esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The HEPD-02 Detector  
The High-Energy Particle Detector realized for the CSES-02 satellite (HEPD-02) is designed to detect electrons, protons and light nuclei in the energetic ranges from 30 to 200 MeV for protons and from 3 to 100 MeV for electrons.  
The detector's dimensions are 403.6 \(\times\) 530 \(\times\) 382.5 mm\({}^{3}\), and its mass is 50 kg. The power consumption, at the highest reachable trigger rate, is 43 W and the data budget is less than 100 Gbit per day.  
It is structured in four main components:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Payload Name** & **Observation Target** \\ \hline \multirow{2}{*}{Particle energy} & High-Energy Particle Detector (HEPD) & Electrons: 3 to 100 MeV \\  & Medium Energetic Electron Detector (MEED) & Protons: 30 to 200 MeV \\ \cline{2-3}  & Electric Field Detector (EFD) & Electrons: 25 keV to 3.2 MeV \\ \hline \multirow{4}{*}{Electromagnetic field} & High-Precision Magnetometer (HPM) & Magnetic field: 10 Hz to 20 kHz \\ \cline{2-3}  & Search Coil Magnetometer (SCM) & Magnetic field: 10 Hz to 20 kHz \\ \hline \multirow{4}{*}{In situ plasma} & Plasma Analyzer Package (PAP) & \
\begin{tabular}{c} Composition: H\({}^{+}\), He\({}^{+}\), O\({}^{+}\) \\ \(N_{i}\); 5 \(\times\) 10\({}^{2}\) to 1 \(\times\) 10\({}^{7}\) cm\({}^{-3}\) \\ \(T_{i}\); 500 to 10,000 K \\ \end{tabular} \\ \cline{2-3}  & Langmuir Probe (LP) &
\begin{tabular}{c} \(N_{i}\): 5 \(\times\) 10\({}^{2}\) to 1 \(\times\) 10\({}^{7}\) cm\({}^{-3}\) \\ \(T_{i}\); 500 to 10,000 K \\ \end{tabular} \\ \hline \multirow{4}{*}{Plasma profile construction} & GNSS Occultation Receiver & TEC by transmit VH/U/L signal \\ \cline{2-3}  & Tri-Band Beacon & TEC by transmit VH/U/L signal \\ \cline{1-1} \cline{2-3}  & Ionospheric & O\({}_{2}\) 135.6 nm and N\({}_{2}\) LBH airglow \\ \hline \hline \end{tabular}
\end{table}
Table 1: CSES-02 instruments and observation targets.
## 3 System Modeling
### Block Diagram and Transfer Function of the Valve-Controlled Actuator System  
Equations (3), (8) and (9) can be transformed into the following form by Laplace transform, respectively.  
\[\left\{\begin{array}{l}\dot{M}_{\mathrm{a}}(s)-\dot{M}_{\mathrm{b}}(s)=2c_{1}x _{\mathrm{v}}(s)-c_{2}[p_{\mathrm{a}}(s)-p_{\mathrm{b}}(s)]\\ \dot{M}_{\mathrm{a}}(s)-\dot{M}_{\mathrm{b}}(s)=\frac{1}{\kappa RT_{\mathrm{s} }}\big{\{}V_{0}s[p_{\mathrm{a}}(s)-p_{\mathrm{b}}(s)]+\kappa A_{\mathrm{p}}d_{ \mathrm{f}}p_{0}s\theta(s)\big{\}}\\ A_{\mathrm{p}}d_{\mathrm{f}}[p_{\mathrm{a}}(s)-p_{\mathrm{b}}(s)]=\Big{(} \frac{1}{2}m_{\mathrm{p}}d_{\mathrm{f}}^{2}+J\Big{)}s^{2}\theta(s)+\frac{1}{2} d_{\mathrm{f}}F_{\mathrm{f}}(s)\end{array}\right. \tag{13}\]  
From the above equations, we can get the block diagram of the valve-controlled actuator systems shown in Figure 5, where \(\dot{M}_{1}(s)=\dot{M}_{\mathrm{a}}(s)-\dot{M}_{\mathrm{b}}(s)\) denotes the mass flow rate difference and \(p_{1}(s)=p_{\mathrm{a}}(s)-p_{\mathrm{b}}(s)\) stands for the pressure difference. This block diagram also clearly describes the effects of \(c_{1}\), \(c_{2}\) and \(F_{\mathrm{f}}\) on the system. In order to facilitate analysis and calculation, we make the following simplification of the system model.  
