esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Engineering  
We employed the'matminer' library to generate a comprehensive set of features from the material compositions for our ML model. Initially, we utilized the ElementProperty featurizer [22] with the \"maggie\" preset to extract a wide array of elemental properties from the compositions. Following this, additional feature sets were generated using various featurizers: AtomicOrbitals [22] to capture information related to atomic orbitals, BandCenter [22] to derive features related to the band center, and Stoichiometry to analyze the material's compositional makeup. We also applied the ValenceOrbital featurizer [22] to obtain insights into the valence electron configurations. After generating these features, we ensured the dataset's integrity by removing duplicates and handling non-numeric and missing values. To refine our feature set, we computed a correlation matrix and established thresholds to identify features with significant correlations to the Tc and to detect highly inter-correlated features. By applying these thresholds, we isolated the most relevant features that have a strong correlation with Tc while minimizing multicollinearity. We applied a comprehensive feature selection strategy based on correlation analysis. First, we calculated the correlation matrix for all numerical features and established two key thresholds to guide the selection process. A threshold of 10.1 was set to identify features with significant correlation to the target variable, the critical temperature Tc. Only those features that exhibited a correlation greater than this threshold were retained. In order to manage multicollinearity, we implemented a second threshold of 10.9 | for inter-feature correlations. For pairs of features where the correlation exceeded this threshold, we evaluated the correlation of each feature with the target variable and retained the one with the stronger correlation, while discarding the other. The clustered heatmap provided in Figure 3 supports this approach by visually illustrating the correlations among the selected features that are most highly correlated. After applying our selection criteria, multicollinearity has been reduced, with fewer widespread clusters of highly correlated features. Some localized clusters of correlated features remain, but the selection process has successfully minimized redundant information while retaining features most relevant to the target variable.
## 4 Case Studies
### Validation of the Genetic Algorithm
#### 4.2.2 IEEE 14-Bus System  
This study optimizes the capacity and location of RPG units in the IEEE 14-bus system in Figure 9. For the IEEE 14-bus system, the proposed GA optimally allocates six RPG units, with a total capacity of 90 MVA (53 MVA in bus 3, 11 MVA in bus 7, 8 MVA in bus 10, 2 MVA bus 12, 7 MVA in bus 13, and 9 MVA in bus 14), in order to minimize variation in voltage, the installation cost of RPG units, and losses. Figure 16 depicts the objective function's standard deviation for offspring members over multiple generations when the proposed GA finds a solution with an objective function of 0.07735. Since the variation converges to zero, the proposed GA determines the fitter single offspring member (i.e., a solution to the optimization problem). Figures 17 and 18 present the effect of optimally allocated RPG units in the test system on voltage
# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 2 Detection Method for Skid Resistance of Asphalt Pavement
### Highway Asphalt Pavement Skid Resistance Detection Method
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Detection Methods**} & \multicolumn{2}{c}{**Characteristics**} \\ \cline{2-3}  & **Advantages** & **Disadvantages** \\ \hline Lateral force coefficient & Intuitively and realistically reflect the & This method is very sensitive to the state of the road, such as cracks and holes; the tire damage is greater [25] \\ \hline Sand spreading method [43; 44] & The principle of fixed-point measurement is simple and the operation is convenient & The human factors are large and the accuracy is not high \\ \hline Digital image & The camera has the advantages of low cost, convenient operation, and no interference with traffic & It is easy to receive the influence of external environment. The judgment of AP skid resistance is greatly affected by the accuracy of image recognition algorithm \\ \hline Energy loss method [45] & The operation is simple and the cost is low & It is easily affected by human factors. In the practical application process, the detection accuracy is relatively low \\ \hline Pendulum instrument method [43; 46] & Fixed-point measurement, simple principle, not only can be used indoors & Both the detection speed and the detection accuracy are relatively low, and traffic needs to be closed \\ \hline Dynamic rotating & It can reflect the change in the road friction coefficient within the speed range of 0–80 km/h and the test results are good, with fewer human interference factors and high credibility \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pavement skid resistance testing methods and characteristics. Figure 3: Schematic diagram of the detection principle and highway model of the smart sensor detection method [35].
