esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Approach for Fault Diagnosis of Wheelset Bearing
### The Advantages of the Mkurt in Extracting Repetitive Transients  
In order to better understand the Mkurt, a short introduction of the kurtosis is presented in this part at first. Kurtosis is a statistical indicator that can be used to quantitatively represent the non-Gaussian of the vibration signal. On account of this property, the impulse-like characteristics of the bearing fault signal can be detected easily. For this reason, the kurtosis has been a popular indicator used to identify the IFB for envelope analysis. Kurtosis can be defined as:  
\[\text{Kurtosis}=\frac{\sum_{t=1}^{N}(x_{n})^{4}}{\left(\sum_{n=1}^{N}x_{n}{}^ {2}\right)^{2}} \tag{11}\]  
However, in some cases, the kurtosis is easily interfered. As mentioned in [32], the kurtosis is prone to make a misdiagnosis when there is a relatively strong impulse in the measured signal or the fault-related impulses enjoys a high rate repetition. The main reason lies in that the cyclostationarity attribute of the bearing fault signal is not considered. Different from kurtosis, a target vector \(t_{n}\) is adopted in Mkurt, which sets the locations and weightings of the target peaks will to be taken. Mkurt can be defined as follows:  
\[\text{Mkurt}= \frac{\left(\sum_{n=1}^{N}t_{n}{}^{2}\right)^{2}}{\sum_{n=1}^{N}t_{ n}{}^{8}}\frac{\sum_{n=1}^{N}(t_{n}x_{n})^{4}}{\left(\sum_{n=1}^{N}x_{n}{}^ {2}\right)^{2}} \tag{12}\]  
This definition is derived from kurtosis and has been modified to multiple peaks at specified positions based on the target vector \(t_{n}\). A simple instance, \(t_{n}=[00001000010000100]^{\text{T}}\)  
Figure 1: (**a**) The original simulated signal; (**b**) the squared envelope signal generated from the IFB of (**a**); (**c**) the AC of (**b**).  
the target vector \(t_{n}\) will be limited only to pick up three peaks in the signal \(x_{n}\): peaks at \(n=5,\,10,\,15\). In addition, another outstanding virtue worth mentioning is that the Mkurt is a normalized index so that it will reach to the maximal value 1 provided a candidate input signal \(x_{n}\) in the direction of \(t_{n}\) is reached. Therefore, the Mkurt has a potential ability to describe the presence and strength of the periodic components with a given period. The detailed derivation of the normalization factor, please refers to [35]. As discussed in Section 2.3, the positions of the periodic peaks are rearranged in the AC domain. Hence, this unique characteristic makes it possible to use Mkurt to identify the strength of concerned fault-related peaks and avoid the impacts of non-interested fault peaks and outliers in the AC domain.
## 2 The definition of scale and relevant terminologies
### Scale threshold and scale domain  
With the development of scale research, scientists have found that the dominant factors which affect the processes change with the scale. [PERSON] and [PERSON] [2] pointed out that the degree of explanation in the variation of normalized difference vegetation index (NDVI) varies with the scale. The variation of NDVI is mainly affected by the local scale topographic orientation when the resolution is finer, which shows the effect of solar radiation on vegetation in terrain areas. When resolution decreases, the elevation gradually becomes the dominant factor to describe the distribution of vegetation [2,14]. Based on these facts, scientists have proposed the concept of scale domain and scale threshold. The scale domain can be considered as the interval in which the phenomenon or the structures are nearly invariable or slowly variable, while they may change dramatically in a different scale domain that is separated by the scale threshold. The understanding of these concepts would benefit the analysis of the processes. In the same scale domain, scaling may be easier as the dominant factors of the processes are the same or similar. However, scaling may become more complex across scale domain due to different dominant factors. Therefore, one of the focuses of scale research is to determine the scale domain and the scale threshold [2].
