esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area, Data, and Methodology
### Methodology
#### 2.3.3 Trend Analysis  
The monotonic trend at these grid locations and precipitation stations was evaluated using a non-parametric rank-based Mann-Kendall (MK) test [87; 88] for statistical significance and Theil-Sen (TS) slope estimator [89; 90] for trend magnitude. MK, in conjunction with TS, has been widely used in trend detection for hydrometeorological time-series data [80]. In ad dition, statistical significance was computed at a threshold \(p\)-value of 0.1 (i.e., 90% confidence level). Finally, we used the pyMannKendall package [91] following [PERSON]'s [92] pre-whitening method to limit the serial correlation contaminated in observations [93].
## 3 Results and Discussion
### Adsorption Isotherm  
The relationship between the equilibrated mercury uptake (\(q_{\text{e}}\)) of sorbent particles and the equilibrated concentration of Hg(II) in solution (\(C_{\text{e}}\)) was used to quantitatively describe the sorption, by way of adsorption isotherms. In this case, experiments were performed under conditions as used in the kinetic studies, but initial Hg(II) concentrations were varied from 60 to 1000 ppb. The adsorption time  
Figure 5: (**a**) Adsorption kinetics; (**b**) pseudo-second-order kinetic plot for Hg(II) adsorption; (**c**) Langmuir adsorption isotherms of uncoated Fe\({}_{3}\)O\({}_{4}\), Ca@Fe\({}_{3}\)O\({}_{4}\), and Cys-C@Fe\({}_{3}\)O\({}_{4}\); (**d**) effect of pH on Hg(II) adsorption by 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\); (**e**) Hg ion speciation diagram with pH as simulated using the Visual MINTEQ 3.1 software (\(C_{\text{Hg}}\)(total) = 1000 ppb); and (**f**) zeta potential of 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) particles at different pH values. Small standard deviations from five identical batch experiments shown in Figure 5a,d implied the high data reproducibility.  
of 60 min is sufficient for adsorption-desorption equilibrium, as the difference in Hg(II) uptake from 60 min treatments and from longer treatment time was found negligible. As depicted in Figure 5c, Hg(II) uptake increased with initial Hg(II) concentration until it reached a saturation point. According to these profiles, the saturated adsorption capacities of uncoated Fe\({}_{3}\)O\({}_{4}\), C@Fe\({}_{3}\)O\({}_{4}\), and 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) were 12, 28, and 90 mg g\({}^{-1}\), respectively. The equilibrium data were then fitted using the Langmuir model (Figure S3a), allowing prediction of equilibrium parameters [56].  
\[S_{ad}=N_{\max}\times(S_{ad}/(K^{-1}+S_{ad})). \tag{4}\]  
In Equation (4), \(S_{ad}\) is the number of ions adsorbed per unit area of sorbent surface (mg g\({}^{-1}\)), \(S_{ad}\) is the equilibrium concentration of ions in solution (mg L\({}^{-1}\)), \(N_{\max}\) is the maximum adsorption capacity (mg g\({}^{-1}\)) corresponding to complete monolayer coverage on the sorbent surface, and K is the Langmuir adsorption constant (L mol\({}^{-1}\)) related to binding strength. From fitting results shown in Table 2 and comparisons with results calculated using the Freundlich model [15] (data shown in Figure S3b), adsorption clearly follows Langmuir behavior indicating formation of a uniform monolayer of metal ions adsorbed on the particle surface [50]. Furthermore, it is important to note that the calculated maximum adsorption capacity of uncoated Fe\({}_{3}\)O\({}_{4}\) (12.56 mg g\({}^{-1}\)) is enhanced by carbon coating (29.85 mg g\({}^{-1}\)) and more so, by cysteine functionalization (94.33 mg g\({}^{-1}\)).
