esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Assessment  
To evaluate the performance for red tide detection, we performed qualitative and quantitative assessments of the RTI and U-Net models trained with four different datasets. For qualitative assessment, we visually compared the ground-truth red tide map and the red tide maps generated through RTI and four U-Net models from a PlanetScope image taken on 29 July 2018. We quantitatively assessed the performance using a confusion matrix to evaluate the accuracy of red tide detection [53]. In the case of the four U-Net models, we utilized patches and 36 images. For RTI, the evaluation was performed by applying predetermined thresholds to 36 images. Lastly, the performance of the RTI and U-net models on 36 images was compared. In Table 4, the \(mr\) and \(nmr\) symbols indicate the red tide and non-red tide pixels in the ground-truth red tide maps, respectively. \(MR\) and \(nMR\) indicate red tide and non-red tide pixels in the predicted red tide maps, respectively.  
Figure 3: U-Net architecture for red tide detection from PlanetScope image.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Left Side of U-Net**} & \multicolumn{2}{c}{**Right Side of U-Net**} \\
**Blocks** & **Layers per Block** & **Blocks** & **Layers per Block** \\ \hline Input & Input layer (32 \(\times\) 32 \(\times\) 4) & Output & Output layer \\  & & & Softmax layer \\  & & & Conv (1 \(\times\) 1) \\ Encoder-1 to 4 & Conv (3 \(\times\) 3)/ReLU & Decoder-5 & Conv (3 \(\times\) 3)/ReLU \\  & Conv (3 \(\times\) 3)/ReLU & & Depth concatenation \\  & Max pooling & & UpConv (2 \(\times\) 2)/ReLU \\  & & Decoder-2 to 4 & Conv (3 \(\times\) 3)/ReLU \\  & & & Conv (3 \(\times\) 3)/ReLU \\  & & & Depth concatenation \\ Encoder-5 & Conv (3 \(\times\) 3)/ReLU & Decoder-1 & UpConv (2 \(\times\) 2)/ReLU \\  & Conv (3 \(\times\) 3)/ReLU & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of encoder and decoder blocks of U-Net.  
Four figure-of-merits (FOMs), namely accuracy, sensitivity, precision, and F-measure, were calculated from the confusion matrix. The accuracy was calculated as [(1) + (4)]/[(1) + (2) + (3) + (4)] for all pixels in the red tide map. It is the most intuitive performance evaluation indicator; however, if data are biased, the accuracy may be limited in interpreting the performance of the model. Additionally, the sensitivity ((1)/[(1) + (2)]) and precision ((1)/[(1) + (3)]) were evaluated using only red tide pixels in the ground-truth and predicted red tide maps. The F-measure, which is the harmonic mean of precision and sensitivity, was evaluated as follows [53]:  
\[\mathrm{F-measure}=\frac{2\times\mathrm{Precision}\ \times\mathrm{ Sensitivity}}{\mathrm{Precision}\ +\mathrm{ Sensitivity}} \tag{4}\]
# Aircraft Target Interpretation Based on SAR Images
## 3 Method
### Four Decoupled Detection Heads (4 DDH)  
The backbone of YOLOv5s uses the three-scale feature map {C3, C4, C5} of 8 times, 16 times, and 32 times down-sampling to predict the targets of the small, medium, and large scales. There are some small targets in our dataset. The experiment demonstrates that the three-scale feature maps make it difficult to detect these small targets. In order to further extract abundant feature information and improve the detection precision for small targets, this paper proposes the four-scale feature map {C2, C3, C4, C5}. After the fusion of feature information in the neck section of YOLOv5s, new four-scale feature maps {P2, P3, P4, P5} are generated as the input of YOLOv5s head to complete the detection and recognition for aircraft targets in the SAR images.  
At the YOLOv5s head section, the channel dimension of the input feature map is squeezed by a convolution operation. The squeezed feature map contains anchor coordinates, confidence, and aircraft category. The feature map is used to complete the task of detection and recognition. The two tasks share one feature map, but the two tasks focus on different feature information, the one feature map is usually not suitable for two tasks. The images from the GF-3 satellite have a large area of complex background and interference from various types of objects; this makes the task of identifying small targets more challenging. To further enhance the extraction ability of feature information and improve the precision, the detector adopts decoupled processing to complete the detection and recognition; the 4 DDH network is shown in Figure 5.  
