esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Review Methodology
#### 3.1.1 Preparation Phase  
This stage involved defining the research questions and selecting appropriate databases for the literature review. The primary databases included Google Scholar, ScienceDirect, OpenAlex, Engineering Village, Emerald, ProQuest, ASCE, and Web of Science. Zotero was used to organize references due to its advanced features. The review was limited to studies published between 1990 and 2024. The study defined the research problem and set clear objectives. An extensive review of the literature was undertaken, with its methodology guided by the PRISMA framework [11; 32; 59; 69; 70; 71; 72; 73]. PRISMA was chosen for its systematic approach to disseminating and screening collected documents, facilitating a thorough review. The PRISMA methodology used in this review comprises four steps:  
Figure 4: Illustration of the hierarchical relationships between artificial intelligence, machine learning, and deep learning.  
Data Search Policy: Databases, search keywords, and limitations were defined.
* Screening and Filtering: Titles and abstracts of the collected documents were evaluated, and irrelevant ones were excluded.
* Eligibility Determination: Full-text documents were assessed to confirm relevance, with non-applicable documents being removed.
* Data Extraction: Relevant data were retrieved for further interpretation.
# Analysis on Free Vibration and Stability of Rotating Composite Milling Bar with Large Aspect Ratio
## 5 Conclusions  
A continuous dynamic model was developed for a rotating composite milling bar. First, the natural frequencies of free vibration were calculated by Galerkin method. Then the stabilities of milling bar subject to deflection-dependent cutting end loads were analyzed in time domain. Furthermore, the model included delay effects due to regenerative, external and internal damping, rotating gyroscopic and inertial force. The results obtained in time domain were compared to the results calculated in frequency domain, and the comparison showed that both results in time and frequency domains were basically consistent.  
The following conclusions can be obtained from the analyses:  
1. Compared with [PERSON]'s model, the model in this study considers not only the gyroscopic effect, internal damping and external damping of the rotating milling bar, but also the effect of the rotary inertia. Moreover, the calculation of this model is simple and convenient, and the results can meet certain accuracy requirements. These advantages are the innovation of this paper.
2. Based on the analysis and calculation results in this paper, the use of composite materials instead of steel for the structural design of milling cutter bar can improve the stability (by 15%) and machining efficiency (increasing cutting speed) to a certain extent.
3. The internal damping affects the stabilities, and the effect increases with the increase of rotating speed. At a high cutting speed, an unstable zone is triggered by internal damping.
4. The amplification of external damping leads to the expansion of the stable zone.
5. Ply angle has a great influence on the stable zone and the largest stable zone is reached at the ply angle of 0.
6. The rotation effect plays a negative damping role in the stability analysis.  
In brief, it has demonstrated that application of composite milling bar can partially improve the chatter stability.  
Conceptualization, J.M. and Y.R.; methodology, J.M.; software, J.X.; validation, J.M. and J.X.; data curation, J.X.; writing--original draft preparation, J.M.; writing--review and editing, Y.R.; project administration, J.M.; funding acquisition, J.M. 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 51605264 and11672166.  
The authors declare no conflict of interest.  
Figure 13: The influence of rotating.
# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
## 4 IHHO-LightGBM Fault Detection Model  
### Data Cleaning and Preprocessing  
Data of the wind turbine-based pitch control system are collected by various sensors on the wind turbine, and then signals are collected by the SCADA system. However, in the process of wind turbine operation, sensors are easily affected by unstable environmental factors and their own anomalies, and the collected data will be missing and abnormal. Therefore, it is very important to clean and preprocess the data.  
Figure 4: The process of IHHO.  
First, we should process the original data and delete the data which vacancy values with the status quantity of \"0\".  
Because the operation of the pitch system directly affects the power output of the wind turbine, the most important parameter of the pitch control system is power output. Based on the principal component analysis of wind turbine data, [37] the Pearson correlation coefficient will be used to analyze the correlation between power output, which used as the main variable, and other parameters in the process of feature selection, and the parameters with a high correlation with the pitch control system will be retained, to perform the second cleaning and processing of the data.  
