esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pan-Sharpened versus Multi-Spectral Images  
An example demonstrating the superior capacity of a pan-sharpened image over a multispectral image for distinguishing the tree canopy is shown in Figure 6. In contrast to the multi-spectral image, the pan-sharpened image has captured the canopy details clearly and thus the pan-sharpened image is effective for distinguishing the plant communities.
## 4 Results and Discussion
### Spatial Distribution Characteristics
#### 4.3.2 Variation of Monthly GPP
Figure 12: Spatial distribution characteristics of the multiyear averaged monthly GPP over the Arctic, (**a**–**l**) refers to January to December. The mean values of the whole Arctic are also shown in the figure for each month. Units of GPP are g C m\({}^{-2}\) d\({}^{-1}\).
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Model Agnostic Meta-Learning
#### 3.5.3 Loss Function for Anomaly Detection  
For anomaly detection, the loss function \(L_{\text{t1}}(\theta)\) can be designed based on either the reconstruction error or the output of a discriminator. In a reconstruction-based approach, the loss function measures how well the model can reconstruct the input data, and it can be defined as:  
\[L_{T_{i}}(\theta)=\Sigma_{(\text{x},\text{y})}{\in}_{D_{i}}\|x-f_{\theta}(x) \|^{2} \tag{12}\]  
where \(f_{\theta}(x)\) is the reconstructed output of the model. This formulation penalizes large deviations between the input x and its reconstruction, thereby helping the model learn normal patterns and identify anomalies as instances with high reconstruction errors.
## 1 Introduction
The above research shows that the FK can be applied to bearing fault diagnosis and extract the fault features effectively. To effectively extract fault features from background noise, this paper proposes a hybrid method based on WPE and FK for bearing fault diagnosis. Firstly, in order to select the optimized wavelet packet basis function and obtain a reconstructed signal with sufficient fault information, various wavelet functions are compared and the energy distribution were analyzed. Secondly, FK analysis is carried out on the reconstructed signal to select the optimal center frequency and bandwidth of the band-pass filter. Finally, the filtered signal is processed with the square envelope spectrum, and the fault features are extracted for bearing fault detection. Compared with the conventional FK analysis, the diagnosis result shows that the proposed method not only can extract the fault characteristic frequency but also optimize the wavelet packet parameters. Therefore, the effectiveness of the proposed method in the fault diagnosis of rolling bearings is verified. The remainder of this paper is outlined as follows. A brief review of the theoretical background of wavelet packet transform and fast kurtogram are presented in Sections 2 and 3. In Section 4, the performance evaluation of the proposed method based on simulation study is introduced. The procedure of the proposed method is demonstrated in Section 5. Section 6 validates the practical applications of the detector via application case studies. Finally, conclusions are drawn in Section 7.
# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 2 How Is a Radiomic Analysis Performed?
### Model Construction  
The aim is to create a model related to a single outcome of interest (diagnosis, tumor response, patient outcome, etc.). When quantitative imaging features are applied to this algorithm, it analyzes the training data and works out a hypothesis to predict a variable.  
The most known algorithms are random forest (RF) [24], least absolute shrinkage and selection operator (LASSO) [25], artificial neural networks (ANNs) [26], support vector machine (SVM) [27], and minimum redundancy maximum relevance (mRMR) [28]. The aforementioned are not meant to be exhaustive, and there is no superior algorithm; one algorithm might work better than another depending on the dataset type, attributes, and hyperparameter tuning [29].
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
###### Abstract  
The Gaussian sampler is an integral part in lattice-based cryptography as it has a direct connection to security and efficiency. Although it is theoretically secure to use the Gaussian sampler, the security of its implementation is an open issue. Therefore, researchers have started to investigate the security of the Gaussian sampler against side-channel attacks. Since the performance of the Gaussian sampler directly affects the performance of the overall cryptosystem, countermeasures considering only timing attacks are applied in the literature. In this paper, we propose the first single trace power analysis attack on a constant-time cumulative distribution table (CDT) sampler used in lattice-based cryptosystems. From our analysis, we were able to recover every sampled value in the key generation stage, so that the secret key is recovered by the Gaussian elimination. By applying our attack to the candidates submitted to the National Institute of Standards and Technology (NIST), we were able to recover over 99% of the secret keys. Additionally, we propose a countermeasure based on a look-up table. To validate the efficiency of our countermeasure, we implemented it in Lizard and measure its performance. We demonstrated that the proposed countermeasure does not degrade the performance.  
