esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Singular Spectrum Analysis  
The Singular Spectrum Analysis (SSA) method was developed since 1970s. SSA is a model-free approach because SSA decomposes an original time series into a trend, seasonal and semi-seasonal components, and residuals based on singular value decomposition (SVD) [28]. The new decomposed series can help us to understand the trend of the original time series and to extract seasonal or monthly components and residuals. The basic concept of SSA consists of four steps: embedding, singular value decomposition (SVD), grouping, and diagonal averaging. The basic SSA algorithm decomposes an initial time series into a new time series which consists of the trend, the seasonal and semi-seasonal components, and residuals. The basic SSA algorithm has two stages: decomposition and reconstruction.
# New Stable Non-Vector Control Structure for Induction Motor Drive
## 3 Mathematical Model of Induction Motor  
The complete mathematical model of the IM presents a nonlinear dynamic system of the 5 th order. In the following, it is assumed that the current control loops are involved in circuits of a standard frequency converter. Due to their high dynamics, their influence is neglected. Then, the current-flux model of the IM presents the 3 rd order system. Its current-flux model [8] is described in the \(\{x,y\}\) reference system by components of the stator currents and rotor fluxes:  
\[\frac{\mathrm{d}\psi_{2x}}{\mathrm{d}t}=-\omega_{g}\psi_{2x}+L_{m}\omega_{g}i _{1x}+(\omega_{1}-\omega)\psi_{2y} \tag{19}\]  
\[\frac{\mathrm{d}\psi_{2y}}{\mathrm{d}t}=-\omega_{g}\psi_{2y}+L_{m}\omega_{g}i _{1y}-(\omega_{1}-\omega)\psi_{2x} \tag{20}\]  
\[T_{mech}=L_{m}\frac{p}{L_{1}}\left(\psi_{2x}i_{1y}-\psi_{2y}i_{1x}\right) \tag{21}\]  
\[\frac{J}{p}\frac{\mathrm{d}\omega_{m}}{\mathrm{d}t}=T_{mech}-T_{L} \tag{22}\]  
where the notation of the parameters and variables is as follows:  
\(i_{1x},i_{1y}\) components of the stator current space vector \(\mathbf{i}_{1;}\)  
\(\omega_{m}\) mechanical angular speed of the rotor;  
\(\omega_{1}\) angular frequency of the stator voltage;  
\(\omega_{2}\) slip angular speed \(\omega_{2}\) = \(\omega_{1}\)-\(\omega_{m;}\)  
\(r_{2}\) rotor phase resistance;  
\(r_{1}\) stator phase resistance;  
\(\psi_{2x},\psi_{2y}\) stator and rotor magnetic flux components;  
\(L_{m}\) main inductance;  
\(L_{1}\), \(L_{2}\) leakage inductances;  
\(\Omega_{g}\) constant \(\omega_{g}=R_{2}\)/\(L_{2}\);  
\(T_{mech}\) mechanical motor torque;  
\(T_{D}\) dynamic motor torque;  
\(p\) number of pole pairs;  
\(J\) moment of inertia;  
\(T_{L}\) load torque.  
From Equations (19)-(22) it is obvious that the IM presents a strongly nonlinear higher-order controlled system of an oscillating character.  
The values of the motor parameters used for simulation are specified in Table 1.  
The characteristics in Figure 2 show the motor model torque and angular speed responses at the step change on the motor inputs in time \(t=0\) s, when the motor is  
\begin{table}
\begin{tabular}{c c c} \hline \(P_{N}=3\) kW & \(U_{1N}=220\) V & \(I_{1N}=6.9\) A \\ \(J=0.1\) kgm\({}^{2}\) & \(r_{1}=1.8\)\(\Omega\) & \(R_{1}=2/3\)\(r_{1}=1.2\)\(\Omega\) \\ \(n_{N}=1430\) rev./min & \(r_{2}=1.85\)\(\Omega\) & \(R_{2}=2/3\)\(r_{2}=1.23\)\(\Omega\) \\ \(p=2\) & \(L_{1}=L_{2}=0.2106\) H & \(\omega_{\mathrm{g}}=R_{2}/L_{2}=5.84\) s\({}^{-1}\) \\ \(T_{N}=20\) Nm & & \\ \hline \end{tabular}
\end{table}
Table 1: Induction motor parameters for modeling and experimentation.  
supplied from a current converter in which the current loop time constant already is compensated. Let us note that according to (19)-(22) in this case, the values of the current vector components of the IM model used for simulation are \(i_{1x}=0\) A, \(i_{1y}=15\) A. At this supply, the angular speed reaches the value of \(\omega_{1}=200\) rad/s.