## 4 Experimental Results
### Evaluation Metrics  
The current study proposes a multi-class classification task for determining the severity of cyberbullying incidents, which are rated as slight, medium, and serious. Performance evaluation was conducted according to the metrics of precision (\(precision_{j}\)), recall (\(recall_{j}\)), and \(F1\) (\(f1_{j}\)) on each label \(j\), and defined as follows:  
\[ACC=\frac{\textit{number of labels correctly classified}}{\textit{Total number of samples}} \tag{19}\]  
\[precision_{j}=\frac{\textit{number of label j correctly classified}}{\textit{number of label j in gold standard}} \tag{20}\]  
\[recall_{j}=\frac{\textit{number of label j correctly classified}}{\textit{number of label j in model predicted}} \tag{21}\]  
\[f1_{j}=\frac{2\big{(}precision_{j}\times recall_{j}\big{)}}{precision_{j}+ recall_{j}} \tag{22}\]  
To evaluate multi-class problems, we used the macro and weighted averaging approaches to compute overall metrics for the three severity labels. The metrics \(PRE_{macro}\) (macro precision), \(PRE_{weighted}\) (weighted precision), \(REC_{macro}\) (macro recall), \(REC_{weighted}\) (weighted recall), \(F1_{macro}\) (macro \(F1\)), \(F1_{weighted}\) (weighted \(F1\)), and \(F1_{overall}\) are defined as follows:  
\[PRE_{macro}=\frac{1}{3}\sum_{j=1}^{3}precision_{j} \tag{23}\]  
\[REC_{macro}=\frac{1}{3}\sum_{j=1}^{3}recall_{j} \tag{24}\]  
\[F1_{macro}=\frac{1}{3}\sum_{j=1}^{3}f1_{j} \tag{25}\]  
\[PRE_{weighted}=\frac{1}{N}\sum_{j=1}^{3}precision_{j}*n_{j} \tag{26}\]  
\[REC_{weighted}=\frac{1}{N}\sum_{j=1}^{3}recall_{j}*n_{j} \tag{27}\]  
\[F1_{weighted}=\frac{1}{N}\sum_{j=1}^{3}f1_{j}*n_{j} \tag{28}\]  
\[F1_{overall}=\big{(}F1_{macro}+F1_{weighted}\big{)}/2 \tag{29}\]  
where \(N\) denotes the number of all samples and \(n_{j}\) denotes the number of \(j\)th labels.
# A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm
## 2 Task Assignment Model
### Task Allocation Model  
Comprehensive consideration of the UAV body constraints, task requirements, cost consumption, effects of task completion and other factors, a task allocation model under multiple constraints is established.  
\[maxf=\alpha\frac{f_{rew}}{f_{cost}} \tag{10}\]  
\[s.t.\ \sum_{i=1}^{N_{u}}\sum_{j=1}^{N_{t}}\sum_{k=1}^{3}x_{ijk}=3 \tag{11}\]  
\[\forall i\in\ [1,N_{u}],d_{i}\leq d_{i}^{max},\tau_{i}\leq\tau_{i}^{max} \tag{12}\]  
\[\sum_{i=1}^{N_{u}}\tau_{ijk}^{c}X_{i}^{j}\geq I_{j} \tag{13}\]  
where \(\alpha\) is the binary flag, which can be expressed as follows.  
\[\alpha=\left\{\begin{array}{rl}1,\ if(9)&\sim(14)\textit{are all }\textit{satisfied}\\ &0,\textit{others}\end{array}\right. \tag{14}\]  
Formula (11) is used to ensure that each task must be completed; in Formula (12), \(d_{i}\) and \(\tau_{i}\), respectively, represent the total flight distance and total time consumed by the UAV \(u_{i}\) to complete all assigned tasks, neither of which can exceed the prescribed upper limit.  
Formula (13) is to ensure that each task is successfully completed, and the resources consumed by the UAV performing the task need to meet the conditions required for the completion of the task.  
In the formulated task allocation model, the cost function and the function of task benefit evaluation are used as the numerator and denominator of the objective function, respectively. By maximizing the objective function proposed, the contradictory variables of task revenue and resource consumption are optimized at the same time. Meanwhile, constraints such as limited resources and task priority are introduced to improve the applicability of the model. The introduction of multiple constraints makes the established model more accurate and further improves the mission execution efficiency of the UAV system.
# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
###### Abstract  
Skeleton-based human action recognition has attracted extensive attention due to the robustness of the human skeleton data in the field of computer vision. In recent years, there is a trend of using graph convolutional networks (GCNs) to model the human skeleton into a spatio-temporal graph to explore the internal connections of human joints that has achieved remarkable performance. However, the existing methods always ignore the remote dependency between joints, and fixed temporal convolution kernels will lead to inflexible temporal modeling. In this paper, we propose a multi-scale adaptive aggregate graph convolution network (MSAAGCN) for skeleton-based action recognition. First, we designed a multi-scale spatial GCN to aggregate the remote and multi-order semantic information of the skeleton data and comprehensively model the internal relations of the human body for feature learning. Then, the multi-scale temporal module adaptively selects convolution kernels of different temporal lengths to obtain a more flexible temporal map. Additionally, the attention mechanism is added to obtain more meaningful joint, frame and channel information in the skeleton sequence. Extensive experiments on three large-scale datasets (NTU RGB+D 60, NTU RGB+D 120 and Kinetics-Skeleton) demonstrate the superiority of our proposed MSAAGCN.  
action recognition; skeleton sequence; graph convolutional network; attention mechanism
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 3 Results and Discussion
### Practical Use Cases of the Proposed Rectenna  
The proposed compact rectenna is particularly well suited for low-power wireless systems. Its unobtrusive design, compact form factor, planar structure, flexibility, low cost, and good performance allow integration across a wide area of practical use cases, including the following:  
* Integrated into sensor nodes in smart homes, environmental monitoring, or precision agriculture. These nodes can function in passive mode, harvesting energy from nearby Wi-Fi routers or cellular base stations, thus eliminating the need for batteries.
* Integrated into wearable devices. The rectenna can be embedded into garments or accessories to harvest energy from ambient RF fields for powering low-energy devices, such as biometric sensors and fitness trackers.
* Integrated into passive tracking tags, enabling battery-free operation by scavenging RF energy from nearby readers or ambient signals for logistics systems.
# Research on the Correlation of Safety Risk of Railway Bridge Construction Based on Meta-Analysis
## 3 Construction of Meta-Analysis Model
### Inclusion Criteria  
To comprehensively identify the factors that influence the risk associated with railway bridges, an extensive search of the existing literature in China was conducted, ensuring the integrity of data collection. This search encompassed published Chinese journal articles as well as master's and doctoral theses, utilizing title, keyword, and subject searches. The China Knowledge Network database was employed for retrieval purposes. The key search terms included \"railway bridge\", \"construction risk\", \"risk identification\", \"risk management\", and their combinations. As a result, a total of 1244 Chinese articles were retrieved.  
Subsequently, specific inclusion criteria were developed for the literature. Firstly, papers without a clearly defined object of study were excluded. Secondly, articles that solely provided qualitative analysis of railway bridge risk factors or presented incomplete examples without proper discussion were eliminated. Thirdly, the selected papers were required to explicitly describe the security risk factors relevant to this study or possess characteristics that could be transformed into security risk factors through analysis. Additionally, these papers needed to have a clear identification process; otherwise, they were rejected. Fourthly, literature with arbitrary risk classifications or vague definitions of risk factors was excluded.  
Following this set of criteria, the full texts of the remaining articles were carefully reviewed. Ultimately, a total of 21 articles were determined to be suitable for inclusion in this research. These comprised six journal papers and 15 Master's theses, as depicted in Figure 1.
## 4 Results and Discussion
### Vortex Shedding Frequencies
\(C_{pb}\) is the base pressure coefficient at \(\theta=180^{\circ}\). \(C_{pm1}\) and \(C_{pm2}\) are the minimum pressure coefficients at the upper and lower sides, respectively. \(\theta_{pst1}\) and \(\theta_{pst2}\) are the corresponding \(\theta\) of \(C_{pm1}\) and \(C_{pm2}\). As shown in Figure 20a, in the single bluff body flow regime, the base pressure of the upstream cylinder increases compared to that of a single cylinder due to the blockage effect. Stagnation points of downstream cylinders are pretty obvious (\(\theta_{pst1}=6.94^{\circ}\), \(\theta_{pst2}=353.06^{\circ}\)). The base pressure of upstream cylinder has two minimal values in the local area, located at \(150^{\circ}\) and \(210^{\circ}\). Meanwhile, two local minimum pressure coefficients occur at \(30^{\circ}\) for cylinder B and \(330^{\circ}\) for cylinder C. Those arresting minimal values occur at the positions where cylinder \(A\) is closest to cylinders B and C, respectively. The pressure coefficients of downstream cylinders both have a decrease at the gap-side area, and are nearly mirror-symmetric about \(\theta=180^{\circ}\). For the deflected gap flow shown in Figure 20b, the pressure coefficients of downstream cylinders become asymmetrical because of the jet flow bias towards cylinder C. In addition, stagnation points of downstream cylinders are slightly smaller (\(\theta_{pst1}=4.49^{\circ}\), \(\theta_{pst2}=355.55^{\circ}\)). As \(L/D\) increases into the anti-phase flow regime, the base pressure of the upstream cylinder turns into negative because the blockage effect becomes weak. \(C_{pm1}\) of cylinder A is smaller than \(C_{pm2}\) of cylinder B, while the opposite condition happens on cylinder C. This is because the flow velocity on the gap side is greater than the flow velocity on the free-flow side. When \(L/D\) goes into the in-phase flow regime, stagnation points of downstream cylinders, \(\theta_{pst1}\) and \(\theta_{pst2}\), become zero. In the co-shedding flow regime, the pressure coefficient distributions of all the cylinders are similar with that of a single cylinder. Figure 19: Strouhal numbers of three cylinders at different \(L/D\).
