esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment  
### Effect of Duty Ratio on Droplet Size and Spray Angle  
The spray droplet volume and spray angle are important parameters for evaluating atomization effect [25]. Since the particle size and spray angle of the pressure nozzle change with the change of the fluid pressure, a laser particle size analyzer (DP-02, Zhuhai Omega Instrument Co., Ltd.) was used to measure droplet volume. Additionally, a single-lens reflex camera (ILCE-5100, Sony Corporation of Japan) was used to photograph the spray angle. The average particle size was tested at different duty cycles, and the results are shown in Figure 13.  
Figure 12: PID control and unregulated step response curve.  
Figure 13: \(Cont\).  
In Figure 13, frequency denotes the percentage of droplets with a certain size in the whole droplet group, and accumulation denotes the percentage of droplets arranged from small to large, to a certain size of droplets accumulating in the whole droplet group. The droplet size at 50% position is denoted as droplet volume diameter D (50). It can be seen from Figure 13 that the droplets produced by different nozzles are more uniform and the droplet spectrum is normal. The droplet size percentage is accumulated according to the order of droplet size from small to large. When the PWM duty ratio is 40% and 100%, the droplet volume diameter values are 99.81 and 96.26 \(\upmu\)m, respectively.  
The spray angles of PWM duty ratios between 40% and 100% are shown in Figure 14, and the particle size and spray angle of the other duty ratios are shown in Table 1. The experimental data in Table 1 demonstrate that the volume of the spray droplets varies between 94 and 100 \(\upmu\)m, when the duty ratio is varied from 40% to 100%. Moreover, the volume of the volume gradually decreases as the duty ratio increases. The coefficient of variation is 5.52%, the spray angle varies between 97\({}^{\circ}\) and 105\({}^{\circ}\), and the coefficient of variation is 2.77%. As the duty cycle increases, the flow rate of the system increases, and the pressure at the nozzle also increases, increasing atomization energy and promoting droplet breakage. The experimental results of droplet size show that different flow rates can be achieved by changing the duty cycle of PWM square wave signal to regulate the flow rate.  
Figure 14: Spray angle of different PWM square wave duty ratios with a duty cycle of 40% (**a**) and a duty cycle of 100% (**b**). **Note:** 0 is the spray angle.  
Figure 13: Droplet spectra of different PWM square wave duty ratios: (**a**) droplet spectrum with a duty ratio of 40%; (**b**) droplet spectrum with a duty ratio of 100%.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 3 Results and Discussion
### Operation Limits  
The research setup has shown the ability to detect microplastics ranging from 0.5 mm to 5 mm, covering the most common sizes found in environmental samples.  
* **Surface scanning and sample analysis:** The sensor scans the surface, up to 20 cm\({}^{2}\) per run, allowing it to analyze a large sample area efficiently. The system can process up to three samples per run, thanks to the three custom-made sample holders and the laser holder's specific design in a C-shaped form;
* **Luminosity and environmental conditions:** One limitation found during testing is the effect of ambient light. Bright environments can cause detection errors, so a black cloth is needed to reduce light interference during the measurements. The sample should be placed 1.2 to 2 cm away to ensure accurate reading;
* **Specificity:** Digital image processing (DIP) techniques improve the sensor's ability to differentiate microplastics from other particles by analyzing their morphological characteristics. This issue is further explored in another published study [28]. This precision is important for reliable environmental monitoring and avoids mistakes in detecting microplastics.  
We evaluated the sensor's performance by calculating its sensitivity and limit of detection (LOD) as follows:  
* **Sensitivity:** sensitivity (S) was determined as the slope of the calibration curve, representing the change in signal intensity per unit concentration of microplastics: \[S=\frac{\Delta V}{\Delta\mathbb{C}}\] (7)  
Equation (7) is the sensitivity equation.  
where \(\Delta V\) is the change in the detected voltage output, and \(\Delta\mathbb{C}\) is the change in microplastic concentration (e.g., grams per liter).  