## 4 Experiments  
### Experimental Environment and Hyperparameter Settings  
Our experimental environment is configured as follows:  
Hardware Specifications:  
Graphics Processing Unit (GPU): NVIDIA GeForce RTX 3090, equipped with 24 GB GDDR6X memory, which provides high parallel computing power necessary for accelerating deep learning tasks such as image generation and model training.  
Central Processing Unit (CPU): AMD EPYC 7601, a 32-core processor with a base clock speed of 2.2 GHz, responsible for handling data preprocessing, model initialization, and other sequential computing tasks.  
Software and Dependencies:  
Programming Language: Python 3.8, serving as the primary language for implementing all experimental workflows.  
Deep Learning Framework: PyTorch (version 1.10.0, compatible with CUDA 11.1), used for building and training neural network models, including the classifier and integration with other deep learning tools.  
Virtual Environment Management: Conda (version 4.11.1) was employed to create an isolated virtual environment, ensuring consistent installation of dependencies (e.g., specific versions of PyTorch and Python libraries) across different operating systems. This is crucial for maintaining reproducibility of experimental results, as inconsistent dependency versions can lead to variations in model performance or runtime errors.  
Key Experimental Parameters:  
Text Input Constraints: The length of input text was limited to less than 77 tokens to align with the input requirements of the CLIP model's text encoder.  
Generated Image Specifications: Images were generated in a resolution of \(512\times 512\) pixels with three color channels (RGB).  
Training Configuration: The image generator ran for 50 iterations per sample, with a total of 100 training epochs. The batch size was set to 20 to balance computational efficiency and model convergence, and the initial learning rate was \(5\times 10^{-5}\), adjusted dynamically during training using a cosine annealing scheduler.  
For text preprocessing, we use the NLTK, re, NumPy, and Pandas packages in Python. For the import and utilization of the CLIP model in text and image encoding tasks, we employ the torch, torchvision, transformers, fifty, and regex packages in Python. Regarding the Stable diffusion model used for image generation, its loading and usage are implemented using the diffusers, accelerate, and safetensors packages in Python. The classifier, on the other hand, is constructed with the help of NumPy and PyTorch in Python.
## 2 Vehicle Modeling for State Estimation
### Measurement, Control Input, and State Vectors  
As for the DDEV equipped with the standard active safety sensors suit, the signals that can be directly measured consist of the steering wheel angle \(\delta_{sw}\), the longitudinal acceleration \(a_{x}\), the lateral acceleration \(a_{y}\), yaw rate \(r\), torque output \(T_{m\_{jj}}\) and rotational speed \(w_{m\_{jj}}\) of each motor.  
As the reduction ratio \(i\) between motor and wheel is known, the wheel speed is simply obtained,  
\[\omega_{ij}=\omega_{m\_{jj}}\cdot i. \tag{1}\]  
Therefore, the measurement vector is given as  
\[\mathbf{y}=\Big{[}\begin{array}{cccccccc}a_{x}&a_{y}&r&\omega_{fl}&\omega_{rl}& \omega_{fr}&\omega_{rr}\end{array}\Big{]}^{T}.\]  
Additionally, the longitudinal tire force can be calculated by the rotational dynamic equation instead of complicated tire models, which is shown below:  
\[F_{t\_{,}ij}=\frac{T_{m\_{jj}}\cdot i-J_{w}\cdot\dot{\omega}_{ij}}{R},\;ij=fl, \;fr,\;rl,\;rr, \tag{2}\]  
where \(J_{w}\) is the wheel rotational inertia; \(R\) is the tire radius and in this study it is assumed to be a constant.  
These four forces and steering wheel angle form the control input vector as follows:  
\[\mathbf{u}=\Big{[}\begin{array}{cccccccc}\delta_{sw}&F_{t\_{,}fl}&F_{t\_{,}fr}& F_{t\_{,}rl}&F_{t\_{,}rr}\end{array}\Big{]}^{T}.\]  
Finally, the vehicle states that need to be estimated include the longitudinal velocity, the lateral velocity, yaw rate and the lateral force of each tire.  
\[\mathbf{x}=\Big{[}\begin{array}{cccccccc}u&v&r&F_{s\_{fl}}&F_{s\_{fr}}&F_{s\_{ fl}}&F_{s\_{rr}}\end{array}\Big{]}^{T}\]
## 3 Gray Value Compression
### Comparative Analysis of the Described Coders  
In this subsection, all the described coders are compared at five compression rates (0.8, 0.4, 0.2, 0.1, and 0.05 bpp) on the test image _horse_. The cumulative results are shown in Table 4.  
