esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 3 Literature Search and Analysis  
We conducted a literature survey of the ISI Web of Science database using the search term TOPIC ensemble AND TOPIC remote sensing. The search was then refined to include research areas Remote Sensing, Geography, Agriculture, Forestry and Environmental Sciences Ecology and document types Articles, Meeting, and Review articles. This yielded a total of 2247 results that we refer to as ensemble literature.  
Research articles accounted for 84% of the records returned, conference papers for 14% and review articles for 2% (Figure 1). The publication years of the refined search records returned ranged from 1979 to 2022. In 2015-2022, more than 100 papers meeting the search criteria were published annually (Figure 1).  
Figure 1: Statistics of ensemble literature in the refined areas.  
We then constructed a co-occurrence network by identifying keywords in the searched ensemble literatures, calculating the frequencies of their co-occurrences, and analyzing the networks to find central words and clusters of themes in the network (Figure 2). The constructed network showed that studies of ensemble learning algorithms were mainly related to classification, and studies of ensemble method use or ensemble models were primarily concerned with RF. In addition, the co-occurrence network suggested that applications concerning crop yield, biomass, rainfall, landslides, and satellite products were connected to ensemble algorithms.
## 3 Results
### In Situ FTIR
#### 3.1.1 FTIR Sample Holder and Neutron Background  
Figure 2 shows a technical drawing of the sample cells designed for the liquid and film samples for the simultaneous FTIR and SANS setup using the KWS-2. The cell is equipped with two ZnSe windows, each with a thickness of 1 mm, which enable a beam path of either 1 mm or 2 mm. A shorter beam path can be obtained by placing an additional ZnSe window with a thickness of 0.5 mm and an appropriate diameter inside of the holder. The chosen geometry allows for an easy filling and a tight closing of cells using the series of lateral screws, appropriate O-rings, and Teflon screws for the top openings used for filling in the samples. Tests of the holders have shown that they remain tight up to a temperature of at least 120 \({}^{\circ}\)C.  
The background yielded by the set of Al-coated mirrors and ZnSe windows in SANS is low (Figure 3) and, despite the weak Bragg reflexes from the ZnSe material, which can be observed in the high-Q scattering conditions (the small spots indicated by arrows in Figure 3c), very good background subtraction for SANS data of high quality can be achieved.  
Figure 3: The two-dimensional SANS patterns corrected for detector sensitivity collected at \(\mathrm{L_{D}=1.5\,m}\) from: empty beam (**a**), when the two Al-coated quartz windows (items 3 and 4 in Figure 1a) are added (**b**), and when the two ZnSe windows are added (**c**); data from the same setup as in (**c**) but collected at \(\mathrm{L_{D}=20\,m}\) are shown in (**d**). The yellow arrows indicate the Bragg reflections from the ZnSe material.  
Figure 2: Technical drawing of the new sample cuvette equipped with ZnSe windows, which is used for the simultaneous FTIR and SANS investigation of samples using the KWS-2. The liquid samples can be filled in by using the top openings, while the black O-rings, the series of screws (violet), and Teflon screws for the top openings provide the cuvette tightness. Cuvettes with 1 mm and 2 mm sample thickness are provided; the sample thickness can be further decreased to 0.5 mm or 0.2 mm by using additional ZnSe windows of appropriate thickness and diameter that can be placed inside of the cuvette, between the yellow windows shown here.
###### Abstract  
Silicon carbide (SiC) materials have demonstrated promising application prospects in modern manufacturing due to their outstanding physical and chemical properties. With its process flexibility and formation feasibility, binder jetting 3D printing technology has become a crucial technical approach to meet the demand for mass production of complex, high-performance SiC components. Addressing the technical challenges of traditional manufacturing techniques in achieving high-quality, complex-shaped SiC components, this paper systematically reviews the application of binder jetting 3D printing technology in fabricating high-quality SiC-based ceramic components, with a particular focus on the regulation of key process parameters affecting SiC green body formation quality and the optimization of post-densification processes. Firstly, this paper elaborates on the powder pretreatment, green part formation process, and post-processing chain involved in this technology, establishes an evaluation index system for formation quality, and provides research directions for rapid prototyping of SiC powders. Secondly, it provides an in-depth analysis of the influence patterns of jetting parameters (e.g., jetting conditions, powder characteristics, binder properties) and various post-processing techniques on the quality of SiC-based components, along with optimization methods to enhance the dimensional accuracy and mechanical properties of 3D-printed SiC components. Furthermore, this paper systematically summarizes advanced characterization methods for evaluating formation quality and demonstrates the technology's application potential across multiple industrial fields through representative engineering cases. Finally, it predicts the future development trends of this technology and discusses potential application expansion directions and key scientific issues in current research, aiming to provide theoretical references for promoting in-depth development of this technology.  