* **Limit of Detection (LOD)**: the LOD is defined as three times the standard deviation of the blank sample (\(\sigma_{b}\)) divided by the slope (S) of the calibration curve: \[LOD=\frac{3\cdot\sigma_{b}}{S}\] (8) Equation (8) is the limit of detection equation.  
This equation ensures that the sensor's detection threshold accounts for natural variability in blank readings, thereby making it reliable for identifying microplastic presence at low concentrations.
## 2 Materials and Methods
### Measurement of Dispersibility  
For identifying the dispersion, aggregation, and precipitation of nanoparticles, a supernatant of a 50 mL centrifuge vial was settled, and its absorbance was measured using an UV-vis spectrometer (Evolution 260 BIO, Thermo fisher scientific, Waltham, MA, USA) in accordance with OECD TG 318. The absorbance of ZnO NPs was measured at 360 nm, followed by the dispersibility measurements.  
Dispersion stability of the particles was analyzed based on the particle-size changes. The ZnO NPs were dispersed in DI water; the test media, each with a concentration of 50 mg/L, manufactured with 40 mL solutions, were obtained. TG 318 recommends that the dispersion of nanomaterials be measured at an interval of 1 h for up to 6 h; however, considering the exposure duration of each test method, such as the freshwater algae growth inhibition test, Daphnia sp. acute immobilization test, and fish acute toxicity test, the particle aggregation was analyzed for up to 96 h. For measurement, the samples were stirred and measured three times via dynamic light scattering (DLS, ELS-Z-2 analyzer, OTSUKA, Tokyo, Japan) to obtain an average of the measured size values.
# Application of Machine Learning Techniques to
## 2 Study Area and Data
### NEX-GDDP-GCMs-CMIP5 Data  
For future climate change analysis, this study used newly developed NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The experiments included in Coupled Model Intercomparison Project 5 (CMIP5) were devised to address the research interrogations in AR4 of the IPCC [44]. The original resolution of most GCMs in the CMIP5 is >100 km, where such coarse resolution cannot provide fine-scale information for impact studies and decision-making at a local scale. The NEX-GDDP dataset is a collection of 21 GCMs. These datasets are bias-corrected and downscaled to a finer resolution (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) using the bias-corrected spatial disaggregation (BCSD) method [45]. The Global Meteorological Forcing Dataset (GMFD), developed by Princeton University, provides the gridded observed climate data and the NEX-GDDP after bias correction and downscaling [46; 47]. Table S1 lists the 21 GCMs in the NEX-GDDP, along with information on the research centers that produce the GCMs.  
emissions scenarios. These scenarios were employed by the IPCC fifth assessment report (AR5) [44]. The NEX-GDDP dataset is publicly available. The comprehensive details to use these scenarios dataset for climate change impact evaluations and adaptations have been provided by IPCC [48]. The CMIP5 data provides multi-model datasets of climate variability, which was used to develop AR5 [44].
