esa-sceva/satcom-chunk-collection huggingface dataset:
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 4 Experimentation and Results
### Comparison of Compression Ratio and Nodes Number  
To further identify the advantages and disadvantages of different methods, we counted the number of nodes compressed with different data and different error ranges. The number of nodes will visually show the compression efficiency.  
In Tables 5 and 6, volume reduction ratio can be defined as follows:  
\[VolumeReductionRatio=\frac{sum}{sum_{0}} \tag{13}\]  
where \(sum\) means the size of data after being compressed and \(sum_{0}\) means the initial data size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline _eps_ & **Method** & **Volume Reduction Ratio** \\ \hline \multirow{4}{*}{0.005} & SbMBR tree & 0.000236 \\  & brute R-tree & 0.002945 \\  & quadtree & 0.000309 \\ \hline \multirow{4}{*}{0.0046} & SbMBR tree & 0.000254 \\  & brute R-tree & 0.002963 \\  & quadtree & 0.000527 \\ \hline \multirow{4}{*}{0.0042} & SbMBR tree & 0.000327 \\  & brute R-tree & 0.002981 \\  & quadtree & 0.001018 \\ \hline \multirow{4}{*}{0.0038} & SbMBR tree & 0.000563 \\  & brute R-tree & 0.003036 \\  & quadtree & 0.001290 \\ \hline \multirow{4}{*}{0.0034} & SbMBR tree & 0.000727 \\  & brute R-tree & 0.003127 \\  & quadtree & 0.001345 \\ \hline \multirow{4}{*}{0.003} & SbMBR tree & 0.000763 \\  & brute R-tree & 0.003200 \\  & quadtree & 0.001727 \\ \hline \multirow{4}{*}{0.0026} & SbMBR tree & 0.000945 \\  & brute R-tree & 0.003363 \\  & quadtree & 0.002072 \\ \hline \multirow{4}{*}{0.0022} & SbMBR tree & 0.001490 \\  & brute R-tree & 0.003418 \\  & quadtree & 0.002509 \\ \hline \multirow{4}{*}{0.0018} & SbMBR tree & 0.001909 \\  & brute R-tree & 0.003654 \\  & quadtree & 0.002800 \\ \hline \multirow{4}{*}{0.0014} & SbMBR tree & 0.002636 \\  & brute R-tree & 0.003890 \\  & quadtree & 0.003236 \\ \hline \multirow{4}{*}{0.001} & SbMBR tree & 0.003581 \\  & brute R-tree & 0.004636 \\  & quadtree & 0.005163 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Volume reduction ratio of different methods under the same sparse data.  
the brute R-tree, it can be seen via the sparse and dense data that the number of nodes of the brute R-tree generally remains stable for different error ranges, with more nodes in the sparse data and fewer nodes in the dense data. The quadtree has a better compression ratio at a larger error tolerance. However, when the error tolerance becomes smaller, the number of nodes of the quadtree immediately grows massively, far exceeding the brute R-tree and SbMBR tree. The SbMBR tree has the best performance in sparse data, while guaranteeing the construction speed and compression ratio in dense data. Overall, it is the most stable and efficient.
# Research on Performance Prediction Model of Impeller-Type Breather
## 4 Data Analysis
### Significance of Factors and Optimal Method
#### 4.2.1 Characteristics of Separation  
Separation efficiency of the breather is defined as the percentage of the mass of oil droplets separated from the breather in the mass of oil droplets entering the breather, which is an important index to evaluate the performance of the breather. Range analysis was made on the separation efficiency in the calculated results to determine the significance and optimal level of the various factors, as shown in Table 2.  