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 3 Architecture Exploration for Backprojection Based Video SAR
### Baseline Architectures of BP-VISAR
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(N_{p}\) & the number of pulses constructing a full-synthetic-aperture \\ \(N_{s}\) & the number of pulses constructing a sub-synthetic-aperture \\ \(N_{FFT}\) & the number of inputs for IFFT in the range compression stage \\ \(N_{v}\) & the number of pixels in rows or columns in video SAR frame of square size \\ \(K\) & the number of frequency samples in the phase history data of each pulse \\ \(M\) & the number of sub-synthetic-apertures constructing a full-synthetic-aperture \\ \(f_{PRF}\) & the pulse repetition frequency in a radar platform \\ \(\alpha\) & the overlap ratio of the synthetic apertures for adjacent video SAR frames (\(=k/M\)) \\ \(\gamma\) & the frame rate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations. padding, rearrangement of the range profile, and the multiplication of phase ramp data. It is subsequently repeated on a pulse by pulse basis. The image formation stage is shown to take most of the processing cycles regardless of the aperture processing type and the output pixel resolution. The image formation consists of range calculation, interpolation of the range profile, and accumulation of the interpolated ranges over the pulses. These operations are repeated for every pixel position to generate a BP-VISAR frame.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 2 Qualitative Identification of Internal Stability Based on Physical Properties of Soils
### Identification Methods Based on Fine Content and Porosity
#### 2.2.2 Semi-Theoretical and Semi-Empirical Formula  
[PERSON] et al. [29] proposed that the fine particle content is the main factor assessing the internal stability of soils. When the internal stability of soil improves once the pores are filled with fine particles, this is known as the optimal fine content. Using empirical methods, a formula to determine whether internal erosion occurs based on the fine particle content was proposed, where \(P_{op}\) is the optimal fine content (%) under ideal conditions [29], as shown in Formula (1),  
\[P_{op}=\frac{0.28+3n^{2}-n}{1-n}\times 100\% \tag{1}\]
## 4 Exact Solution of Catenary Strand Shape Considering Change in Tangent Point  
The pre-deflection of the cable saddle is determined according to the principle that the non-stress length between the two control points of cable saddle is invariable. The tangent point position of the cable strand in the saddle groove is iterated to make the force inclination angle of the cable end coincide with the angle of the tangent point of the cable saddle, and then the tangent point position is determined. Then, according to the geometric relationship between the tangent point and the saddle control point and the equilibrium equation of the suspension segment, the height of the strand at any position can be determined. According to the principle that the non-stress length \(S_{0}\) between the control points of two saddles is invariable, the strand between two adjacent saddles is divided into three sections. The length of the cable segment between saddle control point \(m_{1}\) and tangent point TP1 is \(S_{01}\), the length of the freely suspended cable segment between tangent point TP1 and tangent point TP2 is \(S_{02}\), and the length of the cable segment between tangent point TP2 and saddle control point \(m_{2}\) is \(S_{03}\). Assuming that the stressed length of \(S_{01}\) and \(S_{03}\) is equal to the non-stressed length (through calculation, the difference between the empty cable state is very small, and the error can be ignored), then, \(S_{0}=S_{01}+S_{02}+S_{03}\). For the freely suspended strand \(S_{02}\) between the adjacent tangent points, when the horizontal distance l, height difference h and non-stressed cable length \(S_{02}\) between two end points are determined, the strand shape is unique and satisfies the catenary equations composed of formulae (31) and (32). The sectional calculation diagram of the tangent point of the cable strand is shown in Figure 8. In the figure, \(\alpha\) is the included angle between the left cable saddle and the horizontal axis, and \(\theta_{1}\) and \(\theta_{2}\) are the included angle between the line passing through the arc center and tangent point of the cable saddle and the cable saddle axis, respectively. By iterating the length of \(S_{01}\) and \(S_{02}\) the tangent point position is corrected. When the tangent angle at the tangent point of the saddle is consistent with the force inclination angle at the end of the strand, the tangent point position is determined. Then, according to the catenary formula, the elevation of cable strand at any position can be determined. Figure 8: Calculation diagram of tangent point segment of cable strand. For the free suspension cable, as shown in Figure 6, the rectangular coordinate system is established with the left end point as the origin. As shown in Figure 8, the equation of the suspension cable is as follows:  
\[z=\frac{1}{c}chcx+a+b \tag{31}\]  
\(c=-q/H\), \(a\) and \(b\) are integral constants, which are related to the boundary conditions of suspension cables.
# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 1 Introduction
Deep CNN is a deep learning mechanism that offers enormous success as compared with other conventional methods in different image complexities. Deep learning has recently solved many complex problems and has shown excellent performance in many computer vision and machine learning tasks such as image classification, object detection, speech recognition, voice recognition, natural language processing, and medical imaging. Thus, one of the deep CNN tasks has been utilized for plant disease detection aimed at replacing traditional approaches of diseases detection [10]. In this paper, we investigate the feasibility of using DNN models for detecting olive leaf diseases. The dataset consisted of images from real cultivation fields in the Al Jouf region in the Kingdom of Saudi Arabia that were collected using an autonomous drone that flew over olive farm zones with over 400 trees; the experimental work was conducted between June 2021 and February 2022. The considered drone was equipped with a camera for taking images of the olive leaves, and then the images were transmitted to the cloud to identify the olive leaf diseases using deep learning techniques. The proposed solution in this work consists of a hybrid DL model that includes a combination of ResNet50 and MobileNet named \"MobiRes-Net\" that is used to detect olive leaf diseases. Therefore, our main contributions can be summarized as follows:  
* Building the first suitable dataset to be used for training and testing similar work;
* Conducting a trail work that combines an innovative deep learning framework with a drone to identify three types of olive leaf diseases, and thus, provide accurate diagnoses and helpful recommendations in an efficient and timely manner;Comparing the findings of the proposed work to state-of-art works. The remainder of this paper is organized as follows: Related works are investigated in Section 2; the materials and methods are presented in detail in Section 3; in Section 4, we described implementation and testbed; followed by the experimental results in Section 5; finally, the conclusions are stated.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 4 Materials and Methods
### Timing Synchronization Method  
Based on the new synchronization preamble, a novel timing synchronization method is proposed, which utilizes the ideal correlation properties of the ZC sequences.  
The proposed method is composed of two stages. In the first stage, the ZC sequence that makes up the preamble is used as the coefficients of the correlator. The received signal \(r(n)\) is then input into the correlator to perform a correlation operation with the ZC sequence. Finally, the correlation value signal \(Corr(n)\) is output. In the second stage, the correlation value signal \(Corr(n)\) is used for synchronization detection. The schematic of the proposed method is shown in Figure 3.  
Through the correlation value signal that is output by the correlator, we propose a new timing synchronization algorithm as follows.
## 3 Results
### Effects of SFA-CHCl3 Extract on Cell Cycle Progression of HepG2 Cells  
Flow cytometry was also performed to estimate the effects of the SFA-CHCl3 extract treatment on cell cycle progression in HepG2 cells. As shown in Figure 3, the SFA-CHCl3 extract treatment reduced the cell ratio in the G0/G1 phase for 24 h, 48 h, and 72 h, compared to the control group (90.53 \(\pm\) 0.15%, 87.40 \(\pm\) 0.23, 85.43 \(\pm\) 0.58 vs. 91.67 \(\pm\) 0.55%, respectively) (Figure 3A,B). In contrast, the increase in the cell ratio in the G2/M phase was observed in cell cycle distribution maps. The G2/M phase proportion of HepG2 cells was increased from the control group (2.87 \(\pm\) 0.06%) to the SFA-CHCl3 extract treatment group for 24 h, 48 h, and 72 h (3.87 \(\pm\) 0.06%, 6.26 \(\pm\) 0.31%, and 8.23 \(\pm\) 0.22%). This result revealed that the SFA-CHCl3 extract treatment induced the G2/M phase arrest of HepG2, which resulted in the inhibition of HepG2 cell proliferation.  
Figure 3: Cycle progression of HepG2 cells over the time of SFA-CHCl3 extract treatment. (**A**) Flow cytometry was applied to analyze the cell cycle progression. (**B**) The calculation of percentage of HepG2 cells in cell cycle phases. The percentage of HepG2 cells in G0/G1 phase was reduced from  
24 h to 72 h (* \(p\) < 0.05, 48 h group vs. other groups; # \(p\) < 0.05, 72 h group vs. other groups). The percentage of HepG2 cells in S phase was slightly increased in 48 h and 72 h (** \(p\) < 0.01, 48 h group vs. control group and 24 h group; # \(p\) < 0.05, 72 h group vs. control group and 24 h group). The percentage of HepG2 cells in G2/M phase was increased in 48 h and 72 h (*** \(p\) < 0.001, 48 h group vs. other groups; *** \(p\) < 0.001, 72 h group vs. other groups).