post-quantum cryptography; lattice-based cryptography; Gaussian sampling; CDT sampling; side-channel attack; single trace analysis +
Footnote †: journal: Applied Sciences
## 5 System Outline  
### Transmit Array and Receive Probe Description  
The block diagram and photograph of the system are shown in Figure 5. The transmit array is a uniform linear array with \(N=2\) elements, a designed carrier frequency \(f_{0}=720\) MHz (\(\lambda_{0}=c/f_{0}=0.416\) m, where \(c=299\),792,458 m/s is the speed of light), half-wavelength element spacings \(d=\lambda_{0}/2=0.208\) m and far-field distance \(R\geq 2D^{2}/\lambda_{0}=\lambda_{0}/2=0.104\) m. The antennas used are identical ultra-wideband log-periodic antennas with operating range 600-6000 MHz. The measured antenna radiation  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
0 & 24 & 35 & (s) & 7s \\ \hline Buffer Bits & **BC** & **Information** & **BC** & Buffer Bits \\ \hline \end{tabular}
\end{table}
Table 1: Packet structure with \(N_{c}=100\). The **bold** entries are the modulated information.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Expression** & **Samples** \\ \hline \(N_{s,c}\) & \(T_{c}\cdot f_{s}\) & 7200 \\ \(N_{s}\) & \(N_{s,c}\cdot N_{c}\) & 720,000 \\ \hline \end{tabular}
\end{table}
Table 2: Waveform sample size with \(T_{c}=1\) μs and \(N_{c}=100\).  
pattern and measured \(S_{11}\) parameter are shown in Figure 6. We make the monochromatic assumption in our far-field calculation because of the relative frequency band \(\delta f=\frac{f_{max}-f_{min}}{f_{max}+f_{min}}=\frac{(f_{0}+\Delta f)-f_{0}}{(f _{0}+\Delta f)+f_{0}}\approx 0\)[47].
# Cybersecurity and Safety Co-Engineering of Cyberphysical Systems--A Comprehensive Survey
## 2 Related Work
### Previous Reviews
Specifically, the authors identified two types of integrated methods: the _sequential_ integrated safety and security risk assessment method, and the _non-sequential_ method. The third criterion is the stages of the risk management process (_risk identification, risk analysis, risk evaluation_ that the method addresses. Further, the identified approaches are classified according to how the integration is achieved: _1) Combination of a conventional safety assessment method and of a variation of it to assess security, 2) Combination of a conventional security assessment method and of a variation of it to assess safety; 3) Combination of a conventional safety and a conventional security method;_ and _4) Other - no conventional safety or security assessment method used._ Finally, the survey categorized the approaches considering the _application and the application domain_; four out of seven are methods targeting the transportation domain. [PERSON] [6] surveyed integrated techniques for requirements engineering in CPSs; eight methods focusing on the requirements engineering phase of the lifecycle were identified and analyzed. The techniques were classified as _safety and security requirements_ or _accident analysis_. [PERSON] et al. [7] conducted a systematic literature review of joint safety and security analysis methods. The search was performed using the keywords (\"safety\" AND \"security\" AND \"analysis\") in four scientific databases (IEEE, ACM, Web of Science, and Springer link). Thirty three methods that analyze safety and security of CPSs early in the development phase have been identified. Five characteristics of these methods were considered: _application domain; stage in the system lifecycle; association with relevant standards; existence of validation step; and origin of contribution_. Additionally, the identified methods were classified according to the relationship between safety and security in the analysis process (_Unified, Parallel_), and the overall goal of the analysis (_combined safety and security; security informed safety, safety informed security_). Additionally, the yearly distribution of the reviewed papers, based on their security and safety focus was studied. Finally, [PERSON] et al. [8] surveyed ten methods for safety and security analysis; these included five integrated approaches. The identified approaches were compared considering characteristics such as: _quantitative/qualitative, model-based/system-based, top-down/bottom-up analysis, and hierarchical/dynamic analysis_. The authors identified the pros and cons of each method and the technical gaps of the interplay of safety and security. Most of the integrated approaches analyzed in this work take a qualitative risk management approach.
## 4 Results
### Optimal Spectral Feature Selection  
For selecting optimal spectral features, the parameters in the GA algorithm including the initial population size, the chromosome pairs' crossover probability, the mutation probability and the evolution times were initialized as 100, 0.6, 0.01 and 100, respectively, according to the empirical values from [38]. In the evolution, the chromosomes in the population did not change after 80 iterations, at which point the GA and SVM algorithm stopped, and the optimal spectral features were determined with Ch = [000000001110101] and highest fitness. Then, six spectral features, including D\({}_{\text{green}}\), SD\({}_{\text{green}}\), D\({}_{\text{red}}\), D\({}_{\text{RE}}\), D\({}_{\text{NIR}}\) and SD\({}_{\text{NIR}}\), were used for training the SVM classification model to predict the PWD infection stages of pine trees extracted from the HI.  
