esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Antibiotics by Biochars: A Critical Review
## 4 A Critical Review of Biochar-Based Adsorption Processes
### Modification of Biochars
However, metal modifications generate contamination of water bodies through metal ion shedding [49]. Doping with carbonaceous materials is the introduction of carbonaceous materials (e.g., graphene and carbon nanotubes) into the surface structure of biochars to improve their adsorption efficiency [49; 158]. The increased number of adsorption sites and the increased specific surface area of the biochar improve its adsorption capacity [49; 160; 159]. However, graphene, graphene oxide, and carbon nanotubes are highly expensive materials and cannot be considered practical for large-scale adsorption applications [49]. Non-metallic or heteroatom doping of biochars using nitrogen [161; 162; 163], oxygen [162], sulfur [163], or phosphorus [162] is an efficient modification method to offer increase the stability and adsorption efficiency of adsorbents [49]. The heteroatom doping of biochar provides additional surface functional groups and active sites for adsorption. However, the available research is currently scarce [49]. Other physical modifications, such as steam activation [164] and ball milling [158; 165], generate a higher specific surface area, a higher number of functional groups, and pores in biochars. Physical modifications are environmentally friendly, as they do not use any chemicals during biochar modification [49]. However, they are comparatively less effective than chemical modifications [49]. Molecular imprinting improves the specific adsorption of biochars by creating selective active sites [49]. Molecularly imprinted biochars can be used to remove low-concentration and highly toxic pollutants [49; 166]. Molecularly imprinted biochars have already been used to detect and quantify antibiotic residues at trace levels in food and environmental samples [49; 167; 168]. Molecularly imprinted biochars are reusable, which is their major advantage compared to other biochars [48]. Similar to other modified biochars, molecularly imprinted biochars usually exhibit better adsorption properties for antibiotics than pristine biochars [49].
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 5 Results and Discussion
### Discussions  
Credit card fraud is a huge burden for financial institutions. The advances in e-commerce and digital payment platforms have made credit card fraud more common. This study aimed to utilize machine learning to detect credit card fraud effectively. Meanwhile, since most credit card datasets contain irrelevant attributes that degrade the performance of machine learning algorithms, this study proposed a robust hybrid feature-selection approach comprising filter (information gain technique) and wrapper (genetic algorithm) feature-selection steps, ensuring only the most significant attributes are used for machine learning. This study uses the well-known European Credit Card dataset. The proposed IG-GAW, which uses the ELM as the learning algorithm in the GA wrapper, obtained scores for sensitivity, specificity, AUC, and G-mean of 0.997, 0.994, 0.990, and 0.994, respectively. Additionally, two popular credit risk datasets (German and Taiwan credit datasets) were used to further validate the proposed method's improved performance.  
The proposed IG-GAW outperformed the selected classifiers when trained with the German and Taiwan datasets. The results also showed that the proposed hybrid approach, IG-GAW, achieved superior performance compared to the filter and wrapper methods, i.e., IG-ELM and GAW. Therefore, integrating filter and wrapper techniques in a hybrid setting is a robust approach to detecting credit card fraud. Lastly, the results also showed that introducing feature selection enhanced the ELM's performance, indicating the importance of effective feature selection.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Meteorological Dataset  
In order to quantify and reduce the uncertainties of our results, we use a combination of a 9-member ensemble that consists of three precipitation and three temperature datasets. They are daily records of TerraClimate [[[http://www.climatologylab.org/terraclimate.html](http://www.climatologylab.org/terraclimate.html)]([http://www.climatologylab.org/terraclimate.html](http://www.climatologylab.org/terraclimate.html)) (accessed on 20 May 2023)], CRU4 [[[http://data.ceda.ac.uk//badc/cru/data/cru_ts/](http://data.ceda.ac.uk//badc/cru/data/cru_ts/)]([http://data.ceda.ac.uk//badc/cru/data/cru_ts/](http://data.ceda.ac.uk//badc/cru/data/cru_ts/)) (accessed on 20 May 2023)] and ERA5 [[[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)) (accessed on 20 May 2023)]. AI (Arid Index, P/PET) is calculated by estimates of precipitation (P) and potential evapotranspiration (PET) from TerraClimate. Areas that are not water-limited (AI > 0.65) and hyper-arid (AI < 0.05) are excluded from our calculation. All datasets are remapped to the same resolution as NDVI.