Figure 5 is the Fault detection flow chart of wind turbine pitch system based on improved HHO-LightGBM.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 2 Backgrounds and Security Problems
### Analysis of Attacks in WSNs
#### 2.2.3 Layer-Based Attacks in Network Architecture  
Physical layer attacks: Usually involve activities targeting the physical components of the system, changing the structure or operation of sensor nodes, as well as degrading the quality of collected data, or harming network performance [36]. Different types of attacks on the physical layer include eavesdropping attacks [37]; jamming attacks [28]; attacking a compromised node [38]; and attacking via node copying [39].  
Data link layer attacks: These are activities aimed at communication protocols and processes at the data link layer. Some common attack methods in the data link layer of WSNs are collision attacks; smart jamming attacks; and sleep denial attacks [40]. Figure 4 illustrates the power consumption of the node in different modes including data transmission/reception, idle mode, and sleep mode [41] in which the energy consumption of nodes in sleep mode is the lowest, so attacks are often aimed at blocking nodes from falling into sleep mode so that the nodes quickly exhaust energy [41].  
Network layer attacks: These attacks aim to focus on network protocols and infrastructure. Some of the common network layer attacks are the replay attack and the Sybil attack [43]. Other types of attacks include black hole attacks (Figure 5a) [44], gray hole attacks (Figure 5b) [45], wormhole attacks [46], sinkhole attacks [47], and hello flood attacks [48].  
Figure 4: Energy consumption of network nodes in different modes [42].  
Transport layer attacks: Usually involve activities targeting communication protocols and processes at the transport layer. Some common attacks in the transport layer of WSNs: flooding attacks, attack synchronization, etc. [49; 50].  
Application layer attacks: These are activities aimed at specific applications or services that the sensor network is providing, including attacks on reliability, and malware attacks [50; 51].  
Cross-layer attacks: These are types of attacks that combine and affect layers of the network system. Some typical types of attacks include jamming attacks, DoS attacks, and man-in-the-middle attacks [49; 52].  
Although we have analyzed the attack classification from various perspectives to provide a systematic and comprehensive overview of attacks in WSNs, it should be noted that real-world attacks may satisfy different classification criteria (e.g., an insider attack may be classified as both active and passive, or an eavesdropping attack may fall into both physical and passive attack categories). This reflects the diversity and complexity of modern attacks directed at WSNs.
## 3 Analysis and Discussion of Physical Modeling Results  
Prior to data interpretation, data quality analyses were performed to ensure the reliability of the interpreted results. A slice of the seismic data was first given to analyze the fracture energy distribution, and Figure 6 shows an east-west oriented stacking and post-stack seismic profile in the middle of the model. The top and bottom surfaces of the model were clear, and the model seismic profile was consistent with the model design profile as well as the real seismic profile. The SBLR of the central Ordovician fractures could be recognized, and different fractures showed different SBLR characteristics, which were similar to the SBLR of the fractures in the real seismic profile [37; 42] and were consistent with the numerical forward simulation results. Overall, the quality of the seismic data was sufficient for further reservoir interpretation.  
According to the modeling design profile, the fractured reservoirs were all at the north-south 5000 line. The specific correspondence between the SBLRs in seismic migration profiles and the fractured reservoirs was easily recognized because the physical model had known geometry and physical properties (Figure 7). The seismic profile stretching scale was adjusted using the north-south 5000 line in the model, which facilitated the determination of the SBLR locations of different fractures and the different dips of the fractured reservoirs corresponding to different SBLRs (Figures 8-10).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Physical Model** & **Geological Model** \\ \hline Receiver lines & 23 & 23 \\ Short lines & 29 & 29 \\ Receiver line interval & 35 mm & 350 m \\ Shot line interval & 35 mm & 350 m \\ Trace interval & 2.5 mm & 25 m \\ Shot interval & 5 mm & 50 m \\ Number of traces & 491 & 491 \\ Sampling points & 4096 & 4096 \\ Sampling interval & 0.2 μs & 2 ms \\ Bin & \(1.25\times 1.25\) mm & \(25\times 25\) m \\ Dominant frequency & 500 kHz & 25 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the 3D seismic observation system.  