The change trends of the six selected spectral features with the PWD infection stages for the dataset are illustrated in Figure 6. The D\({}_{\text{green}}\) and D\({}_{\text{RE}}\) decrease with the PWD infection stages, indicating that the reflectance of the pine trees in the healthy stage changes the most while the reflectance of the pine trees in the serious stage changes the least, which is consistent with the change trends of the spectral curves in Figure 4. The SD\({}_{\text{green}}\), D\({}_{\text{red}}\), D\({}_{\text{NIR}}\) and SD\({}_{\text{NIR}}\) for pine trees in the early PWD infection stages are higher and decrease with the PWD infection stages for other pine trees. These change trends indicate that the six optimal spectral features could extract the reflectance changes for the pine trees in different PWD infection stages well.  
Figure 5: The pine tree crown segmentation and labelling results: (**a**) pseudo color image of HI; (**b**) part segmentation result of HI shown in (**a**). The green, light yellow, yellow and brown circles represent the pine tree crowns in healthy, early, middle and serious PWD infection stages, respectively. The red circles represent the final segmentation of the pine trees.  
Figure 6: Comparison the six spectral features selected in GA at different PWD infection stage.
## 3 Numerical Setup and Results  
First, we chose the Wiener model to simulate the various nonlinear factors that appeared in the communication system, as shown in Figure 4. The schematic diagram of the model is shown in the figure below; here, \(d(n)\) is the input signal, \(h_{i}\) are the coefficients of the linear part, and \(g_{i}\) are the coefficients of the nonlinear part [18].  
The original \(d(n)\) is a four-level pulse amplitude modulation signal (PAM-4) that is a random sequence with a value of \(\{-3,-1,1,3\}\)[19]. Here, we set \(M=7\), \(i=-2\), \(j=1\), \(Z=3\), and the specific coefficients are shown in the following formulas. Thus, the input signal first undergoes a linear memory change and is converted to \(q(n)\):  
\[\begin{array}{l}q(n)=0.08d(n+2)-0.12d(n+1)+d(n)\\ +0.18d(n-1)-0.1d(n-2)+0.091d(n-3)-0.05d(n-4)+0.04d(n-5)+0.03d(n-6)+0.01d(n-7) \end{array} \tag{4}\]  
followed by an instantaneous memoryless nonlinearity:  
\[u(n)=q(n)+0.036q^{2}(n)-0.011q^{3}(n)+\
u(n) \tag{5}\]  
where \(\
u(n)\) is Gaussian white noise, \(u(n)\) is the final input signal of the system, and the target of nonlinear channel equalization is to recover \(u(n)\) to \(d(n)\). Here, the bit error rate (BER) is used to verify the system's performance, and the performance of the proposed scheme is demonstrated through parameter optimization and comparison with the single-loop optoelectronic RC scheme.  
The features of the proposed optoelectronic RC scheme and the Wiener model is inherently correlated. First, Equation (4) shows the memory property of the channel, it can be reflected in the delay-loop scheme of the proposed optoelectronic RC. Thus, the optimization of the virtual node time interval (\(\theta\)) and RC size (\(N\)) could influence the  
Figure 4: The input–output relationship of the Wiener model that describes the nonlinear response of an optical transmission system.  
Figure 3: Schematic diagram of the input signal pre-processing process.  
memory property of the proposed optoelectronic RC and improve the correlation between the proposed RC scheme and the Wiener model in terms of memory performance. Second, Equation (5) shows the nonlinear and noise property of the channel, it can be reflected in the nonlinear node (DPol-MZM) and output training process in the proposed optoelectronic RC. Thus, the optimization of the parameters of DPol-MZM would change the nonlinear transfer function of it, and this process could influence the nonlinear property of the proposed optoelectronic RC.
## 3 Path Following Control Strategy
### Guidance System Design
#### 3.2.2 FPLOS Guidance Law  
Then, the FPLOS guidance law is given by:  
\[\psi_{d}=\phi_{p}+arctan\left(-\frac{\hat{y}_{e}}{\Delta}-\hat{\beta}\right) \tag{20}\]  
where \(\Delta\) is the user-specified lookahead distance.  