As shown in Table 2, the rotating speed has the largest influence on separation efficiency among all operating conditions, followed by ventilation rate and average inlet particle size. The temperature and oil/gas ratio have little influence. Number of blades has the largest influence among all structural factors, followed by external diameter of the blades and thickness of the breather. Blade angle and diameter of vent hole have little influence.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 3 Methodology
### Fuzzing and Package Analysis  
Based on the labeling and filtering results, we determined which IDs correspond to data fields containing bits with potential for body behavior. In this phase, fuzzing and packet analysis are used to collect candidate messages that can trigger a vehicle body response.  
First, messages with data fields containing bits labeled B are used as base messages. The approach described in this paper utilizes the idea of fuzzy testing by fuzzifying the bits marked B in the base message. In contrast, the non-candidate bits are left unchanged from the original data, thus allowing the test message to be obtained. An example of test message generation is given in Figure 6. First, based on the tagging and message filtering results, the ID corresponding to the message with the B tag in the data field is found. A real CAN message corresponding to this ID is randomly selected from the obtained messages. Afterward, the bits marked with B are fuzzed (randomized or traversed) according to the message marking results. Finally, the bits not marked with B are left unchanged, and a set of test messages corresponding to this ID is generated. Once the set of test messages has been obtained, the test messages are sent sequentially to the vehicle's CAN network via the OBD-II interface using the same cable combinations as used in the data collection phase. After each test message has been sent, we record each message that triggers the car's behavior and the body response it triggers. After this operation, we can obtain candidate messages that can trigger a specific response from the vehicle.  
Although the fuzzed test messages can trigger most car body responses, there are still some car body behaviors that cannot be triggered by the test messages. The reason for this is that these body behaviors are controlled by the driver rather than by CAN messages, e.g., seat belts and vehicle lights, and CAN messages only characterize the state of the part. In order to obtain a description of the state of these messages, the packet analysis method is used to obtain candidate messages. In order to capture CAN messages containing descriptions of specific states of the body, specific operations are performed for short periods of time, with the CAN message recorded in the CAN network during these periods. Based on the screening and marking results obtained in the previous stage, the CAN traffic captured during a specific operation is screened, and the number of flips  
Figure 6: Test message generation process.  
is calculated based on the bit marked B. If the number of flips matches the number of operations, the marked bit characterizes the status of the part being operated. Since CAN messages usually use 1 to represent the dominant result, the message corresponding to the marker bit of 1 is recorded as a candidate message.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
Finally, our approach was designed to detect botnets in more robust types of IoT devices, which usually have an operating system for constrained devices (e.g., Raspbian and Android) and at least 512 MB of RAM (Random Access Memory). These devices are found today in many homes and offices being used in diverse applications. Examples of this type of device are domestic routers, Raspberry Pi ([[https://www.raspberrypi.org/](https://www.raspberrypi.org/)]([https://www.raspberrypi.org/](https://www.raspberrypi.org/))), Odroid ([[https://www.hardkernel.com/](https://www.hardkernel.com/)]([https://www.hardkernel.com/](https://www.hardkernel.com/))), Google's Chromecast ([[https://store.google.com/product/chromecast](https://store.google.com/product/chromecast)]([https://store.google.com/product/chromecast](https://store.google.com/product/chromecast))) and Roku TV ([[https://www.roku.com/en-gb/](https://www.roku.com/en-gb/)]([https://www.roku.com/en-gb/](https://www.roku.com/en-gb/))). To test our approach, we used the experimental setup created by [21]. The dataset contains data collected from an IoT device, a Raspberry Pi, which was infected by seven different botnets (Hajime, Aidra, BashLite, Mirai, Doflo, Tsunami, and Wroba). The Raspberry Pi emulated the behaviour of a surveillance camera and a multimedia centre. Four one-class classifiers were evaluated, namely Elliptic Envelope (EE), Isolation Forest (IF), Local Outlier Factor (LOF), and One-class Support Vector Machine (OSVM). In our tests, the proposed approach presented a high performance in detecting botnets, considering the different device profiles and the seven botnets, whilst it had a low impact in the host's utilisation of CPU, memory and energy consumption. This paper extends our previous work on detection of IoT botnets [22]. Unlike our previous work, which relied only on OSVM, in this work, we tested three additional algorithms. Besides, we proposedand implemented a novel agent-manager architecture based on HTTPS, detailing its building blocks and how they interact with each other. This work is organised as follows. Section 2 presents the related work, while Section 3 introduces background information about one-class classifiers. In Section 4, the description of the proposed host-based detection approach is presented. Section 5 presents the results and the discussion about them. Lastly, Section 6 provides conclusions and future work.
# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 4 Use of ECG with AI for Clinical Analysis and Diagnosis
### Use of ML with ECG for Heart Health Diagnosis
Third, different research papers on applying machine learning algorithms to diagnose cardiac disorders may present distinct circumstances, such as patient demographics or experimental setups. Direct comparisons might be incorrect or misleading due to these contextual variations, which substantially influence performance indicators and results. Although the research is ongoing and researchers produce excellent, accurate models, real-life implementation and usability in medical setups remain in question. Machine learning algorithms have been primarily used for heart rate-related problems and other heart diseases such as myocardial infarction or general heart diseases such as \"yes\" or \"no\" scenarios. Here are some potential paths for future study in the use of ECG for diagnosis using machine learning for heart disease diagnosis:  
* Develop more robust machine learning algorithms: Often, the machine learning algorithms used today to diagnose ECGs are not resistant to signal noise and distortions. This is a significant drawback since real-world ECG recordings frequently contain noise and artifacts. Developing more robust machine learning algorithms that can better tolerate noise and artifacts should be the primary goal of future research. Figure 4: Machine learning framework using ECG data. * Use more extensive and diverse datasets: Most research on machine learning for ECG diagnosis has relied on relatively small datasets. The results' generalizability is so constrained. Future research should use more extensive and varied datasets to increase the generalizability of the findings. A benchmark dataset should contain a sufficient number of features that are relevant to the class variable as well as a large number of samples from varied characteristics of patients. * Integrate machine learning with other clinical data: Clinical data other than ECG signals can also be utilized to identify cardiac disorders. Information from patient medical records, lab results, and imaging investigations may also be utilized. Future studies should look at ways to better diagnose heart disease by combining machine learning with other clinical data. * Develop mobile and wearable ECG devices: ECG equipment is becoming more wearable and portable. This enables the collection of ECG data outside of a hospital environment. Future studies should look at ways to enhance the early diagnosis of cardiac problems using mobile and wearable ECG sensors. * Make machine learning algorithms more interpretable: Black boxes characterize many current machine learning techniques for ECG diagnosis. As a result, it is challenging to comprehend how the algorithms generate their predictions. Future studies should concentrate on creating interpretable machine learning algorithms so physicians can better comprehend the outcomes of the forecasts.
# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
## 4 Results
### Training Session Results  
Both training sessions reached high mean Average Precision (mAP) and low average loss (a higher mAP) is better, and a lower average loss is better) values. Figure 9 contains two charts of the resulting training sessions of the YOLOv4-tiny and YOLOv4 models [10; 19].  
(a) YOLOv4-tiny's training session lasted 7 h at 30,000 batches. The highest mean average precision achieved was 85.64%, whilst the average IoU was 47.90%. (b) YOLOv4's training session lasted 64 h at 30,000 batches. During training, the highest mean average precision of the model based on the test dataset was 91.28%, whilst the average IoU was 65.10.  
We developed an application capable of calculating the Intersection over Union (IoU) and accuracy metrics of our trained models using a validation dataset, Figure 10. Additionally, the application provides visual overlays of the groundtruth information on top of the detected objects.  
The training charts of YOLOv4-tiny and YOLOv4 in Figure 9a,b provide a valuable visual understanding of the effectiveness of our dataset for both models, based on the rate at which the average precision and average loss changed. Both models were trained with a total of 30,000 max batches.  