Figure 5: Three-dimensional seismic data acquisition.  
Figure 6: Seismic profiles of the east-west 4600 line. **(a)** Stacking profile. **(b)** Post-stack migration profile.  
Figure 7: The migration profile of the north–south 5000 line. The red, blue, and green boxes contain the seismic response of the fractured reservoirs in the top of the Yingshan Formation, the fourth, member of the Yingshan Formation, and the Penglaiba Formation, respectively.  
Figure 8: Model and results in the top of the Yingshan Formation. The profile location is identical to that of the red box in Figure 7. (**a**) Model of fracture reservoirs. (**b**,**c**) are the color and waveform plots of the migration results, respectively. (**d**) Frequency attribute.  
Figure 9: Model and results in the fourth member of the Yingshan Formation. The profile location is identical to that of the blue box in Figure 7. (**a**) Model of fracture reservoirs. (**b**,**c**) are the color and waveform plots of the migration results, respectively. (**d**) Frequency attribute.
## 3 Face Recognition System
### Face Recognition Algorithms
#### 3.2.2 Holistic Approach  
While algorithms for face recognition based on the geometric approach make use of local features of the face, those based on a holistic approach analyze the face image as a whole. The main advantage of this approach is the spared time in face detection, since there is no need for time-consuming operations of accurately localizing face landmarks such as the mouth, eyes, or the nose. The whole face image is used for analysis, and as such, operations are performed to its pixels in order to create a face model. Depending on the conditions in which the images are taken, sometimes pre-processing is needed, such as histogram equalization in order to lower the influence of environmental factors in recognition results.
# Bioactivity and Delivery Strategies of Phytochemical Compounds in Bone Tissue Regeneration
## 4 Bioactive Phytochemicals and Bone Signaling Pathways
### Bone Cells Differentiation Activity  
Phytomolecules are not only responsible for bone resorption inhibition, but also promote bone formation by aiming at osteoblasts differentiation. For example, EGCG positively acts on osteoblast differentiation and MSC proliferation by upregulating BMP2 and Runx2 expression [91]. Likewise, myricetin can promote osteoblast differentiation and activity, by targeting SMAD-1/5/8, downstream of BMP signaling [92]. It has been demonstrated that myricetin and Baicalin affect osteoblast and osteoclast differentiation and function also through Wnt/\(\beta\)-catenin pathway [93; 94]. Additionally, due to structural similarity to mammalian estrogens, some polyphenols can bind estrogen receptors (Ers) that are called phytoestrogens [95]. Among them, vanillic acid upregulates the expression of osteoblastic differentiation markers, i.e., Runx2, OCN, and OPG, by activating ERs pathway [96]. Rutin, instead, downregulated the RUNX suppressor genes [97] and exerts its osteogenic effect through an ER-mediated mechanism [98]. In addition, phyto-derived neurotransmitters such as dopamine, commonly found in fruit and vegetables (in particular bananas), promote VEGF and bFGF expression, leading to enhanced angiogenesis and osteogenesis [99]. Furthermore, several studies have highlighted the crucial role of polyphenols in regulating gene activation or silencing through epigenetic modifications such as DNA methylation and histone modification [100]. Resveratrol is one of the main activators of Sirt, a known NAD-dependent deacetylase, which induces a conformational change in proteins, translating into an increase in enzymatic activity [101]. Resveratrol induces the MSC differentiation into osteoblasts via a very complex mechanism, which could be direct or indirect. Indirectly, resveratrol, through the interaction of Sirt1 with nuclear receptor co-repressor (NcoR), inhibits peroxisome proliferator-activated receptor gamma (PPARy) [102], while directly activating RUNX2 transcription factor by forming a Sirt1-Runx2 complex [103]. Resveratrol-mediated activation of Sirt1 enhances phosphorylation of downstream kinases involved in osteoblastic differentiation, such as PKB/Akt, SMAD1/5/8, AMPK, and MAPKs [104; 105]. Furthermore, quercetin stimulates osteoblast differentiation through the stimulation of the expression of TGF-81, BMP-2, and Runx2, via activation of ERK1/2, p38, and JNK MAPKs [106]. Finally, curcumin regulates the expression of genes implicated in RANKL-induced osteoclast differentiation through the suppression of NF-kB [107]. Table 1 recaps the phytochemical-related bone regeneration signaling pathways.  