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 4 Proposed Methodology
The results from phase 2 are recorded in another file, representing the calculated outcomes for numerous simulations with datasets exhibiting different characteristics. Phase 3 is where the practical application of our approach comes into play. Here, we apply AI classifiers to create a model that can accurately predict the accuracy of a new, unseen agricultural dataset. Our approach is a crucial step in its validation. For the new dataset, we extract feature information and estimate its accuracy by applying all AI algorithms and exploring all possible data segmentations. The configuration that achieves the highest accuracy is then recommended to the user, demonstrating the practicality and relevance of our research. During phases 2 and 3, we employed machine learning techniques such as training, data preprocessing, cleaning, testing, and validation to obtain the desired results in our experiments. In phase 3, the data generated from phase 2 served as the output of the machine learning algorithms, and we repeated the same steps as in phase 2. One key benefit of our approach is the elimination of the need for exhaustive reruns of AI algorithms. This time-saving feature allows us to identify the algorithm that yields the best results more efficiently. Our approach's predictive capability relies on a pre-trained model, which estimates outcomes based on the dataset's characteristics rather than the specific data  
Figure 2: Use of the proposed model on unseen agricultural data. points within the dataset. This not only saves time but also enhances the accuracy of our predictions, providing a more reliable tool for agricultural data analysis. Finally, the best predicted results are selected based on the scenario, and we present the recommended algorithm and its performance metrics. In our study, we took a systematic approach to selecting the most effective AI-powered models for specific agricultural applications, which represents a significant advancement over the more generalized discussions found in the current literature. This was based on the following areas: (a) algorithm optimization and selection; (b) comprehensive testing and validation; and (c) practical application and scalability. This manuscript not only adds to the body of knowledge already in existence by offering a detailed examination of AI applications in agriculture, but it also makes novel contributions to the fields of algorithm selection and data analysis techniques. Its method of algorithm improvement and realistic scalability marks a major advancement in the application of AI to improve the sustainability and productivity of agriculture. As a result, we present a tool that empowers researchers in the agricultural domain to make informed decisions when selecting the most suitable AI model based on the type and characteristics of their data. This model is a valuable resource that minimizes time and effort by streamlining the algorithm selection process and contributing to more effective and accurate decision making in agricultural data analysis.
## 7 Conclusions  
In conclusion, the growing trend of organisational collaboration with third-party entities for data acquisition, while enhancing decision-making and operational efficiency, also introduces significant privacy concerns. These external partners often engage in the collection and utilisation of personal and sensitive data, necessitating compliance with government privacy regulations to protect user privacy through robust privacy measures. However, third-party organisations may have varying policies, emphasising the need for comprehensive privacy frameworks that, in conjunction with legal regulations, emphasise standardisation, privacy management, and risk assessment. Such frameworks offer holistic approaches to address the challenges arising in data-sharing partnershipships.  
In this study, we conducted an analysis of privacy breaches in the Facebook and Cambridge Analytica case with a specific focus on issues related to data collection, transparency, and accountability. We compared the NIST and Five Safes privacy frameworks to assess their potential to mitigate these privacy violations. Our analysis reveals that these frameworks serve distinct but complementary purposes, both offering valuable guidance in addressing the complexities of this scenario.  
Therefore, this study urges organisations, policymakers, and individuals to actively engage in dialogues and initiatives that promote a culture of privacy that is deeply rooted in ethical practices and informed by robust frameworks like NIST and Five Safes.  
In future work, there are several research directions to be explored in the practical implementation and adoption of privacy frameworks like NIST and Five Safes in organisations, which focuses on the real-world challenges and solutions in integrating these frameworks into data-sharing practices. Furthermore, the exploration of emerging technologies such as federated learning, implementation of differential privacy, and homomorphic encryption, and their impact on data privacy could present rich knowledge for future studies. These areas of research are crucial for advancing our understanding of how to use these cutting-edge technologies with robust privacy measures, thus developing a more secure and privacy-aware digital environment.  