When \(F_{\mathrm{f}}=0\), we can obtain the transfer function of the displacement \(x_{\mathrm{v}}\) to the rotation angle \(\theta\), and it can be written as the following standard form.  
\[\frac{\theta_{x}(s)}{x_{\mathrm{v}}(s)}=\frac{K_{\mathrm{T}}\omega_{\mathrm{n }}^{2}}{s(s^{2}+2\zeta\omega_{\mathrm{n}}s+\omega_{\mathrm{n}}^{2})}, \tag{14}\]  
where its gain \(K_{\mathrm{T}}\), natural frequency \(\omega_{\mathrm{n}}\), and damping ratio \(\zeta\) are expressed as follows, respectively.  
\[\left\{\begin{array}{l}K_{\mathrm{T}}=\frac{2c_{1}RT_{\mathrm{s}}}{A_{ \mathrm{p}}d_{\mathrm{f}}p_{0}}\\ \omega_{\mathrm{n}}=\sqrt{\frac{2\kappa A_{\mathrm{p}}2^{2}d_{\mathrm{f}}^{2} p_{0}}{(m_{\mathrm{p}}d_{\mathrm{f}}^{2}+2J)V_{0}}}\\ \zeta=\frac{1}{2}c_{2}\kappa RT_{\mathrm{s}}\sqrt{\frac{m_{\mathrm{p}}d_{ \mathrm{f}}^{2}+2J}{2\kappa A_{\mathrm{p}}^{2}d_{\mathrm{f}}^{2}p_{0}V_{0}}} \end{array}\right.. \tag{15}\]  
In addition, we let \(x_{\mathrm{v}}(s)=0\), then we obtain the transfer function of the friction force \(F_{\mathrm{f}}\) to the rotation angle \(\theta\).  
\[\frac{\theta_{f}(s)}{F_{\mathrm{f}}(s)}=-\frac{\frac{K_{\mathrm{T}}\omega_{ \mathrm{n}}^{2}}{4c_{1}A_{\mathrm{p}}}\Big{(}\frac{V_{0}}{\kappa RT_{\mathrm{s} }}s+c_{2}\Big{)}}{s(s^{2}+2\zeta\omega_{\mathrm{n}}s+\omega_{\mathrm{n}}^{2})}. \tag{16}\]  
Therefore, the total output of the valve-controlled actuator system is  
\[\theta(s)=\theta_{x}(s)+\theta_{f}(s)=\frac{K_{\mathrm{T}}\omega_{\mathrm{n} }^{2}x(s)-\frac{K_{\mathrm{T}}\omega_{\mathrm{n}}^{2}F_{\mathrm{f}}(s)}{4c_{ 1}A_{\mathrm{p}}}\Big{(}\frac{V}{\kappa RT_{\mathrm{s}}}s+c_{2}\Big{)}}{s(s^{2 }+2\zeta\omega_{\mathrm{n}}s+\omega_{\mathrm{n}}^{2})}. \tag{17}\]  
Figure 5: Block diagram of the valve-controlled actuator systems.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method  
### Laser Scanners  
In this study, a commercial TLS and three handheld MLSs (MLS-01, MLS-02, and MLS-03) are used for experiments. TLS-01 was used for generating reference data, and the detailed method will be described in Section 3.4.1. MLS-02 and MLS-03 are the same model, but MLS-02 has been in use for over two years, whereas MLS-03 is newly purchased.  
The model names of the scanners are intentionally withheld; however, they are MLS devices that are widely used in the field. Our goal is to present a repeatable method for analyzing error distribution patterns in MLS point clouds and to confirm whether each device exhibits a unique pattern. The proposed analysis and experiments are reproducible regardless of the scanner model. However, accuracy-related specifications play a crucial role in our analysis of error distribution patterns; therefore, we have provided them in Table 1.  
