esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### PAW Modulates the Root Hair Density through Root Developmental Genes  
Root hair is one of the important organs in plants. It controls osmotic pressure to absorb moisture and increases the area where the root comes into contact with the soil [29]. In _Arabidopsis_, the development of root hairs is determined by the location of the epidermal cells [30]. The epidermis cells adjacent to two cortical cells develop into root hairs, while epidermis adjacent to one cortical cell does not develop into root hairs [31; 32]. _GFP_ fusions with target genes such as _GLABRA2 (GL2), WEREWOLF (WER)_, and _CAPRICE (CPC)_ expressed in the root epidermal have been used to study root epidermal patterns in a previous study [26]. The visualization of the GFP helps to differentiate the cell structure and development process and clearly shows any alteration in the root. In _Arabidopsis_, _cob19-1_ was shown to have root hair defects. The _COBL9_ gene is one of the important biomarkers as a root hair-specific gene and has a crucial role in root hair formation during development [33]. By using the double mutant _WER:GFP/cob19-1_, we expected to see a clear effect of the PAW treatment in epidermal cell patterning which can help us to understand the morphological structure at the single cell level, especially the root hairs. Furthermore, PAW treatment via _cob19_ can help to confirm if the root hair phenotype is dependent on a single pathway or broader gene regulatory networks. The difference in the morphology of the root hairs between the DW and PAW treatments at days 5 and 7 of the developmental stage was confirmed using the _WER:GFP/cob19-1_ mutant line. The results showed at 5 days after planting that there were no differences in the root length, but the root hair number and root hair length were changed between the DW and PAW treatment. In detail, the number of root hairs was increased by 14.5 \(\pm\) 1.37 in the DW treatment. This was increased by 40.66 \(\pm\) 5.71 and 27.50 \(\pm\) 5.54 for the PAW5 and PAW19 treatments, respectively. The length of the root hair was decreased by 138.35 \(\pm\) 11.11 and 85.05 \(\pm\) 11.65 um for the PAW5 and PAW19 treatments compared with 261.21 \(\pm\) 100.37 um for the DW treatment. These results show that the PAW treatment did not affect the total root length, but the root hair phenotypes were altered for the plant harboring the mutation in the _COBRA-LIKE9_ genes, especially during the early developmental stage. In addition, the length of the root hairs was shortened, but the number of root hairs was increased for all PAW treatment conditions (Table 4, Supplementary Figure S2).
# Fractional Order Processing of Satellite Images
## 2 Definitions and Formulations  
This section presents and compares the image treatment algorithms.
## 4 Discussion
### Contributions and Limitations of EVI Assessment  
The integration of the Genetic Projection Pursuit (GPP) model for weight determination addresses a critical limitation in traditional vulnerability assessments that rely on subjective expert judgment or arbitrary weighting schemes. By optimizing projection directions through genetic algorithms, our approach ensures that indicator weights reflect the actual data structure and relationships, enhancing the objectivity and reproducibility of vulnerability assessments. The multi-dimensional indicator system incorporating both natural (FVC, HQI, LST, slope) and anthropogenic (population density, ISC) factors provides a comprehensive representation of ecological vulnerability drivers. This holistic approach captures the complex interactions between urbanization pressures and environmental responses, offering insights that single-factor analyses would miss. The inclusion of topographic variables (slope) is particularly valuable in the Beijing-Tianjin-Hebei context, where mountainous areas in the north exhibit fundamentally different vulnerability patterns compared to the plain regions [28].The temporal analysis framework linking EVI changes to urbanization intensity gradients reveals dynamic vulnerability patterns that static assessments cannot capture. The classification of areas into different ISC density zones enables the identification of threshold effects and non-linear responses in ecological systems, providing crucial information for understanding when and where urbanization pressures exceed ecosystem resilience capacities.  