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 1 Introduction
### Problem Description  
Methods of segmentation based on the proximity of pixels are region-based. The authors in reference [60] Propose coarse-to-fine kidney segmentation using kernel fact hunt and adaptive region-growing, modifying attention construction limitations. [PERSON] et al. [PERSON] et al. [61] Utilizing native restraints prevents adjacent organs from affecting deformable models. [PERSON] et al. [PERSON] et al. [62] The level-set procedure generates a deformable model, probabilistic form, voxel communication, and stochastic haste purpose for segmentation presentation; the atlas-based technique uses functional diagrams for orientation and consistent comments. [PERSON] et al. [PERSON] et al. [63] Two-step multi-atlas image registration technique for renal segmentation using random forest and machine learning classifier. [PERSON] et al. [PERSON] et al. [64] Random forest algorithm classifies dynamic contrast-enhanced images. [PERSON] et al. [PERSON] et al. [65;66] Earlier attempts to resolve this issue have shown result promise. This study uses five lightweight architectures from the SE-ResNet family designed to optimize resources while maintaining high precision. These architectures are valuable for achieving comparable outcomes to current methods while requiring less space and training time. The study evaluates SE-ResNets' ability to classify kidney tumor images using F1-score, IoU scores, and the KiTs 19 dataset.  
Figure 1: Diagram depicting human kidney anatomy and renal cell carcinoma development [13].
###### Abstract  
Accelerometers have been widely used for motion analysis. The effect of initial sensor orientation (ISO) on the derived range of motion (ROM) is currently unexplored, limiting clarity in understanding error. This two-step study systematically explored the effect of ISO on the error of accelerometer-derived range of motion (ROM) and the effect of a proposed correction algorithm. Accelerometer data were used to compute peak and through-range ROM across a range of ISO and movement angular velocities up to 148\({}^{\circ}\) s\({}^{-1}\) compared to an optoelectronic gold-standard. Step 1 demonstrated that error increased linearly with increasing ISO offsets and angular velocity. Average peak ROM RMSE at an ISO of 20\({}^{\circ}\) tilt and twist was 5.9\({}^{\circ}\) for sagittal motion, and for an ISO of 50\({}^{\circ}\) pitch and 20\({}^{\circ}\) twist, it was 7.5\({}^{\circ}\) for frontal plane ROM. Through-range RMSE demonstrated errors of 7-8\({}^{\circ}\) for similar ISOs. Predictive modeling estimated a 3.2\({}^{\circ}\) and 3.7\({}^{\circ}\) increase in peak and through-range sagittal plane error for every 10\({}^{\circ}\) increase in tilt and twist ISO. Step 2 demonstrated error reduction utilizing mathematical correction for ISO, resulting in <1\({}^{\circ}\) mean peak error and <1.2\({}^{\circ}\) mean through-range ROM error regardless of ISO. Accelerometers can be used to measure cardinal plane joint angles, but initial orientation is a source of error unless corrected.  
accelerometers; human motion; joint angle; motion analysis; sensors Article
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 6 Challenges of Deep Learning
### Quantity of Hidden Layers  
The quantity of hidden layers can be important [157]. Using two hidden layers in a model of deep learning is the baseline. Employing further hidden layers will increase the complexity of the computation. However, adding more layers means an analytical model that is deeper. Hidden layers play an important role in the performance of a neural network, especially when dealing with complex problems where accuracy and time complexity are the main constraints. A hidden layer is necessary in an artificial neural network if and only if the data must be separated nonlinearly. If the data can be separated linearly, then no hidden layers are required. If the data are not very complex or have few dimensions or features, neural networks with one or two hidden layers will work. When data have many dimensions or features, 3-5 hidden layers can be used to achieve optimal results. An appropriate number of hidden layers reduces the training time required for high accuracy. A large number of hidden layers slows down the training process of a neural network. Consequently, if the goal of the application is to improve accuracy, then large numbers of hidden layers are the best solution; however, if time complexity is the primary constraint of the application, then large numbers of hidden layers will not be the most suitable solution. Adding unnecessary layers can also result in overfitting. Hence, before designing the neural network, training database samples must be analyzed to estimate the number of hidden layers needed.
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 2 Literature Review
### Handoff Techniques
#### 2.1.3 Two Antenna Scheme  
This methodology clarifies that two remote interface cards are given to the vehicles while each card has an antenna. These two antennas deal with common base usefulness. One antenna has given an undertaking to check the quality of the flag and perform handoff when required. The other antenna conveys flawlessly, which results in less parcel misfortune and hands-off time necessity.