The cellular responses resulting from the activation of different biological pathways underline the importance of natural bioactive molecules and their ability to modulate inflammatory processes, oxidative stress, and cellular differentiation.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Probabilistic Modeling (PM)  
Incorporating probabilistic modeling techniques into finite element modeling for simulating fatigue crack growth involves accounting for uncertainties in material properties, loading conditions, and environmental variables [172; 173]. These methodologies offer a more authentic evaluation of crack growth patterns by recognizing the inherent variability present in real-world situations. They enable the identification and measurement of uncertainties in input variables (such as material characteristics, loading intensities, and residual stresses) that have the potential to influence crack propagation [174; 175; 176]. Advantages of these approaches include enhanced predictive capabilities, realistic modeling, improved risk assessment, and greater safety by identifying critical areas susceptible to failure. However, challenges exist, such as computational complexity, data requirements for reliable characterizations, difficulties in defining appropriate probability distributions, and the complexity of interpreting probabilistic results. Several prominent probabilistic techniques employed in finite element modeling for simulating fatigue crack growth encompass Monte Carlo Simulation [177; 178], Reliability Analysis [179], Bayesian Methods [180], and Response Surface Methodology [181].
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 4 Results  
We first calibrate the parameters for seven correction methods (i.e., SMP, GPM-Linear, GPM-Nonlinear, GPM-CDF, SMP-Linear, SMP-Nonlinear, and SMP-CDF) based on GPM (or SMP) and CMPA precipitation dataset during May to September of 2015 to 2016. We then evaluate these methods by comparing the corrected precipitation derived from these seven methods with the CMPA precipitation in May to September, 2017.
## 4 Discussion  
The results obtained require division into linear and nonlinear model selection algorithms. The division generated makes it possible to analyze the results according to the model type and to identify the sequence of each feature. Tables 12 and 13 show the algorithms by group, the feature selection order, and the mean.  
The averages calculated and reported in Tables 12 and 13 indicate two different behaviors depending on the model performance (linear or nonlinear). Figure 8 shows an alternative way to visualize the performance between linear and nonlinear models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & **Variance** & **Mutual** & \multicolumn{4}{c}{**Sequential Backward Deep Tree Values**} & \multirow{2}{*}{**Mean**} \\ \cline{2-2} \cline{4-6}  & **Threshold** & **Information Gain** & **2** & **3** & **4** & **5** \\ \hline Intensity & 6 & 5 & 7 & 7 & 7 & 6.5 \\ R & 5 & 7 & 1 & 6 & 6 & 5.17 \\ G & 2 & 3 & 5 & 3 & 4 &  
After dividing the models into linear and nonlinear groups, we validated the ordinal elimination variables on a scale from 1 to 7 and tested the distributions with the Kruskal-Wallis test (Table 14) [61]. The sequence elimination distribution for the proposed models is shown in Figures 9 and 10 (linear and nonlinear models, respectively).  
The linear model indicated that the essential characteristic was intensity, while the least significant was the duty cycle (Figure 9 and Table 14). If the appropriate sequence for any variable is required, the mean value can be found in Table 12. This means that the elimination order for the linear models was duty cycle, G, R, W, B, frequency, and intensity.  
The nonlinear model found that the most crucial characteristic was intensity, while the least important was W (white color), with sufficient significance \(p<0.05\). If the correct sequence of the other variables is required, we can rely on the mean values for the feature distribution (Table 13). Overall, the elimination sequence was W, G, B, duty cycle, frequency, R, and intensity.  