bin d 2025 15 6488 +
Footnote †: journal: Applied Sciences  
doi: 10.3390/app15126488
## 4 Discussion
In terms of practical implications, this finding, together with previous fNIRS results, may suggest that in situations aimed at sharing common goals or fostering behavioral changes, persuasive strategies must be acted at the beginning of the communication, because it seems that the initial phase is the one in which both the P-der and P-ded share a sense of togetherness at the physiological level. Fourthly, an increase in the dissimilarity index for HRV was found when the speaker was the P-der compared with when the speaker was the P-ded. This indicates that the HRV patterns between the two individuals were more different (higher dissimilarity) when the person speaking was playing the role of the P-der rather than the P-ded. HRV is often used as a measure of stress control and emotional regulation. A higher HRV dissimilarity index may reflect different strategies or capacities for stress regulation between individuals during their interaction. HRV synchrony has previously been observed in conditions involving linguistic synchronization (such as participants singing hymns and mantras in a choir) [30]. The dyadic analysis was not concerned with changes in individual HRV (for either the P-der or P-ded). A few studies have investigated psychophysiological variations during persuasion [53]. For instance, previous research reported changes in cardiovascular arousal due to narrative persuasion, i.e., successful persuasion was characterized by lower HRV [54]. Instead, the focus of this work was, for the first time, on the interaction between the two participants as a pair (dyad). The dissimilarity index highlighted that when the P-ded was speaking, the two individuals appeared to employ different approaches to stress management. Although single-subject analysis could have provided more detailed insights regarding the differences in stress management strategies between the two subjects, the results did not show statistically significant differences tied specifically to the roles of P-der and P-ded in the two conditions. Indeed, the individual subject analysis revealed no significant differences, while interesting effects emerged in the dyadic analysis. This suggests that the dyad rather than the individual subject serves as the more relevant statistical unit. Thus, interpreting dyadic dynamics and dissimilarity is not only more interesting but potentially more appropriate given the interpersonal nature of the phenomenon. In individual analyses, these differences are not apparent. Dyadic analysis provides added value by revealing descriptive effects of the interactive dynamics that would otherwise remain undetected. When we focus solely on single-subject analyses, we miss how the pair interacts and how these dynamics influence the neurophysiological signals. Even if individuals A and B do not exhibit intrinsic differences, the interaction between them creates a distinction that is evident when considered as part of a dynamic process.
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 2 Method and Model Establishment  
In order to achieve the UAV sound recognition, it is necessary to model and analyze the sounds and recognition methods. This chapter mainly describes a modeling study on the theories and methods utilized in the research. Firstly, we studied the propagation and collection of sound by modeling and analysis, and the data form for collection and the parameters used for collection are clarified. Secondly, by analyzing the classical MFCC feature extraction model, we discovered the mismatch between the classical method and the UAV sound frequency band. We also propose the frequency band feature extraction method. Finally, through CNN model analysis, we establish a two-dimensional (2-D) CNN recognition model to recognize the 2-D features of UAVs.
