esa-sceva/satcom-chunk-collection huggingface dataset:
# A Discrete Hidden Markov Model for SMS Spam Detection
## 2 Related Work
### Hidden Markov Model for Spam Detections  
HMM and its variants have found a wide variety of applications. There was a hierarchical hidden Markov model (HHMM) for real-time finger motion synthesis [38], a hierarchical multivariate HMM with reactive interpolation functionality for full-body motion reconstruction [39], a combining speaker-specific Gaussian mixture model (GMM) with a syllable-based HMM for speaker recognition [40], and a Spherical-Self Organizing Map (S-SOM) with HMM for classifying sets of time series [41], to list just a few examples. HMMs have also laid a solid foundation for their applications in NLP, including part-of-speech tagging in many languages [42; 43] and name entity recognition [44].  
However, based on our literature review including the latest review papers about SMS spam detection techniques [4; 45; 46], there is no report on using HMM for SMS spam detection based on the word order. [PERSON] and [PERSON] [47] used HMM for SMS spam detection on byte-level, which is the low communication level of SMS delivery. [PERSON] and [PERSON] [48], as forerunners in this field, proposed a HMM for detecting spam mail in 2007. The paper focused on obfuscated words detection, such as the example in the paper, _money_ or _mo.ney_ for the word _money_. Therefore, instead of words, they focused on spam detection at the language character level, such as letters in English. They treated characters in spam emails like a DNA chain and used a similar DNA chain classification method for spam emails. [PERSON] et al. [49] built a HMM for detection and classification of duplicate bug reports (BRs) by focusing on the relation of current BRs and incoming BRs. Similarly, [PERSON] et al. [50] put forward a topic-based HMM for spam tweet filtering and predicted the tweet sample classification based on an assumed high dependency among successive tweets. [PERSON] et al. [51] used HMM in spam detection over internet telephony in voice, which belongs to a far different research field from this study. The existing work focused on emails, bug reports, and tweets, etc. Unlike the existing work, we aim to use a hidden Markov model for spam SMS detection based on the word order, which is a new application of HMM.
# Systematic Analysis of Risks in Industry 5.0 Architecture
## 7 Applications
### Intelligent Healthcare  
A real-time, intelligent hospital is what Industry 5.0 aims to build. Within the healthcare industry, technology can offer remote monitoring solutions. It is crucial to improving the doctors' quality of life. Doctors may concentrate on infected patients and give effective data for better treatment during the COVID-19 pandemic using this smart healthcare technology [9]. These days, doctors use ML models to aid in the diagnosis of patients' illnesses. Intelligent wearable, a patient's medical data can be continuously captured in real time by such smart watches and sensors and stored in the cloud [20].
# An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning
## 2 Materials and Methods
### Layers of CNN Model Architecture  
* **Convolution layer:** In this layer, the input dataset is convolved with a kernel or weight. A feature map is considered the output of this layer. The stride parameter determines how much the kernel convolves with the input data. By learning from the diverse input signals, this operation is considered as a feature extractor.  
**Conv1D (filters, kernel-size, strides, activation = none)** Filters: Number of sliding windows; Kernel-size: The size of the sliding window;  
Figure 5: The orange part of the signal represents k minutes of the inter-ictal phase.  
Figure 6: Proposed deep learning method.  
Strides: Modifies the amount of movement over a sample of data;  
Activation: The activation function is a mathematical port that connects the current neuron's input to its output, which is passed to the next layer as input.  
**Pooling layer:** This is a down-sampling layer that uses the pooling process to lower the input data's dimension (reduce the amount of computation performed in the network) and increase the robustness of feature extraction while keeping all the important information. Average, max, and sum are all considered as pooling layer operations. The max-pooling operation is used more often in modern deep learning architecture.  
Most used in 1D \(\rightarrow\)**MaxPooling1D (pool-size = 2)**  
Pool-size: take the maximum value in a defined spatial window.  
