esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Setup
### Analysis of Experimental Results of Different Datasets with Different Feature Selection Methods  
In the classification evaluation, precision is chosen as the evaluation metric, indicating the proximity of the value to 1 corresponds to higher classification accuracy. The bold font in Table 3 represents instances where the precision value of FSNT is equal to or greater than that of other feature selection algorithms. The performance of four feature selection methods (CSFFS, XGBFS, RFHFS, and FSNT), along with a non-Baseline feature selection algorithm, is compared using eight classification algorithms. Across most datasets, once the number of features reaches 20, the explanatory variance ratio exceeds 90%. Further increasing the number of features does not result in a significant improvement in the explanatory variance ratio. Consequently, these feature selection methods all choose 20 features and their classification accuracy is compared in combination with different classifier ratios. This finding aligns with the analysis presented in literature [49]. Table 3 presents the precision results of three datasets across eight classifiers: KNN, SVM, decision tree, random forest, Gaussian naive Bayes, neural network, logistic algorithm, and AdaBoost. The precision values in the table are reported with four decimal places. The bold values within each row indicate instances where FSNT outperforms the other three methods. Regarding the Student-mat dataset in Table 3, FSNT achieves the highest precision value under the SVM classification algorithm, surpassing the other three feature selection algorithms by 5%. Conversely, under the logistic algorithm, CSFFS, XGBFS, RFHFS, and FSNT achieve the same optimal precision value, but it is 4.72% lower compared to the scenario without a feature selection algorithm. Additionally, when considering the Gaussian naive Bayes algorithm, the FSNT algorithm once again achieves the highest precision value, demonstrating the best classification accuracy among the student performance datasets. Notably, the FSNT algorithm outperforms the CSFFS algorithm by 2%, the XGBFS algorithm by 5.52%, the RFHFS algorithm by 1.02%, and the non-feature selection algorithm by 5.54%, showcasing its distinct advantages. For the Student Archive dataset, the precision value of four of the eight classification algorithms for FSNT feature selection is the best. Among them, under the random forest
## 2 Related Works
### Automatic Sentiment Analysis  
Lastly, we introduce the automatic sentiment analysis utilized for analyzing sentiment polarity. Sentiment analysis, otherwise known as attitude and opinion mining, refers to the use of natural-language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information [9]. It aims to determine the attitude of a speaker, writer, or other subjects with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event. Sentiment analysis involves classifying attitudes in text into categories like \"positive\", \"negative\", or \"neutral\" [33]. Generally, current sentiment analyzers often represent an attitude with the following key factors [34]:  
Object: an entity which can be a person, event, product, organization, topic, etc.;  
Attribute: a feature of objects with respect to which evaluation is made;  
Attitude orientation or polarity: the orientation of an attitude on an object representing whether the attitude is positive, negative, or neutral;  
Attitude holder: the person or entity that expresses the attitude.  
The definitions of \"object\" and \"attitude\" in sentiment analyzers of attitude can match those of \"concept\" and \"attitude\" in psychological measurements. Although \"attribute\" has different definitions in automatic methods and psychological measurements, we do not need to match them in this work because we are not working on aspect-based attitude analysis [35]. It is noted that state-of-the-art sentiment-based attitude measurements also do not clearly distinguish between explicit and implicit attitudes bias [36; 37; 38]. In this study, we employ pre-trained machine-learning models [39] to analyze the sentiment conveyed in statements. Pre-trained machine-learning methods refer to the process of initially training a model on a large-scale dataset to acquire knowledge of the underlying language structure, followed by fine-tuning for specific tasks. Specifically, two sentiment analysis models, namely Stanford CoreNLP [40] and Baidu Sentiment Analyzer [[https://ai.baidu.com/tech/nlp_apply/sentiment_classify](https://ai.baidu.com/tech/nlp_apply/sentiment_classify)]([https://ai.baidu.com/tech/nlp_apply/sentiment_classify](https://ai.baidu.com/tech/nlp_apply/sentiment_classify)) (accessed on 12 March 2024), were employed. These two models combine probabilistic models, machine learning, and linguistic knowledge to analyze sentiments of statements.  