## 4 Evaluation Results and Discussion
### Performance Analysis
#### 4.2.3 Analysis of the First Scenario of \(VNR_{1}\)
Similarly, almost 50% of the paths with the lowest delay profiles shown in Table 12, highlighted by the \(L\)-based methodology, correspond to the same group of mediocre results, based on the outcome of QUAKE. Furthermore, 76 of 162 paths in the second most prominent \(L\) sets of the delay-oriented counterpart correlate with the penultimate group of QUAKE values, consequently leading to unsuitable resource allocations that may compromise the performance of the served application. Summarizing the results of the first experimental scenario, it becomes evident that the criticality of the emergency communications application (\(VNR_{1}\)) renders the monothematic evaluation mechanisms obsolete. The QUAKE framework outperformed its competitors in both the trial with fixed starting and ending nodes and the one with all paths calculated, proving its effectiveness regardless of scenario parameters. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Path Ordering** & **PJ** & **L** & \(RNR^{norm}\) & \(RCR^{norm}\) & **QUAKE** & **Hop Count** \\ \hline
**BAF** & 0.114 & 0.070 & 0.066 & 0.302 & 0.110 & 2 \\
**BEF** & 0.034 & 0.132 & 0.213 & 0.535 & 0.141 & 2 \\
**BAF** & 0.080 & 0.149 & 0.164 & 0.709 & 0.179 & 3 \\
**BCDGF** & 0.159 & 0.175 & 0.139 & 0.360 & 0.184 & 4 \\
**BCDEF** & 0.125 & 0.158 & 0.172 & 0.709 & 0.201 & 4 \\
**BAEF** & 0.170 & 0.114 & 0.180 & 0.709 & 0.203 & 3 \\
**BCDGF** & 0.261 & 0.175 & 0.074 & 0.419 & 0.224 & 5 \\
**BEDGF** & 0.102 & 0.237 & 0.246 & 0.709 & 0.231 & 4 \\
**BCDEAF** & 0.170 & 0.175 & 0.123 & 0.884 & 0.239 & 5 \\
**BEDHGF** & 0.205 & 0.237 & 0.180 & 0.767 & 0.271 & 5 \\
**BAEDGF** & 0.239 & 0.219 & 0.213 & 0.884 & 0.293 & 5 \\
**BAEDHGF** & 0.341 & 0.219 & 0.148 & 0.942 & 0.333 & 6 \\ \hline \end{tabular}
\end{table} 
Table 10: Produced path enumeration results for case of \(VNR_{1}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**QUAKE Value Groups**} & \multicolumn{8}{c}{**Path Length (in Hops)**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{5-8}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
0.041–0.141 & 12 & 23 & 4 & 0 & 0 & 0 & 0 & 39 \\
0.141–0.241 & 0 & 3 & 42 & 59 & 10 & 0 & 0 & 114 \\
0.241–0.341 & 0 & 0 & 0 & 11 & 77 & 66 & 12 & 166 \\
0.341–0.441 & 0 & 0 & 0 & 0 & 1 & 14 & 23 & 38 \\ \hline
**Total** & 12 & 26 & 46 & 70 & 88 & 80 & 35 & 357 \\ \hline \end{tabular}
\end{table} 
Table 11: \(VNR_{1}\) use case—QUAKE classification of all paths per path length category.
## 4 Results and Discussion
### Comparison of Face Image Inpainting Algorithms  
#### 4.2.1 Evaluation Metrics  
The quality of the inpainting results was mainly measured by the structural differences between the restored text images and the original complete images. The human brain can easily distinguish between two images, but this is not easy for computers. Image similarity measurement is not simply obtained through distance calculation. Currently, the Structural Similarity Index Measure (SSIM) and peak signal-to-noise ratio (PSNR) are two commonly used metrics for evaluating image processing results. SSIM is an index that measures the similarity between two images by comparing their brightness, contrast, and structure. The SSIM value ranges from \(-1\) to \(1\), with \(1\) indicating that the two images are identical and \(-1\) indicating that they are completely different. PSNR is an objective standard for evaluating images based on the mean squared error, with units in dB. The larger the value, the smaller the distortion.
## 1 Introduction
Figure 1: Additional components for stiffness modification: (**a**) curved damper [44]; (**b**) fluid viscous damper [45]; (**c**) buckling-restrained brace [46]; (**d**) steel diagonal brace [43]; (**e**) springs [48]. The proposed BMUA framework can be also applied along with repair/retrofitting techniques. In a schematic form, the post-repair condition \((\mathbf{M},\mathbf{K})_{post-R}\) is not fully unknown when \((\mathbf{M},\mathbf{K})_{post-R}\) are limitedly known due to structural damages or the coupling effect. \[(\mathbf{M},\mathbf{K})_{post-R}=(\mathbf{M},\mathbf{K})_{pre-R}+\Delta\mathbf{k} \tag{2}\]  