**Batch normalization:** A method for training deep neural networks that normalizes each contribution to a layer for every small batch. This slows down the learning process and reduces the number of training epochs required to train and build deep neural networks.  
**Dropout:** Overfitting is avoided by setting input units to zero at random with a rate of frequency at each step during the training period. Inputs that are not set to zero are scaled up by 1/(1-rate) so that the total sum remains the same for all inputs.  
Dropout (rate = 0.1-0.5)
###### Abstract  
When fires break out in subway station halls, traditional smoke extraction (TSE) systems are employed with the aim of preventing smoke from spreading to the platform and passageways. The functionality of TSE systems under the influence of external winds needs to be further explored. Based on a numerical method, this study investigated the effect on TSE systems under the influence of external wind. A numerical model was established and validated by means of full-scale field tests to ensure accuracy. Subsequently, the validated model was applied to study the effect of the external wind directions and speeds on the smoke diffusion distance. The results showed that when all entrances and exits were on the windward side, the external wind direction led to serious longitudinal diffusion of the smoke toward the side with fewer entrances and exits of the station hall, and the diffusion distance increased with increasing wind speed. The diffusion distance reached a maximum value of 61.32 m when the outdoor wind was 5 m/s, which was 67.9% higher than that under no wind. When all the entrances and exits were on the leeward side, the external wind had little influence on the degree of smoke spread, with the greatest smoke diffusion distance being only 4.76% longer than that under no wind. When two entrances and exits were on the windward side and the other on the leeward side, the external wind caused smoke to spread to a passageway, and the degree of smoke spread was more unfavorable at higher wind speeds, with the longest diffusion distance being 7.28 m. To prevent smoke from spreading to passageways and to effectively shorten the longitudinal diffusion distance of smoke, an optimized smoke control (OSC) system was proposed, employing center and passageway smoke barriers, which were able to shorten the diffusion distances by 35.45%, 13.64%, and 2.35%. In particular, smoke diffusion did not occur in passageways. This study provides a reference for the fire safety engineering design of subway stations.  
Keywords:external wind; numerical method; full-scale test; smoke control system +
Footnote †: journal: Applied sciences
## 3 Analysis and Results
### Qualitative Analysis
#### 3.2.3 Detail Information of BIM  
Based on the historical data of on-site BIM-related work, detailed information was classified, and its weight throughout the entire process was examined. In Project 1, the main issues addressed by BIM were modeling (33.25%), followed by quantity verification (17.68%) and modeling materials (10.29%) (Figure 4). Similarly, in Project 2, the primary issues were modeling (35.61%), followed by quantity verification (13.41%) and process schedules (9.76%). Therefore, in both construction projects, tasks related to modeling constitute the largest proportion of BIM-related work.  
Figure 4: (**a**) The proportion of detail information in Project 1. (**b**) The proportion of detail information in Project 2.  
Figure 3: (**a**)The proportion of location information in Project 1. (**b**) The proportion of location information in Project 2.
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 2 Algorithm
### Signal Denoising
#### 2.2.3 Ewt  
Adaptive filtering has some limitations. Sometimes, the acceleration signals do not reflect the true MA, or the MA is in a range that is similar to or even overlaps with the true heart rate. In these cases, the spectral peaks corresponding to the true heart rate often cannot be found by RLS filtering alone. Therefore, the algorithm uses EWT to extract the portion of interest in the PPG signal. Different from RLS filtering, EWT analyzes the signal from the perspective of the frequency domain. The misjudgment caused by the close proximity of MA to the true heart rate and the strong MA signal can be effectively avoided by EWT in some cases.  
(1) The EWT Principle  
EWT is an adaptive signal decomposition method for non-stationary signals proposed by [PERSON] (2016). The traditional wavelet transform requires screening of suitable wavelet bases, and the selection of wavelet bases affects the decomposition results, while the decomposition of EWT is based on the spectral information of the signal to be decomposed.  
