esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 7 Conclusions  
In modern systems such as self-driving and semi self-driving vehicles, intelligent intrusion detection systems have become a vital security application. These vehicles, networks and devices are subjected to different types of attacks that have a direct effect on the development and use of self-driving vehicles. The intelligent IDS introduced here has the capability to detect grey hole and rushing attacks in VANETs. These network types can assure secure self-driving and semi self-driving by CAMs and control data that are swapped between the different vehicles in a zone. Rushing attacks and grey hole attacks attempting to bring about a drop in some or all incoming messages, which can lead to a direct consequence to the lives of passengers, vehicles and drivers. In addition, without security, self-driving and semi self-driving vehicles will not be able to achieve their function in providing safety and comfort when being operated.  
We created an intelligent security system that can secure external communication system for self-driving vehicles. We designed the IDS to be used for training and testing with fuzzified data through the use of SVM and FFNN. This system can produce two system outcomes, which we have been able to generate and simulate in ns-2. We also studied the behaviour of all vehicles to detect normal and abnormal behaviour in VANETs. We applied monitoring and analysis of the trace files that were generated in network simulator to detect grey hole and rushing attacks. The trace file defines the behaviour of the network using receive, send, forward, move and drop. A possible additional extension of this system is to study these attacks with different AI techniques like Fuzzy Petri Nets (FPNs).  
This research has been supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant EP/K004638/1 (project named RoBoSAS).  
[PERSON] designed the proposed system and simulated it within different conditions for measuring the efficiency of the security system and response system for Automatic driving cars. [PERSON] had a substantial role in improving the proposed system throughout the scientific notes. [PERSON] had the essential and distinct role in writing and designing and simulating the system throughout his continuous feedback and scientific guidance and advice. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 2 System Model
### Signalling Through Precoder Matrix Indices  
The system model depicted in Figure 1 employs transmitted reference signals applied to precoders, \(G_{i}\), with dimensions corresponding to the number of transmitting and receiving antennas (i.e., \(N\times N\)). These reference signals are pre-known to the receiver, enabling the recovery of information related to the precoders. During initialisation, the precoder \(G\) is selected from a collection of random unitary operators with complex coefficients (\(g_{i,j}\in\mathbb{C}\)).  
Consider the precoder matrix \(G\) defined as  
\[G=\begin{bmatrix}g_{1,1}&g_{1,2}&\cdots&g_{1,N}\\ g_{2,1}&g_{2,2}&\cdots&g_{2,N}\\ \vdots&\vdots&\ddots&\vdots\\ g_{N,1}&g_{N,2}&\cdots&g_{N,N}\end{bmatrix} \tag{1}\]  
where \(g_{i,j}\in\mathbb{C}\) are complex coefficients. The selection of \(G\) is based on a set of random unitary operators. The relationship between the transmitted signal \(x\), the precoder \(G\), and the received signal \(y\) can be expressed as  
\[y=Gx+n \tag{2}\]  
where \(n\) represents the noise vector. Precoders \(G_{1}\) and \(G_{2}\) are derived from a combination of the received signals by both parties ([PERSON] and [PERSON]), the CSI between them, and an information-bearing codebook that maps an \(n\)-bit binary tuple to a codebook matrix. The codebook of a finite set of precoder matrices is shared among both parties. Each matrix is identified by an index called the precoder matrix index (PMI). The private data or  
Figure 1: Intelligent network for B5G service-based architecture.  
