esa-sceva/satcom-chunk-collection huggingface dataset:
# Insider Threat Detection Using Machine Learning Approach
###### Abstract
The number of features detected will usually be very large and may lead to the overfitting problem in machine learning when the number of features far exceeds the number of data examples. As such, we use a feature dimensionality reduction algorithm to select the most important features, and then use the data to train our detection system. Detecting anomalies aims to uncover any strange behaviour on the computer system and catch risky user behaviour before data are lost. It uses acceptable or usual behaviours in the system and quantifies them in an attempt to isolate irregular behaviour, categorising them as intrusive or not. The system will detect anomalous behaviour in an organisation by using machine learning algorithms that detect anomalies (abnormal activities) in user behaviour to ensure that the security of the system is maintained and information is kept safe. Thus, the aims of this study are threefold: (1) to develop an intelligent machine learning model utilising the most recent advances in machine learning approaches coupled with feature engineering to detect anomalies of potential insider threats based on the CERT insider threat dataset, (2) to ensure that the system pipeline is streamlined and designed to fit into any organisational log file structure, and (3) that the system has the capability to easily adapt to changes to log file structure. Such a system would be able to detect zero-day attacks in real time. The system has the ability to recognise anomalous user behaviour compared with the user baseline. Once the system is installed on the network, it can be used to monitor incoming user actions that are written on the corresponding logs and send appropriate messages to the security officer. A new user added to the system will require the training phase to be repeated in order to enrol the user into the system. An important consideration in cyber security nowadays is to build a system with strong guarantees of resiliency in response to attacks. Formal verification is an approach used to prove a system's compliance with the required security properties [5; 6; 7]. Machine learning algorithms may be verified during both the data preparation and training phase, as well as the verification of the machine learning system itself [8; 9]. Currently, research efforts are still far away from being able to verify the entire machine-learning pipeline [10]. However, the formal verification of random forests, SVMs, and NNs have been studied [10; 11; 12]. Before the system is deployed in an organisation, formal verification of the system should be conducted, both for the data pre-processing and training stage, as well as verification of the system itself. The rest of this paper is organised as follows. Section 2 reviews and evaluates existing anomaly detection and classification machine learning approaches. Section 3 describes the utilised CERT dataset and gives our proposed system's overall methodology. Section 4 shows and discusses the results of ML models used to solve the insider threat problem. Finally, we conclude with the conclusion in Section 5.
## 4 Experiments and Discussion
### Parameter Settings and Evaluation Metrics  
There are three parameters in the DA-RNN: the number of road segments input each time \(T\), the size of hidden states for the encoder \(m\), and the size of hidden states for the decoder \(p\). The optimization method is grid search. The search range of \(T\) is (5, 10, 15, 20, 25). We set \(m=p\) for simplicity. The search range of \(m=p\) is (16, 32, 64, 128, 256).  
Figure 3: The single route selected. Source: Baidu Maps.  
The prediction accuracy of bus arrival time \(t_{i}\) can be evaluated by the following metrics.  
(1) Root Mean Squared Error (RMSE):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(t_{i}-\hat{t}_{i}\right)^{2}} \tag{16}\]  
(2) Mean Absolute Error (MAE):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|t_{i}-\hat{t}_{i}\right|\]  
(3) Mean Absolute Percentage Error (MAPE):  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{t_{i}-\hat{t}_{i}}{t_{i}}\right| \times 100\%. \tag{18}\]
# Impact of Audio Delay and Quality in Network Music Performance +
## 6 Discussion
### Lessons Learned  
Our study of the effects of audio delay and audio quality on the QoE of NMP took place with actual musicians that had no previous experience with such studies or with remote performance. Some of the tips that we could offer to researchers undertaking similar studies are the following:  
* Start with a small pilot study. Studies where the participants must travel to your premises to run experiments are costly to organize and execute, therefore it is critical to first test your setup with fewer participants. This allows you to debug your experimental setup (does the audio and video get recorded properly?) and assess your questionnaire (are all the questions relevant?).