Conceptualisation, S.R.G., T.Z., M.B. and Y.J.; formal analysis, S.R.G.; investigation, S.R.G.; writing--original draft preparation, S.R.G.; writing--review and editing, S.R.G., T.Z., M.B. and Y.J.; supervision, T.Z. All authors have read and agreed to the published version of the manuscript.  
The work has been supported by the Cyber Security Research Centre Limited, whose activities are partially funded by the Australian Government's Cooperative Research Centres Programme P23_000_19_0027.  
The authors [PERSON] and [PERSON] were employed by Charles Sturt University. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Proposed Algorithm for Multi-Agent Configuration Change
### Reward Function
The third layer represents the potential agents' velocity \(\hat{v}_{i}\) and \(\hat{\omega}_{i}\), adjusted by utilizing the weighted average velocity of its neighbors \((\hat{v}_{j},\hat{\omega}_{j})\) and the desired velocity \(v_{d}\) as a reference to adapt the flight direction. In addition, the angular velocity is mainly set to enhance the flight smoothness; thus, the angular velocity of the agent can only be determined by that of neighbors \((\hat{\omega}_{i}-\hat{\omega}_{j})^{2}\). The final layer, known as the rotation maneuver penalty \(pen(i=1,\ldots,N)\) for the formation, imposes a cost on turning maneuvers to adjust the turning rate of the agent in the event of a sudden change in velocity. This term can be utilized in scenarios where trajectories devoid of turning maneuvers are favored, even though it results in a longer time to reach the target. In this paper, a moment estimation method is assigned to this penalty term, and defined as follows:  
\[ \begin{array}{ll}pen&=E[\mathbb{U}^{2}]-E[\mathbb{U}]2\\ &=(\frac{1}{N_{s}}\sum\limits_{i=1}^{N}\mathbb{U}_{i}^{2})-(\frac{1}{N_{s}} \sum\limits_{i=1}^{N}\mathbb{U}_{i})^{2},\\ &=\frac{1}{N_{s}}\|\mathbb{U}\|_{2}^{2}-\frac{1}{N_{s}^{2}}\|\mathbb{U}\|_{1} ^{2}\end{array}  \tag{31}\]  
where \(E[\cdot]\) is mathematic expectation, and \(N_{s}\) is the number of sampling points. The squared distance vector \(\mathbb{U}\in\mathbb{R}^{N}\) is defined as follows:  
\[\mathbb{U}=(\|\hat{\omega}_{i2}-\hat{\omega}_{i1}\|_{2}^{2},\ldots,\|\hat{ \omega}_{iN_{s}}-\hat{\omega}_{iN_{s}-1}\|_{2}^{2}), \tag{32}\]In addition, based on the assumption that the graph information of each sampling point is independent, the gradient of _pen_ concerning \(\hat{\omega}_{i,j}\) is calculated as follows:  
\[ \begin{array}{ll}\frac{\partial pen}{\partial\hat{\omega}_{i,j}}&=\frac{4}{N_ {i}}(\mathrm{U}_{n-1}-\frac{\|U\|}{N_{i}})(\hat{\omega}_{i,j}-\hat{\omega}_{i,j -1})\\ &-\frac{4}{N_{i}}(\mathrm{U}_{n}-\frac{\|U\|}{N_{i}})(\hat{\omega}_{i,j+1}- \hat{\omega}_{i,j})\end{array} , \tag{33}\]  
where \(\mathrm{U}_{n}\in\mathrm{U}\), and the quasi-Newton method [32] is employed to solve the proposed optimization problem and would generate an optimal formation sequence to feedback the agent. Thus, the reward function (30) is designed to evaluate the current experience between the state and the action, where the agent can improve the strategies' learning effect \(\pi_{\theta}\) to instruct the next state \(s^{i}_{t+1}=\left(p,\
u,\omega\right)^{i}_{t}\) (Algorithm 2). ```
1 Initialize:\(\theta\) and parameters for policy \(\mathbf{\pi}_{\theta}\), and \(\phi\) for critic \(V(s)\), \(\mathbf{p}^{*}_{d}\forall n\).