secret information serves as an index for these precoding matrices within the universally known codebook. The actual secret information bits are not shared with any entities in the system; instead, the receiver decodes the mapped codebook elements to retrieve the codebook indices. The codebook that maps an \(n\)-bit binary tuple \(b\) to a precoder matrix \(C(b)\) is defined as  
\[C(b)=G_{PMI(b)} \tag{3}\]  
where \(PMI(b)\) is the precoder matrix index associated with the binary tuple \(b\). Due to the Rayleigh fading channel differences between [PERSON] and [PERSON], the precoding matrix of a multiple input multiple output (MIMO) system is known only to them, not to any potential interceptor or eavesdropper. Therefore, the PMI is also utilised to map secret information tuples. The detection probability \(P_{d}\) for a given PMI scheme can be evaluated using  
\[P_{d}=\Pr(P\hat{M}I=PMI|y,G) \tag{4}\]  
where \(P\hat{M}I\) is the estimated precoder matrix index at the receiver. Furthermore, the bit error rate (BER) is given by  
\[\text{BER}=\frac{1}{n}\sum_{i=1}^{n}\Pr(b_{i}\
eq\hat{b}_{i}) \tag{5}\]  
where \(b_{i}\) and \(\hat{b}_{i}\) represent the \(i\)-th bit of the transmitted and received binary tuples, respectively.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
###### Abstract  
In this paper, we present a radial basis function (RBF) and cubature Kalman filter (CKF) based enhanced fusion strategy for vision and inertial integrated attitude measurement for sampling frequency discrepancy and divergence. First, the multi-frequency problem of the integrated system and the reason for attitude divergence are analyzed. Second, the filter equation and attitude differential equation are constructed to calculate attitudes separately in time series when visual and inertial data are available or when there are only inertial data. Third, attitude errors between inertial and vision are sent to the input layer of RBF for training. After this, through the activation function of the hidden layer, the errors are transferred to the output layer for weighting the sums, and the training model is established. To overcome the problem of divergence inherent in a multi-frequency system, the well-trained RBF, which can output the attitude errors, is utilized to compensate the attitudes calculated by pure inertial data. Finally, semi-physical simulation experiments under different scenarios are performed to validate the effectiveness and superiority of the proposed scheme in accurate attitude measurements and enhanced anti-divergence capability.  
Keywords: attitude measurement; vision and inertial fusion; sampling frequency discrepancy; divergence; RBF; CKF +
Footnote †: journal: Applied Sciences
## 1 Introduction
The use of zero-thickness interface elements has significantly improved the modeling of discontinuities and failure surfaces, especially in soils governed by the Mohr-Coulomb criterion. These elements allow for the accurate simulation of progressive failure and localized shear bands. Advanced approaches, such as those introduced by [PERSON] et al. [25], automate mechanism identification using optimization algorithms and have set a benchmark in the field. Accurately evaluating the ultimate bearing capacity of shallow foundations is essential for safe and cost-effective geotechnical designs. While analytical methods offer insights under idealized conditions, numerical approaches are better suited for complex problems. However, methods like discontinuity layout optimization (DLO) and traditional finite element limit analysis (FELA) often face challenges, such as convergence instability, mesh sensitivity, and limited scalability. Moreover, adaptive failure detection methods can obscure physical interpretation and demand high computational effort. This study addresses these limitations by exploring a structured finite element approach with predefined failure mechanisms and zero-thickness interface elements. It is guided by two questions: (1) Can such a model match the accuracy of traditional upper-bound methods? (2) How well does it balance computational efficiency with the ability to capture progressive failure in cohesive and C-phi soils? This study presents a numerical framework based on a self-developed finite element program that incorporates zero-thickness interface elements governed by the Mohr-Coulomb failure criterion. The model assumes a homogeneous, isotropic, linearly elastic soil mass with predefined failure surfaces. A series of parametric studies is conducted to examine how different failure geometries influence the predicted bearing capacity. By integrating mesh optimization with upper-bound limit analysis, the proposed method yields accurate and computationally efficient estimations of collapse loads. The results not only validate the effectiveness of the approach, but also highlight its potential for simulating progressive failure mechanisms with high fidelity. Finally, this research contributes a reliable tool for geotechnical design and offers a practical pathway to integrating discontinuity modeling within upper-bound limit analysis for shallow foundations.