Table 1 summarizes the key metrics related to accuracy. Point (range) accuracy refers to the systematic distance error of a single laser pulse, indicating how far the average measurement deviations from the actual surface. 3D accuracy incorporates angular errors in addition to range errors, representing the total 3D positional deviation that may occur at a given distance, particularly in the context of TLS performance. Relative accuracy refers to the final point cloud accuracy of MLS, encompassing all accumulated error throughout the SLAM process. This study primarily focuses on relative accuracy. Rather than analyzing internal SLAM components or individual error stages specified by the manufacturer, our interest lies in how the accumulated error manifests as bias in the final MLS point clouds and how accurately it reproduces actual surfaces.
###### Abstract  
The imaging technique provides an efficient non-intrusive way for studying local sediment transport with a low rate in open-channel flows. It aims to track all sediment trajectories above the background consists of similar particles (i.e., top-view images of the channel). For this area of interest, currently used imaging methods can be summarized as a two-step framework for identifying and matching active bed-load particles. While effective against a simple and clear background, the two-step approach fails to yield accurate and uninterrupted Lagrangian paths for the sediment particles against complex image background consists of similar particles. This study presents a three-step approach to improve the accuracy of the tracking method. The first two steps of this approach based on image subtraction, centroid exaction and Kalman filter entail improvements to those of the classic methods. The third step based on the nearest neighbor algorithm and spline interpolation manage to identify broken chains and connect them to reconstruct uninterrupted Lagrangian paths of the sediment particles. The verification against simulated images and experimental data shows that the improved three-step approach yields more accurate estimation of bed-load kinematics than the classic two-step method.  
imaging technique; particle tracking method; Kalman filter; global connection sediment transport +
Footnote †: journal: Applied Sciences
# Clustering of a Health Dataset Using Diagnosis Co-Occurrences
## 2 Materials and Methods
### Statistical Analysis: A New Hierarchical Agglomerative Clustering Algorithm
#### 2.2.2 New Quality Indicators
Furthermore, as their comparison is meaningful, we can introduce a global indicator for the clustering algorithm, namely the Membership Ratio (MR) criterion:  
\[\text{MR}(\text{S})=\frac{\text{RR}_{\text{intra}}(\text{S})}{\text{RR}_{ \text{inter}}(\text{S})} \tag{5}\]  
This global clustering quality indicator is constructed in the same spirit as most classical ones like the Dunn index [33], by comparing an indicator of intra-quality of clusters to an indicator of inter-quality of clusters. The goal of the clustering is to construct a clustering solution \(\text{S}^{*}\) that will maximize this indicator, \(\text{S}^{*}=\text{argmax}_{\text{S}}\text{MR}(\text{S})\). However, the MR indicator has the particularity of being non-distance-based, and therefore it requires an adapted approach to be optimized.
## 3 Results and Discussions
### Influence of Reaction Parameters on the IC\({}_{50}\) Values  
Figure 1 shows the theoretical calibration curves with varying K\({}_{\text{d1}}\) values for two initial concentrations of labeled antigen. Note that the position of the calibration curve and, accordingly, the IC\({}_{50}\) value both depend on the binding constant and on the concentrations of the reagents used.  
By varying several parameters of the assay simultaneously, it is possible to adjust the parameters of the calibration curve to achieve the desired signal intensity and IC\({}_{50}\). Thus, Figure 2 demonstrates the combined effect of the initial concentrations of the labeled antigen and antibodies on the shape and position of the calibration curve. The signal intensity is determined by the smaller of the parameters \([Ab]_{0}\) and \([Ag^{*}]_{0}\). Both parameters affect the IC\({}_{50}\) in three ways: (i) a decrease in the concentration of antibodies decreases the IC\({}_{50}\); (ii) with an excess of antibodies, a decrease in \([Ag^{*}]_{0}\) leads to a decrease in the amplitude of the signal change and an increase in the IC\({}_{50}\); (iii) with a lack of antibodies, a decrease in \([Ag^{*}]_{0}\) leads to a decrease in both the amplitude and IC\({}_{50}\).  
By reducing \([Ab]_{0}\) and \([Ag^{*}]_{0}\), a shift in IC\({}_{50}\) towards lower values can be achieved, which potentially accords with a lower limit of detection. However, this process is accomplished by a decrease in detected signals, and thus, reliable measurements may become impossible. Thus, the low limit of detection reached depends on the minimum concentration of the label that can be detected in a given assay format. The effect of changes in the concentrations of the immunoreagents used in the analysis on the cross-reactivity values requires additional consideration, which is given below.  
