esa-sceva/satcom-chunk-collection huggingface dataset:
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 3 Results
### Experimental Results  
The results of this study indicate that the new network structure performs well in segmenting complex urban roads, fine and narrow roads in wild forests, and shaded roads. Additionally, the evaluation metrics demonstrate an improvement compared to other networks, with DANet achieving a _Re_ of 70.87%, _road IoU_ of 65.31%, and _F1-score_ of 71.08%, as illustrated in Figure 9.  
The results of segmenting different types of roads, including urban roads, irregular roads in the field, and complex roads in the city, are displayed in the figure using the DANet semantic segmentation model.
## 1 Introduction  
Biliary atresia (BA) is a perinatal hepatic inflammatory disease that results in rapid obliteration of the biliary tree, leading to cirrhosis and requiring liver transplantation. The etiology of BA is not fully understood, but multiple factors including genetic predisposition, immune dysregulation, toxins, and infections are thought to ultimately result in an inflammatory cascade within the liver [1]. Although a well-controlled acute inflammatory response is essential for the resolution of tissue injury, a dysregulated inflammatory response can lead to the development of pathological inflammation and devastating long-term consequences [2]. Thus, understanding how perinatal hepatic inflammation is propagated is an essential prerequisite for developing therapeutic targets that can interrupt this inflammatory cascade and alleviate the need for liver transplantation in patients with BA.  
The innate immune system, particularly through the actions of monocytes, plays a crucial role in the initiation and resolution of inflammation [3, 4, 5, 6]. There are two main subtypes of monocytes: Ly6C\({}^{\text{CH}}\) classical monocytes are pro-inflammatory [3, 7, 8], and Ly6C\({}^{\text{Lo}}\) non-classical monocytes are pro-reparative [9; 10; 11; 12]. In a murine model of BA, we have demonstrated that the relative abundance of Ly6C\({}^{\text{Lo}}\) non-classical monocytes promotes the resolution of periodat liver inflammation and confers protection to liver disease in neonates [12]. However, the development and differentiation of Ly6C\({}^{\text{Lo}}\) non-classical monocytes in neonatal mice are not well understood. In adult murine bone marrow, the transcription factor Nr4a1 has been shown to play a critical role in mediating the differentiation and survival of Ly6C\({}^{\text{Lo}}\) non-classical monocytes [13]. Nr4a1 is an orphan nuclear receptor and a member of the Nr4a family of intracellular transcription factors [14; 15]. The Nr4a1 family is widely expressed across various tissues, and its diverse scope of actions includes DNA repair, cell proliferation, differentiation, apoptosis, migration, metabolism, and inflammation [14; 16]. Such diverse roles stem from the fact that Nr4a1 activity is tissue- and environment-specific [17; 18; 19].  
Given the importance of Ly6C\({}^{\text{Lo}}\) non-classical monocytes in promoting the resolution of perinatal liver inflammation, and our current understanding of Nr4a1 as a monocyte regulator in the adult murine bone marrow, we sought to investigate the effects of inflammation on Nr4a1 expression in a murine model of BA. Understanding the factors that regulate non-classical monocytes during perinatal inflammation is particularly important, because the early dysregulation of monocyte development can contribute to detrimental sequelae of BA.
## 3 Results
#### 3.1.1 Comparison of \"Single-Layer\" and \"Double-Layer\" Layouts  
Figure 3a,b clearly demonstrate that the \"single-layer\" layout was unable to obtain height information. With only 18 sets of sampling data, accurate localization was not possible, resulting in erroneous localization areas. However, the detector exhibited a preliminary capability to distinguish anomalous regions. By increasing sampling data along the z-axis, as shown in Figure 3c,d, the \"double-layer\" layout achieved a degree of z-axis resolution. This increase in sample size enhanced prediction capability, potentially eliminating prediction errors.  
Comparing the SMR between single and double layers, as illustrated in Figure 4, revealed that increasing the sample size enhanced the response to anomalous regions. When the SMR fell below 10, prediction capability decreased, and external interference increased. Therefore, in this experiment, to improve prediction accuracy, efforts were made to ensure the SMR of anomalous regions reached or exceeded 10.  