DDH squeezes the input channel dimension by 1 \(\times\) 1 convolution first, which is used to decrease the number of parameters and increase operation speed; then, the output is divided into two branches. The top branch is responsible for the recognition task, and first extracts the feature information through two 3 \(\times\) 3 convolution operations, and another  
Figure 5: The 4 DDH network.  
\(1\times 1\) convolution operation is used to adjust the channel dimension of the feature map to the number of categories of predicted aircraft; there are seven types of aircraft in our datasets. The bottom branch is responsible for the detection task, and first extracts the feature information through two 3 \(\times\) 3 convolution operations. The feature map is divided into two more, the top one is used to predict the anchor coordinates by \(1\times 1\) convolution operation, and another is used to predict the confidence by \(1\times 1\) convolution operation. 4 DDH can improve the efficiency and precision of detection and recognition, especially for small targets.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 4 Overview of Traditional ACO and PSO Algorithms  
Ant colony optimization (ACO) is a searching method determined by means of previous experience [30] and enlightened by the food-hunting process of ants. When ants walk through a path, they release a material called pheromone, and pheromone can volatilize after a while. In the beginning, ants choose each path with equal probability, and the path with the shorter length costs less time for traveling. Therefore, the path with shorter length will be travelled by more ants in a given time, and the pheromone in those paths become higher. In cycles, more ants will choose the short path for food hunting to reduce the total time cost. ACO is applied to address the traveling salesman problem (TSP) [14; 39] originally and it has been widely used in various fields. The working procedure of ACO is described in Figure 3.  
Figure 3: The workflow of ACO.  
PSO is another intelligent heuristic algorithm which utilizes the wisdom of the swarm [31; 40; 41]. Each particle commonly represents a whole solution for the problem, and the fitness function evaluates the performance of the solution represented by the particle. During each iteration, a local and a globally-optimal solution are marked for updating the location of the particles. Particles search for better solutions according to local and global optimal solutions. The working procedure of PSO is described as in Figure 4.
## 7 Modification Strategies for Sludge-Derived Biochar
### Metal Loading and Composite Formation  
To impart specific catalytic functions, sludge biochar is often used as a support for metal nanoparticles or as a matrix to embed metals in situ. Although the high ash content of sludge (rich in Fe, Al, and Ca) means that some metal presence is inherent, additional loading is required for superior performance. Through soaking sludge in FeCl\({}_{3}\) or FeSO\({}_{4}\) before pyrolysis, Fe-loaded SDB could be obtained. The yields of iron on carbon showed excellent performance for Fenton and persulfate activation [44]. [PERSON] et al. [106] investigated the use of magnetic SDB for the removal of various dyes in wastewater.  
These methods offer advantages such as high efficiency, speed, simplicity, reusability, and environmental sustainability. [PERSON] et al. [41] prepared Cu-doped SDB for bisphenol A degradation in high-salinity wastewater (100 mg/L, PDS = 0.5 g/L, catalyst = 0.7 g/L) by PDS activation. The removal process was found to be dominated by \({}^{1}\)O\({}_{2}\). In contrast to [PERSON]'s study, [PERSON] et al. [107] improved bisphenol S removal using PDS through CuFe\({}_{2}\)O\({}_{4}\)-doped SDB activation to produce reactive oxygen species, and the main reactive species was the sulfate radical. By endowing SDB with magnetic properties, the multifunctional adsorbents can use an external magnetic field to efficiently recover from treated water [108]. Magnetic SDB often refers to Fe\({}_{3}\)O\({}_{4}\)-laden sludge-derived biochar, which is retrievable by magnet and also catalytically active. [PERSON] et al. [106] used magnetic SDB to remove various dyes in wastewater. Except for serving as recyclable adsorbents, magnetic SDBs have been recently used as electron shuttles as DIET facilitators in digesters for enhancing methane production [109]. The integration of magnetism into biochar through the doping of magnetic nanoparticles represents a significant advance in wastewater treatment technology. Beyond sole substrate, sludge is co-pyrolyzed with another waste to form composite biochar, such as red mud (Fe/Al source), clay minerals, or agricultural biomass [110]. The sludge-red mud magnetic biochar was used to adsorb TC and catalyze PDS. Unlike conventional SDB, the combination of Fe-driven radical activation and the nonradical pathway facilitated by carbon-based functional groups synergistically enhanced the persulfate-driven oxidation of TC. Alternatively, the incorporation of SDB with conductive nanostructures such as carbon nanotubes or graphene oxide enables the fabrication of hierarchical composite electrodes exhibiting enhanced electrical conductivity and robust electrochemical stability, which is particularly advantageous for energy storage applications, including supercapacitors and electrocatalytic systems [111]. In summary, the combination of sludge and dopants often yields synergistic effects. Researchers are increasingly leveraging SDB as a scaffold to anchor active nanophases (metals, metal oxides, etc.,), thus turning a waste into a value-added composite material.