# The Significance of Calcium in Photosynthesis
## 2 Mechanisms of Ca\({}^{2+}\) Involved in Stomatal Movements
However, both artificially induced [Ca\({}^{2+}\)]\({}_{\text{cyt}}\) oscillation [56] and H\({}_{2}\)O\({}_{2}\) treatment [57] can cause stomatal closure in mutants. Moreover, H\({}_{2}\)O\({}_{2}\) affects stomatal movement by activating [Ca\({}^{2+}\)]\({}_{\text{cyt}}\) channels [58], and CAS is involved in the generation of H\({}_{2}\)O\({}_{2}\), which induces [Ca\({}^{2+}\)]\({}_{\text{cyt}}\) oscillation by activating [Ca\({}^{2+}\)]\({}_{\text{cyt}}\) channels and then causes stomatal closure. Some reports showed that [Ca\({}^{2+}\)]\({}_{\text{cyt}}\) oscillations can also be induced by the inositol 1,4,5-trisphosphate (IP\({}_{3}\)) under external stress [59, 60, 61], and this phenomenon is related to CAS [61]. Additionally, NADPH oxidases RbohD and RbohF, which are also known as respiratory burst oxidase homologues, are both involved in stomatal movement [15, 16]. Moreover, they have two Ca\({}^{2+}\)-binding EF-hand motifs [17]. RbohD/F [62] and phosphatidic acid (PA) produced by phospholipase D (PLD) [63] are related to ROS production, and the _PLDa1_-null _Arabidopsis_ mutant [64] and rbohD/F depletion mutant [16] are insensitive to the pathway in which ABA induces stomatal closure. RbohD/F and PA are speculated to function in the same pathway. Further research showed that PA interacts with RbohD/F to increase NADPH activity, thereby affecting ROS production [64]. In addition, NO is involved in this pathway and plays a vital role [65]. When the activities of phospholipase C (PLC) and PLD are inhibited, NO-induced stomatal closure is also prevented [65]. Thus, NO acts upstream in the PLC and PLD pathway [65]. However, different studies showed that NO occurs in the downstream events of the pathway, in which PLD generates PA [64]. Stomatal movement is co-regulated by a signal network including the Ca\({}^{2+}\) signal transduction pathway, plant hormone pathway, and ROS signal pathway, but the relationship between NO and PA on the stomatal movement pathway needs further research. Other signaling molecules involved in stomatal movement have been discussed in detail in another review [66].
## 5 Security Analysis
### Game-Based Security Argument  
To formalize the security properties of the proposed temperate blind signature (TBS) scheme, we briefly outline its resistance to existential forgery under chosen message attacks (EUF-CMA), within the context of a game-based security model.  
Security model: We consider the classical game-based model for blind signature schemes. In this model, an adversary \(\mathcal{A}\) interacts with a signing oracle and attempts to produce a valid message-signature pair \((M^{*},\sigma^{*})\) such that  
* \(M^{*}\) was never queried to the signing oracle in unblinded form.
* \(\mathsf{Verify}(M^{*},\sigma^{*},T)=\mathsf{True}\) under public key \(K_{\text{pub}}\).  
Assumption: We assume the hardness of RSA inversion (or ECDLP, depending on instantiation), and that the random oracle model is in place for the blinding factor generation and verification operations.  
Security argument: Under these assumptions,  
* The blindness of the scheme ensures that the signer cannot learn \(M\) from \(M^{\prime}=M\cdot r\text{ mod }n\), due to the semantic independence provided by the multiplicative random factor \(r\).
* The unforgeability follows from the inability of the adversary to invert the RSA/ECC trapdoor function or to produce valid \(\sigma^{*}\) for unqueried messages without any knowledge of \(K_{\text{priv}}\).
* The tempering parameter \(T\) is public and deterministic per application; it does not compromise the hardness assumption.  
Thus, we claim that the TBS scheme satisfies EUF-CMA security in the random oracle model under standard assumptions.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
###### Abstract  
In order to enhance the anti-submarine capability of multi-unmanned aerial vehicles (multi-UAVs) in the unknown sea environment and improve the search efficiency, in this paper, we propose a rule-inspired-multi-ant colony (RI-MAC)-based UAV cooperative search algorithm. First, a special sea area anti-submarine search model is established, including an association rule-driven target probability map (TPM) model, a UAV kinematics model, and a sensor model. The novel model has the characteristics of rule linkage, which effectively improves the accuracy of target detection probability in unknown environments. Secondly, according to the established search model, a multi-objective utility function based on association rules is derived. In order to solve the problem of multi-objective optimization, an RI-MAC algorithm based on association rules is proposed, and a pheromone update method using threat avoidance is designed to optimize the search path of multi-UAVs. Finally, a simulation experiment is conducted to verify the effectiveness and superiority of the proposed search algorithm.  