Figure 4: Comparison of SMR values for “single-layer” and “double-layer” layouts. The SMR values for the “double-layer” layout were higher than those for the “single-layer” layout. When the SMR fell below 10, localization accuracy decreased, resulting in erroneous regions, as shown in Figure 3b.  
Figure 3: Results using 19.0% moisture content grain. (**a**) “Single-layer” layout with “Cross-shape” along the z-axis; (**b**) Hotspot localization results for the “single-layer” layout, showing lower response intensity and inability to obtain height information, resulting in localization errors; (**c**) “Double-layer” layout along the z-axis; (**d**) Hotspot localization results for the “double-layer” layout, demonstrating enhanced response intensity and improved anomaly resolution capability along the z-axis, but poor resolution capability along the y-axis. The red boxes in the layout diagrams indicated the currently detectable areas (analyzed and explained in Sections 3.2 and 3.3).
# Real-Time Detection of DDoS Attacks Based on Random Forest in SDN
## 3 Materials and Methods
### DDoS Attack Detection  
#### 3.2.1 Datasets  
The CIC-DDoS2019 dataset is a public dataset created by the Canadian Institute for Cyber Security (CIC). The dataset contains data on network traffic from DDoS attacks generated using a variety of attack tools and techniques. The dataset includes 15 scenarios of DDoS attacks, each representing a different combination of attack tools, targets, and victim networks. The attacks are generated in a controlled environment and the network traffic is captured by a set of sensors deployed on the victim's network. The dataset contains a total of 80 million network flows, including both legitimate and malicious traffic. Each traffic is characterized by 88 features, which include information such as source and destination IP addresses, protocol type, packet size, and duration. The CIC-DDoS2019 dataset is intended to be used for research purposes, such as developing machine learning models for DDoS detection and mitigation. The dataset is available for download from the CIC website and can be used for non-commercial purposes free of charge. As a first choice,  
Figure 4: Traditional versus software defined networking.  
we have extracted 82 features from the CICDoS2019 dataset, with a total of 330,000 records, and the dataset contains the latest DDoS attacks. This dataset can simulate the real data flow features to the maximum extent possible, compensating for the shortcomings and limitations of the previous dataset. The dataset was first pre-processed with a series of data type conversions, feature encoding, default value padding, redundant data removal, and data balancing. Then, to unify the magnitudes, the data also need to be normalized. The data are normalized according to equation \(x\), which converts the original data to values in the interval [0,1].  
\[x_{i}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{5}\]  
\(x_{i}\) is the original data, \(x_{min}\) is the minimum value of that feature data, and \(x_{max}\) is the maximum value of that feature data. Finally, the CIC-DDoS2019 dataset was divided into 67% of the training set for model training and 33% of the test set for model prediction.
# Advancements in Phytoremediation Research in South Africa (1997-2022)
## 5 Outlook of Phytoremediation in South Africa  
This study reveals a slow but positive growth rate in publications. Notwithstanding the low annual growth rate of 4.49%, the research is published in high-impact journals, suggesting that the quality of studies remains commendable despite the limited volume. The prospect of further research is immense, especially considering the rich biodiversity and the availability of native hyperaccumulators uniquely suited to South African ecosystems. Indigenous species like _Berkheya coddii_ and _Phragmites australis_ have demonstrated efficacy in extracting heavy metals from contaminated soils, underlining the suitability of local species for phytoremediation projects.  
Looking forward, several key aspects must be considered to fully harness the potential of phytoremediation in South Africa. Firstly, strategic investments in research and development are crucial. Governmental support, alongside incentives for private sector involvement, can increase the existing research base and drive innovations in phytoremediation techniques tailored to local environmental conditions. Secondly, enhancing international collaborations, particularly with leading nations like the USA, which currently holds the highest collaborative link strength with South Africa, could expedite knowledge transfer and the adoption of advanced methodologies.  