## 2 Material and Methods
### Dataset  
Landsat images (TM, ETM and OLI/TIRS), as a medium resolution image, were used widely to derive imperviousness in environmental studies [31; 32; 33; 34; 35; 36]. Hence, Landsat 8 OLI/TIRS image (collection 1 Level-2) which provides surface reflectance data (path/row: 89/79) in July 2016 was used in this study ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), (accessed on 20 October 2021)). Cloud-free Landsat 8 OLI/TIRS images and that with minimal system-induced errors such as stripping or bad pixels which have higher quality rate for image analysis purposes were selected. No atmospheric correction and no image to image registration were needed because Level-2 Surface Reflectance data are atmospherically and geometrically corrected. High resolution and cloud-free Google Earth images in July 2016 were used to obtain imperviousness data for evaluating the accuracy of Landsat-derived imperviousness data. Population data at the suburb scale was obtained from Australian Bureau of statistics ([[https://itt.abs.gov.au/](https://itt.abs.gov.au/)]([https://itt.abs.gov.au/](https://itt.abs.gov.au/)), (accessed on 15 July 2021)). Population data were available for 154 suburbs for the year 2016. Therefore, 154 suburbs from a total of 310 suburbs were selected for this study. Area of the selected suburbs was obtained from polygon layer of suburbs provided by the Australian Government website ([[https://data.gov.au/](https://data.gov.au/)]([https://data.gov.au/](https://data.gov.au/)), (accessed on 10 August 2021)). Residential polygons were derived from SEQ land use map in 2015.
## 4 Inversion Effect Analysis
### Effect of Forward Travel Time Algorithms  
We present two forward travel time calculation methods, FMM and MSFM. In this section, we will compare the inversion effects of these two algorithms in a fault model. In order to control the variables, we set the default constraint method as the dynamic prior model composite constraints (DPMCC) and selected an efficient and stable LSQRs algorithm for linear inversion. Both methods use the t0 difference method to obtain the priori model. The offset distance is 4 m, the shot spacing is 2 m, and the trace spacing is 1 meter. A total of 49 seismic sources and 97 receivers are used.  
Figure 12a is the inversion result of the FMM algorithm, and Figure 12b is the inversion result of the MSFM algorithm. We can see from the figure that when FMM is used as the forward travel time calculation method, there are more false anomalies in the reconstructed velocity field, which are mainly concentrated near the fault.  
Figure 11: Error fitting curve.  
Figure 12: Tomographic inversion results of the fault models using different forward travel time algorithms. (**a**) FMM inversion result; (**b**) MSFM inversion result.  
As shown in Figure 13, the effect of MSFM is better than that of FMM when the number of iterations is the same. In summary, the MSFM proposed in this paper is a better choice for tomography forward calculations.
## 3 Methodology
### Dataset  
The experiments were conducted using the CICIoV2024 dataset [8], which stands out as the most recent and comprehensive benchmark for advancing cybersecurity research in the Internet of Vehicles (IoV) domain, specifically in CAN environments. The dataset was developed using a fully intact inner structure of a 2019 Ford vehicle, using the widely adopted standard CAN protocol for intra-vehicular communication among Electronic Control Units. While the CAN protocol is efficient for real-time communication, its lack of built-in security features exposes it to various cyber threats, making intrusion detection systems (IDS) essential for vehicular safety and reliability. This dataset captures vehicular network traffic, including both benign and malicious scenarios. It includes six different classes--benign traffic and five types of attacks: Denial-of-Service (DoS), Gas Spoofing,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hardware** & **Software** \\ \hline Processor: Intel® CoreTM i7-8750H & Windows 11, 64-bit \\ CPU @ 2.20 GHz, 2.21 GHz & Jupyter Lab 4.2.5 \\ RAM: 16.00 GB, SSD: 1 TB + 256 GB & Python 3.12.7 \\ Graphics card: Nvidia® GeForceTM GTX 1050 Ti 4 GB VRAM & Optuna 4.0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the experimental setup - ASUS laptop.  
Speed Spoofing, Steering Wheel Spoofing, and Revolutions Per Minute (RPM) Spoofing. Each attack scenario reflects realistic exploitation methods targeting the CAN bus, such as flooding the network to disrupt communication (DoS) or manipulating critical control data such as speed, steering angle, and gas pedal position.  
