., the values of the displacements in individual coordinate axes from the transformation matrix). For the purpose of the evaluation, values \(\Delta\)X, \(\Delta\)Y, \(\Delta\)Z were calculated as root mean square error of displacements in individual axes from all profiles (i.e., 3 profiles \(\times\) 3 passages for SLAM scanners and 3 profiles \(\times\) 1 measurement for static scanners).  
Lastly, the locally ICP-transformed profiles were compared with the reference profiles using the cloud/cloud distance function, yielding the RMSE\({}_{\text{ICP-profile}}\) value characterizing the local cloud accuracy.