# Performance of CRTS-II Ballastless Track-Bridge Structural System Rebars under Fatigue Loading Test
## 3 The Result of the Experiment
### Rebar Strain Amplitude under Dynamic Load
In order to analyze the cumulative increase in the strain amplitude of the steel rebars under the action of multi-stage fatigue load, the strain amplitude ratio of each stage of the fatigue test was accumulated, and the cumulative value of the strain amplitude ratio \(D_{N}\) is calculated according to the following formula:  
\[D_{N}=\frac{\epsilon_{N}^{i}}{\epsilon_{0}^{i}}.D_{i-1} \tag{1}\]  
where \(\epsilon_{N}^{i}\) is the strain amplitude under the \(N\)-th cycle of \(i\)-th stage, \(\epsilon_{0}^{i}\) is the initial strain amplitude of the \(i\)-th stage, \(D_{i-1}\) is the final cumulative value of the strain amplitude ratio at \((i-1)\)-th stage, \(D_{0}=1\). The cumulative evolution curve of strain amplitude ratio is obtained throughout the experiment, as shown in Figure 5. The cumulative strain-amplitude ratio of the steel rebars in stage 1 decreases first and then increases. The cumulative strain-amplitude ratio at the L/2 section and L/4 section reaches the minimum value at the 3 millionth fatigue loading cycle, with a decrease of 2.86% and 3.44%, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multicolumn{4}{c}{**Stage 1**} & \multicolumn{4}{c}{**Stage 2**} \\ \cline{2-7}  & **Initial Ratio** & **End Ratio** & **Increasing Rate (\%)** & **Initial Ratio** & **End Ratio** & **Increasing Rate (\%)** \\ \hline L/2 & \(1.00\) & \(0.98\) & \(-2.10\) & \(2.09\) & \(2.14\) & \(5.32\) \\ L/4 & \(1.00\) & \(0.97\) & \(-2.96\) & \(2.10\) & \(2.16\) & \(6.43\) \\ \hline \multirow{2}{*}{**Section**} & \multicolumn{4}{c}{**Stage 3**} & \multicolumn{4}{c}{**Stage 4**} \\ \cline{2-7}  & **Initial Ratio** & **End Ratio** & **Increasing Rate (\%)** & **Initial Ratio** & **End Ratio** & **Increasing Rate (\%)** \\ \hline L/2 & \(3.19\) & \(3.26\) & \(7.01\) & \(5.54\) & \(5.70\) & \(15.40\) \\ L/4 & \(3.31\) & \(3.39\) & \(8.61\) & \(5.67\) & \(5.82\) & \(14.85\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The strain amplitude ratio (\(\Delta\varepsilon_{N}/\Delta\varepsilon_{0}\)). Figure 5: The cumulative evolution of the strain amplitude ratio. The cumulative strain-amplitude ratio gradually increases in stages 2, 3 and 4, and finally reaches the maximum value at the end of the test, with an increase of 5.46% and 5.32%, respectively at L/2 and L/4 sections. The cumulative strain amplitude ratio of the midspan section is slightly larger than that of the L/4 section during the whole experiment. The growth rate of the cumulative strain amplitude is similar in all the sections. It decreases rapidly in the early stage, increases slowly in the middle stages and increases rapidly in the late stage.
## 4 Link Reliability Evaluation
We also evaluated the link reliability when the transmitter and the receiver were deployed at the different floors of the building. The transmitter was at the lower level of the floor. The vertical distance between the two nodes was around 3.7 m. Figure 14 shows the average PDR and RSSI measurement as a function of various transmit powers between \(-13\) and 7 dBm. A significant improvement was observed when the developed quasi-isotropic antenna was used. One interesting observation is that the proposed antenna provides around 3 dB gain of average RSSI with respect to those using the omni-directional antenna pair when the transmit power is greater than \(-9\) dBm. It also provides PDR greater than 70 for transmit power between \(-11\) and \(-9\) dBm while the omni-directional antenna pair has low PDR of less than 35%.
# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 2 Materials and Methods  
### Samples Preparation  
In this study, 17 types of commercial rice consisting of polished and unpolished rice were purchased from local markets in Australia (Table 1). The rice was stored in its original packaging at room temperature until used for experiments. Before cooking, the rice samples were washed and strained with tap water three times. The amount of water was added to the rice according to the recommended ratio provided on the packaging with slight modification after pre-cook testing (Table 1). The rice was cooked using the steam cooking mode in a combi steamer oven (Convotherm 4 easyDial 6.10 GS, Welbilt Deutschland GmbH, Egling, Germany). After cooking, six cooked rice kernels were drawn from the samples to test the cooked rice. Two transparent plastic plates were used to press the rice kernels between the plates, and the rice was considered fully cooked if no white core was observed. This step is important to ensure the rice samples were thoroughly cooked before fluffing the rice with a fork and cooling it to room temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class ID**} & **Product** & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Brands**} & \multirow{2}{*}{**Abbreviation**} & **Rice-to-Water** & **Cooking Time** \\  & & & & & **Ratio (v/v)** & **(min)** \\ \hline
1 & & Khoshihikari \({}^{\text{a}}\) & SunRice & KHO & 1:1 1/2 & 25 \\ \hline
2 & & Sushi rice \({}^{\text{a}}\) & SunRice & SRS & 1:1 1/2 & 25 \\ \hline
3 & & Bomba \({}^{\text{a}}\) & La Perla & BMB & 1:2 & 25 \\ \hline
4 & & Calasparra \({}^{\text{a}}\) & Cooperativa del & \multirow{2}{*}{CLP} & \multirow{2}{*}{1:2} & \multirow{2}{*}{25} \\  & & Campo & & & \\ \hline
5 & & Arborio \({}^{\text{b}}\) & Woolworths & ARB & 1:2 & 25 \\ \hline
6 & (Polished rice) & Calrose \({}^{\text{b}}\) & SunRice & CLS & 1:1 1/2 & 25 \\ \hline
7 & & Long grain \({}^{\text{c}}\) & Woolworths & LGW & 1:1 1/2 & 25 \\ \hline
8 & & Jasmine \({}^{\text{c}}\) & SunRice & JSR & 1:1 1/2 & 25 \\ \hline
9 & & Jasmine-organic \({}^{\text{c}}\) & Macro & JOM & 1:1 1/2 & 25 \\ \hline
10 & & Basmati \({}^{\text{c}}\) & Riviana & BSR & 1:1 1/3 & 25 \\ \hline
11 & & Basmati \({}^{\text{c}}\) & SunRice & BSS & 1:1 1/3 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of commercial rice samples used in the study, including class ID, product category, type, brandabbreviation, rice-to-water ratio, and cooking time. Abbreviations: v: volume.