Figure 1: Theoretical calibration dependences of equilibrium competitive immunoassay at different values of the equilibrium dissociation constant of the antibody–unlabeled-antigen complex. The quantities inserted into Equation (15) are shown in the sidebars. (**a**) shows the variance of the constant K\({}_{\text{d1}}\) from 0.1 to 10 nM at \([Ag^{*}]_{0}=20\) nM; (**b**) shows the same variance of the K\({}_{\text{d1}}\) constant at \([Ag^{*}]_{0}=2\) nM.
# BDS-3 Time Group Delay and Its Effect on Standard Point Positioning
## 2 Methodology  
### General Observation Model  
The observation equation of code \(P\) can be expressed as [25]  
\[P_{r,j}^{S}=\rho_{r}^{S}+c\cdot dt_{r}-c\cdot dt^{S}+d_{trop}+\gamma_{j}^{S} \cdot I_{r,1}^{S}+\left(d_{r,j}-d_{j}^{S}\right)+B_{j}+\varepsilon_{r,j}^{S} \left(P_{r,j}^{S}\right) \tag{1}\]  
where \(r\), \(S\) and \(j\) refer to the receiver, satellite and frequency, respectively; \(\rho_{r}^{S}\) denotes the geometric distance between the satellite \(S\) and the receiver \(r\); \(c\) represents the speed of light; \(dt^{S}\) and \(dt_{r}\) are satellite clock offsets and the receiver clock difference between the local time and the system time in seconds, respectively; \(I_{r,1}^{S}\) indicates the slant ionospheric delay on the frequency \(f_{1}^{S}\); \(\gamma_{j}^{S}\) refers to the frequency-dependent multiplier factor (\(\gamma_{j}^{S}=\frac{(f_{1}^{S})}{f_{j}^{S}}\)); \(d_{r,j}\) and \(d_{j}^{S}\) represent the uncalibrated code delays (UCDs) at the receiver and satellite end, respectively; \(B_{j}\) is the code biases on frequency \(j\). \(\varepsilon_{r,j}^{S}\) (\(P_{r,j}^{S}\)) are the noise and multipath error for code observations, respectively.  
Currently, the broadcast ephemeris of BDS-3 is divided into two types. One broadcast ephemeris refers to the old signals (BII and B3I); we call this broadcast ephemeris the old broadcast ephemeris. The other broadcast ephemeris refers to the new signals (B1C and B2a); we call this broadcast ephemeris the new broadcast ephemeris. Compared with the orbit model of BDS-3 with the old broadcast ephemeris, the orbit model of BDS-3 with the new broadcast ephemeris is different, which has been introduced in the BDS-3 ICD [21; 25].  
Note that the BDS-3 satellite clock is referred to in both the old and new broadcast ephemerides with respect to the B3I signal. Hence, the BDS-3 application using other signals or combined signals differing from the conventional reference signal should apply TGD corrections, which are essential for code-based timing, positioning and ionosphere modelling [10; 26]. The correction models of TGD for BDS-3 will be derived and extended to various situations in the next subsection in detail.
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 5 Experiments and Results
### Fabrication of the Encrypted Hologram  
In the experiment, the encrypted binary CGH was fabricated by using the approach described in Section 3. Figure 2 depicts the original watermark and its corresponding reconstruction. The image, whose size is \(64\times 64\), in Figure 2a was employed to generate the encrypted hologram. Here, \(SX(1)=0.352\), \(SY(1)=0.865\), \(SZ=0.752\), \(\alpha=0.998\), \(\beta=3\), \(\gamma=4\), \(T=10^{4}\), \(t1=3528\), \(t2=7832\) and \(t3=6832\), respectively. Figure 2b is the encrypted binary CGH. Figure 2c shows the reconstruction of Figure 2b with the all correct keys.  
Figure 1: Flowchart of proposed watermarking method. (**a**) watermark embedding process; (**b**)watermark extraction process.