Although automated sentiment analysis has been widely studied and applied in fields such as public opinion analysis and human-machine dialogue, there has been no research yet on applying sentiment analysis techniques to identify people's biases. This not only requires us to theoretically attempt to correlate the key elements of sentiment analysis with the elements of bias measurement in the field of psychology but also necessitates the design of reasonable automated methods to apply this correlation to real-world data.
## 2 Materials and Methods
### Experimental Set-Up  
The measurements required for the validation of thermo-electric models were performed in the experimental field of the Institute of Energy Technology, Faculty of Energy Technology, University of Maribor. The experimental field consists of nine dual-axis PV tracking systems with additional measuring equipment (shown in Figure 1). One dual-axis PV tracking system consists of twenty series-connected 260 W\({}_{\text{p}}\) silicon PV modules with a total installed power of 5.2 kW\({}_{\text{p}}\). Each PV tracking system is equipped with a pyranometer for solar radiation measuring, four temperature sensors for measuring the operating temperature of the PV module on the backside and a DC/AC inverter. The DC/AC inverter does not include measurements of AC and DC current, voltage and output power data, therefore a Power Meter (Siemens SENTROM PAC4200--AC measurements) and a Hall Effect Sensor (T201 DCH100--DC measurements) are additionally installed. The entire system is connected to a several-local-and-one-global PLC (Siemens S7-300), while the data is displayed in a SCADA software system that allows easy monitoring and management. All meteorological and experimental data are sampled every 5 min. Several detailed information on the accuracy and calibration time of the measuring devices are shown in Table 1.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 5 Malware Detection Techniques
### Behavior-Based Malware Detection  
Utilizing tracking tools, the behavior-based malware detection method conducts an analysis of the programmed software's activities to ascertain whether it can be classified as malicious. Despite variations in code, the majority of new malware can still be identified through its behavior, as this has remained constant, despite the fact that malware may not always act regularly when operating in an isolated environment (virtual machine, sandbox environment). Consequently, benevolent codes could erroneously be labeled as malevolent. The proposed technique on Windows OS has the potential to detect both unknown malware and malware that has been previously secured. The proposed technique provides a more extensive perspective on commonplace malicious software behavior, along with the specific activities that malicious software is known to execute. Behavior-based identification commences with the unveiling of behaviors through the application of one of the aforementioned methods, and a dataset is constructed by extracting the characteristics through data mining. Subsequently, ML algorithms are employed to select and label specific features of the information [48; 53; 54]. The schema for the behavior-based malware detection technique is given in Figure 9.  
Figure 8: Heuristic-based Malware Detection Schema.  
Behavior-based malware detection involves a multi-step process for effective identification and classification. The detection schema typically consists of three main steps, each leveraging different techniques and methodologies.
# The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins
## 3 The Relationship of Chemical Structure and the Antioxidant Ability of Peptides
### The Relationship of Peptide Structure and Its Antioxidant Activity
#### 3.2.3 Amino Acids Sequence
The iron chelating capacity was inferred involving the -NH group of the imidazole ring of the histidine residue, especially when His is located at the C-terminal extremity, which can chelate the Fe\({}^{2+}\) ion through coordination bond. Moreover, as being located at the \(N\)-terminal of the peptides, the \(\beta\)-Ala can drastically enhance the chelation capacity of His. However, the effect does not happen to \(\alpha\)-Ala, which cannot form two hydrogen bonds as easily as \(\beta\)-Ala. Moreover, the single indole ring of the Trp residue was not involved in the iron chelation mechanism. However, the best half maximal inhibitory concentration values measured using metal chelating activity were shown by LPWRPATNVF, AWFS and YGIKVGYAIP to be 0.001 \(\upmu\)M, 0.002 \(\upmu\)M and 0.087 \(\upmu\)M, respectively [13]. The metal chelating activity of the three peptides may be related to the combined effects of the indole, benzene and phenol rings existing in Trp, Phe and Tyr, respectively. Furthermore, the values of the chelating capacity indicate that the synergetic effect of two phenol rings in YGIKVGYAIP may be considerable poorer than that between an indole ring and a benzene ring in LPWRPATNVF and AWFS. Trp and Phe will even improve the synergistic action due to the larger space provided for metal ions. Therefore, the interaction of amino acids and the relative location in the sequence can significantly influence the antioxidant capacity of peptides. A sequence of hydrophobic amino acid residues like Val or Leu at the \(N\)-terminus containing Pro, His or Tyr, is characteristic of antioxidant peptides. In the sequence, Pro