* Manage the expectations of participants. Explain to the participants what they need to know (you will perform with your own instrument, you will be recorded, there will be 20 repetitions of the performance, the experiment will take 30 min), without telling them what they should not know (we will be changing the audio delay) to avoid bias.  
* Fine-tune your experimental setup. Participants get easily bored during experiments; therefore, it is important that the questionnaires are short (otherwise, participants start providing random answers) and the setup between experiments is streamlined (otherwise, mistakes may be made, causing delays and the need for additional repetitions). This is where the pilot study helps.
* Record everything. Most usability studies rely on questionnaires, but the cost of also recording audio and video is low enough to allow recording everything (of course, the participants must provide informed consent for this). Having these recordings allows troubleshooting (why did I get odd answers to this questionnaire?) and may even allow different modes of analysis (such as audio tempo evolution, which we did not even consider before the experiments).
* Consider alternative experimental designs. Future work could unify delay and sampling rate variations in a single setup, allowing the application of MANOVA or factor analysis on a reduced set of dependent variables. This might reveal interactions between delay and quality that were impossible to notice in our study where two different setups were used to vary audio delay and audio quality.
## 3 Comparison of Finite Element Simulation of Almen Test and Almen Test
### FE Simulation of Almen Test
#### 3.1.2 Material Model  
In the previous studies [16; 33; 34; 35], strain rate dependency of the Almen strip on the deformation behavior was assumed negligible even though high strain rates were engaged in the Almen strip during shot peening. However, the strain rate will have effects on the deformation profile, i.e. arc height, of the Almen strip. Hence constitutive equation which relates stress and strain to the current conditions of strain rate is necessary to describe the deformation behavior of the Almen strip under shot peening. In this study, the Johnson-Cook model is used for high strain rate deformation.  
\[\sigma=\left[A+B(\overline{\epsilon})^{n}\right]\left[1+C\ln\left(\frac{ \dot{\overline{\epsilon}}}{\dot{\overline{\epsilon}}_{0}}\right)\right]\left[ 1-\left(\frac{T-T_{0}}{T_{m}-T}\right)^{m}\right] \tag{3}\]  
\(A\), \(B\), \(C\), \(m\) and \(n\) are material constants to be determined from deformation tests. \(\dot{\overline{\epsilon_{0}}}\) and \(T_{0}\) are reference values of strain rate and temperature, respectively. \(\dot{\overline{\epsilon}}\) and \(T\) is the strain rate and temperature, respectively. \(T_{\rm m}\) is the melting temperature of the material.  
The material parameters of the Johnson-Cook model used in this study were taken from the literature [16]. Figure 3 shows the strain rate dependent flow stress behavior of the Almen strip used in FE simulation.  
Figure 2: (**a**) Boundary condition of the Almen strip when a modified Almen strip holder is used; (**b**) Enlarged meshes at a part.
# A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks
## 4 Generic Fuzzy Inference System
### Application of Rule List  
The IF-then rules are defined and configured by the user to correspond with a logical fuzzy set to allow the system to calculate the degree of membership. The rules follow a forward chaining link wherein everything left of the THEN is named the antecedent and everything right of THEN is the consequent, indicative of forward chaining that uses existing facts and its rule base to derive new acts. Rules are comprised of subjective understanding and are defined to relay a logical statement, for example, the simple rule list below contains 3 If- then rules:  
* Rule 1: 'If temperature is warm AND humidity is high THEN climate is ok'
* Rule 2: If temperature is warm OR Humidity is low THEN climate is good'
* Rule 3: 'If temperature is not warm THEN climate is undesirable'  
In the above rule list, rule 1 contains 2 fuzzy linguistic sets, 'temperature is warm' and 'humidity is high', the rule then follows on to produce a fuzzy output of 'temperature is ok'. The fuzzy operators commonly used are the AND operation + OR operation, this is to allow a logical configuration of rule (other popular operators include min + max but they are irrelevant to this implementation). Rule 1 showcases the AND operator implying when the temperature is warm AND when the humidity is high then the climate is OK. Both statements must be true in order for the consequent to be true. Rule 2 shows the OR operator suggesting when the temp is warm or the humidity is low then climate is good, the OR operator allows more flexibility in the rule as it does not need a positive result (i.e., both antecedents do not need to be true) for the first set rather it allows both sets to not be dependant upon each other, as opposed to rule 1 wherein the climate is OK only if the temp is warm AND humidity is high. Rule 3 portrays a statement in which only 1 set is being used and still allowing a result.  