## 5 Simulation and Discussion
### The Design of the Experimental Prototype
#### 5.1.2 Energy Storage System  
The bladder accumulator is adopted in the hydraulic wind turbine experimental prototype to store the extra hydraulic energy for its lower costs and fast response. The advantage of the energy storage system is that: when the wind does not provide enough energy for electrical energy, the generator can continue to produce electric energy for a long time with the hydraulic energy stored in the energy storage system. Its main parameters are described in Table 2.
## 3 Evaluation
### Setup  
We conducted preliminary experiments that showed that the following parameters, used in this paper, provide the best trade-off between crackle detection performance and the computational cost: sampling rate \(f_{s}\) = 4410 Hz, window size \(N\) = 18 samples (approximately 4.1 ms), hop size of four samples (approximately 0.9 ms), and a number of FFT points equal to twice the window size in samples.
## 2 Materials and Methods
### Extracting Faces from Videos and Data Transformation Processes  
Extracting faces from videos and preparing these faces to meet the model's requirements form the foundation of the training and evaluation processes. This process is carried out using the extract_faces_from_video function, leveraging tools such as OpenCV and MTCNN. Face detection is performed on each video frame, converting the faces into a format that the model can process and preparing them for training through data transformation processes. The main stages of these operations are illustrated in Figure 2.  
In the initial stage, video files are split into frames using OpenCV [21]. The video opened with the cv2.VideoCapture command is processed frame by frame. Face detection on the frames is performed using an MTCNN. The MTCNN detects the position of faces in each frame, marking these faces with rectangular boxes. The coordinates of the faces  
Figure 2: Diagram of the process for extracting faces from videos and data transformation.  
(x, y, width, height) are identified, these rectangles are cropped, and only the face regions are processed. During this operation, the suppress_stdout function is employed to prevent unnecessary outputs. Images are converted into the PIL_Image format using the Pillow library and prepared for data transformation processes. The progress is displayed to the user using the tqdm library, enhancing the traceability of the process [22].  
Data transformation processes are carried out to make the face images suitable for the model's input. These transformations are defined via the data_transforms object and assembled using the transforms.Compose function. The first transformation, transforms.Resize((224, 224)), resizes all images to 224 \(\times\) 224 pixels. This resizing is crucial because the model's input layer expects a specific size, preserving important details in the images while optimizing memory usage. Images are then converted into tensor format using transforms.ToTensor(). The tensor structure provides a three-dimensional representation of RGB color channels, allowing the model to process the data efficiently. Additionally, scaling pixel values to a range between 0 and 1 ensures that the model can adapt to varying lighting and color conditions.  
These processes ensure that each video frame's faces are processed only with the data required by the model, improving the efficiency of the training process. The cropping, scaling, and tensor conversion of faces enable the data to be arranged according to the model's requirements and eliminate inconsistencies. This structure, created during the training process, allows the model to learn more effectively and produce more accurate results in deep learning applications.