tends to interrupt and change the secondary structure of: the peptide molecule, which may increase the availability of the amino acid residues of the peptide sequences to act as antioxidants [53]. Leu and the Ser-Leu/Th r-Leu/Pro-Leu motifs in their sequences, particularly at the N- and C-terminus, seem to contribute to their superior scavenging activities [54]. According to the results obtained by [PERSON] and [PERSON], antioxidant activity is correlated with structure related to the electron properties, hydrophobicity, stericity, and hydrogen bonding properties of amino acids for tripeptides [55]. The electron properties of amino acid residues are very important, and bulky hydrophobicity at the C-terminal is also closely related to the antioxidant activity. The results suggest that the electronic, hydrogen-bonding properties and location of the amino acids, along with the steric properties of the amino acid residues at the C- and N-termini may be the root of the antioxidant activity of the whole peptides.
## 4 Hardware and Implementation Results
And a series voltage generator, such as a DVR, can be utilized to add the harmonics for use in multi-frequency systems [2; 36; 37]. The simulation results, together with laboratory data, show that harmonic injection maintains voltage stability and load quality throughout all dynamic scenarios, including load changes and mode transitions. The proposed LVDC back-to-back system architecture includes modular design features that enable future scalability for connecting higher-capacity loads and additional distributed energy resources (DERs), including photovoltaic (PV) arrays and battery storage systems, and electric vehicle charging units. System expansion can be achieved through converter parallel operation or by upgrading existing power stage capacities while preserving the established control strategy and multi-frequency coordination. The system uses industrial components that are widely available, including standard three-phase inverters together with LC filters and digital control platforms. The control software manages harmonic injection and separation operations without requiring special hardware components, thus minimizing both capital costs and system flexibility. A comprehensive economic evaluation exceeds the current study boundaries, yet preliminary assessments indicate that the power routing complexity from harmonic-based control methods provides multiple operational advantages. The system achieves better power transfer efficiency through harmonic channels without raising peak voltage levels and optimizes line capacity utilization, and delivers high-quality 50 Hz voltage to loads. The system operates in dual mode to enable peak load reduction and self-consumption optimization, which leads to long-term operational cost savings. The proposed system presents beneficial features for real-world implementation in smart distribution systems and flexible microgrids because it offers modularity and high-power quality together with efficient infrastructure utilization.
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 1 Introduction
First, the volatility of attributes in real scenarios is considered, and a time-series decision matrix is constructed based on the average growth rates to make the results closer to reality. Then, hash functions are used to map the decision opinions to different dimensions and express the similarity through the Hamming distance, yielding clustering results with high stability and cohesion. In this paper, we proposed a new Locality-Sensitive Hashing-based Random Sample Consensus algorithm (LSH-RANSAC) to enhance the fitting accuracy and efficiency of the data point set. The LSH-RANSAC combines LSH and RANSAC to improve the efficiency and robustness aiming at modeling parameter fitting of central data point sets that contain outliers. Our algorithm shows a great enhancement compared with three common linear fitting algorithms above due to the efficient outlier removing process. This would greatly improve the linear fitting process under noisy environments, which would give benefits to auto-driving, AI recognition, and industrial components generation.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 2 Development of BMS
### Sensor Measurement
#### 2.1.2 Temperature Measurement  
Temperature measurement for battery applications utilizes sensors that change resistance with temperature. Common sensor types include thermistors, thermocouples, and RTDs. RTDs are widely used to measure battery temperatures due to their accuracy [17; 34]. In this study, PT100 Adafruit MAX31865 sensor breakout boards were chosen due to their low power consumption (1.6 \(\mu\)W to 1.8 mW) including 2, 3, and 4 wire compatibility with high accuracy and stability [35]. The relation between the resistance of the temperature sensor and the temperature was obtained based on [PERSON]'s equation [36], as shown in Equation (5):  
\[R(t)=R_{0}\left(1+\alpha t+\beta t^{2}\right) \tag{5}\]  
where, \(R_{0}=100\;\Omega\) (resistance at 0 \({}^{\circ}\)C) and \(\alpha=3.90830\times 10^{-3}\), \(\beta=-5.775\times 10^{-7}\). Five temperature sensor breakout boards were used, with one attached to each battery cell represented in Figure 1 and an additional sensor on board for ambient temperature monitoring. This setup was calibrated by comparing the ambient temperature of the room to the ambient temperature collected by the sensor.