A key strength of CICIoV2024 [8] is its realism, which extends beyond the fidelity of the network traffic to include its imbalanced nature, where benign data significantly outweigh malicious instances. The data imbalance reflects real-world conditions and presents a challenge for IDS development, as minority attack classes are more difficult to detect. In addition, the features of the dataset, detailed in Table 2, include three formats--binary, decimal, and hexadecimal--providing flexibility. In this research, we used only the decimal representation of the dataset, which provides the data as numerical values suitable for machine learning analysis.  
Figure 1: ML models evaluation process.
# Green Synthesis Method and Application of NaP
## Results and Discussion
### The NaP Zeolite to Remove Ammonia Nitrogen
#### 3.7.2 Influence of pH  
The adsorption of NaP zeolite on ammonia nitrogen in simulated wastewater with different pH values is shown in Figure 8. The ammonia nitrogen removal rate is proportional to the pH from 3 to 7, and inversely proportional to the pH from 7 to 10. Because under acidic conditions, the ammonia nitrogen in the wastewater mainly exists in the form of NH4\({}^{+}\), a large amount of H\({}^{+}\) in the wastewater competes with NH4\({}^{+}\) for adsorption, resulting in a decrease in the adsorption amount of ammonia nitrogen by the NaP zeolite. Under alkaline conditions, ammonia nitrogen in the wastewater exists in the form of molecular NH3, resulting in a decrease in adsorption capacity. Therefore, the optimum removal rate of ammonia nitrogen by synthetic NaP zeolite is pH = 7. This is consistent with the better selectivity of the modified zeolite reported in the literature at pH = 7 [49; 50].
## 5 Discussion  
In this study, both statistical learning (PCR and PLS) and machine learning (SVR and RFR) models were built using various approaches to observe and compare their performances to SEP. At first the dataset was split into 75:25 ratios for model building an independent test set. A minimum of three PPG signals were acquired from each human subject. The data were split into training and test sets randomly without any prior stratification. This experiment was useful for identifying the dominating features and some of the key model parameters in prediction of glucose with the lowest error. Statistical learning methods performed well overall and first derivative and derivative features dominated in models built with both statistical and machine learning methods.  
There was an around 8.5 mg/dL reduction in SEP in the subject wise rigorous testing. The key observation here is that the models that performed well in the earlier experiment maintained good performances in subject wise testing too. PCR and PLS models built with first derivative characteristics points performed well in both types of experiments. The SEPs achieved with these models were comparatively lower than the others. Furthermore, the lowest SEP for these models were recorded for the same no. of principal components. This shows the possibility of using the first experiment for the selection of dominating features, optimum model, and key model parameters which can then be used for real tests.  
In this study, PCR- and PLS-based models generally outperformed the models based on SVR and RFR, as seen in Figure 13. This could be attributed to the limited dataset used in the study. [PERSON], [PERSON] et al. used PPG signals acquired from smartphones for the classification of the glucose level. Their model, with an accuracy of over 80%, can predict one of the three diabetic status--_normal_, _borderline_, and _warning_ of a subject. However, knowing the quantitative information of the actual glucose level is more important than knowing the diabetic status. We have demonstrated that our model can solve that problem by predicting the actual glucose level with an error of less than 20 mg/L. First derivative and second derivative characteristic points were the dominating features with the machine learning models. SVR based models performed comparatively well in prediction in both experiments, whereas RFR models performed poorly in both the experiments.  
Figure 13: Comparison of performances of different regression techniques.  
Since most of the glucose levels in the dataset were in the range of 70-150 mg/dL, the efficacy of the proposed model is limited to prediction of glucose level within the mentioned range. We anticipate an improvement in the models with an increase in the amount and variety in the data in the near future. Robustness, platform independency tests, and user side app development for instant analysis will be performed in the near future.
## 3 Proposed Methodology
### Adversary Attack Model
#### 3.5.2 Selective Packet-Dropping by the Gray Hole Node  
The behavior of the gray hole node is unpredictable because it changes rapidly from normal to malicious, so it is a challenging task to detect this behavior in MANETs. Gray hole nodes receive the RREQ packet from the source node and check-in the routing table for validity. If it has a valid route for the destination, the gray hole node sends a bogus RREP, with a high sequence number and a minimum hop count, towards the source node, or else sends the normal RREP. The gray hole node is smart enough to behave like a genuine node in order to hide from security mechanisms. During the route discovery process, it sends the correct sequence number but at the time of data transmission, its behavior becomes malicious and it starts dropping selective data packets, which is an undesirable situation. The main intention of the gray hole node is to degrade the network's performance.