Figure 3: Fuzzification of Input variable (Temperature).
## 5 Simulations and Experiments
### Simulation Experiments  
The UWA environments were modeled from a point of Taean in the West Sea of S. Korea, and the UWA channel was modeled by Bellhop based on the environments. The doppler spread was set as 2 Hz, which was measured at the same location [32; 33]. The delay profile and sound velocity profile (SVP) of the UWA channel are shown in Figure 9.  
The BER obtained by the UWA channel in Figure 9 is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Real Dolphin** & **Proposed Method** & **Conventional Method (Ref. [13])** \\ \hline
3.72 & 3.81 & 2.81 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average MOS result for the 2 nd assessment.  
Figure 9: Simulation channel (**a**) delay profile, (**b**) sound velocity profile (SVP).  
In Figure 10, the red- and green-line denote the BERs of the proposed- and the conventional-methods, respectively. When the conventional TFSK signals passed through the multipath channels, the time spreading occurred, which caused the detection ambiguity for the zero-chirp rate whistles. Thus, the BER of the conventional method had an error floor at \(10^{-2}\), even though the SNR is large, e.g., SNR \(>-10\) dB. However, the BER of the proposed method did not have the error floor at \(\Delta t\) of 5 ms which satisfies the orthogonality requirement by Equation (7) and showed less than \(10^{-4}\) at a \(-5\) dB SNR, which was an acceptable value for the practical communications.
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 4 Results
### Preliminary Intercomparisons  
#### 4.3.1 VAD Lidars  
In this section, we use a data set that is the result of merging all lidars in VAD mode that fulfill the filtering criteria. Despite the CNR filtering, some data were found, where Bura measured near-zero wind speeds and 3E measured realistic values of more than 5 m\(\cdot\)s\({}^{-1}\). These outliers were found within periods of near-zero temperatures (a comparison of Bura 10 min wind speeds with those from the Hovsere meteorological mast at a similar level also showed these data). Therefore, 10 min intervals with an absolute difference of more than 5 m\(\cdot\)s\({}^{-1}\) between the wind from Bura and 3E at 150 m were excluded. The result is a data set with 4640 10 min means.  
Figure 8a panel shows a scatter plot of the 126-m ASL 10 min mean wind speeds estimated from the collocated vertical profiling lidars WLS66 and Alize in position 2, in order to get an idea of the quality of the data. The agreement is generally good (Pearson's linear correlation coefficient \(R^{2}\) is 1.00). Alize generally measures slightly higher wind speeds than WLS66 (\(\approx\)3%). The mechanism responsible for this is yet unknown but it might be due to both the difference in the lidars' measuring volumes (Alize has a larger probe volume but measures over a smaller horizontal measuring area than WLS66) and the flow inhomogeneity near the cliff.  
A similar comparison is illustrated in Figure 8b panel between 3E and Bura at 150 m ASL, which are located in pos. 4 and 5 and separated \(\approx\)550 m, where the wind speeds are also in good agreement (1% bias and \(R^{2}=0.99\)).
## 4 Results and Discussion
### Accuracy Analysis Using Confusion Matrix
#### 4.1.3 Naive Bayes Mainly Targets the Text Classification Industry  
It is primarily used for clustering and classification purposes [43]. The underlying architecture of naive Bayes depends on conditional probability. It creates trees based on their likelihood of happening. These trees are also known as Bayesian networks. As shown  
Figure 11: SVM confusion matrix.  