## 3 Results
### Evaluation of Landslide Susceptibility Models
#### 3.3.3 Map/Model Comparison  
To better investigate the similarity of the models, we calculated the LSI and susceptible zone difference between them (Figure 8). The residual maps present the differences in the LSI and susceptibility zones. Five differences in the susceptibility classes can be distinguished (0-4 zones difference), as presented in Table 6. Green color represents no difference in the susceptible zones between these two models, while light red and light blue represent one susceptible zone difference (1,\(-\)1). According to Table 6 almost half of the map (47%) presents no difference in landslide susceptibility zones, and 42% differs in one susceptibility zone. Based on the difference between the left and right images, it can be seen that negative and positive values exist between these two sides of the study area, respectively (Figure 8). This is because the \"Lososina\" model was adjusted to the \"Lososina\" area and the \"Grodek\" model was adjusted to the \"Grodek\" area.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model/** & & & & & **Landlide Pixels within** & **Percentage** & \\
**Modeling Area** & **Class** & **Validation Area** & **Pixels in Domain** & **\%** & **the Specific Class** & **of Total** & **SCAI** \\ \hline \multirow{6}{*}{Grodek} & Very low & & 894,142 & 0.08 & 1006 & 0 & 194.50 \\ \cline{2-6}  & Low & & 3,390,427 & 0.30 & 22,136 & 0.01 & 33.48 \\ \cline{2-6}  & Moderate & & 6,373,115 & 0.56 & 324,619 & 0.13 & 4.27 \\ \cline{2-6}  & High & & 6,344,671 & 0.55 & 953,086 & 0.38 & 1.45 \\ \cline{2-6}  & Very High & & 2,841,621 & 0.25 & 1,185,391 & 0.48 & 0.52 \\ \cline{2-6}  & Very low & & 1,236,346 & 0.11 & 3176 & 0 & 70.74 \\ \cline{2-6}  & Low & & 2,948,451 & 0.26 & 196,954 & 0.09 & 2.72 \\ \cline{2-6}  & Moderate & & 4,448,486 & 0.39 & 924,964 & 0.45 & 0.87 \\ \cline{2-6}  & High & & 2,431,304 & 0.21 & 786,222 & 0.38 & 0.56 \\ \cline{2-6}  & Very High & & 370,523 & 0.03 & 166,095 & 0.08 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & & \(R_{ind}\) \\ \hline
**Area** & **“Lososina” [\%]** & **“Grödek” [\%]** \\ \hline “Lososina” area & 90 & 46 \\ “Grödek” area & 57 & 86 \\ Total & 72 & 68 \\ \hline Total average & & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(R_{ind}\) calculated for very high and high susceptibility classes.  
The correlation index calculated for both susceptible maps is 0.697. This value is quite similar to the total average landslide density in the high and very high susceptibility zones for the entire study area (compare with Table 5). Therefore, the overall accuracy of both of these susceptibility maps for the entire investigated region can be estimated at 70%. However, it must be mentioned that the accuracy changes from region to region. In the area where the landslides used for modeling are located, 90% accuracy can be expected, while in the area where the landslides used for modeling are not preserved (validation area), an LSM accuracy between 46% and 57% can be expected.