The elimination order for the duty cycle and R in the linear and nonlinear models suggests that they are nonlinear features, mainly because several linear algorithms selected them as the first variables to eliminate but nonlinear algorithms selected them as the most important ones.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & \(\mathbf{F_{score}}\) & \(\mathbf{P_{value}}\) \\ \hline Linear & 16.27232 & 0.012364 \\ Nonlinear & 17.65278 & 0.007161 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Values obtained with the Kruskal-Wallis test.  
Figure 9: Order of elimination for features in linear models.
## 4 Simulations
### Maximum Speed along the Surface  
The maximum speed at which the task can be executed is computed as follows. Consider that the maximum force error tolerated by the system is \(e_{max}=3\) N, and controller gain \(K_{p}=0.005\) (close to the one in Table 1). Then, locating these values in the design curves of Figure 13, one obtains a region containing the possible values of disturbance magnitude \(M\) that can be compensated (see Figure 20). The maximum value of \(M\) is the one located in the upper right corner of the rectangular region delimited by \(e_{max}=3\) N and \(K_{p}=0.005\). Note that the maximum value is \(4M\). Then, the maximum speed at which the task can be executed is computed as \(4M=4\times 0.00314=0.0126\) m/s.  
Figure 19: Simulation of the force control system: sinusoidal disturbance with variable frequency.  
Figure 18: Curves of steady-state error \(e_{\mathrm{ss}}\) in terms of control magnitude \(K_{GM}\) and sinusoidal frequency.  
For the case of time-varying disturbances, the maximum speed at which the task can be executed is computed using the curves presented in Figure 18. Considering a maximum error of \(e_{max}=3\) N and a selection of control magnitude \(K_{GM}=1.5\), the maximum velocity can be estimated with the frequency value associated with the closest curve to the upper right corner of the rectangular region delimited by \(e_{max}=3\) N and \(K_{GM}=1.5\). In Figure 21, this frequency value is 2.5 rad/s. Then, assuming a wavelength \(\lambda=0.1\) m and considering the maximum frequency of 2.5 rad/s, the maximum velocity is \(0.1\times(2.5/2\pi)=0.0199\) m/s.
## 2 Literature Review  
### Life Cycle Assessment  
The life cycle assessment (LCA) framework is a powerful tool for assessing the environmental impacts of products, systems, or processes throughout their life cycle. Life cycle sustainability assessment is increasingly applied in the building and construction sector, with significant economic, environmental, and social impacts [18]. Recent studies have focused on analyzing the environmental aspects of seismic retrofitting of buildings using LCA. For example, a group of researchers compared the environmental performance of reinforced concrete column jacketing and dissipation devices, concluding that the latter is more environmentally friendly than traditional solutions [28]. Similarly, another study evaluated three seismic retrofitting methods for an old RC building: concrete shear wall reinforcement, RC column jacketing with shear wall reinforcement, and base isolation using high-damping rubber bearings and viscous fluid dampers. Of these, the base isolation method was found to have the least environmental impact [29]. Another group of researchers conducted an LCA of three seismic retrofitting alternatives for a reinforced concrete building: RC column jacketing, beam weakening, and shear walls. Among them, shear walls were found to have the highest environmental impact [17]. Similarly, another research analyzed and compared the environmental performance of four seismic retrofitting strategies: FRP-based strengthening solution, FRP-RC jacketing-based strengthening solution, installation of RC shear walls, and base isolation. Their results indicate that base isolation has the lowest environmental impact [30]. Another study conducted an LCA of seismic retrofitting of RC columns, comparing three traditional methods: concrete jacketing, steel jacketing, and carbon fiber incorporation; steel jacketing proved to be the best alternative from an economic and environmental point of view [26].  
The building and construction sector faces significant challenges on the social sustainability front. The criteria for assessing social sustainability in the built environment vary considerably, highlighting the need for a standardized framework [31]. This lack of consistency can be attributed to the ambiguity surrounding the definition of social sustainability criteria. Notably, no relevant studies currently address social life cycle assessments of seismic retrofitting alternatives, underscoring a significant knowledge gap.