## 1 Introduction
The linear attenuation coefficient on CT reconstruction can then be calculated via an inverse Radon transform operation  
\[\mu(\mathbf{r},\lambda)=-\mathbf{\mathcal{R}}^{-1}\bigg{[}ln\bigg{(}\frac{I(x,\theta,\lambda)}{I_{0}}\bigg{)}\bigg{]} \tag{2}\]  
Figure 2: MCT beamline layout. The distribution of the linear attenuation coefficient in the single wavelength case can also be expressed in terms of \(\beta\), the distribution of the imaginary component of the complex X-ray refractive index as \(\beta(\mathbf{r},\lambda)=-\frac{\lambda}{4\pi}\ \mathbf{\mathcal{R}}^{-1}\Big{[}ln \Big{(}\frac{I_{(x,\beta,\lambda)}}{I_{0}}\Big{)}\Big{]}\). For low density or low Z materials, the X-ray attenuation generates low contrast [22]. By comparison, in the hard X-ray regime, the contribution of \(\delta\) is typically three orders of magnitude greater than the contribution of \(\beta\). This has led to utilizing the refraction in hard X-ray phase-contrast imaging becoming an increasingly widespread technique for the non-destructive imaging of low-density samples [3; 23]. Therefore, to obtain the distribution of \(\delta(\mathbf{r},\lambda)\) we use:  
\[\delta(\mathbf{r},\lambda)=-\mathbf{\mathcal{R}}^{-1}[\delta(\lambda).\ T(x,\theta)] \tag{3}\]  
where \(T(x,\theta)\) is the retrieved thickness for a projection angle, \(\theta\). For homogeneous samples, this can be calculated using the \"TIE-Hom\" algorithm (also known as Paganin's method), as follows [24]:  
\[T(x,\theta)=-\ln\Big{[}\mathbb{F}^{-1}\bigg{(}\frac{\lambda}{4\pi\beta+z\delta \lambda u^{2}}\mathbb{F}\bigg{[}\frac{I_{z}(x,\theta,\lambda)}{I_{0}}\bigg{]} \bigg{)}\bigg{]} \tag{4}\]  
where \(u\) is the Fourier variable conjugate of \(x\), \(\mathbb{F}\) is the Fourier transform operator, \(\mathbb{F}^{-1}\) is the inverse Fourier transform, and \(I_{z}(x,\theta,\lambda)\) is the intensity at a propagation distance with a fixed value of \(z\). The TIE-Hom algorithm is predominantly useful for imaging approximately homogeneous low-density samples. Equation (4) is implemented using the propagation-based phase-contrast method that is often more straightforward compared to other phase-sensitive X-ray-imaging techniques [4; 25]. The emphasis of the current paper is on the early development and progress of micron size resolution imaging (of mm-scale objects) at the MCT beamline including both propagation-based phase-contrast and absorption-contrast imaging modalities. More detail on the image resolution will be presented in terms of the results obtained during hot commissioning. Other imaging modes such as grating-based, speckle-based, and nano-CT are still in the process of development at the MCT beamline.
# Deep Learning for COVID-19 Diagnosis from CT Images
## 2 Materials and Methods
### Datasets
#### 2.1.1 COVIDx CT-2A  
COVIDx CT-2A [68] is an open-access dataset. At the time of writing, it is composed of 194,922 CT images from 3745 patients from 15 different countries, between 0 and 93 years old (median age of 51), with strongly clinically verified findings. Every image belongs to a particular class verified by expert pathologists. In particular, the classes are COVID-19, indicating CT images of COVID-19 positive patients, pneumonia indicating CT images of patients with pneumonia not caused by COVID-19, and normal, indicating CT images of patients in normal conditions.  
The countries involved are part of a multinational cohort that consists of patient cases collected by the following organisations and initiatives from around the world:  
1. China National Center for Bioinformation (CNCB) [49] (China);
2. National Institutes of Health Intramural Targeted Anti-COVID-19 (ITAC) Program (hosted by TCIA [69], countries unknown);
3. Negin Radiology Medical Center [70] (Iran);
4. Union Hospital and Liyuan Hospital of the Huazhong University of Science and Technology [71] (China);
5. COVID-19 CT Lung and Infection Segmentation initiative annotated and verified by Nanjing Drum Tower Hospital [72] (Iran, Italy, Turkey, Ukraine, Belgium, some countries unknown);
6. Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) [73] (countries unknown);
7. Radiopaedia collection [74] (Iran, Italy, Australia, Afghanistan, Scotland, Lebanon, England, Algeria, Peru, Azerbaijan, some countries unknown).  
Figure 2 shows some sample images taken from the dataset.