Figure 10: Intersection over union examples using YOLOv4-tiny and YOLOv4. Comparison of a model’s prediction bounding box with the ground truth’s bounding box. The percentage represents the IoU calculated using DarkHelp and OpenCV. Frame location at Koronisia, Nisida Balla (location at Koronisia, Nisida Kamakia (GPS Pin)), received on 23 March 2023. (a) IoU comparison results using the **YOLOv4-tiny** model. Average IoU: 86.93%. (b) IoU comparison results using the **YOLOv4** model. Average IoU: 90.22%.  
Figure 9: Training session charts of **YOLOv4** and **YOLOv4-tiny**. Training session on 6 April 2023. (a) Training session of YOLOv4-tiny model; (b) training session of YOLOv4 model.  
During the initial phase of the training session, we can observe a significant decrease in the average loss. That was due to a scaling factor given by the configuration files of the model. In the first few batches, the learning rate was gradually scaled up, accelerating the rate at which the model learned.  
These steps were also an important milestone for the model, to change its learning behaviour and find the best-possible result. Similar to the initial phase of the training session, it scaled the learning rate based on the number of batches [12]. Throughout the entirety of both training sessions, the mAP showed a gradual increase, depicting a progressive improvement in accurately detecting objects until it converged to a stable state.  
Further advancing into the training session, beyond 30,000 batches increased the risk of the model encountering the overfitting problem [32].
# Improved Detection and Response via Optimized Alerts: Usability Study
### Background
A focus on data-driven security operations addresses and enables discussions related to challenges that security analysts face, as well as opportunities for improvements such as applied machine learning and visualization. The specific business problem is: organizations risk data breach, loss of valuable human resources, reputation, and revenue due to excessive security alert volume and a lack of fidelity in security event data. A Cloud Security Alliance survey illuminated the problem further. With an average of two billion transactions a month at the average enterprise, IT security professionals say that 40.4% of alerts received lack actionable intelligence to investigate, and another 31.9% report ignored alerts due to false positives [12]. [PERSON] that as much as 25% of a security analyst's time is spent processing false-positive alerts, commonly erroneous security alerts or false indicators of confidence, before focusing on true-positive findings. Every hour an analyst spends on the job, 15 min are wasted on false positives, leading the typical organization to waste between 286 and 424 h per week on false positives [13]. In addressing this problem, improving the efficiency of security analysts can be helpful. In a survey that examines specific areas where high- and low-performing SOCs diverge, with a focus on the challenges both groups struggle with, Ponemon found key data points in the differences and similarities between the two classes of SOCs. Even highly effective SOCs suffer from job-related stress affecting security analysts, where 55% of respondents from high-performing SOCs rated their stress level as a 9 or 10 on a 10-point scale. Twenty-two percent of survey respondents rated their SOC as ineffective, citing a lack of visibility into the attack surface and a lack of timely remediation as the core factors [14]. To examine opportunities for increased efficiencies, this study used a survey questionnaire based on the Technology Acceptance Model (TAM) to test for statistical differences between security analysts' responses regarding perception and usability of text-based alert output (TAO) versus visualized alert output (VAO).
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
## 2 Methodology  
### LSSVM Model  
With the increase in sample data and the complexity of sample relationships, traditional SVMs tend to lose noise immunity, resulting in a decrease in computational speed. To address this issue, [PERSON] et al. [26] proposed the LSSVM as an improvement over the original SVM. The LSSVM replaces the inequality constraints in the SVM with equations and utilizes a least-squares linear equation as the loss function. This modification transforms the training process from quadratic programming to solving a system of linear equations, thereby reducing computational complexity and increasing computational speed.  
Common nonlinear kernel functions include the radial basis kernel function (RBF), polynomial kernel function, and sigmoid kernel function [27]. The RBF was chosen for this study due to its superior performance in practical applications. The RBF is known for its ability to handle nonlinear relationships and its robustness in various scenarios, making it more suitable for the complex nature of the data in this research compared to other kernel functions such as polynomial or sigmoid functions. The basic principles and operational steps of the LSSVM are described in the literature [28].