Considering the periodicity of 2\(\pi\) and [PERSON]'s criterion, the Fourier spectrum \(\omega\) is restricted to 0-\(\pi\). [0, \(\pi\)] is divided into N segments, and each segment is defined as \(\Lambda\)n = [\(\omega_{n-1}\), \(\omega_{n}\)]. Centered on \(\omega_{n}\), the length of the transition intervals in each band is 2\(\tau_{n}\). The empirical wavelet transform can be defined as a set of band-pass filters in the frequency bands, as shown in Figure 5:  
Figure 4: Comparison of PPG spectrograms before and after RLS filtering. (**a**) Spectrogram before RLS filtering; and (**b**) spectrogram after RLS filtering.  
Figure 3: Block diagram of RLS filtering.  
Application of EWT in the Algorithm  
Here, the spectrum of the PPG signal is considered to be divided into three segments. From Figure 6a, it can be seen that there is a strong MA in the neighborhood of the true heart rate, and the amplitude corresponding to the MA is larger than the amplitude of the true heart rate. The two dashed lines in Figure 6a show the segmentation line of the EWT, and Figure 6b-d show the spectrograms of the three signals ewt1, ewt2, and ewt3, which are decomposed by this signal after the EWT. The ewt3 is selected for spectral peak tracking by the position of the segmentation boundary with respect to the heart rate of the previous window. After EWT, the amplitude of the signal corresponding to the MA is reduced and slightly lower than the amplitude of the true heart rate, which facilitates the subsequent spectral peak tracking.
## 3 Results and Discussion
### Model Validation
#### 3.1.2 Rectisol  
To validate the Rectisol process model, the simulated results were compared with the experimental data. The flowsheet result of Rectisol is presented in Appendix C. In [PERSON] et al. research, Rectisol is designed to process syngas from acid gas shift reactor prior to the synthesis of ammonia at 1000 tons/day [36]. The Rectisol columns are designed to remove most of the dissolved CO\({}_{2}\), H\({}_{2}\)S, and COS. The syngas, composed of H\({}_{2}\), N\({}_{2}\), Ar, CO, CH\({}_{4}\), CO\({}_{2}\), H\({}_{2}\)S, and COS, is fed to the bottom of the column, the chilled methanol is fed to the top of the Rectisol column. As the gas flows upward in the Rectisol column, downward flowing methanol absorbs H\({}_{2}\)S, COS, and CO\({}_{2}\), so the syngas leaving the top of Rectisol column is nearly sulfur and CO\({}_{2}\) free. Liquid methanol is withdrawn from the bottom of the column; the results are presented in Table 6. The simulation results are compared with the experimental results. There is a small discrepancy between the experimental data and simulation results but it is not significant. Therefore, the Rectisol model could be considered as a valid model to simulate Rectisol process.
# Development of Task-Space Nonholonomic Motion Planning Algorithm Based on Lie-Algebraic Method
## 4 Conclusions  
In this paper, the Lie-algebraic method of motion planning for nonholonomic systems, originally designed to plan within the configuration space, has been extended to also work in a task-space. There are some advantages of planning within the task-space:  
* a smaller than the configuration space dimension either increases redundancy and facilitates optimizing a motion or allows to decrease the number of parameters of controls,
* it allows to define and solve many practical tasks, especially in obstacle-cluttered environments when some components of the configuration vector are not important. However, there are also some disadvantages:
* a new source of singularities introduced by the output mapping,
* the impossibility to plan only within the task-space, without referring explicitly to the configuration space (usually the task-space dimension is smaller than the configuration space dimension, thus no unique mapping between the two spaces exists).  
Some general observations based on simulation results:  
* Depending on the planning purpose, the Newton algorithm, extensively used in the proposed algorithm, can be applied with or without optimization. The first version (low computational costs) should be used for tasks with no extra requirements or as a first try solution.
* A small redundancy in setting the search space size (\(=\dim(\mathbf{p})\)) is advised not to cause a significant increase of computational costs, while preserving flexibility and solvability of planning tasks.  
An initial value of the parameter vector \(\mathbf{p}\) highly impacts solvability of the planning task and also the resulting trajectory shape. Therefore, the multi-start technique is recommended, especially when planning is performed in the off-line mode.  