Several limitations constrain the interpretation and application of our EVI results. The temporal resolution of EVI assessment (5-year intervals from 2005 to 2020) may miss rapid environmental changes or short-term fluctuations that could be ecologically significant [29]. Climate variability, extreme weather events, and sudden policy interventions can cause vulnerability patterns to change more rapidly than our assessment framework can capture [30]. Furthermore, the Habitat Quality Index calculation relies on fixed coefficients derived from national standards that may not adequately reflect local ecological conditions or species-specific habitat requirements in the Beijing-Tianjin-Hebei region. The assumption of linear relationships between land cover types and habitat quality may oversimplify complex ecological processes and species-habitat relationships.
## 3 Analysis of the Practical Engineering Data and Results
### Establishment of Predicting Models Based on ANFIS, ANN, PCA-ANN, and PCA-ANFIS Models
By contrast, the PCA-ANFIS and PCA-ANN models had four nodes in the input layer and one node in the output layer according to the results of PCA. After conducting multiple fitting trials, the RMSE value was set to \(6.5\times 10^{-4}\), the iteration number was set to 136 epochs, and the structures with different MFs for each input 2-2-4-3 topography were found to have the lowest values of RMSE. The choice of membership functions also influenced the accuracy results, with the generalized bell being selected to train and check the network based on its testing performances. Overall, this study aimed to improve the accuracy of carbonation depth forecasting in concrete structures by using PCA and ANFIS models to reduce the dimensionality of the input data and test the combination of these models.
# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 2 Related Work  
### Corruption Processing Method  
Raw point clouds usually have corruption, including noise and outliers. Extensive research efforts over the years have resulted in various denoising approaches specifically designed to address different forms of corruption [14; 15]. For instance, statistical methods are used to categorize outliers [7], while local smoothing is adjusted using additional input parameters [16], etc. However, these traditional algorithms, while aiming to remove noise, tend to smooth out crucial geometric features, such as sharp boundaries and geometric texture details. As a result, point cloud corruption processing algorithms must strike a balance between smoothing and preserving important features. Furthermore, certain classical algorithms require users to input parameters like search radius, which can significantly impact the results and potentially render parameter-based algorithms less effective if users are uncertain about selecting optimal parameters. These challenges have a detrimental impact on the practical applications of point cloud processing in real-world scenarios.  
Deep learning corruption processing methods typically employ a sampling strategy to reduce computational effort. However, most sampling methods are limited by noise sensitivity, are not data-driven [9; 17], or ignore spatial distributions [18]. SO-Net [19] utilizes an unsupervised neural network, the self-organizing map (SOM), to exploit the spatial distribution of point clouds. Then, PointNet++ [9] is used for multiple smaller sampling \"nodes\". However, SO-Net does not incorporate adaptive sampling. Under the assumption of local label consistency, several productions use the geometric center of the voxel grid to represent the sampled points uniformly [20], ignoring differences in the effect of point distribution. These approaches, however, are extremely sensitive to noise and cannot simultaneously understand the spatial distribution of sampled points. TriangleNet [21] is designed to extract features invariant to position, rotation, and scaling disturbances. Although Triangle-Net achieves excellent robustness in the case of extreme corruption, its performance on clean data is not as strong. There are other works to improve  
Figure 2: Visualizations of ModelNet40-C. The ModelNet40-C dataset consists of 15 corruption types that represent different out-of-distribution shifts in the real-world applications of point clouds.  
the robustness of models by denoising and upsampling, voting on downsampling point clouds, and exploiting the relative positions of local features.  
In summary, traditional methods for cleaning point cloud corruption heavily rely on the expertise and suitability for specific applications, thus posing challenges in handling the noise data of a general nature without requiring additional user intervention. On the contrary, the deep learning approaches offer the advantage of processing corruption without human intervention, but their complexity and lack of accuracy often limit their effectiveness for real-world tasks.
## 6 Conclusions  
1. The proposed methodology let us to formulate a S-N curve and to determine its desired probabilistic percentiles, as well as to determine the designed reliability, as it is made for the A572 Gr.50 steel analyzed in this paper.  