**Remark 2**: _Different from the existing LOS guidance in [15; 20], \(\Delta\) of this paper changes with the movement of the USV. The greatest advantage of this proposed guidance strategy is to optimize \(\Delta\) through the fuzzy method, thereby producing an aggressive behavior to decrease \(y_{e}\) faster. Here, the inputs of the designed fuzzy system are \(y_{e}\) and \(\dot{y}_{e}\), and the output is \(\Gamma\). the lookahead distance \(\Delta\) is designed as \(\Delta=\Delta_{\min}+\Gamma\left(\Delta_{\max}-\Delta_{\min}\right)\)._  
The velocity \(u_{p}\) is designed to stabilize \(x_{e}\) as the virtual input, which is determined by:  
\[u_{p}=U\cos\left(\psi-\phi_{p}\right)-U\sin\left(\psi-\phi_{p}\right)\hat{ \beta}+k_{1}\tilde{x}_{e} \tag{21}\]  
where \(k_{1}\) denotes a positive constant.  
From (13) and (21), the update law \(\theta\) can be described by:  
\[\dot{\theta}=\frac{U\cos\left(\psi-\phi_{p}\right)-U\sin\left(\psi-\phi_{p} \right)\hat{\beta}+k_{1}\hat{x}_{e}}{\sqrt{{x^{\prime}_{d}}^{2}\left(\theta \right)+{y^{\prime}_{d}}^{2}\left(\theta\right)}} \tag{22}\]  
Based on the above analysis, (15) can be rewritten as:  
\[\left\{\begin{array}{l}\dot{\hat{x}}_{e}=-k_{1}\hat{x}_{e}+\dot{\phi}_{p} \hat{y}_{e}-k_{x}\tilde{x}_{e}\\ \dot{\hat{y}}_{e}=-c_{1}\hat{y}_{e}-\dot{\phi}_{p}\hat{x}_{e}-k_{y}\tilde{y}_{e }\end{array}\right. \tag{23}\]  
where \(c_{1}=\frac{U}{\sqrt{\Delta^{2}+\left(\hat{y}_{e}+\Delta\hat{\beta}\right)^{ 2}}}\).  
**Theorem 2**: _Consider the application of (20) and (22) in the system (23). If we choose \(k_{1}>\frac{k_{x}}{2}\), \(c_{1}>\frac{k_{y}}{2}\), the errors \(\hat{x}_{e}\) and \(\hat{y}_{e}\) are bounded, and the system (23) is UIUB._  
Proof of Theorem 2: Design a Lyapunov function as follows:  
\[V_{2}=\frac{1}{2}\hat{x}_{e}^{2}+\frac{1}{2}\hat{y}_{e}^{2} \tag{24}\]Differentiating (24) and combining (23) and [PERSON]'s inequality [15], one has:  
\[\begin{split}\dot{V}_{2}&=-k_{1}\hat{x}_{e}^{2}-c_{1} \hat{y}_{e}^{2}-k_{x}\hat{x}_{e}\tilde{x}_{e}-k_{y}\hat{y}_{e}\tilde{y}_{e}\\ &\leq-\left(k_{1}-\frac{k_{x}}{2}\right)\hat{x}_{e}^{2}-\left(c_{ 1}-\frac{k_{y}}{2}\right)\hat{y}_{e}^{2}+\frac{k_{x}}{2}\hat{x}_{e}^{2}+\frac{ k_{y}}{2}\hat{y}_{e}^{2}\\ &\leq-2\lambda V_{2}+\Omega\end{split} \tag{25}\]  
where \(\lambda=\min\left\{k_{1}-\frac{k_{x}}{2},c_{1}-\frac{k_{y}}{2}\right\}\), \(\Omega=\frac{k_{x}}{2}\hat{x}_{e}^{2}+\frac{k_{y}}{2}\hat{y}_{e}^{2}\).  
Integrating on both sides of (25) yields:  
\[V_{2}\leq\left(V_{2}\left(0\right)-\frac{\Omega}{2\lambda}\right)e^{-2\lambda t }+\frac{\Omega}{2\lambda} \tag{26}\]  
Obviously, \(V_{2}\) is eventually bounded by \(\frac{\Omega}{2\lambda}\), and the system (23) is UUB.
# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 2 Methodology
### Error Functions
The error functions \(f_{1}\) and \(f_{2}\) account both for the spatial distribution; the error functions \(f_{3}\), \(f_{4}\) account for the spectral distribution; the error function \(f_{5}\) is a hybrid measure and accounts for spectral and spatial distribution together, and, finally, the function \(f_{6}\) highlights the areas that are highly not correlated and hence possibly changed. **Remark 1**. : _Note that the functions \(f_{4}\) and \(f_{5}\), i.e., SAM-MEAN and SMSADM, have been experimentally proven to remarkably improve the classical SAM [31] and SSCC [32], when HS images are analyzed; see [27] for additional details, and refer to [33] and references therein for other types of error functions._
###### Abstract  
In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the different sizes of vehicles, occlusions, and other real-time traffic scenarios. To address the vehicle detection and tracking issues, an intelligent and effective scheme is proposed which detects vehicles by You Only Look Once (YOLOv5) with a speed of 140 FPS, and then, the Deep Simple Online and Real-time Tracking (Deep SORT) is integrated into the detection result to track and predict the position of the vehicles. In the first phase, YOLOv5 extracts the bounding box of the target vehicles, and in second phase, it is fed with the output of YOLOv5 to perform the tracking. Additionally, the Kalman filter and the Hungarian algorithm are employed to anticipate and track the final trajectory of the vehicles. To evaluate the effectiveness and performance of the proposed algorithm, simulations were carried out on the BDD100K and PASCAL datasets. The proposed algorithm surpasses the performance of existing deep learning-based methods, yielding superior results. Finally, the multi-vehicle detection and tracking process illustrated that the precision, recall, and mAP are 91.25%, 93.52%, and 92.18% in videos, respectively.  
convolutional neural networks; deep SORT; Kalman filter; vehicle detection and tracking; YOLOv5 2023
## 4 Results and Discussion  
### Weathering Profile  
The results on weathering indices of CIA and WIG of the weathering profile are shown in Figure 3. The values of CIA in the profile vary in a range of 44.6 to 74.0, and WIG values vary from 19.5 to 78.3, which indicates an incipient to intermediate weathering degree mostly. Although the weathering degree is increasing gradually from fresh bedrock at the bottom to the weathered debris in the middle parts and then to topsoil, an abrupt variation at the boundary of PM-1 and PM-2 is recognized at the platform.  
The geochemical lithogenes of LG01 and LG03 of samples from the weathering profile were coded on GGC (Geochemical Gene Coding) software [1], and then the acidic similarities (\(R_{\text{Acidic}}\)) were calculated and are illustrated in Figure 3.  
With respect to the bottom rock, the \(R_{\text{Acidic}}\) values of LG01 and LG03 are all \(\leq\)20%, which indicates that the bottom sample is the 33 type of LG_CR compositionally. With respect to the other rock and debris (below 5 m in profile depth), they are basic-like materials on LG01 classification and intermediate-like on LG03 classification; therefore, they belong to the 32 type of LG_CR compositionally, except the top material in the sub-profile of PM-2, which is the 22 type of LG_CR and is located near the boundary of the platform or sub-profiles, while the soils (above 5 m in profile depth) are 11, 21, 22, or 32 types of LG_CR, which indicates the lithology of the weathering profile is heterogeneous compositionally or soils were mixed with different sources compositionally. The lithological variation in the upper soils in the profile may be explained by its location near the petrological boundary of the conglomerate (K\({}_{1}t^{1}\)) and the basalt with clay and shale in the middle and upper part (N\({}_{3}\)) (Figure 2b).  
In a word, the geochemical lithology of the weathering profile shows a gradual variation from basic-like in the bottom rock to acidic-like at the upper soils compositionally with heterogeneity.
## 3 Materials and Methods
### Analysis Methods
#### 3.2.2 Spatial Analysis: Moran's I  
To assess spatial autocorrelation of waiting times, Moran's I was used to determine whether high waiting times are clustered in specific geographic areas. The formula for the index is:  
\[I=\frac{n}{W}\cdot\frac{\sum_{i,j}w_{ij}(x_{i}-\overline{x})\left(x_{j}- \overline{x}\right)}{\sum_{i}\left(x_{i}-\overline{x}\right)^{2}}\]  
where:  
* \(n\) is the total number of observations;
* \(W\) is the total sum of spatial weights \(w_{ij}\);
* \(w_{ij}\) are the spatial weights between locations \(i\) and \(j\), which form part of the spatial weights matrix \(W\);
* \(x_{i}\) and \(x_{j}\) are the values of the variable of interest at locations \(i\) and \(j\), respectively;
* \(\overline{x}\) is the mean of the variable \(x\).  
The spatial weights matrix WW was constructed using the kk-nearest neighbors (KNN) method, assigning each point its kk closest neighbors based on Euclidean distance. This method is suitable for data with irregular spatial distribution, although it may leave some points \"disconnected\" (i.e., with no assigned neighbors) in very isolated or low-density areas. This issue is addressed in Section 4.2, where the effect of including and excluding such points in the calculation of Moran's I is demonstrated.  
This analysis provided an initial view of the spatial correlation of waiting times prior to the clustering process.