## 3 Proposed Algorithm  
The coordinate of the selected pixel is \((i,j)\). The pixel value is denoted as \(F(i,j)\). The values of the neighboring eight pixels with the specified step-sizes are as follows: \(F(i-s,j-s)\), \(F(i-s,j)\), \(F(i-s,j+s)\), \(F(i,j-s)\), \(F(i,j+s)\), \(F(i+s,j-s)\), \(F(i+s,j+s)\), and \(F(i+s,j+s)\), which can be expressed in matrix form as follows:  
\[P=\left\{\begin{array}{l}F(i-s,j-s);\\ F(i-s,j);\\ F(i-s,j+s);\\ F(i,j-s);\\ F(i+s,j-s);\\ F(i+s,j+s);\end{array}\right\}, \tag{2}\]  
The model of the pixels in the infrared image is shown in Figure 7. The pixel value of a selected pixel, \(F(i,j)\) (yellow box in Figure 7), is considered to be related to the neighboring eight pixels, whose step-sizes are 2 (green boxes in Figure 7). For convenience, the neighboring pixels are expressed by \(a_{i}\), arranged in clockwise order from the top left corner.  
Figure 6: Signal flow chart.  
Figure 7: Model of pixels in infrared image.  
To find the relationships between \(F(i,j)\) and the neighboring pixels, we subtract the values of neighboring pixels from the value of \(F(i,j)\) and arrange them in a column matrix, \(L\), as follows:  
\[L=\left\{\begin{array}{l}X(i,j)-a_{1}\\ X(i,j)-a_{2}\\ X(i,j)-a_{3}\\ X(i,j)-a_{4}\\ X(i,j)-a_{5}\\ X(i,j)-a_{6}\\ X(i,j)-a_{7}\\ X(i,j)-a_{8}\end{array}\right\}, \tag{3}\]  
The differences between \(F(i,j)\) and the neighboring pixels shows the correlations between them. The correlation matrix, \(R\), representing the correlation between the neighboring pixels is obtained from the difference matrix, \(L\), as follows:  
\[R=L\times L^{T}, \tag{4}\]  
The differences between \(F(i,j)\) and the neighboring pixels are denoted as \(\Delta_{1}\)-\(\Delta_{8}\). After calculating the correlation between \(F(i,j)\) and the neighboring pixels, the value of \(F(i,j)\) is calculated as follows:  
\[F(i,j)=\sqrt{\sum\limits_{i=1}^{8}\sum\limits_{j=1}^{8}\Delta_{i}\Delta_{j} \mu_{i}a_{j}}, \tag{5}\]  
Equation (5) can be written as follows:  
\[F(i,j)=\sqrt{P^{T}\times R\times P}, \tag{6}\]  
The value of \(F(i,j)\) obtained from the process above is not the exact value, as the sum of the correlation coefficients is not equal to 1. Thus, to obtain the real value of \(F(i,j)\), the value above must be normalized as follows:  
\[F(i,j)=\frac{F(i,j)}{(\sum\limits_{i=1}^{n}\Delta_{i})}, \tag{7}\]  
The infrared image can finally be obtained by calculating the value of every pixel using the method presented above.
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 2 Materials and Methods
### Materials
As shown in Figure 1, comparing the pre- and post-fire images reveals radiometric differences between them. These differences are evident even within the image mosaic of the same day when acquired with different sensors (e.g., Figure 1c,d). Preprocessing for data harmonization could help balance the radiometric response of the images. However, to present a robust and automatic methodology and to test the characteristics of the SuperDove images, we decided not to perform this preprocessing. The effects of these radiometric differences are discussed in later sections. Also, Table 1 provides details of the image-capture dates for the four areas selected for the study. To select the study area, the public geospatial information provided by the CEMS for each of the events was used.