association-driven probability map; collaborative search; rule-inspired multi-ant colony algorithm
# A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm
## 5 Simulation Experiment and Result Analysis
### Comparison with Other Optimization Algorithms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & **MOFECO** & **NSGA-II** & **MOPSO** & **PESA-II** & **KnEA** & **NSLS** \\ \hline ZDT1 & \(1.40\times 10^{-4}\) & \(1.54\times 10^{-4}\) (+) & \(6.94\times 10^{-4}\) (+) & \(1.72\times 10^{-4}\) (+) & \(5.36\times 10^{-5}\) (-) & \(5.04\times 10^{-4}\) (+) \\ ZDT2 & \(4.02\times 10^{-6}\) & \(2.36\times 10^{-5}\) (+) & \(2.00\times 10^{-3}\) (+) & \(5.75\times 10^{-4}\) (+) & \(5.59\times 10^{-6}\) (+) & \(3.80\times 10^{-3}\) (+) \\ ZDT4 & \(2.39\times 10^{-5}\) & \(2.81\times 10^{-5}\) (+) & \(9.30\times 10^{-3}\) (+) & \(2.09\times 10^{-2}\) (+) & \(1.42\times 10^{-2}\) (+) & \(1.81\times 10^{-2}\) (+) \\ ZDT6 & \(3.50\times 10^{-7}\) & \(4.02\times 10^{-6}\) (+) & \(9.13\times 10^{-2}\) (+) & \(6.10\times 10^{-3}\) (+) & \(4.36\times 10^{-6}\) (+) & \(7.30\times 10^{-3}\) (+) \\ DTLZ2 & \(4.67\times 10^{-4}\) & \(1.20\times 10^{-3}\) (+) & \(4.70\times 10^{-3}\) (+) & \(1.40\times 10^{-3}\) (+) & \(5.03\times 10^{-4}\) (+) & \(5.56\times 10^{-4}\) (+) \\ DTLZ4 & \(1.43\times 10^{-3}\) & \(1.10\times 10^{-3}\) (-) & \(4.60\times 10^{-3}\) (+) & \(1.42\times 10^{-3}\) (\(\sim\)) & \(4.24\times 10^{-4}\) (-) & \(6.05\times 10^{-4}\) (-) \\ DTLZ5 & \(3.56\times 10^{-5}\) & \(2.30\times 10^{-4}\) (+) & \(1.40\times 10^{-3}\) (+) & \(2.19\times 10^{-4}\) (+) & \(5.00\times 10^{-4}\) (+) & \(1.46\times 10^{-5}\) (-) \\ DTLZ6 & \(4.82\times 10^{-7}\) & \(4.82\times 10^{-6}\) (+) & \(5.20\times 10^{-3}\) (+) & \(1.80\times 10^{-3}\) (+) & \(4.73\times 10^{-6}\) (+) & \(4.85\times 10^{-6}\) (+) \\ DTLZ7 & \(9.60\times 10^{-3}\) & \(3.00\times 10^{-3}\) (-) & \(1.01\times 10^{-2}\) (+) & \(2.70\times 10^{-3}\) (-) & \(1.50\times 10^{-3}\) (-) & \(2.90\times 10^{-3}\) (-) \\ WFG2 & \(6.25\times 10^{-2}\) & \(8.77\times 10^{-2}\) (+) & \(7.12\times 10^{-2}\) (+) & \(6.44\times 10^{-2}\) (+) & \(6.51\times 10^{-2}\) (+) & \(6.66\times 10^{-2}\) (+) \\ WFG3 & \(5.29\times 10^{-2}\) & \(1.85\times 10^{-1}\) (+) & \(1.46\times 10^{-1}\) (+) & \(1.63\times 10^{-1}\) (+) & \(1.21\times 10^{-1}\) (+) & \(1.62\times 10^{-1}\) (+) \\ WFG4 & \(1.57\times 10^{-2}\) & \(1.68\times 10^{-2}\) (+) & \(3.61\times 10^{-2}\) (+) & \(2.58\times 10^{-2}\) (+) & \(1.59\times 10^{-2}\) (\(\sim\)) & \(4.39\times 10^{-2}\) (+) \\ MaF1 & \(7.10\times 10^{-3}\) & \(3.97\times 10^{-3}\) (-) & \(9.72\times 10^{-3}\) (+) & \(4.29\times 10^{-3}\) (-) & \(1.75\times 10^{-3}\) (-) & \(6.14\times 10^{-3}\) (-) \\ MaF2 & \(3.67\times 10^{-3}\) & \(8.13\times 10^{-3}\) (+) & \(4.88\times 10^{-3}\) (+) & \(3.79\times 10^{-3}\) (+) & \(4.35\times 10^{-3}\) (+) & \(6.19\times 10^{-3}\) (+) \\ MaF12 & \(1.88\times 10^{-2}\) & \(2.01\times 10^{-2}\) (+) & \(4.11\times 10^{-2}\) (+) & \(2.20\times 10^{-2}\) (+) & \(2.01\times 10^{-2}\) (+) & \(5.83\times 10^{-2}\) (+) \\ + & / & 12 & 15 & 12 & 10 & 11 \\ - & / & 3 & 0 & 2 & 4 & 4 \\ \(\sim\) & / & 0 & 0 & 1 & 1 & 0 \\ \hline \hline \end{
# Decellularized Scaffolds for Skin Repair and Regeneration
## 3 Decellularization Methods
### Main Decellularizing Agents
#### 3.2.1 Detergents
In a mouse wound healing model, despite important loss of GAG content, alterations in elastic fibers, and important tissue stiffening compared to native skin, this acellular matrix supported the establishment of host fibroblasts and formation of new blood vessels after a 6-month graft. A patented protocol, reported to be one of the MatrACELL(r) decellularization methods applied to produce the commercial human acellular matrix DermACELL(tm), also mentions use of NLS combined with nucleases to decellularize tissues [104]. These authors have published a comparison of this