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 3 FLATSIM Architecture
### Architecture of the FLATSIM Production Module  
The technical environment of the production module includes the HPC and the GPFS, a shared PostgreSQL  
DBMS, and the mutualized datalake (composed of several data storage spaces). The architecture of the production module (Figure 8) is based on 2 sub-components:  
Figure 7: General architecture for production and delivery.  
The FLATSIM application is in charge of referencing data, orchestrating processing steps and submitting them as unitary jobs to the scheduler. The data cataloged by the application server are stored on the GPFS, while their metadata and status of processing steps' jobs are registered in the PostgreSQL database.  
Through the integration, each step of the NSBAS workflow has been divided into unitary processes, which can be performed in isolation and parallel for each unique data (SLC or interferograms) in different jobs spread on the HPC's nodes. During their execution, a job's temporary data are written on the local storage of the node, and, at the end of the execution, output data are transferred and cataloged by the FLATSIM application server, to be used as input of the next step jobs. Once the output data are cataloged, the job also updates its status in the application server.  
The FLATSIM application server is also responsible for the processing sequencing. It monitors the production status of each step, and when one step is completed, based on its parametrization, its internal logic, and the data cataloged, it identifies the jobs to process for the next step, and submit these new jobs to the distributed resource manager (DRM) PBS-PRO, that will dispatch them on the HPC nodes. At any time during the production, the operators can interrogate the FLATSIM application server to request information on the production status, and act on products or jobs if necessary.  
During the first step of the production, FLATSIM gets SENTINEL-1 Level-1C products via direct privileged access on the PEPS platform (developed by CNES in the context of collaborative ground segments of Copernicus program) and stores them in the application server catalog. After the packaging step, the FLATSIM output products are transferred on the mutualized datalake, which is accessible by both the production module and the delivery module. At the same time, these products metadata are sent to the delivery module to be registered in its database.  
Figure 8: Production module architecture and components.  
By default, the scheduling system in the CNES HPC allows a project to submit jobs on up to 800 cluster cores. Under these conditions, processing a Sentinel-1 swath covering 5.5 degrees of latitude to generate a 8 looks resolution (about 100 m pixel size) time series over 5 years' time depth requires approximately 10,000 h of CPU time and 60 h of real time.
# Adversarial Training Methods for Deep Learning: A Systematic Review
## 4 Findings
### Summary of the Findings  
The adversarial attacks and defenses are currently one of the hottest topics in this research field. One of the most effective ways to defend against these attacks is adversarial training. The current increase in the number of published papers indicates that there are still many opportunities for the development of adversarial training.  
In Section 4, the common adversarial sample generation techniques within adversarial training are analyzed. In this section, we summarize the adversarial training methods based on their primary goals. In summary, the most popular adversarial training methods are the FGSM and PGD, which account for 20 and 35 papers, respectively. [PERSON] et al. [13] proposed the PGD adversarial training as a universal adversarial training method for first-order adversaries. The method implemented by this paper is used as a standard benchmark method for adversarial training and evaluation. There are eighteen papers proposing an efficient adversarial training schema using modified FGSM or PGD for the fast adversarial training process, and one paper heavily modified the adversarial sample generator to achieve the same goal. Seven papers proposed an improvement to traditional PGD adversarial training to reduce the overfitting problem on complex data and improve the adversary generation for every instance of training data. Six papers mainly considered new regularization terms in adversarial training to improve the model robustness. Five papers proposed a semi-supervised adversarial training process with four papers focused on PGD adversaries, while one paper proposed a unique adversarial sample generation method. There are five papers proposing a training method that utilizes black-box adversaries' generation, four of which include a generative network or auto encode-decode architecture. Two papers proposed a solution to train neural networks against unseen adversarial samples.