2. In the proposed methodology, steps 1-7 let us to determine the maximum and minimum strength S-N values, based on which Weibull parameters are determined. Steps 8-12 let us to determine the \(\beta\) and \(\eta\) parameters, as well as the log-standard deviation used to determine the S-N percentiles. Steps 13-15 let us to determine the desired S-N percentiles.  
Figure 4: Goodman failure theory analysis.  
3. The stress/strength analysis given in Section 5.1 let us determine the reliability of the designed component. The formulation of Section 5.2 lets us perform the failure theory analysis by using the derived lower S-N percentile as the lower allowed value in the analysis.  
4. Since the proposed methodology let us analyze variant stress, then it will be useful not only for designers, but also for structural and mechanical practitioners.  
5. The log standard deviation \(\sigma_{g}\) value used to formulate the probabilistic S-N curve percentiles is determined from the minimum and maximum stresses values, so its value is unique.  
6. Although the focus in this paper was to determine the probabilistic S-N curve for the ASTM A572 Gr.50 steel material, it can be used to determine the probabilistic S-N curve for any material.  
7. The key features of the given S-N methodology are: (a) the derived S-N curve is based on the true stress-true strain analysis; (b) the Weibull distribution analysis is based on the maximum and minimum strength values given by the fatigue strength methodology; and (c) the Probabilistic S-N curve percentiles given by the corresponding log-mean (\(\mu_{g}\)) and log-standard deviation (\(\sigma_{g}\)) values are based on the \(\lambda_{1}\) and \(\lambda_{2}\) values.  
Conceptualization, A.M., M.R.P.-M., J.M.B.-C.; methodology, A.M., M.R.P.-M.; data analysis, A.M., J.M.B.-C.; writing--original draft preparation, A.M., M.R.P.-M.; writing--review and editing, A.M., M.R.P.-M., J.M.B.-C.; supervision, M.R.P.-M.; funding acquisition, A.M., M.R.P.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
Secondly, silver possesses a bactericidal action and remains in the demineralized and hypomineralized areas, hardening them and preventing their reinvasion by means of a series of metabolites. Thirdly, ammonia stabilizes the solution and maintains the pH [17]. Furthermore, in dentin, SDF favors the maintenance the collagen matrix's integrity due to its ability to inhibit bacterial metalloproteinases, which play an important role in the enzymatic degradation of collagen. Potassium iodide (KI) decreases blackening of dentin caused by SDF [18]. The combined use of SDF/KI and bioactive restorative materials such as GICs has been proposed in minimally invasive procedures for the treatment of deep carious lesions, aiming to preserve viable demineralized dentin by exploiting their remineralization ability [19]. One problem that could arise is the adaptation of GICs to cavity walls and their bond strength to SDF/KI-treated dentin. A recent systematic review concludes, not without highlighting the methodological disparity among the included studies, that the application of SDF/KI has no adverse impact on bond strength [20]. Interestingly, some studies even indicate that it increased the bond strength of GICs on SDF/KI-treated dentin [10]. To our knowledge, there are no studies that evaluate the adaptation between RMGICs and dentin when the latter is treated with SDF/KI, nor the influence of the use or absence of a dentin conditioning system before the application of the ionomer. Accordingly, the objective of the present in vitro study was to evaluate the adaptation of a high-viscosityresin-reinforced glass ionomer (Riva Light Cure(r)) to dentin walls treated with different conditioning systems, with or without the application of SDF/KI, by means of scanning electron microscopy. The null hypothesis was that the use of SDF/KI does not influence the adaptation of the glass ionomer to cavity walls regardless of dentin conditioning.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.1 Face Restoration  
Face restoration comprises two primary steps: initially, a model is employed for face unmasking to remove the mask and restore the hidden facial regions. Subsequently, another network is utilized to identify or verify the unmasked face. The primary objective within this category of the MFR task is to restore the face to its original, unmasked state. While the unmasking of faces and masked face recognition have traditionally been treated as distinct tasks, there are relatively few endeavors that amalgamate both within a single model. An example of such integration is evident in LTM [92], depicted in the final row of Table 7.  