Our future research will concentrate on applications of the Lie-algebraic method in difficult environments (narrow passages, non-convex obstacles) in the task-space. In such environments, the planning should be performed through selection of sub-goals on the way to the target position. The main difficulty is the sub-goal selection is caused by the fact that nonholonomic systems are not governed by the Euclidean geometry, but rather the sub-Riemannian one [19]. The Lie-algebraic method is local and general-purpose, thus it is particularly well suited for solving tasks in partially known or dynamic environments.  
Conceptualization, A.M. and I.D.; methodology, A.M. and I.D.; software, A.M.; validation, A.M. and I.D.; formal analysis, A.M. and I.D.; resources, A.M. and I.D.; writing--original draft preparation, I.D.; writing--review and editing, I.D.; visualization, A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Wroclaw University of Science and Technology within Grant No. 821 110 41 60.  
The authors declare no conflict of interest.
## 5 Results
### Reaction Time Analysis  
The initial step in our data analysis involved conducting a normality assessment to determine if the data follows a normal distribution. For this purpose, we utilised both the Shapiro-Wilk and Anderson-Darling tests. The Shapiro-Wilk test, introduced by [56], is widely recognised for its efficacy in evaluating the normality of a dataset. The Anderson-Darling test, developed by [57], assesses the data's alignment with a specific distribution (in this instance, the normal distribution) by placing added emphasis on the tails. This phase was crucial as it directed our subsequent choice of either parametric or non-parametric statistical tests, based on the data's distributional properties.  
Data for the ADAS functions trained through video (AP-On and LKA) and those trained through the paper-based user manual (CA and ACC) were both found to deviate from a normal distribution, as confirmed by both normality tests. Given these results, traditional parametric tests like Analysis of Variance (ANOVA), which require data to follow a normal distribution, were not suitable for this study. Therefore, we chose to use the Mann-Whitney U test, a non-parametric method recommended for analysing datasets that do not meet the normality assumption [58]. The null hypothesis for this test asserts that the distributions of the two groups are identical, meaning there is no difference between them. When the null hypothesis is not true, it suggests that there is a statistically significant difference between the two groups. A \(p\)-value below a predetermined threshold \(p<0.05\) would lead us to reject the null hypothesis, indicating that one group tends to have higher or lower values than the other. Thus, a statistically significant result from the Mann-Whitney U test suggests that the observed differences between the groups are not attributable to random variability, but rather signify an underlying disparity between them [58]. These conclusions are drawn with careful consideration of the methodological choices employed and the distributional characteristics inherent to the data.
## 3 State-of-the-Art MIMO-SDR Systems
### Mimo-Sdr Waveforms
#### 3.2.3 MIMO-OFDM Variants
Therefore, it offers lower spectral sidelobes and computation latency, in addition to robustness to multipaths in high-loss environments. FBMC-OQAM uses QAM mapping, with in-phase and quadrature components that are diphase in time by half the symbol period, and a well-designed pulse-shape filtering to satisfy the orthogonality propriety. Hence, the interferences between the adjacent bands do not affect the quality of transmission for this scheme. In fact, the received data do not suffer from either ISI or ICI due to the orthogonality condition imposed between the adjacent bands. The diagram of an FBMC-OQAM baseband transceiver is depicted in Figure 10. \(\mathrm{s_{k}(m)}\) represents the transmitted symbol with the frequency and time indexes k and m, respectively. \(\mathrm{x_{k}(2~{}m)}\) and \(\mathrm{x_{k}(2~{}m+1)}\) are gathered by calculating the real and imaginary components of the symbol resulting from the mapping stage, respectively. g(t) is the time response of the symmetrical pulse-shaping filter [90]. Figure 9: UF-OFDM transceiver.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
###### Abstract  
Focusing on the global integrity monitoring performance of the BDS-3, the integrity failure model and effect analysis (IFMEA) of the system space segment and the ground control segment were carried out, and the system integrity risk tree model was established according to the IFMEA results. To evaluate the system's integrity risk probability more reasonably, this paper establishes an abnormal event prediction model with a Bayesian method, based on actual operation data, under a situation in which the BDS-3 just opened service. With statistical analysis results of system anomalies since 27 December 2018--which is the date the BDS-3 began providing basic service--according to the anomaly prediction model, the system's integrity risk probability under the 95% confidence limit was calculated to be approximately 0.9e-7/h, meeting the design index requirements of 1e-7/h. The analysis results also show that the main risk factors affecting the integrity of BDS-3 are 'pseudorange measurement anomaly' and'miss-detection of satellite autonomous integrity monitoring'. The results are important references with practical engineering significance for improving the integrity performance of BDS-3.  