# Nitrite: From Application to Detection and Development
## 6 Intelligent Detection Methods
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Nomenclature**} & \multicolumn{1}{c}{**Definitions and Explanations**} \\ \hline AgNPs@PANI/rGO/GCE & A glassy carbon electrode (GCE) modified by silver nanoparticles modified polyaniline/reduced graphene oxide complexes \\ FeTMPyP/Sr2 Nb3O10 & A complex consisting of tetramethylpyproylidine cationic iron complex (FeTMPyP) \\  & with strontium niobate columbium (Sr2 Nb3O10) in a layered chalcogenide structure \\ PDA & Polydopamine (PDA), which can form a uniform coating on a wide range of surfaces \\  & and enhance material stability and functionality \\   CuO NPs/CC & Carbon fiber complexes modified by copper oxide nanoparticles \\   Ag-coated Nanofiber SERS Platform & Platform with superb surface-enhanced Raman scattering effect from \\  & silver-coated nanofibers \\   AuNPs-PTH/SGN/GCE & Composite combining gold nanoparticles, polythiophene and graphite nanosheets \\  & with glassy carbon electrodes \\   AuNP/MnOx-VOx/ERGO/GCE & An innovative composite system combining the advantages of gold nanoparticles, \\   AuNP/MnOx-VOx/ERGO/GCE & manganese oxides, vanadium oxides and graphene oxide with glassy \\  & carbon electrodes \\   PdAg/C/TP/GCE & Composite structure combining palladium-silver alloy, carbon material and \\  & triphenylamine on a glassy carbon electrode \\  & Cell lysate conjugate to glassy carbon electrode \\  & amine-functionalized graphene oxide \\   Bi/HCNFs-SPE & A composite material formed by combining bismuth with hyperbranched carbon \\  & nanofibers and applying it in a solid phase extraction technique \\   Au/Ni-60s NTs & Composites composed of gold, nickel and nanotubes \\   PA-TaCoPc@ZnO & A polyaniline-linked tetra amino cobalt phthalocyanine surface functionalized ZnO \\  & hybrid nanomaterial \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._tion techniques, including spectroscopy combined with electrochemical methods, which can achieve the combined advantages of high sensitivity, high selectivity, and low cost; (3) intelligent data processing and analysis [121] using big data, cloud computing, and other technologies to intelligently process and analyses the detection results to improve detection efficiency and accuracy.
# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
###### Abstract  
The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data and detailed physical measurements derived from simulations. We simulated RBC behavior in a microfluidic channel. The simulation results provided the basis for generating data on which we applied machine learning techniques. We analyzed the surface-area-to-volume ratio of RBCs as an indicator of elasticity, employing statistical methods to differentiate between healthy and diseased RBCs. The Kolmogorov-Smirnov test confirmed significant differences between healthy and diseased RBCs, though distinctions among different types of diseased RBCs were less clear. We used decision tree models, including random forests and gradient boosting, to classify RBC elasticity based on predictors derived from simulation data. The comparison of the results with our previous work on deep neural networks shows improved classification accuracy in some scenarios. The study highlights the potential of machine learning to automate and enhance the analysis of RBC elasticity, with implications for clinical diagnostics.  
red blood cell; elasticity; machine learning; surface-area-to-volume ratio; decision trees Article
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 2 Two-Dimensional (2D) Materials in Organic Light Emitting Diodes (OLEDs)
### Composite Layers Based on 2D Materials
#### 2.2.4 Composite EIL Based on 2D Materials  
Gao reported OLEDs with a device structure of ITO/NPB/Alq\({}_{3}\)/Cs\({}_{2}\)CO\({}_{3}\):graphene/Al, in which a Cs\({}_{2}\)CO\({}_{3}\):graphene composite was utilized as electron injection layer (EIL) [57]. Maximum luminance and CE of the OLEDs with Cs\({}_{2}\)CO\({}_{3}\):graphene composite EIL are 7690 cd/m\({}^{2}\) and 2.02 cd/A, which are 2.07 and 2.59 times higher than those of the reference device with conventional LiF EIL, respectively. The improved performance can be attributed to the enhanced electron injection capacity after the doping of graphene.