where the \((\mathbf{M},\mathbf{K})_{post-R}\) and \((\mathbf{M},\mathbf{K})_{post-R}\) are the pre-repair and post-repair structural systems characterized by mass \(\mathbf{M}\) and stiffness \(\mathbf{K}\), respectively. Once both mass and stiffness are precisely estimated for both systems, repair and retrofitting works can be confidently implemented to understand the full spectrum of structural elements using quantified retrofitting techniques (herein, stiffness modification, \(\Delta\mathbf{k}\)). In the proposed framework, \(\Delta\mathbf{k}\) is fully known because the design and actual contribution of existing retrofitting techniques are well estimated or analyzed before the implementations. The outline of the presented work is as follows: The formulations of the proposed BMUA are first described in Section 2, including new eigenequations, analytical formulations of optimal parameters and associated uncertainty. Subsequently, the capability of the proposed BMUA is evaluated by two numerical examples in Section 3; probabilistic damage detection is also performed. Finally, conclusions and a summary are presented in Section 4.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 5 Overview of Reviewed Datasets for ML-Powered IDS
### Cic-Ids2017  
All of the CIC datasets are created by the Canadian Institute for Cybersecurity, sometimes collaborating with an external institution [27]. In the case of CIC-IDS2017 [89], it is the first IDS dataset that the CIC created, back in 2017. The dataset is labeled, imbalanced, and was generated based on the most common attacks in 2016. The researchers aimed to create naturalistic benign background traffic and set out to mimic a real network traffic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Year** & **DDoS** & **\# of Records** & **\# of Features** & **References of Papers** \\ \hline KDDCUP 1999 [88] & 1999 & - & 4,898,431 & 41 & [78] \\ DARPA2000 [92] & 2000 & ✓ & 200,000+ & 41 & [54] \\ NSL-KDD [93] & 2009 & - & 150,000+ & 43 & [33; 66] \\ ISCX 2012 [94] & 2012 & ✓ & 2,450,324 & - & [31] \\ CTU-13 [95] & 2014 & ✓ & 150,000,000 & - & [31] \\ UNSW-NB15 [96] & 2015 & - & 250,000+ & 48 & [56; 57; 66; 70] \\ SNMP-MIB Dataset [97] & 2016 & - & 4998 & 34 & [44] \\ CIC-IDS2017 [89] & 2017 & ✓ & 2,830,743 & 80 & [29; 68; 69; 71; 79] \\ N\_BaIoT [98] & 2018 & - & 7,062,606 & 23 & [40] \\ Mirai [99] & 2018 & ✓ & 750,000+ & 115 & [56] \\ CSE-CIC-IDS2018 [100] & 2018 & ✓ & 16,232,943 & 79–83 & [52; 68; 69; 80] \\ DS2 OS [101] & 2018 & ✓ & 355,902 & 11 & [76] \\ CIC-DDoS2019 [102] & 2019 & ✓ & 94,000+ & 74 & [39; 61; 65; 68; 72] \\ BoT-IoT [26] & 2019 & ✓ & 73,360,900 & 46 & [28; 30; 45; 55; 56; 58; 61– \\ IoT23 [103] & 2020 & ✓ & 325,307,990 & 23 & 63; 67; 73; 75; 76; 78; 82] \\ TON\_IoT [85] & 2020 & ✓ & 22,000,000+ & 22–52 & [35; 57; 58; 55; 59; 61; 75] \\ IoTID20 [104] & 2020 & - & 100,000+ & 86 & [36] \\ Application-Layer DDoS & 2020 & - & 346,869 & 78 & [47] \\ Dataset [105] & & & & & \\ IoT-CIDDS [77] & 2021 & ✓ & 95,299 & 21 & [77] \\ ETF IoT Botnet [106] & 2021 & - & 2245 & 9 & [56] \\ Edge-IIoTset [83] & 2022 & ✓ & 20,952,648 & 61 & [10; 35; 41; 42; 43] \\ X-IIoTID [87] & 2022 & - & 820,834 & 68 & [35] \\ MedBIoT [51] & 2022 & - & 17,845,567 & 100 & [50; 51] \\ CICIoT2023 [107] & 2023 & ✓ & 45,000,000+ & 47 & [46; 59; 74; 76] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of public datasets (✓: includes DDoS attacks in the dataset; \(\cdot\): not included or unknown).  
capture in a complete network configuration. Seven types of attacks are represented in the dataset, including DoS, DDoS, Brute Force, Heartbleed, Web, Infiltration, and Botnet. Almost 2,300,000 of the records account for benign traffic, while the remaining 500,000 are attacks, with the DoS (250,000) and DDoS (128,000) attacks accounting for over half of the attack records. The researchers mention that the dataset contains over 80 features, though in all analyses with this dataset, including the researchers' own [89], only 80 or fewer features are extracted. Therefore, this dataset is considered to be imbalanced, and, similar to the previously discussed datasets, different mitigation strategies for this imbalance are employed by further research [108].