Figure 10: K-NN confusion matrix.  
in Figure 12, of the 184 cases that were tested, the test correctly determined 23 patients and 155 healthy subjects. Therefore, the accuracy of the trial was equal to 96.74%.  
1.4 Decision Tree (DT) Is a Supervised ML Method to Solve Classification, Prediction, and Feature Selection Problems  
It aims to predict the target class based on the rules learned from the specified dataset. As a result of the 184 cases shown in Figure 13 that were tested, the test correctly determined 35 patients and 146 healthy subjects. Therefore, the accuracy of the trial was equal to 98.37%.  
1.5 Random Forest (RF) Is a Supervised Machine Learning Algorithm Used Widely in Classification and Regression Problems  
It builds decision trees on different samples and takes their majority vote for classification and their average in case of regression. As presented in Figure 14, of the 184 subjects tested, the test correctly determined 29 patients and 152 healthy cases. Therefore, the accuracy of the test was equal to 181 divided by 184 (98.37%).  
Figure 12: NB confusion matrix.  
Figure 13: DT confusion matrix.  
4.1.6. Linear Discriminant Analysis Is a Statistical Technique That Can Classify Individuals into Mutually Exclusive and Exhaustive Groups Based on Independent Variables [44]  
In this model, as shown in Figure 15, of the 184 cases tested, the test determined 24 patients and 153 healthy subjects correctly. Therefore, the accuracy of the trial was equal to 177 divided by 184 (96.19%).
# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Machine Learning
#### 3.1.3 Predict and Minimize Transportation Emissions  
Many cars still use fossil fuels. Fossil fuels are essential to our lives, but they pollute the environment. In this section, we review papers investigating prediction and minimization of vehicle emissions.  
In [33], the authors proposed a three-layer perceptron neural network to learn and predict carbon emissions. The authors exploited five features: global positioning systems (GPS), carbon emissions, roads, POIs, and meteorology. The prediction results were good compared to Gaussian naive Bayes, linear regression, logistic regression, stacked denoising autoencoder, and deep belief network. Moreover, the authors of [34] estimated fuel consumption and emissions by exploiting GPS data. They proposed an N-dimensional representation to analyze and visualize GPS data. Another study [35] demonstrated that pavement quality, traffic volume, and the climate of roads affect energy consumption and CO\({}_{2}\) emissions. The proposed ranking algorithm was called recommend paths.  
Furthermore, the authors of [36] developed an urban atmospheric dataset by installing a pollution measurement platform on Google Street View vehicles and repeatedly sampling all distances in an area of about 30 km\({}^{2}\). An analysis of these collected data revealed that the map of annual weekly NO, NO\({}_{2}\), and black carbon results had a steeply persistent pattern of contamination of up to 5 to 8 times. The authors of [37] proposed a system that recommends routes and vehicle speeds that minimize NOx emissions and use on-demand route optimization. Finally, In [38], a spatial econometric model was used to study the impact of smart transportation on CO\({}_{2}\) emissions in China.