LTM [92] proposes an innovative approach to enhance masked face recognition through the utilization of amodal completion mechanisms within an end-to-end de-occlusion distillation framework. This framework comprises two integral modules: the de-occlusion module and the distillation module. The de-occlusion module leverages a generative adversarial network to execute face completion, effectively recovering obscured facial features and resolving appearance ambiguities caused by masks. Meanwhile, the distillation module employs a pre-trained general face recognition model as a teacher, transferring its knowledge to train a student model on completed faces generated from extensive online synthesized datasets. Notably, the teacher's knowledge is encoded with structural relations among instances in various orders, serving as a vital posterior regularization to facilitate effective adaptation. Through this comprehensive approach, the paper demonstrates the successful distillation and transfer of knowledge, enabling the robust identification of masked faces. Additionally, the framework's performance is evaluated across different occlusions, such as glasses and scarves. Notably, impressive accuracies of 98% and 94.1% are achieved, respectively, by employing GA [93] as an inpainting method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Year** & **Dataset** & **Accuracy** & **mAP** & **Rank-1** \\ \hline IAMGAN-DCR & & & & 86.5\% & 42.7\% & 68.1\% \\ (VGGFace2) [21] & 2020 & MFRS-REC & & & 82.3\% & 37.5\% & 67.4\% \\ (CAISA-Webface) [21] & & & & & \\ \hline \multirow{2}{*}{LPD [22]} & \multirow{2}{*}{2020} & MFV & 97.94\% & - & - \\ \cline{3-6}  & & MFI & 94.34\% & 49.08\% & - \\ \cline{3-6}  & & Synthesized LFW & 95.70\% & 75.92\% & - \\ \hline \multirow{3}{*}{LTM [92]} & \multirow{3}{*}{2020} & LFW & 95.44\% & - & - \\ \cline{3-6}  & & AR1 & 98.0\% & - & - \\ \cline{3-6}  & & AR2 & 94.1\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of masked region discarding and face restoration-based methods for masked face recognition.  
Figure 9: Three directions in masked face recognition (MFR): face restoration, masked region discarding, and deep learning-based approaches.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 4 Cognitive Radio
### Cognitive Radio Functions  
A general overview of the different functions in a typical CR system (CRS) is shown in Figure 2. There are five basic functions of a CRS described as follows:  
1. **Spectrum Sensing (SS):** A typical CR device is equipped with a radio front-end to scan (sense) its immediate electromagnetic environment for the presence/absence of primary user (PU) signals. In this case, PU refers to the licensed owner of the spectrum. There are different approaches proposed for SS, including the use of the energy detector (ED), matched filter, cyclostationary detector (CD), eigenvalue method, covariance method, and prediction-based approaches [72]. Another method currently deployed for spectral awareness is the geolocation database approach [28; 73]. In this case, a central database comprising geographic coordinates and the respective RF signal distributions in such environments is established. Consequently, CR devices would connect to these databases in order to acquire PU information before decision is made.
2. **Spectrum Decision Making:** Following the measurement of the energy content of a specified band, the CR device progresses to the spectrum decision-making phase wherein decision is made concerning the presence/absence of PU signals in the band. In the case where only the signal's energy values are known, then decision is made based on whether the received energy values exceed a specified threshold value or not. However, in cases where certain characteristics aboutthe PU signal are known a priori, for example, the cyclic frequency, then the CD technique can be deployed, albeit at the expense of long processing times.
3. **Spectrum Access:** Spectrum access entails the use of information pertaining to the absence of PU signals in a band (white space) by carefully designed CR MAC protocols in order to adjust transmission parameters. This ensures that the new operating radio spectral can be conveniently and efficiently used for opportunistic communication.