## 3 Results
### The Comparison of Patients' Characteristics Distribution  
To further evaluate the effectiveness and utility of the proposed MTLNFM model, we conducted a case analysis on an independent testing set. Specifically, we compared the distribution of clinical phenotypes of the cohort predicted by MTLNFM and STLNFM, along with true distribution of patients' clinical profiles of the same cohort. As shown in Table 2, the clinical characteristics of the cohort generated by MTLNFM were notably closer to the true distributions than those produced by STLNFM. For numerical features, such as hospital length of stay, the STLNFM model exhibited significant deviations, particularly for patients with shorter stays (less than 10 days). This discrepancy led to instability in the calculation of standard deviations for clinical variables such as lactate, APACHE, and SOFA scores, highlighting the model's vulnerability to sparsity in subgroups. Similar results were observed in categorical features, where STLNFM exhibited biased predictions for patients with specific comorbidities. For instance, the model consistently overestimated hospital stays for individuals with chronic kidney disease (CKD), obesity, dementia, and cancer, assuming prolonged hospitalization regardless of actual outcomes. In contrast, MTLNFM \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictive Task** & **Models** & **Accuracy** & **Precision** & **F1-Score** & **AUROC** \\ \hline \multirow{4}{*}{Hospital Length of Stay} & LR & 0.7263 & 0.7324 & 0.8376 & 0.6432 \\  & XGBoost (raw) & 0.6766 & 0.6931 & 0.7189 & 0.6264 \\  & XGBoost (embed) & 0.6866 & 0.7378 & 0.8015 & 0.5877 \\  & FM & 0.7343 & 0.7401 & 0.8410 & 0.6189 \\  & MTLNFM & 0.7383 & 0.7448 & 0.8424 & 0.6772 \\ \hline \multirow{4}{*}{Mortality} & LR & 0.7224 & 0.7294 & 0.6396 & 0.7626 \\  & XGBoost (raw) & 0.7267 & 0.7100 & 0.7015 & 0.7244 \\  & XGBoost (embed) & 0.7085 & 0.6701 & 0.6565 & 0.7344 \\  & FM & 0.6906 & 0.6719 & 0.6088 & 0.7337 \\  & MTLNFM & 0.7234 & 0.7202 & 0.6489 & 0.7754 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._demonstrated a greater capacity to account for variability among patients, effectively avoiding such overgeneralizations. Its multi-task architecture allows the model to draw on shared knowledge across tasks, enabling more accurate predictions. These findings suggest that MTLNFM provides a more balanced and clinically realistic modeling approach. Additional analyses for the other two prediction tasks (clinical frailty scale and mortality) are presented in Supplementary Material S3. In addition to comparing the predicted and true distributions, we further quantified the overall deviation to intuitively assess the robustness and generalization capabilities of the MTLNFM model. For numerical features, we computed the absolute error between
## 2 Data and Methodologies
### Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & **Initial land Resolution** & \begin{tabular}{c} **Land** \\ **Surface** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Vegetation** \\ **Distribution** \\ \end{tabular} & \
\begin{tabular}{c} **Plant** \\ **Function** \\ **Types** \\ \end{tabular} &
\begin{tabular}{c} **Available** \\ **Outputs** \\ \end{tabular} & **References** \\ \hline \multirow{2}{*}{**BNU-ESM**} & \multirow{2}{*}{\(2.8^{\circ}\times 2.8^{\circ}\)} & The & \multirow{2}{*}{Dynamic} & \multirow{2}{*}{10} & NPP and & [PERSON] et al. (2014) \\  & & & Land Model & & NEP & [36] \\ \hline \multirow{2}{*}{**IPSL-CM5A-LR**} & \multirow{2}{*}{\(3.75^{\circ}\times 1.9^{\circ}\)} & \multirow{2}{*}{ORCHIDEE} & \multirow{2}{*}{Static} & \multirow{2}{*}{12} & NPP and [PERSON] et al. (2013) [37] \\ \hline \multirow{2}{*}{**MPI-ESM-LR**} & \multirow{2}{*}{\(2.8^{\circ}\times 1.9^{\circ}\)} & \multirow{2}{*}{SEIB-DGVM} & \multirow{2}{*}{Dynamic} & \multirow{2}{*}{10} & NPP & [PERSON] et al. (2012) [38] \\ \hline \multirow{2}{*}{**NorESM1-ME**} & \multirow{2}{*}{\(2^{\circ}\times 1^{\circ}\)} & Community & \multirow{2}{*}{Dynamic} & \multirow{2}{*}{12} & & [PERSON] et al. (2012) [39]; \\  & & & version 4 & & & [PERSON] et al. (2010) [40] \\ \hline \multirow{2}{*}{**CEVSA2**} & \multirow{2}{*}{\(0.1^{\circ}\times 0.1^{\circ}\)} & \multirow{2}{*}{/} & \multirow{2}{*}{Static} & \multirow{2}{*}{12} & NPP and & [PERSON] et al., (1998) \\  & & & & NEP & [6]; [PERSON] et al. (2017) [30] \\ \hline \hline \end{tabular} Note: Net ecosystem productivity (NEP), net primary productivity (NPP).  