substitute with other commercial matrices, finding the lowest DNA content in the DermACELL(tm) scaffold; however, other independent studies are needed to confirm and complete the characterization of this matrix. More studies also are needed to describe in greater detail the effects of NLS on ECM during decellularization processing and to evaluate its compatibility with acellular matrix production using other tissues. No use of CHAPS, a zwitterionic detergent reported for the decellularization of thin tissues (lungs and trachea), has been tested for producing scaffolds for skin repair. The thickness of the tissues used for skin repair may not be compatible with adequate decellularization using this detergent [28].
## 3 Model and Methodology
### Solution Procedure
#### 3.2.1 Discrete Artificial Bee Colony
If only execution modes are selected, the activity stays in its current activity list order; the modes are swapped/inserted; and the resource constraints are checked. Figure 4: Artificial bee colony flowchart. Refer to Figure 5 for the following example showing how these operators work: Suppose a swap between Activities 3 and 7 results in a feasible solution; Figure 4(a) shows the result of this operation. If the same two activities are chosen, but only to swap their execution modes, Figure 4(b) shows the result. Figure 4(c) shows a graphical representation of inserting Activity 7 before Activity 4, and finally, Figure 4(d) shows the result of inserting the execution mode of Activity 7 before that of Activity 4.
## 1 Introduction
Other deep learning approaches use expert visualization of the red color in composite images for the training phase to provide _Sargassum_ occurrences (ERIS-NET, [17]). The asset of such methods is to take into account the full spectrum of reflectance instead of focusing only on the red-edge and NIR domains. _Sargassum_ aggregations are often subject to move at depths greater than 1 m as a result of the wind (>4 m/s) and of the roughness of the sea in Caribbean waters [18]. When the _Sargassum_ are immersed in the water column, the above water reflectance red edge/NIR peak is strongly attenuated as a result of the high pure seawater absorption coefficient in these spectral bands. Therefore, the performance of the current _Sargassum_ algal detection indexes, which are all based on red-edge/NIR bands, could significantly decrease, thus making more challenging the detection _Sargassum_ of aggregations present in the ocean [19]. The originality of the current study is to take into account the immersion depth of _Sargassum_ for improving their detection from satellite and for estimating their fractional coverage. [PERSON] et al. [20] proposed a direct semi-analytical radiative transfer model in shallow waters that is able to simulate the water leaving reflectance from the bio-optical properties, depth, and benthic habitat composition. [PERSON] et al. [21] recently adapted the [PERSON]'s model to analyze the influence of immersion depth and fractional coverage on the surface reflectance. Since the semi-analytical [PERSON]'s model can be inverted, it will be used here to retrieve the _Sargassum_ aggregation parameters, especially _Sargassum_ depth and fractional coverage, from the surface reflectance derived from Sentinel-3/OLCI observations. The paper is organized as follows: the study area in the Caribbean Sea, the Sentinel-3/OLCI satellite data and the methodology used to estimate in-water and surface _Sargassum_ aggregation properties are described Section 2. The performances of the methodology and the application to Sentinel-3/OLCI data are outlined Section 3. The implementation of a neural network approach for reducing the computational time for processing a large OLCI dataset is presented in Section 4. Finally, the comparison of the proposed approach with a standard surface _Sargassum_ detection method is discussed in Section 5.