Furthermore, phytoremediation aligns well with South Africa's commitment to sustainable development, as outlined in its National Development Plan and Green Economy Strategy. This alignment presents a distinct opportunity for mainstream phytoremediation as a cornerstone of environmental policy, especially in mining-impacted areas like Limpopo and Gauteng provinces, where the ecological and public health risks are most acute. Incorporating phytoremediation into national remediation frameworks could lead to large-scale restoration projects, thereby contributing to both ecological balance and socio-economic uplift Finally, the future of phytoremediation in South Africa is promising but dependent upon improved research output, strategic collaborations, and policy integration. By capitalizing on its unique biodiversity and fostering a supportive research environment, South Africa can lead the way in pioneering phytoremediation technologies that not only restore degraded landscapes but also set a global benchmark for sustainable environmental management.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 4 Data Acquisition and Handling  
The sampling of an observed physical phenomena and the conversion of the samples into compatible numerical values for computer analysis is called data acquisition. The real-world conditions are measured with fluctuations in electrical signals of certain material in reference to those conditions. By nature, these signals are analog, which is amplified and converted into digital signals. The digital signals are packets of 0 s and 1 s, which are converted into the decimal system by an interpreter. The processed data is stored in the R/W memory of the computer. A simple data acquisition system (DAS) is outlined in Figure 5.  
Figure 4: The model’s exterior frame and setting of the components.  
Figure 5: An outline of data acquisition system.  
With the abovementioned procedure, the developed prototype receives and processes the environmental data sets. The measurements are monitored through IDE terminal and saved in the SD card for future assessment. The connected sensors are called one after the other in the following order according to the prototype's programming code:  
* Globe temperature (Tg) sensor
* Air temperature (TAIR), and relative humidity (RH%) sensor
* Anemometer
* RGB and illuminance sensors
* GPS
* Particulate matter (PM2.5) sensor
* Sound detector  
The data received from the Tg, Ta, and RH% sensors are converted and saved in the international system of units (SI). Although additional coding can be made for unit conversion or calculating heat index, our data remains confined to air temperature, globe temperature, and relative humidity from these two sensors (Figure 6).
## 5 Discussion  
This paper emphasized the concept of the saturation of the PRPD pattern. The visibility and stability of the patterns can be improved by increasing the measurement duration when the testing voltage is near PDEV. This process might mine the weak PD signal under normal operation voltage for online monitoring maintenance.  
The results clearly demonstrated the crucial role of measurement duration in defect type recognition accuracy in epoxy resin materials with void defects. Moderate to high accuracy for defect recognition was observed when testing the CNN model with one-minute duration PRPD patterns recorded from the lower RPDIV to the PDEV. However, certain misclassifications occurred. Remarkable improvements in accuracy were achieved when using one-hour duration PRPD patterns recorded at the PDEV. Furthermore, the CNN model successfully recognized void defects at different positions with 100% accuracy.  
Future research could focus on developing adaptive thresholding methods that dynamically adjust the detection threshold based on signal characteristics. This would enable a more accurate identification and classification of weak PD signals, thereby improving the overall performance of defect recognition algorithms. Furthermore, the test objects can be practical equipment, such as power cables, transformers, etc.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, and writing--original draft preparation, C.-H.C. and C.-J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Void Type** & **Overall Testing Accuracy (\%)** & **Testing Accuracy (\%) at PDEV Data** \\ \hline Void\_Top & 100 & 100 \\ \hline Void\_Center & 100 & 100 \\ \hline Void\_Bottom & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testing accuracies of CNN model with one-hour duration for PRPD data.
## 3 Results
### Range-of-Motion (ROM) Improvement  
Hip mobility and range-of-motion (ROM) restoration were important outcomes assessed across studies. [PERSON] et al. (2019) [47] reported that participants in the trunk stabilization group demonstrated greater improvements in hip flexion and internal rotation ROM compared to those who performed pelvic strengthening alone. [PERSON] et al. (2021) [19] primarily focused on movement efficiency rather than specific ROM outcomes, though general improvements in mobility were observed in the strengthening group.  
[PERSON] et al. (2016) [5] found no significant differences in ROM between the surgical and physiotherapy groups at the two-year follow-up, indicating that both interventions led to similar long-term mobility improvements. [PERSON] et al. (2021) [29] observed that the surgical group showed significantly better ROM outcomes in hip flexion and abduction, though differences in other movement directions were not statistically significant. Similarly, [PERSON] et al. (2019) [48] found that, while the surgical group exhibited greater gains in hip flexion ROM, no major differences were noted in other directions when compared to physiotherapy.  