# Experimental and Numerical Investigation of the Mesoscale Size Effect in Notched Woven Composites
## 2 Materials and Methods
### Numerical Model
Smaller, unrelated clusters were filtered out to remove noise. The centroids of the main clusters were assumed to be the centers of the fiber tows, with their coordinates defined with DIC. The map was first built by fitting a geometrically exact grid to these points using optimization to determine its x-y spacing and the in-plane rotation angle. In the next step, the full map of the two material orientations was constructed building rectangles around the tow centers, with dimensions obtained from the optimized grid spacing. The structured FEM mesh (Figure 3f) was finally generated from parametrized geometry in GMESH. It is composed of a single layer of four-node Belytschko-Tsay shell elements. Six through-thickness integration points were used to represent the three composite layers, and the material was specified per every integration point. Each of the threeplies in the real composite was modeled via two through-thickness integration points in the shells, with alternating materials, representing the interwoven tows of fibers in a simplified fashion. A mesh convergence study was conducted to identify the appropriate mesh size. The unnotched specimens were simulated with a mesh size of 0.5 mm to have 10 \(\times\) 20 elements for each tow of the woven texture. In open hole specimens, the mesh refinement in correspondence with the hole was considered, with a minimum mesh size of 0.05 mm. An orthotropic linear elastic material model, \"MAT_54 in LS-Dyna, was used. A Python script was used to apply the appropriate material direction to every integration point according to the map previously described. The material properties of the modeled tows were identified with an optimization process minimizing the difference between the numerical and DIC surface strain maps at a given applied load. More details about the material properties identification are given in the following section.
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
## 4 Experimental Results
### Tortuosity Classification  
First of all, we need to grade each image into different tortuosity levels. However, the number, length and curvature of curvilinear of each image are different, so it is a key problem to evaluate different tortuosity characteristics effectively [1]. As previous section stated, we should consider more about the measurement of a small part of the high tortuosity segments. Therefore, the tortuosity measurement for a whole image is calculated by the weighted average:  
\[M=\frac{\sum_{i=1}^{N}l_{i}\times m_{i}}{\sum_{i=1}^{N}l_{i}}, \tag{14}\]  
where \(l_{i}\) is the length of the \(i\)th curvilinear structure. \(N\) is the number of segments in a curvilinear structure map. For each \(i\)th curvilinear structure with length (\(l\)), the \(m_{i}\) is the tortuosity metric score. The tortuosity metric can be any one of the metrics in Table 1 as well as our proposed Kexp.  
Here, we use 14 tortuosity metrics for experiment. The 14 features are the values of tortuosity metrics, which are listed in Table 1. First, we extract each image feature as a 14 dimensional vector, then the tortuosity classification is by the Support Vector Machine (SVM) with image features. For instance, as four-level grading is used to measure tortuosity, the one versus one strategy used by SVM for multi-class classification leads to six SVM classifiers, each of which is trained to discriminate one class from another.  
SVM is a well-established classification technique and has been proved to be able to produce satisfactory results on many applications, in particular when the training data is not big enough as required by deep learning. We only have 400 images in this study, from our experience, this would be too small for deep learning models, and SVM is well suitable in the problem at this scale. In the experiment, we leverage the SVM in the Matlab, and all SVM parameters are set to default values. The parameters may not be the optimal but will provide a benchmark for future studies (The goal of classification here is just to verify the validity of this new method).
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
## 2 Theoretical Analysis and Simulation of the Improved SPGD Algorithm
### AdaDelSPGD Algorithm and Simulation
(a) three-channel; (b) seven-channel. rithm is too slow. At the same time, when the gain is too large, the traditional algorithm can not stabilize the phase-locked output. The relationship between the number of iterations required for convergence and the number of sub-beams of the two algorithms is presented in Table 1. The convergence speed of the AdaDelSPGD algorithm has improved by 38.4% and 44.4% for three-channel and seven-channel laser array systems, respectively. Therefore, the AdaDelSPGD algorithm can reduce the number of iterations, and the convergence effect is better than the traditional algorithm.