\end{table} 
Table 1: Key features of the four Earth system models (ESMs) and CEVSA2 model used in this study.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 4 Proposed Relay Selection Scheme
### Singular Value Decomposition (SVD)-Based Relay Selection in 3-Dimensional (3D) Channel Model
The singular values of the matrix are obtained by calculating the SVD of the 3D channel matrix between the BS and relays. Then, the sum of the singular values is obtained, and the relay having the largest value is selected. As a result, the proposed relay selection scheme provides the method for selecting the appropriate relay using the SVD in the 3D channel environment.
###### Abstract  
Background: Acne scars significantly impact skin texture and esthetics, necessitating effective treatment modalities. This study evaluates the efficacy of erbium glass laser therapy in improving atrophic acne scars using advanced image analysis techniques. Materials and methods: Twenty patients with mild to moderate atrophic scars underwent two sessions of 1550 nm erbium glass laser treatment. The clinical photographs were analyzed using a Gray-Level Co-occurrence Matrix (GLCM) to assess changes in contrast and homogeneity across the grayscale and RGB channels. The analysis revealed statistically significant improvements post-therapy, including reduced contrast and increased homogeneity, indicating a smoother and more uniform skin texture. The blue and green channels demonstrated the greatest sensitivity to surface-level textural changes, while the red channel exhibited the smallest differences, reflecting its deeper penetration and reduced sensitivity to surface alterations. Conclusions: These findings underscore the value of quantitative imaging techniques in dermatology for objectively evaluating therapeutic outcomes and optimizing treatment strategies. Erbium glass laser therapy emerges as a non-invasive and effective solution for acne scar management.  
Keywords:image analysis; GLCM; clinical photography; acne scars +
Footnote †: journal: Applied Sciences
## 2 Literature Survey
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Valueable Research Contribution** & **Filter Method Used** & **Datasets Used** & **Year** \\ \hline [PERSON] et al. [58] & Sequential forward selection & Thyroid dataset & 1997 \\ [PERSON] et al. [59] & First ranker algorithm & Clinical datasets for text categorization & 2011 \\ [PERSON] et al. [60] & Correlation feature Selection & PIMA Indian diabetes & 2014 \\ [PERSON] et al. [61] & Chi-square error detection & Corel images dataset & 2015 \\ [PERSON] et al. [62] & Relief-F and Sequential Forward Selection & Healthcare datasets & 2015 \\ [PERSON]. [63] & Principal Component Analysis & Ultrasound kidney images & 2015 \\ [PERSON] et al. [64] & Principal Component Analysis & Heart disease dataset & 2016 \\ [PERSON] et al. [65] & Relief and Rough Set (RFRS) method & Statlog (Heart) dataset & 2017 \\ [PERSON]. [66] & Relief, minimal-Redundancy-Maximal- & Heart Cleveland Dataset & 2018 \\ [PERSON] et al. [67] & Linear Forward Selection technique & High dimensional datasets from various fields & 2019 \\ [PERSON] et al. [68] & Mutual Information & Chronic datasets & 1994 \\ [PERSON] et al. [69] & Correlation based feature selection & Coronary Artery & 2016 \\ [PERSON] et al. [70] & Fast correlation-based feature selection & High dimensional medical datasets & 2003 \\ (FCRF) algorithm & & Heterogeneous datasets & 2009 \\ [PERSON] et al. [71] & Sequential backward elimination & Heterogeneous datasets & 2009 \\ [PERSON] [72] & One-R and Consistency Based Subset & Hepatitis dataset & 2015 \\ [PERSON] et al. [73] & Soft Set Based Feature Selection Approach & Lung Cancer dataset & 2010 \\ [PERSON] et al. [74] & Relief-F method & Tumour dataset & 2011 \\ [PERSON] et al. [75] & Information Gain & Breast cancer and diabetes dataset & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An outline of relevant study on wrapper based attribute selection. to tackle the complexity of disease datasets. Among the different studies discussed in this section, various filter, wrapper and hybrid attribute selection methods were implemented for dimensionality reduction in chronic disease datasets. Figure 4 summarizes the overall use of these attribute selection methods discussed in our analysis.