From careful examination of Table 4, it can be concluded that all of the described changes in grey level compression did not improve the quality of the reconstructed image. Changing the compression algorithm alone does not produce better results, so a modification of the input data stream is required.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**bpp** & **JPEG** & \begin{tabular}{c} **JPEG** \\ **2000** \\ \end{tabular} & **EEDC** & \begin{tabular}{c} **EEDC-** \\ **Arith** \\ \end{tabular} & \begin{tabular}{c} **EEDC-** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **EEDC-** \\ **LZ77** \\ \end{tabular} & \
\begin{tabular}{c} **EEDC-** \\ **LZ78** \\ \end{tabular} &
\begin{tabular}{c} **EEDC-** \\ **LZW** \\ \end{tabular} \\ \hline
0.8 & 1.65 & 1.39 & 2.08 & 2.49 & 2.24 & 3.01 & 3.77 & 3.62 \\
0.4 & 2.91 & 2.29 & 2.67 & 3.03 & 2.88 & 3.51 & 3.92 & 3.84 \\
0.2 & 6.20 & 3.78 & 3.80 & 3.98 & 3.86 & 4.33 & 4.71 & 4.58 \\
0.1 & 10.96 & 6.75 & 5.94 & 6.50 & 6.31 & 7.12 & 7.90 & 7.33 \\
0.05 & / & 12.52 & 9.31 & 10.20 & 10.08 & 14.22 & 15.20 & 14.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of average absolute error for image _horse_ and all modified compression algorithms.  
Figure 7: AAE for image _horse_ and modified gray values compression with LZ77 coding.
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 2 Materials and Methods
### Datasets
#### 2.5.3 Fundus Image Registration (FIRE) Dataset  
We used category S of the Fundus Image Registration (FIRE) Dataset [47] to evaluate the proposed approaches. This dataset contains 14 image pairs with 2912 \(\times\) 2912 pixels resolution. The images were captured using a Nidek AFC-210 fundus camera.
# An Improved Cutting Force Model for Ultrasonically Assisted Grinding of Hard and Brittle Materials
## 2 Model Development
### Cutting Force Model in UAG  
Substituting Equations (18), (23), (26) and (28) into Equation (4), one can obtain the axial cutting force of the tool in UAG:  
\[\begin{array}{ll}F_{a}=&K^{-12/13}\frac{N_{c}}{2A}\Big{(}\frac{N_{c}}{180}n _{r}(R_{i}+R_{o}-r_{c})C_{Lh}\frac{\pi}{2A}\Big{)}^{-12/13}\big{(}2(A+h_{e})R_ {om}v_{F}\big{)}^{12/13}\bigg{(}2H_{V}\tan\beta\sqrt{2+\tan^{2}\beta}\bigg{)} ^{-1/26}\\ &+\int_{0}^{\alpha}C\pi r_{c}(R_{o}-r_{c}+r_{c}\sin\alpha)\cos\alpha(F_{c1}{ }^{\prime}+F_{s1}{}^{\prime})da\\ \end{array} \tag{29}\]  
The feed direction cutting force of the tool in UAG is:  
\[\begin{array}{ll}F_{f}=&\tau 2R_{o}C\big{(}a_{p}-r_{c}\big{)}^{17/9}\Big{(}v_{F} \frac{60}{n_{r}}R_{o}\Big{)}^{8/9}(K_{s}N_{s}2L_{S}C_{Lh})^{-8/9}\\ &+\int_{0}^{\alpha}2C\pi r_{c}(R_{o}-r_{c}+r_{c}\sin\alpha)\sin\alpha(F_{c1}{ }^{\prime}+F_{s1}{}^{\prime})da\\ \end{array} \tag{30}\]  
where if \(a_{p}>r_{c}\), \(\alpha=\pi/2\); if \(a_{p}<r_{c}\), \(\tau=0\).  
Figure 8 shows the trajectory of an abrasive grain during one cycle of ultrasonic vibration.  