Overall, these findings suggest that both conservative and surgical treatments improve ROM, though surgery appears to offer greater short-term improvements in hip flexion. Exercise-based rehabilitation, particularly core stabilization and targeted mobility training, remains an effective approach for enhancing overall movement efficiency.
## 3 Particle Contact Test
### Results  
Table 2 shows a comparison between the test and the theoretical analysis, and they are in good agreement. The heights of the conical cores are all about 2.9 mm and are not listed here since their changes are very small for different normal force grades. It is clear that in the normal-tangential contact test, the normal force grade has a great influence on the features of particle breakage. In the low-normal force level, the size of the conical core increases with the increasing normal force grade, while the inclination of the conical core and the rupture surface is fixed. In the high-normal force level, the inclination of the conical core and the rupture surface decreases with the increasing normal force grade, and the conical core has a fixed size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(F_{\text{ncr}}\) (kN) & \(S\) (mm2) &
\begin{tabular}{c} **Predicted \(S\)** \\ **(mm2)** \\ \end{tabular} & \(\theta\) (\({}^{\circ}\)) & **Predicted \(\theta\) (\({}^{\circ}\))** \\ \hline
6.015 & 19.97 & 21.18 & 18.4 & 17.8 \\
6.416 & 20.56 & 22.60 & 19.3 & 17.8 \\
6.817 & 24.91 & 24.01 & 15.8 & 17.8 \\
7.218 & 23.62 & 25.43 & 17.1 & 17.8 \\
7.619 & 25.79 & 26.84 & 15.6 & 17.8 \\
7.700 & 27.33 & 26.91 & 14.9 & 16.3 \\
7.780 & 25.30 & 26.91 & 13.7 & 14.1 \\
7.860 & 26.68 & 26.91 & 13.1 & 11.5 \\
7.940 & 27.34 & 26.91 & 10.3 & 8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the predicted and measured values including the inclinations of the rupture surface and the conical core, and the bottom areas of conical core. The heights of the conical cores are not listed due to the small differences.  
Figure 7: An illustration of the rock rheological testing system. The roller gasket is used to eliminate the influence of friction between the arc-shaped support and the loading component.  
Figure 8 shows the relationship between \(F_{ncr}\) and \(F_{lcr}\), including the theoretical predictions and the data measured in the particle contact test. Obviously, when \(F_{ncr}\) < 7.639 kN (\(\frac{\sqrt{F_{lcr}^{2}-F_{ncr}^{2}}}{\mu}\)), the relationship between \(F_{lcr}\) and \(F_{ncr}\) presents a titled straight line with a slope of 0.32 (\(\mu\)); when \(F_{ncr}\) \(\geq\) 7.639 kN (\(\frac{\sqrt{F_{lcr}^{2}-F_{ncr}^{2}}}{\mu}\)), the \(F_{lcr}\)-\(F_{ncr}\) curve is a circular arc centered at the origin of the coordinate. The test under normal contact conditions [48] can be regarded as a specific situation where the tangential force on the contact surface is zero (see the purple circle in Figure 8). From the plot, the data measured in the test are in good agreement with the predicted values. If the normal force is too small, the relative slippage of the two spheres is too large, bringing about a secondary occurrence of local compaction rather than the overall breakage. This situation is too complex and therefore is not discussed in this paper.