## Appendix A Time Domain Admittance Array
\begin{array}{rl}\Psi(x,y,t)&=\frac{\text{sgn}(x)\text{sgn}(y)}{12\pi}\int_{0 =r}^{\varsigma_{0}t}(c_{0}t-v)^{3}f(x,y,v)\text{d}v\\ &+\frac{\text{sgn}(y)\text{H}(x)}{4\pi}\Bigg{\{}|y|\Big{(}c_{0}^{2}t^{2}-x^{2} +\frac{y^{2}}{3}\Big{)}\cosh^{-1}\Big{(}\frac{c_{0}t}{|y|}\Big{)}\\ &-c_{0}t\bigg{(}\frac{c_{0}^{2}t^{2}}{6}-x^{2}\Big{)}\tan^{-1}\Bigg{[}\bigg{(} \frac{c_{0}^{2}t^{2}}{y^{2}}-1\bigg{)}^{1/2}\Bigg{]}\\ &-\frac{7}{6}c_{0}ty^{2}\bigg{(}\frac{c_{0}^{2}t^{2}}{y^{2}}-1\bigg{)}^{1/2} \Bigg{\}}\text{H}(c_{0}t-|y|)\\ &+\frac{\text{sgn}(x)\text{H}(y)}{4\pi}\Bigg{\{}|x|\Big{(}c_{0}^{2}t^{2}-\frac {y^{2}}{6}\Big{)}\cosh^{-1}\Big{(}\frac{c_{0}t}{|x|}\Big{)}\\ &-c_{0}t\bigg{(}\frac{c_{0}^{2}t^{2}}{6}-x^{2}\Big{)}\tan^{-1}\Bigg{[}\bigg{(} \frac{c_{0}^{2}t^{2}}{x^{2}}-1\bigg{)}^{1/2}\Bigg{]}\\ &-\frac{5}{5}c_{0}tx^{2}\bigg{(}\frac{c_{0}^{2}t^{2}}{x^{2}}-1\bigg{)}^{1/2} \Bigg{\}}\text{H}(c_{0}t-|x|)\\ &+\frac{c_{0}t}{4}\bigg{(}\frac{c_{0}^{2}t^{2}}{6}-x^{2}\bigg{)}\text{H}(x) \text{H}(y)\text{H}(t),\end{array} \] (A4)  
where we used \(r=(x^{2}+y^{2})^{1/2}>0\) with  
\[ \begin{array}{rl}f(x,y,v)&=\frac{1}{2r}\Big{[}\frac{1}{(v^{2}/x^{2}-1)^{1/2 }}+\frac{1}{(v^{2}/y^{2}-1)^{1/2}}\Big{]}\\ &-\frac{1}{16}\frac{y^{4}}{x^{2}v^{3}}\frac{1}{(v^{2}/y^{2}-1)^{5/2}}\Bigg{[} 3\frac{v^{8}}{y^{8}}+6\frac{v^{6}}{y^{8}}\Big{(}\frac{x^{2}}{y^{2}}-1\Big{)} \\ &+\frac{v^{4}}{y^{4}}\Big{(}15\frac{x^{4}}{y^{4}}-10\frac{x^{2}}{y^{2}}+3 \Big{)}+4\frac{v^{2}}{y^{2}}\frac{x^{2}}{y^{2}}\Big{(}1-5\frac{x^{2}}{y^{2}} \Big{)}+8\frac{x^{4}}{y^{4}}\Bigg{]}\\ &-\frac{1}{2}\frac{x^{2}}{y^{3}}\frac{1}{(v^{2}/x^{2}-1)^{1/2}}-\frac{3}{16} \frac{v}{x^{2}}\frac{1}{(v^{2}/y^{2}-1)^{5/2}}\Bigg{(}3\frac{v^{4}}{y^{4}}\\ &+2\frac{v^{2}}{y^{2}}\Big{(}\frac{x^{2}}{y^{2}}-3\Big{)}+\frac{x^{2}}{y^{2}} \Big{(}3\frac{x^{2}}{y^{2}}-2\Big{)}+3\Bigg{)}\\ &+\frac{3}{4}\frac{v}{x^{2}}\frac{1}{(v^{2}/y^{2}-1)^{3/2}}\Big{(}\frac{v^{2}} {y^{2}}+\frac{x^{2}}{y^{2}}-1\Big{)}.\end{array} \] (A5)  
The calculation of the convolution integral in Equation (A4) is computationally the most exacting task. An efficient way to remedy the issue is the recursive-convolution technique ([28], Appendix H). Alternatively, one may apply integration by parts and write  
\[ \begin{array}{rl}\int_{v=r}^{c_{0}t}(c_{0}t-v)^{3}&f(x,y,v)\text{d}v=-(c_{0} t-r)^{3}\partial_{v}^{-1}f(x,y,r)\\ &-3(c_{0}t-r)^{2}\partial_{v}^{-2}f(x,y,r)-6\,(c_{0}t-r)\partial_{v}^{-3}f(x,y,r)\\ &-6\,\partial_{v}^{-4}f(x,y,r)+6\,\partial_{v}^{-4}f(x,y,c_{0}t),\end{array} \] (A6)  
where \(\partial_{v}^{-n}\) denotes the \(n\)-th integration with respect to \(v\). Thanks to the relatively simple form of \(f(x,y,v)\) (see Equation (A5)), the required integrals can be readily found analytically, thus enabling exact and fast computation of the TD admittance array elements via Equations (A1), (A2), and (A4).