## 2 Materials and Methods
### Quality Control and Data Preprocessing  
Raw sequencing reads were subjected to quality control using FastQC (version 0.11.2). Low-quality reads and adapter sequences were trimmed using Trimmomatics v0.39 [23]. The statistics for the number of reads and bases are summarized in Table 1.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### Credal Decision Trees  
Credal Decision Trees (CDTs) is a classifier that was originally developed to solve classification problems [55; 56; 57]. To address the issue of the increasing uncertainty caused by the development of overly complex decision trees, the blocking classification process was introduced. This approach prevents excessive tree growth by implementing a stopping criterion during the generation of credal decision trees (CDTs). Specifically, if a tree split leads to increased uncertainty, the algorithm immediately halts further splitting, ensuring a more balanced and interpretable model [58; 59]. The uncertainty of the trusted dataset is calculated as follows:  
\[Eu(x)=NG(x)+RG(x) \tag{3}\]  
where \(Eu\) represents the overall uncertainty value of the credal set, \(NG\) represents a non-specific function, and \(RG\) represents a general random function in the credal set. Furthermore, \(X\) is the framework of the entire credal set [57; 60]. The probability interval of each variable can be calculated and determined using an imprecise probability model. Let \(Z\) be a variable, with \(zj\) representing its value; then, \(p(zj)\) represents the \(zj\) probability distribution:  
\[p\big{(}Z_{j}\big{)}\in\bigg{[}\frac{n_{zj}}{N+s},\frac{n_{zj}+s}{N+s}\bigg{]},j=1,\ldots,k \tag{4}\]  
where \(N\) represents the size of the sample dataset, while \(n_{zj}\) represents the frequency of an event occurring (\(Z=zj\)); \(s\) is a hyperparameter, and its value is selected from 1 or 2 [60; 61].
## 5 Detection in UWB Positioning Algorithms
### Machine Learning For UWB In NLoS  
To improve the accuracy of UWB positioning systems, specific NLoS mitigation techniques are required for various applications. Figure 13 illustrates a block diagram of a complete UWB precise IPS, which starts by fixing the anchors for the coordinate system and locates the mobile UWB tags within the indoor environment. The collected raw data will be used by an additional processing step for NLoS detection, which is performed using an ML classification algorithm that has been pre-trained with the raw measurement data. This model is used to mitigate the NLoS effects. For NLoS classification, the ML is used to classify the LoS and NLoS by different signal features or CIR values. For NLoS error correction, the ML can be applied for predicting the error \(e_{i,j}\) accurately. The actual range \(r_{i,j}\) between the \(i\)-th anchor \((x_{i},y_{i},z_{i})\) and the \(j\)-th tag \((x_{i},y_{i},z_{i})\) is defined as:  
\[r_{i,j}=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}+(z_{i}-z_{j})^{2}}. \tag{10}\]  
The estimated distance \(d_{i,j}\) in term of the coordinate of the \(i\)-th anchor and the \(j\)-th tag is determined through calculating the signal round trip time of the ToA. The positioning of the \(i\)-th anchors is known and constant; therefore, the estimated distance \(d_{i,j}\) in a three-dimensional scenarios is given as:  
\[d_{i,j}^{LoS}=r_{i,j}+\epsilon_{i,j}, \tag{11}\]  
where \(\epsilon_{i,j}\) is the measurement error.  
For NLoS conditions, the signal direct path is reflected or blocked due to the presence of obstacles; therefore, there will be further signal propagation delay, resulting in NLoS error \(e_{i,j}\). The estimated distance \(d_{i,j}\) can be calculated as:  
\[d_{i,j}^{NLoS}=r_{i,j}+\epsilon_{i,j}+\epsilon_{i,j}, \tag{12}\]  
where \(e_{i,j}\) is the independent positive measurement bias error. Finally, the corresponding ranging error \(\delta_{i}\) can be expressed as:  
\[\delta_{i}=\left\{\begin{array}{ll}\epsilon_{i,j},&LoS,\\ \epsilon_{i,j}+\epsilon_{i,j},&NLoS.\end{array}\right. \tag{13}\]  
Figure 12: Detection of UWB positioning system. (**a**) Signal propagation in LoS scenario. (**b**) Signal propagation in NLoS scenario. (**c**) Positioning algorithm in LoS scenario. (**d**) Positioning algorithm in NLoS scenario.  
The ML algorithm can now be trained to classify the LoS and the NLoS component for the UWB signal. Therefore, high-accuracy NLoS classification and accurate prediction of \(e_{i,j}\) is crucial for precision IPS.