## 4 Statistical Properties of Significance Tests: A Simulation Study
For instance, compare the values of power for two different scenarios that yield \(\Delta_{x}=-1\): for \((\Delta,\gamma,x)=(-1,-1,0)\), the power is 0.29, whereas for \((\Delta,\gamma,x)=(-0.5,-1,0.5)\), the power is 0.89. Note that in these two scenarios we have the same value of the linear slope: \(\gamma=-1\), and in the first scenario the value of \(\Delta\) is more extreme (\(\Delta=-1\)) than in the second scenario (\(\Delta=-0.5\)). Yet, the value of power in the second scenario (0.89) is much larger than that in the first scenario (0.29). Such a drastic difference in power is due to the fact that in the second scenario we are testing a contrast at the level of engagement \(x=0.5\) that is much closer to the average observed level of engagement (\(\overline{x}_{E}=0.62\)) than the level of engagement in the first scenario (\(x=0\)). Of note, \(\Delta_{x}=-1\) can be also obtained for \(\Delta=-1\), \(\gamma=0\) and any value of \(x\). In this case, the linear slope \(\gamma\) is zero, and the mean treatment difference is equal to \(-1\) for any level of engagement \(x\). The corresponding values of power range from 0.29 (for \(x=0\)) to 0.93 (for \(x=\overline{x}_{E}=0.62\)). As another example, consider \((\Delta,\gamma,x)=(-0.5,-1,0.5)\) for which \(\Delta_{x}=-1\), and \((\Delta,\gamma,x)=(-1,-1,0.3)\) for which \(\Delta_{x}=-1.3\). Despite that in the latter case the treatment difference is more extreme (\(\Delta=-1\) and \(\Delta_{x}=-1.3\)) than in the former case (\(\Delta=-0.5\) and \(\Delta_{x}=-1\)), the value of power in the latter case is smaller (0.84) than that in the former case (0.89). This is because the engagement level in the latter case (\(x=0.3\)) is more distant from the average observed level of engagement (\(x=\overline{x}_{E}=0.62\)) than the engagement level in the former case (\(x=0.5\)). Overall, based on the results from Figure 2 we highlight two important observations: 1) The level of engagement at which the group comparison is made affects statistical power. 2) The values of power of the ANCOVA test and the two- sample \(t\)-test are very close. This implies that both ANCOVA and a two-sample \(t\)-tests are appropriate for the inference on treatment difference at the average observed level of engagement. These findings highlight the importance of clearly formulating study objectives, because sample size and power depend on the choice of the primary estimand. In what follows, we discuss some statistical considerations that can be useful at the study planning stage.
# Narrowband Internet of Things (NB-IoT):
## 4 Narrowband-IoT Possible Deployment Strategies
### Asynchronous NB-IoT Deployment in All Small Cells  
This deployment strategy is employed in such a way that NB-IoT is enabled in all small cells by using different physical resource blocks. This implementation may avoid the interference between NB-IoT UEs from different small cells; however, this may result in co-channel interference between NB-IoT and LTE UEs that are using the same radio resources. When deploying under this strategy, it is imperative to implement proper frequency planning as well as proper power configuration for NB-IoT devices. As seen from the state of the art, some works have proposed blanking of the radio resources to the adjacent cells for the resources that are already occupied by NB-IoT even in the cells that NB-IoT is not enabled. However, blanking of the resources is a wastage of resources, so, there should be some other means such as frequency hopping to avoid wastage of resources (blanking) as well as to mitigate interference.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 3 Results
### Anomaly Detection Model Comparison
#### 3.2.2 Evaluation on Accelerated Wear Test Data  
The results of applying the LSTM, STD and LOF models to the data from the accelerated wear test 1 are depicted in Figure 12. For this, all models were trained based on the first 500 measurements with model parameters of the ROC curves that yielded the best compromise between high TPR and low FPR values. It can be seen that all models correctly identify the anomalies at the end of the time series. The LOF model detects the outlier around measurement 1000 as an anomaly. Given a maintenance action decision criterion of 10 detected anomalies in the last 24 measurements, maintenance actions would have been triggered at the end of the data set for all models and a false alarm would have been triggered around measurement 1000 for the LOF model and for many more time ranges for the STD model. The AD models' behaviour on the second data set are summarized in a similar manner in Figure 13. In this scenario, the models were trained  
Figure 11: Results of the anomaly detection models based on synthetic data set 2.  
Figure 10: Results of the anomaly detection models based on synthetic data set 1.  
using the first 200 measurements with the same model parameters. It can be seen that the LSTM model and the STD model detect more anomalies than the LOF model along the time series. The apparent anomaly at the end of the time series is detected by all models.The LSTM triggers two false alarms around measurement 300. The STD model triggers many false alarms. Summarising, the STD shows more false alarms compared to other models. The LOF and LSTM model detect only the apparent anomalies with a low false alarm rate. Hence, it is suggested that either the LOF model or LSTM model is used in CATS as the AD model.