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 6 Conclusions and Future Work  
This paper demonstrated the performance of DL and UL NOMA PD in a 5G network with and without 64 \(\times\) 64 MIMO technologies. The BER and SE performance of DL NOMA was investigated and analyzed for various distances, power location coefficients, transmitted power, and BW, whereas the average capacity rate and OP performance of UL NOMA were examined for various distances, SNR, and BW. The DL NOMA system results showed that using 64 \(\times\) 64 MIMO enhanced the performance of BER, and SE, and solved the near-far user's problem, where the performance of all users becomes close to each other's for different transmitted power, distance, and power location coefficients parameters when compared without MIMO DL NOMA performance. The results demonstrated that the 64 \(\times\) 64 MIMO DL NOMA enhances the BER performance for the best user U4 from \(10^{-1.7}\) to \(10^{-5.2}\) at 80 MHz BW, and from \(10^{-1.5}\) to \(10^{-5}\) at 200 MHz BW at a transmitter power of 40 dBm. In contrast, the SE performance for the best user U4 is improved by 0.8% bps/Hz for 80 MHz BW and by 1.01% bps/Hz for 200 MHz BW at a transmitter power of 40 dBm. The UL NOMA systems results obtained using 64 \(\times\) 64 MIMO enhanced the average capacity rate performance by 12 bps/Hz, reduced the OP by 0.0150 for 80 MHz BW at SNR of 1 dB, improved the average capacity rate performance by 12 bps/Hz, and decreased the OP by 0.0120 for 200 MHz BW at SNR of 0.17 dB for the best user U4. In general, an increase in BW increases BER and average capacity rate while decreasing SE and OP. MIMO significantly improves the performance of all users. In the future, it will be looked into how MIMO cooperative NOMA and cognitive radio work together.  
Conceptualization, M.H.; methodology, M.H. and M.S.; software, M.H.; validation, M.H., M.S. and K.H.; formal analysis, M.H., M.S. and K.H.; investigation, R.S. and R.A.; resources, R.S., M.A. and R.A.; data curation, R.S., M.A. and R.A.; writing--original draft preparation, M.H.; writing--review and editing, M.A., R.S. and R.A.; visualization, M.H. and K.H.; supervision, M.S.; project administration, M.S.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNNURSP2022R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Figure 18: OP against SNR for four users with varying distances, with 64 \(\times\) 64 MIMO and BW 200 MHz for UL NOMA.
## 2 Research Object and Numerical Analysis Model
### Parameter Definitions  
The Reynolds number is defined by:  
\[Re=\frac{\rho U_{\text{in}}D_{\text{h}}}{\mu} \tag{1}\]  
where \(\rho\) is the fluid density, \(U_{\text{in}}\) is the inlet averaged velocity, and \(\mu\) is the viscosity coefficient of the fluid. \(D_{\text{h}}\) is the hydraulic diameter, and it is given by:  
\[D_{\text{h}}=\frac{4A}{P_{0}} \tag{2}\]  
where \(A\) and \(P_{0}\) are the area and perimeter, respectively.  
The rotation number is:  
\[Ro= \frac{\omega D_{\text{h}}}{U_{\text{in}}} \tag{3}\]  
where \(\omega\) is the rotational angular velocity.  
The Nusselt number is obtained by:  
\[Nu= \frac{qD_{\text{h}}}{(T_{\text{w}}-T_{\text{f}})\lambda} \tag{4}\]  
where \(T_{\text{w}}\) and \(T_{\text{f}}\) are the temperature of the wall and the mass flow average inlet temperature, respectively. \(\lambda\) is the fluid thermal conductivity. \(q\) is the heat flux.  
\(f\) is the resistance factor and it is presented as follows:  
\[f= \frac{\Delta pD_{\text{h}}}{2\rho LU_{\text{in}}^{2}} \tag{5}\]  
where \(\Delta p\) and \(L\) are the pressure drop of the channel and the flow length of the fluid, respectively.  
Reference Nusselt number \(Nu_{0}\)[19] and \(f_{0}\)[27] can be calculated by the following equations:  
\[Nu_{0}=0.023 Re^{0.8}Pr^{0.4} \tag{6}\]  
\[f_{0}=0.507 Re^{-0.3} \tag{7}\]  
where \(Pr\) is the Prandtl number.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Surface** & **PS\({}_{1}\)** & **PS\({}_{2}\)** & **PS\({}_{3}\)** & **SS\({}_{1}\)** & **SS\({}_{2}\)** & **SS\({}_{3}\)** \\ \hline \(\delta/\text{mm}\) & 1.44 & 2.16 & 1.80 & 2.40 & 2.16 & 1.80 \\ \(r/\text{mm}\) & 5.22 & 7.83 & 6.53 & 8.70 & 7.83 & 6.53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Protrusion structure size of each surface.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Case1** & **Case2** & **Case3** & **Case4** & **Case5** & **Case6** \\ \hline \(d/D\) & 0 & 0.06 & 0.12 & 0.18 & 0.24 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thickness of the turning vanes in different model.  
Figure 2: The protrusion structure diagram.  
TP is the comprehensive thermal performance, which can evaluate the cooling channel properties comprehensively, and it can be calculated as:  
\[TP=\left(\frac{Nu_{\text{all}}}{Nu_{0}}\right)\times\left(\frac{f}{f_{0}}\right)^ {-1/3} \tag{8}\]  
where \(Nu_{\text{all}}\) is the average Nusselt number of all heating surface including PS, SS, and tip wall surface.