## 2 Methods  
### CFD Method  
The finite element method (FEM) and computational fluid dynamics (CFD) are two numerical analysis methods commonly utilized in engineering and scientific research. The FEM focuses on solid mechanics problems such as structural stress and strain analysis, as well as electromagnetic and thermal field simulations. In contrast to the finite element method (FEM), computational fluid dynamics (CFD) method is better suited to simulating and analyzing fluid flows and their complicated interactions with heat transfer, mass transfer, and chemical reactions. CFD has numerous advantages for dealing with fluid dynamics problems, particularly in simulating multiphase flows, turbulence features, and free surface and interfacial difficulties. Its capabilities are demonstrated in the simulation of dynamic and transient flow problems, such as transient pressure fluctuations of fluids in piping systems, vibration response in fluid-structure interactions, and heat exchange efficiency analysis in thermal-fluid interactions. CFD is the preferred method when the problem involves fluid dynamics.  
This study investigated the optimization of water jet decontamination performance parameters at a submergence depth of 5000 m. Therefore, computational fluid dynamics was chosen.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Collective Contribution to Reliable AI in Healthcare
#### 4.5.1 Hybrid Privacy-Preserving Techniques in Healthcare AI
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature** & **Benefits** & **Challenges** & **Additional Considerations** & **Integration with Existing Systems** & **Resource and Cost Implications** \\ \hline Real-time & Immediate accuracy and consistency checks & Constant monitoring and updating needed & Balancing accuracy with system load & Seamless integration required & May increase operational costs \\ \hline Adaptability to Evolving Data & Keeps pace with new medical data and practices & Complexity in maintenance and updates & Aligning with maintained and advancements & Requires flexible system design & Resource-intensive for ongoing updates \\ \hline Anomaly Detection & Identifies and corrects data errors & Resource-intensive for computing resources & Prioritization in data areas & Must align with AI objectives & Involves investment in advanced hardware \\ \hline Scalability & Efficient handling of large data volumes & Technical hurdles in scaling algorithms & Developing scalable solutions & Compatibility with large datasets & High cost for scaling infrastructure \\ \hline Integration with AI & Enhances the effectiveness of AI & Alignment of algorithms with AI objectives needed & Ensuring compatibility and effectiveness & Smooth integration is essential & Requires dedicated resources for setup resources for setup \\ \hline Data Accessibility & Centralized control & Distributed access enhances availability & Ensuring equitable access & Improves data availability & Requires effective data distribution \\ \hline Ethical Considerations & Ensures adherence to moral and ethical guidelines & Balancing innovation with ethical constraints & Inclusion of diverse stakeholder perspectives & Ethical considerations & Ensures adherence to moral and ethical guidelines \\ \hline Privacy Protection & Safeguards patient data against breaches & Implementation of robust security measures & Compliance with data protection laws and standards & Privacy Protection & Safeguards patient data against breaches \\ \hline Transparency and Accountability & Enhances trust and reliability in AI applications & Challenges in explaining AI decisions to non-experts & Regular audits, clear documentation, and error reporting & Transparency and Accountability & Enhances trust and reliability in AI applications \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comprehensive summary of advanced data validation algorithms: benefits and challenges. in healthcare AI to address complex privacy concerns, showcasing a blend of multiple privacy-preserving strategies to handle the intricacies of this field effectively.