## 2 Operating Principle and Modes
### Non-Inverting and Inverting Buck Operation
For non-inverting voltage buck operation with any polarity of the input voltage, we get  
\[V_{L}(t_{c})=V_{s}-V_{o}V_{L}(t_{nc})=-V_{o} \tag{6}\]  
so  
\[(V_{s}-V_{o})t_{c}-V_{o}t_{nc}=0 \tag{7}\]  
\[V_{s}t_{c}=V_{o}(t_{c}+t_{nc}) \tag{8}\]  
Figure 5:0  
Figure 5:1  
By employing a similar methodology, the voltage step-down inverting behavior of the proposed circuit can also be validated with equivalent circuits and mathematical equations. This mode produced an inverted form of the input voltage at the output, and this is simply turning on the low-frequency transistors \(T_{r4}\), \(T_{r5}\) and \(T_{r2}\), \(T_{r6}\) for positive and negative input voltage by maintaining the switching role of \(T_{r1}\) and \(T_{r3}\) unchanged. The power transfer loops of Figure 6a-d explain how the variable inverting outputs with voltage buck capabilities are obtained from a constant input voltage. The mathematical validation of this behavior can be proved by employing a similar approach as used for a non-inverting buck operation. The value of the inductor voltage during the conducting and non-conducting interval for any value of the input voltage may be depicted as  
Figure 5:2  
Figure 5:3  
Average inductor voltage gives  
Figure 5:4  
Figure 5:5  
Figure 5:6  
Figure 5:7  
The results of Equations (10) and (16) validate the voltage non-inverting and inverting buck operation, indicating that output is always controllable with the PWM control.
## 3 Materials and Methodology
### Complex Network
#### 3.3.3 Node Criticality  
Critical node mining and assessment are important elements in complex network research when complex network theory is applied to risk analysis. In general, the criticality of nodes depends on the network model and topology. Currently, scholars have proposed various node criticality assessment methods from different perspectives, such as centrality algorithm [24], K-Shell decomposition [45], and PageRank algorithm [46]. In particular, the PageRank algorithm is a specialized algorithm developed by Google to measure the importance of a specific page relative to other pages in a search engine, and is a classic method for identifying the criticality of nodes in a directed complex network [47].  
The PageRank algorithm assigns an initial PageRank value to each node in the directed network to indicate the importance of the node in its initial state, and then iterates until convergence [48]. During the iteration, each node assigns its current PageRank value equally to all connected edges of the node, and then each node obtains a new PageRank value by summing up the PageRank values assigned to all edges pointing to this node. The process iterates until the result converges to obtain the final PageRank value of the node. Note that the PageRank values of the nodes form a probability distribution on the network, so the sum of the PageRank values of all nodes will be 1. The PageRank value of node \(i\) can be defined as:  
\[PR_{i}=\gamma\sum_{j\in V}\frac{a_{ji}}{d_{out}(j)}PR_{j}+\frac{1-\gamma}{N} \tag{14}\]  
where \(\gamma\in[0,\,1)\) is a damping factor ensuring the algorithm never gets stuck in a \"sinking node\"; \(N\) denotes the total number of nodes in the network.  
The interaction network of seafarers' unsafe acts and their influencing factors to be constructed in this study belongs to a directed weighted network, therefore, considering the weighted characteristics of the network, the weighted PageRank algorithm is used to perform the node criticality analysis, which is expressed as [49]:  
\[PR_{i}=\gamma\sum_{j\in V}\frac{w_{ji}}{s_{out}(j)}PR_{j}+\frac{1-\gamma}{N} \tag{15}\]  
where \(w_{ji}\) is the weight of the edge of node \(j\) pointing to node \(i\); \(s_{out}(j)\) is the sum of the weights of all output edges of node \(j\).