The units of parameters involved in the formula are shown in Table 1.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 1 Introduction
### Survey Methodology  
To identify and review existing studies on Intrusion Detection Systems in the Internet of Vehicles, with a particular emphasis on the use of Generative AI techniques, a comprehensive and methodologically rigorous literature search was conducted. The goal was to capture state-of-the-art approaches, evaluate their performance, and analyze research trends within this emerging domain.  
A systematic search of the literature was conducted using Google Scholar. The search aimed to identify relevant studies published between 2015 and 2024, capturing recent trends and advancements in both ML/DL-based IDS and Generative AI-based IDS techniques applied in vehicular networks.  
The search was performed using combinations of the following keywords:  
(\"Intrusion Detection System\" OR \"IDS\")  
AND (\"Internet of Vehicles\" OR \"IoV\" OR \"Vehicular Network\" OR \"V2X\" OR \"CAN bus\") AND (\"Machine Learning\" OR \"Deep Learning\" OR \"Generative AI\" OR \"GAN\" OR \"Transformer\" OR \"LLM\" OR \"BERT\" OR \"GPT\").  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Survey**} & \multirow{2}{*}{**Time Span**} & **ML-Based** & **DL-Based** & \multicolumn{3}{c}{**Generative AI-Based IDS**} & **Network** \\  & & **IDS** & **IDS** & **GANs-IDS** & **Transformer-IDS** & **VAEs-IDS** & **Focus** \\ \hline [PERSON] et al. [1] & — & ✓ & Limited & x & x & x & IVNs + V2X \\ \hline [PERSON] et al. [2] & 2017–2024 & ✓ & ✓ & x & x & x & IVNs \\ \hline [PERSON] et al. [3] & 2016–2022 & ✓ & ✓ & x & x & x & IVNs \\ \hline This Survey & 2015–2024 & ✓ & ✓ & ✓ & ✓ & ✓ & IVNs + V2X \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative taxonomy of AI-based IDS surveys in vehicular networks.  
Titles, abstracts, and keywords were manually reviewed to assess the relevance of each paper. Only studies offering technical depth were considered. To ensure relevance and quality, the following criteria were used to select the surveyed papers:
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion  
### Performance Evaluation Parameters  
This section describes the performance evaluation of the proposed MIDS algorithm in terms of the following:  
* The number of alive nodes.
* Packet delivery ratio.
* Latency.
* Network throughput.
* Consumed energy during the communication phase.  
For the third route, both conditions are false; ts_3 = 4 is not less than the tht and the hop count is one. Hence, the first route is fulfilling the criteria for a malicious node. So, CH (89) detects the first neighbor CH (17) as a wormhole and sends a dummy RREQ to it on the first route; the receiver will receive that dummy request from the CH (94) on the same route so the receiver will detect the CH (94) as the wormhole node. Both these nodes will be isolated, and an alarm will be generated for the other nodes to update their routing entries eliminating these nodes. So, basically, CH (17) and CH (94) were forming a tunnel and accessing the sensitive information of the network.  
Figure 4: Sinkhole/wormhole detection. Each color shows distinct cluster. Triangle in each cluster is cluster head (CH). purple triangle in the center is base station.
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 6 Numerical Simulation Results Analysis
### Impact of Signal Frequency on Estimation Results  
For an array with fixed inter-element spacing, the beam patterns of different beamforming methods vary with frequency, raising the question of whether this variability affects the performance of beamforming-based methods for estimating radiated noise power. Additionally, it is also worth discussing whether the MLE method is subject to this issue. Due to variations in the channel transfer functions at different frequencies, the SNR of the received data fluctuates. For a VLA with 1 m inter-element spacing, Figure 5 presents the SNR of the received data under different frequency conditions, and Figure 6 provides the estimation results and variances.  
It can be observed that the SNR of the received data remains nearly constant at different frequencies. Except for the CBF method, the estimation results and variances of the other methods are largely unaffected by frequency variations. For the CBF method, based on the Fraunhofer distance condition under the far-field assumption, it becomes increasingly challenging to satisfy the far-field condition as the frequency rises at a fixed distance. This leads to a rise in both estimation error and variance at higher frequencies. The NFBF method exhibits relatively large estimation error and variance, while the MFP method achieves the CRLB but still suffers from significant estimation error. The RPCA method demonstrates relatively smaller estimation error and variance. In contrast, the MLE method achieves the smallest estimation error, and its estimation variance reaches CRLB, thereby demonstrating optimal performance in radiated noise estimation.  