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
### Remote Sensing of Vegetation in Drought
#### 3.1.3 Leaf Chlorophyll Content  
The leaf chlorophyll content is inversely proportionate to the intensity of the drought, the variation in leaf chlorophyll content is assessed to analyze the intensity and impact of drought-induced vegetation stress [92]. Satellite information coupled with radiative models, such as the PROSPECT model [93], was mainly used for assessing the chlorophyll content [75]. The spectral properties of Sentinel-2 can efficiently estimate the seasonal variation in leaf chlorophyll content [94,95]. The leaf chlorophyll content was estimated from the temperate forest using the invertible forest reflectance model, a radiative transfer-based model. The derived leaf chlorophyll content was validated and statistically correlated with the field measured leaf chlorophyll content using CCM-300 and wet laboratory analysis. It was observed that the spectral properties of Sentinel-2 outperformed the spatial capabilities of the RapidEye in the leaf chlorophyll content estimation [96]. Similarly, an inversive invertible forest reflectance model based on the forest light interaction model was applied to estimate the needle chlorophyll (C\({}_{\text{a+b}}\)) of the coniferous forest. C\({}_{\text{a+b}}\) derived from airborne hyperspectral images and field data were used for validating Sentinel-2, the red-edge-based chlorophyll index outperformed other indices. The object-based [PERSON]'s thresholding and [PERSON]'s binarization techniques were applied to vegetation segmentation in hyperspectral data. The derived C\({}_{\text{a+b}}\) registered a linear relationship with the inversion model and a nonlinear relationship with the indices [97]. Compared to other onboard bands of Sentinel-2, several studies highlighted the efficiency of the red-edge band in estimating the biophysical parameters, especially in leaf chlorophyll content [98]. Similarly, [PERSON] et al. [99] modeled the Red-Edge Position (REP) for the forest canopy reflectance and transmittance using the simulated Sentinel-2, HyMap, and resampled radiative transfer modeling. The REP was calculated from Polynomial Fitting (PF), Linear Extrapolation (LE), and 4-Point Linear Interpolation (4 PLI) methods and its optimized version of Sentinel-2 and HyMap. The researchers conclude that the REP derived from 4 PLI methods outperforms other methods and can efficiently estimate the canopy chlorophyll (Chl \(\times\) LAI).
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 2 Classical FCM and Modifications on Its Objective Function
### An \(L_{2}\) Regularization Term  
One of the modifications on the objective function is adding an \(L_{2}\) regularization term to refrain the centroids of the clusters from obtaining large values, thus helping to avoid overfitting. The objective function becomes:  
\[J(X,\ U,\ V;\ m,\ C,\ \lambda)=\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij}^{m}d^{2} \big{(}x_{i},\ c_{j}\big{)}+\lambda\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij}^{m} \big{\|}c_{j}\big{\|}^{2} \tag{2}\]  
Here, \(\big{\|}c_{j}\big{\|}^{2}\) represents the squared Euclidean norm of the \(j\)-th cluster center, thus penalizing centers that lie far from the origin. This modification is very useful once the data are previously normalized using z-score or min-max normalizations, which centralize the original data points. Application of the regularization term stimulates simpler models, typically leading to better generalization of data and less susceptibility to noise and outliers [16]. Furthermore, this modification makes the gradient descent procedure more stable, as the regularization adds a smooth constraint in the optimization framework (avoiding local minima). The idea of this modification has been used in hard clustering by [PERSON] et al. to address the high-dimensional noise anomaly.
## 4 Plant-Bacteria Interaction Regulates Plant Protein and Metabolites Production
### Bacteria-Mediated Plant Metabolite Biosynthesis for Salt Stress Mitigation (Indirect Mechanism)
#### 4.2.2. Phytohormones Production in Plant  
Plant hormones, commonly referred to as phytohormones, are a class of naturally occurring chemical compounds that regulate plant physiological processes at low concentrations. These hormones influence plant growth differentiation and development by regulating a variety of processes. ABA regulates water relations not only by decreasing transpiration via ABA-induced stomatal closure but also by promoting water flow via ABA-induced increase in activity of water channel aquaporins [67]. Salt stress reduces the availability of water, resulting in decreased bulk and relative water content in plants. _Bacillus subtilis_ IB22 treatment was found to increase root ABA content and reduced shoot ABA accumulation by up regulating (_HvNCED2_) and down-regulating ABA catabolic genes (_HvCP707A1_) in barley grown under 100 mM salt concentration [56]. Volatile organic compounds (VOCs) released by _B. subtilis_ GB03 can activate phytohormone signaling in _Arabidopsis thaliana_, producing auxin, cytokins, salicylic acid, and gibberellins [68].