# Online Determination of Boron Isotope Ratio in Boron Trifluoride by Infrared Spectroscopy
## 2 Experimental Method
### Procedure for Online Measurement  
The online isotopic ratio determination system was set on the device used for boron isotope separation, as shown in Figure 3. The BF\({}_{3}\) gas was extracted from a pipe where the gas flow and the pipeline can be controlled by valve. The BF3 flowed through a gas cell that was equipped with two CaF\({}_{2}\) windows because the weatherability and corrosion resistance of CaF\({}_{2}\) are superior to KBr under atmospheric and BF\({}_{3}\) conditions. The whole gas cell was fitted to the IR spectrophotometer by which the infrared spectrogram could be obtained directly.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 7 Experiments
### Experiment about Accuracy and Adaptivity: NSL-KDD  
#### 7.3.1 Introduction to NSL-KDD  
NSL-KDD [34] is an optimized version of the well-known data set KDD. It solves some of the inherent problems of the original data set and has been frequently cited by researchers. The amount of records in NSL-KDD training and testing sets are reasonable, enabling the affordable completion experiments on the complete set without the need to randomly select a small portion. Consequently, the evaluation results of different research work would be consistent and comparable. There are 41 features in NSL-KDD data sets and the samples are labeled into two classes: benign and malicious. Furthermore, the attacks can be classified into four categories: (1) denial of service, (2) probing, (3) user to root, and (4) remote to local. A detailed introduction can be found in [38].  
Figure 8: Correlation between accuracy and number of possible values.
# Analysis of the State of Learning in University Students with the Use of a Hadoop Framework
## 2 Materials and Methods
### Data Analysis Process
#### 2.3.2 Ingest, Data Storage  
For storage, the framework uses the HDFS, which is the main component of the Hadoop ecosystem. HDFS allows for storing massive data sets with structured, semi-structured, and unstructured data types such as images, video, sensor data, etc. It is optimized for storing large amounts of data and maintaining multiple copies to ensure high availability and fault tolerance [49]. HDFS, is a distributed system based on Java that allows the obtaining of a vision of the resources as a single unit. It does this by creating an abstraction layer as a single file system. It is responsible for storing the data in several nodes, maintaining its metadata. By distributing data across multiple storage nodes, it increases processing speed, parallelism in operations, and enables data replication. It is based on the concept that moving processing is much faster, easier, and more efficient than moving large amounts of data, which can cause high latencies and network congestion. HDFS provides applications with the ability to access data where it is stored. HDFS is master-slave, it is based on two main components, NameNodes and DataNodes.  
The NameNode, is the master or main node of the system, is in charge of managing its access and storing its metadata. It resembles a table of contents, in which blocks of data are assigned to DataNodes. Because of this, needless disk space, but more computational resources [50]. This component is the only node that knows the list of files and directories in the cluster, the file system cannot be used without the NameNode. The DataNodes correspond to the nodes of the cluster that store the data. It is responsible for managing the node storage. They generally use basic hardware with multiple disks and a large capacity.  
Due to their typology, they allow an increase to the capacity of the system in a horizontal way effectively and at a reduced cost.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 4 Analytical Procedure
### CNN Model
#### 4.1.2 Model B  
The response spectra of ground motion data were applied to model B. Five percent of damped acceleration, velocity, and displacement response spectra were generated from the time domain acceleration, velocity, and displacement histories at the bedrock, as presented in Figure 6. In addition, they were assigned to input data. Five percent of damped acceleration response spectra calculated from acceleration histories measured on the surface were for output data. The process of model B is shown in Figure 7. CNN architecture B consists of a convolution layer with 500 kernels with the size (21,1) without a pooling layer. Mean squared error (MSE) was also adopted to loss function and Rectified linear unit (ReLU) was to activation function.  
Figure 5: Overview of model A.  
Figure 6: Input data preprocessing for model B.  