Figure 4: Radiated noise estimation results under different snapshot numbers, (**a**) estimation results, (**b**) estimation variances.  
Figure 5: The SNR of received data at different frequencies.
## 3 Materials and Methods
### Dataset Description and Preparation  
#### 3.1.1 Dataset Source and Characteristics  
The study utilized a lung cancer prediction dataset obtained from Kaggle. Kaggle is an open-source platform for data scientists and ML professionals connected to Google LLC. The dataset comprises clinical features, offering a complementary approach to studies focused primarily on imaging data and CNNs [17, 20]. Unlike some previous work with smaller sample sizes [14], this dataset includes information on 309 patients with 16 unique attributes (Table 2), providing a robust foundation for analysis. The dataset's dependent variable is the \"Lung_Cancer\" attribute, with the remaining fifteen attributes serving as independent predictors. The \"Lung_Cancer\" attribute uses two categories: 1 to represent patients with lung cancer and 2 to represent patients without it. Figure 1: Proposed data flow model. A detailed statistical analysis, including descriptive statistics, was conducted to understand the dataset's distributions, central tendencies, and variabilities. This analysis is essential for identifying potential biases, informing the preprocessing steps, and interpreting the model performance. Table 2 presents the dataset's characteristics, including attribute descriptions, data types, means, and standard deviations. Table 2 describes the dataset used in the study. It details the characteristics of each attribute (feature), including the data type and basic descriptive statistics like the mean and standard deviation for numeric attributes. This table is essential for understanding the raw data before performing any preprocessing or analysis. The data were primarily analyzed based on positive and negative cases among the correlation of Age and Gender. Figure 2 indicates that most patients' distribution ranged between the ages of 55 and 75. Weka (Waikato Environment for Knowledge Analysis) was utilized in this study to automate several ML tasks. Weka provides pre-built implementations of various machine learning algorithms and tools for data preprocessing, making it efficient for functions such as duplicate record removal, missing value imputation, outlier identification and removal, feature selection, data normalization, and the training and evaluation of machine learning classifiers [11]. The utilization of Weka facilitated the standardized and efficient implementation of the common ML workflow steps. However, NN models were custom implemented using Python 3.11 along with the Keras and TensorFlow libraries, providing greater flexibility in designing and training the deep learning architectures. This hybrid approach allowed us to leverage the strengths of Weka's automation and Python's customizability.
###### Abstract  
Infections with gastrointestinal nematodes (GINs) adversely affect meat color in lambs. Although white-rot fungi (WRF) pretreatment increases nutritional value and fiber digestion of corn straw for lambs, whether it can improve meat quality of lambs infected with GINs is unknown. The objective of this experiment was to study effects of feeding WRF-pretreated corn straw on the health and meat quality of lambs infected with GINs. Sixteen healthy Ujumqin lambs were orally drenched with 3 rd-stage GINs larvae and randomly divided into two dietary treatments of control (CON) and WRF diets for 70 days of feeding. Results showed that feeding WRF-pretreated corn straw decreased \(L^{*}\) and \(b^{*}\) values (\(p<0.05\)) and increased \(a^{*}\) value (\(p<0.01\)) of both longissimus thoracis et lumborum (LTL) and semimembranosus (SM) muscles of lambs infected with GINs. Feeding WRF-pretreatment corn straw decreased fecal egg count (\(p=0.014\)) and increased packed cell volume (\(p=0.013\)) of lambs from 28 d of feeding and increased plasma iron content (\(p=0.008\)) of lambs from 56 d of the feeding. Feeding WRF-pretreatment corn straw decreased myosin heavy-chain (MyHC)-I (\(p=0.032\)) and MyHC-II\(\alpha\) (\(p=0.025\)) content in LTL muscle and MyHC-I (\(p=0.022\)) and MyHC-II\(\beta\) (\(p=0.048\)) in SM muscle of lambs. In conclusion, although there were no significant changes in the content of most amino acids or increased intensity of better flavor compounds, meat quality and health of lambs infected with GINs was significantly improved by feeding WRF-pretreated corn straw due to increased PCV and meat color and tenderness.  
**Keywords:** corn straw; gastrointestinal nematode; lamb; meat quality; white-rot fungus