BDS-3; SISRE; integrity risk tree; IFMEA; GNSS integrity monitoring Article
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
###### Abstract  
Power load forecasting plays an important role in power systems, and the accuracy of load forecasting is of vital importance to power system planning as well as economic efficiency. Power load data are nonsmooth, nonlinear time-series and \"noisy\" data. Traditional load forecasting has low accuracy and curves not fitting the load variation. It is not well predicted by a single forecasting model. In this paper, we propose a novel model based on the combination of data mining and deep learning to improve the prediction accuracy. First, data preprocessing is performed. Second, identification and correction of anomalous data, normalization of continuous sequences, and one-hot encoding of discrete sequences are performed. The load data are decomposed and denoised using the double decomposition modal (LVMD) strategy, the load curves are clustered using the double weighted fuzzy C-means (DBFCM) algorithm, and the typical curves obtained are used as load patterns. In addition, data feature analysis is performed. A convolutional neural network (CNN) is used to extract data features. A bidirectional long short-term memory (BLSTM) network is used for prediction, in which the number of hidden layer neurons, the number of training epochs, the learning rate, the regularization coefficient, and other relevant parameters in the BLSTM network are optimized using the influenza virus immunity optimization algorithm (IVIA). Finally, the historical data of City H from 1 January 2016 to 31 December 2018, are used for load forecasting. The experimental results show that the novel model based on LVMD-DBFCM load c-l curve clustering combined with CNN-IVIA-BLSTM proposed in this paper has an error of only 2% for electric load forecasting.  
power load forecasting; LVMD; DBFCM; CNN; BLSTM; IVIA
## 2 Materials and Methods
### Performance Metrics  
In this study, performances of the models were evaluated using three performance metrics, such as precision (P), recall (R), and F\({}_{1}\)-score (F1). Precision is a measure that indicates how many of the positive predictions are actually correct. Recall indicates how many positive cases were predicted out of all the positive cases. F\({}_{1}\)-score is a measure that combines both precision and recall. Precision is an important measure when there is a high cost associated with false positive. On the other hand, recall is important when the cost of false negative is high. F\({}_{1}\)-score is a measure that is used when a balance between precision and recall is expected. It is usually described as the harmonic mean of these two metrics. The metrics are calculated as below:  
\[\textit{Precision}=\frac{\textit{True Positive}}{\textit{True Positive}+\textit{False Positive}}\]  
\[\textit{Recall}=\frac{\textit{True Positive}}{\textit{True Positive}+\textit{False Negative}}\]  
\[\textit{F}_{1}\text{-}\textit{Score}=2\times\frac{\textit{Precision}\times \textit{Recall}}{\textit{Precision}+\textit{Recall}}\]  
There were two stages of the experiment: Identification of the best feature set and determination of the minimum size of the training set to produce the best performance. Figure 1 depicts the workflow of the entire experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Report Type** & **Abbreviation** & **Number of Reports** \\ \hline Interventional Radiology & IR & 273 \\ Mammography & MA & 167 \\ Magnetic Resonance Imaging & MRI & 1010 \\ Nuclear Medicine Technique & NM & 655 \\ Ultrasound & US & 644 \\ Computed Tomography & CT & 2741 \\ X-ray & XR & 4749 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of dataset.