After predicting the ground response on the surface through model B, a weight moving average is applied to the prediction for reducing noise and errors. The moving average is used to analyze data points by creating a series of averages of different subsets of the entire dataset. Equation (5) presents the mathematical calculation of the weight moving average we implemented.  
\[SA_{pre_{i}}=\frac{1 Sa_{pre_{i-4}}+2 Sa_{pre_{i-3}}+3 Sa_{pre_{i-2}}+4 Sa_{i-1}+5 Sa_{ pre_{i}}+4 Sa_{pre_{i+1}}+3 Sa_{pre_{i+2}}+2 Sa_{pre_{i+3}}+1 Sa_{pre_{i+4}}}{25} \tag{5}\]  
where \(Sa_{pre_{i}}\) is the \(i\)th prediction of a ground motion by model B, and \(SA_{pre_{i}}\) is the prediction after applying the weight moving average.
# Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
## 4 Conclusions  
In this work, we proposed a data-driven reduced-order model based on the CNN-DCNN framework to predict the flow field over a series of airfoils. The effects of training parameters are studied, then the model prediction results are compared with those obtained by CFD calculation, to evaluate the performance, accuracy, and extensionality of the proposed CNN-DCNN model, as well as its capability to capture the intrinsic characteristics of the flow dynamics. The following conclusions can be summarized:  
1. Using SDF representation of airfoil geometry, with Reynolds number as an auxiliary element fed into the neural network, the trained CNN-DCNN model is able to learn  
Figure 17: Comparison of the pressure and velocity contours for three cases: (a) case 4: NACA9712, AOA = 0\({}^{\circ}\), \(Re\) = 2.5 \(\times\) 10\({}^{6}\); (b) case 5: NACA2412, AOA = 5\({}^{\circ}\), \(Re\) = 6 \(\times\) 10\({}^{6}\); (c) case 6: NACA6212, AOA = 3\({}^{\circ}\), \(Re\) = 8 \(\times\) 10\({}^{6}\).  
from the implicit constitutive relation of Reynolds stress and accurately map the geometries of the airfoils to the corresponding flow field.
2. The CNN-DCNN model shows the remarkable predicting ability for the flow field over NACA0012-series airfoils with prediction accuracy up to 98.23% for the pressure field and 99.60% for velocity field.
3. The prediction process of CNN-DCNN model is faster than a corresponding CFD calculation by three orders of magnitude, which leaves broad prospects in the application of aerodynamic design and optimization, especially considering the good extensionality of the model working with flow conditions with unseen Reynolds numbers.
4. The CNN-DCNN model can capture the spatial structure of the flow field, achieving accurate prediction results around the flow separation region, comparison with reconstructed field by POD method indicates that it is able to retain the essential information of coherent structures from highly non-linear flow systems.  
Conceptualization, M.-Y.W. and W.-T.W.; methodology, M.-Y.W. and Y.W.; validation, X.-Y.Y.; writing--original draft preparation, M.-Y.W.; writing--review and editing, Y.W. and Z.-H.C.; supervision, M.-Y.W. and N.A.; All authors have read and agreed to the published version of the manuscript. [PERSON]-Y.W. and Y.W. contributed to this paper equally.  
This research was funded by the Natural Science Foundation of Jiangsu Province (Grant No. BK20201302, Grant No. BK20220954) and the Fundamental Research Funds for the Central Universities (No. 30919011401).  
Not applicable.  
The authors declare no conflict of interest.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 5 Experimental Results
### A Showcase: Switching Overlay in GNS3  
Previous sections show that using GNS3 to emulate a fat-tree data center topology may not be the best option. Instead, we use programmable and scalable emulators such as Mininet or Kathara. Nonetheless, GNS3 may be very helpful to demonstrate small uses cases for teaching or training purposes.  
In this sense, the reference fat tree was built in GNS3 using FRR routers. For simplicity, OSPF was used to provide multi-path layer 3 connectivity among all the nodes in the fabric. After that, VXLAN and EVPN were configured to have a transparent layer 2 (an important requirement presented in Section 2.6). Using this setup, the migrations of VMs among PODs in the fabric were successfully performed, keeping their IP addresses unchanged. This experiment is fully reproducible and available at [69].