4. **Spectrum Mobility:** Information regarding the presence of PU signals in a band (black space) is used to ensure hand-off/change of transmission parameters in order to prevent interference to PU operators. This ensures seamless communication between CR devices in the new white spaces.
5. **Spectrum Sharing:** Spectrum sharing guarantees effective communication between CR devices and coexistence with PU transceivers without inflicting harmful interference. This is achieved by specific protocols designed to operate below a predefined interference level.
# Non-Magnetic Circulator Based on a Time-Varying Phase Modulator
## 4 Discussion  
The time-varying modulation method allows for the integration of the circulator into the chip, which is an unparalleled advantage over conventional circulators with external bias fields. Although the devices that constitute the time-varying modulated circulators are passive devices, transistors, etc., which can be realized by modern technology, it is a very critical engineering problem to make the control signal generated by the oscillation  
Figure 11: S-parameters of the designed circulator. (**a**) S11, S21, and S31. (**b**) S12, S22, and S32. (**c**) S13, S23, and S33.  
circuit as accurate as possible and to make the insertion loss as small as possible in terms of technology, which requires further research and experimental demonstration.
## 2 HHT Theory and Signal Recognition
### HHT Theory  
In 1998, [PERSON] et al. [37] proposed a more effective nonlinear signal instantaneous time-frequency conversion technology, namely the Hilbert-Huang transform, which can identify the frequency characteristics of a signal at different time points, and provided a new idea for evaluating the stability of surrounding rock and other equipment failures. The algorithm uses empirical mode decomposition (EMD) signal filtering rules to decompose the signal into several characteristic components in the time domain, and then transform and combine their components. The transformation method can overcome the problem of insufficient signal frequency resolution.
## 5 Discussion  
In this research, a subset of CSE-CIC-2018 dataset is used, and six feature reduction approaches have been investigated, including Linear Regression, Boruta, Random Forest with IncMSE, Random Forest with IncNodePurity, LASSO, and Autoencoders. The four sets captured from Boruta, LASSO, and two from Random Forest approaches were captured with the other six reduced sets fetched from autoencoders. Then, they were fed into three single-model classifiers as Decision Tree, Naive Bayes, and neural networks, and two ensemble methods as Random Forest and XGBoost.  
To determine the level of overfitting, we calculated the Root-Mean-Squared Error (RMSE) value between the train and test datasets for each model. We observed that the Decision Tree model demonstrated the lowest RMSE value when trained on the reduced feature set derived from autoencoders 2 (AE2), AE3, AE5, and AE6. This finding indicated that the structure of AE2 was particularly well-suited, showing comparable effectiveness to having dropout rates of 0.25%, 0.5%, and 0.75% on this architecture, respectively. Consequently, AE6 with a 75% dropout rate proved to be the least resource-intensive structure capable of producing signatures for all attack vectors while exhibiting the least amount of overfitting.  
We evaluated our findings by implementing various classification approaches, such as XGBoost [16], Random Forest [17], and deep autoencoder as used in [15], among others. As depicted in Figures 6-9, the Decision Tree model achieved the best results when applied to the reduced feature sets obtained from AE2, AE3, AE5, and AE6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**K** & **Boruta** & **RF-IncMSE** & **RF-IncNode** & **K** & **LASSO** \\ \hline
1 & 90.89 & 90.80 & 90.79 & 2 & 90.95 \\ \hline
3 & 92.76 & 92.71 & 92.73 & 4 & 92.85 \\ \hline
5 & 93.51 & 93.53 & 93.43 & 6 & 93.48 \\ \hline
7  
With regard to the efficiency of different approaches in our experiments, only Random Forest took a long time and the rest were somehow similar to each other. It is also worth mentioning that we removed the result of Principal Component Analysis (PCA) to change the feature space after implementing the autoencoders, as autoencoders already encompass PCA in a nonlinear fashion. Therefore, there is no need to have both PCA and autoencoders.