## 3 Results and Discussion
### Results and Analysis
#### 3.2.2 Effect of the Inner Ring Speed on the Slip Rate and Temperature  
The test conditions are as follows: the radial load is 130 N; the lubricating oil quantity is 5 mL/min; the inner ring speeds are 1050 r/min, 1230 r/min, 1410 r/min, 1590 r/min, and 1770 r/min. The measured minimum load is 125 N, the benchmark is 130 N, and the error of them is 3%. Figure 7a shows that the temperature in the contact area increases continuously with the increase of the inner ring speed. From the general trend, the slip increases with the increase of the inner ring speed. The rotary accuracy of the clamping mechanism is higher in the improved test rig. The load has inevitably small fluctuations that result from the roller clamping end having run-out within 0.02 mm. Due to the extremely light load (radial load is less than 200 N), this small load fluctuation also has a significant effect on the temperature rise and slip rate in the contact area. For example, when the speed is 1590 r/min and the load is 125 N, the slip rate is 0.54. When the speed is 1770 r/min and the load is 126 N, the slip rate is 0.53. As the load in the working conditions fluctuates slightly, the load under the condition of a rotating speed of 1770 r/min is larger than that under the condition of 1590 r/min, and so the slip has a slight decreasing tendency. The overall trend showed that the higher the rotational speed, the higher the slip rate and temperature. Figure 7b is the scatter diagram of load for the five cases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Benchmark** & **Measured Value** & **Error** \\ \hline
70 N & 64 N & 8\% \\
100 N & 95 N & 5\% \\
130 N & 127 N & 2\% \\
160 N & 151 N & 5\% \\
190 N & 181 N & 4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The error of radial load.  
Figure 6: (**a**) Temperature and slip rate of the contact area under different radial loads. (**b**) Scatter diagram of load.
## 3 Methodology  
Figure 2 summarizes the hybrid multi-process ISA mapping procedure, which is a progressive process consists of four main stages. Initially, all the pre-processed remote sensing data were used to provide the spectral and spatial information for subsequent steps. Then, the object-based support vector machine (SVM) model was adopted to identify impervious characteristics and distinguish the impervious/pervious area, and later combined with the LSMA method to estimate further ISA fraction in the study area. The estimation results based on this object-based SVM-LSMA were used to compare with the results based solely on LSMA method. Lastly, the two approaches were compared by assessing their potential benefits and tradeoffs in urban spatial pattern.  
Figure 1: The study area of Guangzhou, China.  
Figure 2: Flowchart of impervious surface analysis: Gray boxes represent inputs and general processes, white boxes represent specific methods and white boxes with dashed borders represent results.
## 4 Conclusions  
In conclusion, a hollow-core THz Bragg fiber with a defect layer is proposed. The tunable temperature characteristic of the liquid water filled in the proposed THz fiber is analyzed. The results show that the core power fraction is more than 98% over the frequency range of 0.3 THz to 1 THz. The confinement loss and the low-frequency side of the dip near 0.5 THz can be controlled by the temperature of the liquid water. The temperature sensitivity of the proposed THz fiber is obtained at 0.09614 dB\(\cdot\)m\({}^{-1}\)/K at 0.45 THz. With advantages like low loss and cost, controllability, and easy fabrication, the presented fiber has potential applications in THz tunable devices.  
Conceptualization, J.S. and M.S.; methodology, J.S. and M.S.; software, M.S.; validation, M.S., J.S. and L.T.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S. and J.S.; supervision, D.X., H.C. and J.Y.; project administration, W.X., H.B. and Z.X.; funding acquisition, J.S. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant numbers 61905177, 61705164, and 81802118; Natural Science Foundation of Tianjin City, grant numbers 19 JCQNJC01400 and 19 JCQNJC01600; Education Commission Research Project of Tianjin, grant number 2019 KJ015; the key Technologies R&D Program of Tianjin, grant number 18 YFZNGX00040; Open Project Funding of Key Laboratory of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, grant number 2018 DCKF002; and the China Postdoctoral Science Foundation funded project, grant numbers 2019M651035 and 2019M661013.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The available data has been stated in the article.  
This manuscript has not been published yet and is not even under consideration for publication elsewhere.  
The authors declare no conflict of interest.