## 3 Feature Extraction
### Spatial Domain Feature Extraction
This matrix reflects the degree of correlation among different channels and also illustrates the data distribution within the same channel. As shown in Figure 2, this study has chosen to employ the Pearson correlation coefficient matrix for analyzing the correlation among various channels of EMG signals. The Pearson correlation coefficient gauges the strength and direction of the linear relationship between two variables, calculated through the product of covariance and the standard deviations of the two variables. By leveraging the elements of the covariance matrix, we can determine the covariance between any two variables, subsequently deriving their Pearson correlation coefficient. This correlation assists in quantifying the linear relationships between variables, providing a comprehensive understanding of the associations between data structures and features. In sEMG analysis, it can be employed to investigate the synergy among different muscles, contributing to the understanding of physiological features related to movement and posture control.
## Appendix A Phase-Field Fracture Models  
### Cohesive Phase-Field Fracture Model  
The cohesive phase-field model (CPFM) releases fracture energy progressively compared to the instantaneous dissipation in standard models. The CPFM is effective for quasi-brittle materials and is insensitive to the length scale parameter \(l_{c}\).  
The damage degradation function in CPFM is defined as  
\[\omega(\phi)=\frac{(1-\phi)^{2}}{(1-\phi)^{2}+a_{1}\phi+a_{1}a_{2}\phi^{2}+a_{1 }a_{2}a_{3}\phi^{3}}, \tag{11}\]  
with coefficients \(a_{1}\), \(a_{2}\), and \(a_{3}\) given by  
\[\begin{cases}a_{1}=\frac{2 EG_{c}}{\sigma_{u}^{2}\pi l_{c}},\\ a_{2}=2\left(-2k_{0}\frac{G_{c}}{\sigma_{u}^{2}}\right)^{\frac{2}{3}}-\left(p +\frac{1}{2}\right),\\ a_{3}=\begin{cases}0&\text{if}\quad p>0,\\ \frac{1}{2}\left[\frac{1}{8}\left(\frac{d_{c}\sigma_{u}}{G_{c}}\right)^{2}-(1+ a_{2})\right]&\text{if}\quad p=0,\end{cases}\end{cases} \tag{12}\]  
where \(E\) is the elasticity modulus, \(p\) is an exponent related to material properties, and \(k_{0}\) and \(d_{u}\) represent the initial slope and ultimate crack opening, respectively.  
The CPFM crack geometric function is defined as  
\[\alpha(\phi)=2\phi-\phi^{2}, \tag{10}\]  
resulting in \(c_{0}=\pi\).  
The history variable is defined as  
\[\begin{cases}H(\mathbf{x},t)=\max\Bigl{\{}\psi_{\text{eq}}^{0},\max\psi_{\text{eq}}( \epsilon(x,t))\Bigr{\}},\\ \psi_{\text{eq}}^{0}=\frac{1}{2E}\sigma_{u}^{2},\end{cases} \tag{11}\]  
where \(\psi_{\text{eq}}^{0}\) is a threshold for damage initiation.
## 3 The Deployment Models
### Inspection Using Cryptographic Functions  
The second category of the deployment models is inspection using cryptographic functions. We can further classify it into two sub-categories: TLS inspection with a private session key, and TLS inspection with searchable encryption [58; 59; 60].
## 3 Results and Discussion
### Cross-Validation (CV) and Partial Least Squares Regression  
To better understand the experimental results in this section, we will discuss them separately. Firstly, we will discuss the results obtained in leave-\(k\)-out CV, secondly we will analyze the results obtained by splitting the data set into calibration/validation subsets (80/20%, respectively), and finally the implications of the potential misinterpretation of the results will be addressed.