## 3 Results
### Analysis of Research Fields
#### 3.4.2 Keyword Co-Occurrence and Clustering Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial** & **Cluster** & **Keyword (Frequency)** & **Representative Research Direction** \\
**Number** & & & \\ \hline \multirow{4}{*}{\#0} & \multirow{4}{*}{Vegetation Indices} & Machine Learning, Unmanned Aerial Vehicle (UAV), Biomass, Index, Yield, Chlorophyll & Crop monitoring and health assessment. Core application: Agriculture and ecological monitoring are the dominant focuses. \\  & & & \\ \hline \multirow{4}{*}{\#1} & \multirow{4}{*}{Structure From Motion} & UAV, Photogrammetry, Lidar, Structure From Motion, Accuracy, Models, Segmentation, Height, Topography, Point Clouds, Performance, River, Lidar Data, Image Processing, UAV Imagery, Density, Reconstruction, Tree Species Classification, Terrestrial, Digital Photogrammetry, Infrared Thermography, Terrain & Dominant 3D Modeling Technology: The frequency of SfM significantly exceeds that of LiDAR, indicating that cost-effectiveness remains the primary factor in technology selection, rather than absolute accuracy. \\ \hline \multirow{4}{*}{\#2} & Object-Based Image & Random Forest, Management, Dynamics, Variability, Scale, Airborne Lidar, Water, Patterns, Biodiversity, Support Vector Machine, Landscape, Plants, Google Earth Engine & Image segmentation and object-based classification: Extended applications of traditional remote sensing image analysis methods. \\ \hline \multirow{4}{*}{\#3} & Remote Sensing & Remote Sensing, Unmanned Aerial Vehicle, Classification, Imagery, Precision Agriculture, Model, Leaf Area Index, Forest, Management, Crop & Fundamental remote sensing models and monitoring methods: A concentrated manifestation of application-driven research, with precision agriculture being the strongest demand driver. \\ \hline \multirow{4}{*}{\#4} & \multirow{4}{*}{Deep Learning} & Vegetation Index, Object Detection, Deep Learning, Feature Extraction, Chlorophyll Content, Computer Vision, Unmanned Aerial Vehicles, Fusion, Semantic Segmentation, Image Segmentation, Image Classification, Convolutional Neural Networks, W-Net, Spatial Resolution, Information, Land Cover, Time Series, Framework, Vegetation Mapping & Intelligent recognition and automated data processing: High-frequency terms are primarily established computer vision (CV) models, with a notable absence of new model terminology specifically adapted to remote sensing characteristics. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Clusters and high-frequency words of 10 UAV-RS-related research.
## 4 Discussion  
### Comparison of Classification Accuracy  
When MLC was used for supervised classification, the accuracy of classification results obtained from different types of NDVI datasets varied greatly. Using raster validation samples for accuracy evaluation can reflect many problems. The overall accuracy of single-scene NDVI data was 52.32%, and the Kappa coefficient was 0.36. The classification effect was not good due to the lack of time-series information and the multiple values of the same type of land cover in a single image, resulting in excessive misclassification. Among them, the classification effect of Water was the best, with an F1-score of 0.72. Due to the asymmetry between actual ground truth and input samples that is mainly caused by the low area ratio and the large spectral difference, the classification effect of tidal flats was the worst, with an F1-score of only 0.01. Considering that the vegetation cover varies greatly in different periods, the mean NDVI of 12 months was used as the classification basis. Compared with single-scene NDVI, the classification accuracy of the mean NDVI was significantly improved, with an overall accuracy of 62.15% and Kappa coefficient of 0.45. Among them, the F1-score of agricultural land was 0.67, followed by buildings, vegetation, and water, but there was significant room for improvement. In order to give full play to the vegetation-sensitive characteristics of NDVI and more accurately reflect the change in vegetation cover in a period of time, the 12-month time-series NDVI was used for supervised classification. The overall accuracy of this method is 80.40%, and the Kappa coefficient is 0.71. Among them, buildings and agricultural land have achieved great classification effects, and the classification effect of difficult-to-distinguish tidal flats has also been significantly improved.  
In view of the advantages of time-series NDVI, this study carried out change detection based on it. The changed land was regarded as an independent sample type. MLC and U-Net were, respectively, used for classification. Due to the complexity and diversity of the variable land-cover classes and the difficulty of feature extraction, the overall accuracy of the MLC decreased to 69.65%, and there were a lot of \"excessive\" phenomena. However,  
Figure 10: Confusion matrix results at 902-point validation data. (**a**) MLC confusion matrix. (**b**) U-Net confusion matrix.  
the deep-learning method could retrieve deeper feature information so as to effectively distinguish the \"Changed land\" from other land classes, with a total accuracy of 78.05%.