esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Design of an Innovative Coil for Magnetic Stimulation
### Design of the Stimulation Coil  
According to the objectives of medical research, the most common target of TMS is the cortex, which means that the minimal strength of the induced electrical field at a location of 15-mm depth should not be less than 40 V/m. Thus, to meet the requirements and achieve higher performance, the coils are designed in quad coil form and arranged in X-shape. The function of the little coils is to improve the focality of the stimulations and that of the big coils is to intensify the strength of the stimulation. Figure 6 shows the structure of the proposed coil from the front and top views, and the parameters of the coil are listed in Table 1.  
Figure 7 shows the prototype of the proposed coil for TMS. A polymethyl methacrylate (PMMA) holder was designed and used to secure the coil. As shown in Figure 8, for a more accurate simulation of the electromagnetic field, a three-dimensional human head model is established. Different tissues are reflected in the model by multilayers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Big-Coils** & **Little-Coils** \\ \hline Turns & 12 & 10 \\ Coil diameter & 15.6 cm & 10 cm \\ Wire radius & 0.3 cm & 0.3 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the proposed coil.  
Figure 6: (**a**) Front view of the proposed coil, (**b**) top view of the proposed coil, and (**c**) the location of the compared plane (8 cm beyond the coil plane).  
Figure 7: The prototype of the proposed coil for TMS.  
According to previous research, the electromagnetic properties of the tissues are listed in Table 2[13].
## 5 Experiment Design
### Evaluation Metrics  
To evaluate the effectiveness of the proposed model, assessments are performed using metrics derived from the confusion matrix, including true positive (TP), true negative (TN), false positive (FP), and false negative (FN) classifications. From these metrics, additional performance measures such as Accuracy, F1-Score, Precision, Recall, and G-Mean are calculated using Equation (11) to Equation (15). Additionally, key performance indicators are reported, including the fitness value on the test set, the number of features selected, and the runtime.  
* Classification Accuracy: This metric determines the percentage of accurately classified instances relative to the total number of classifications. It is calculated as: \[Accuracy=\frac{TP+TN}{TP+TN+FN+FP}\] (11)
* Precision (P): This refers to the ratio of correctly identified outlier instances to the total number of instances predicted as outliers: \[Precision=\frac{TP}{TP+FP}\] (12)
* Recall (R): This is often referred to as sensitivity, which measures the proportion of actual outlier instances correctly identified as outliers. It is computed by dividing the number of true positive predictions (TP) by the total number of actual outlier instances: \[Recall=\frac{TP}{TP+FN}\] (13)
* F1-Score (F-Measure): This is the mean of the precision and recall, defined as: \[F1=2*\frac{Recall*Precision}{Recall+Precision}\] (14)
* G-Mean: To evaluate the performance of a classification algorithm when dealing with imbalanced data, sensitivity and specificity can be combined into a single metric. This metric, called the geometric mean (G-Mean), provides a balanced measure of both factors. It is calculated as follows: \[G-Mean=\sqrt{Recall*Precision}\] (15)
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 4 Discussion and Conclusions
In this work, we proposed the use of case-based reasoning for solving this task. Our results connected the formalization and conceptualization of the domain, building a case model, prototyping a case base, and evaluation. At the same time, the case model reflects the basic concepts and relationships describing the main characteristics of forest fires; in turn, the case base accumulated information about 45,000 thermal points in Irkutsk Oblast for 2017-2020. The efficiency of the approach proposed is evaluated from a formal and substantive point of view using the example of the Bodaibinsk and Kazachinsk-Lena districts. Based on the results of the assessment, it can be concluded that it is necessary to use a set of different methods (data mining, neural networks) for more accurate forecasting. In this context, the use of cases helps create training samples and ensures the formation of preliminary decisions, which can be further refined. The application and elaboration of other methods to solve the forecasting problem as well as the refinement of the web service are the directions of further research. Conceptualization and methodology, O.N. and A.Y. ; software, A.Y. and N.D.; validation, A.Y., J.P. and O.N. ; writing--original draft preparation, A.Y. ; visualization, J.P. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Science and Higher Education of the Russian Federation, Grant No. 075-15-2020-787 for implementation of Major scientific projects on priority areas of scientific and technological development (the project \"Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory\"). Not applicable. The authors declare no conflict of interest.
## 3 System Design of Mobile Robot Person-Following System
### Person Identification  
Many methods for identifying objects based on their texture, color, or both have been studied. These methods include HOG [29], scale-invariant feature transformation (SIFT) [30], and the HSV color space [31]. In this study, we adopted the HSV color space to extract the clothing color for identifying the target person, which is robust to illumination [32]. This color space was used effectively in an indoor environment under moderate illumination changes in our previous work [7]. HSV channels were applied to each of the ROIs for color segmentation to identify the color of the clothes. Segmentation is an important technique in computer vision, which reduces computational costs [33]. To extract the color feature in real time, the image color was filtered by converting the live image from the RGB color space to the HSV color space. Then, the colors of the clothes were detected by adjusting the HSV ranges to match the color of the target person. Next, morphological image processing techniques, such as dilation and erosion, were used to minimize the error and remove noise. Finally, the color of the clothes was detected in rectangular regions at different positions and areas determined according to the contours, as shown in Figure 2b. Invalid areas that were very small were filtered out using a minimum threshold value. As depicted in Figure 2c,d, once the person identification module identifies the target person, it sends the basic information of the person (position, angle, and area of the boundary box) to the control system, as described above.
## 4 Performance Evaluation  
This section evaluates the RAD system from the viewpoints of agility, robustness and efficiency through in-detail simulation, in occurring faults, attacks, and elephant flows. We compare the performance in the cases with and without RAD system. Table 2 shows the used tools and technologies for the simulation.  
Utilizing the Mininet simulation tool, we set up our network topology as shown in Figure 2. This topology is general for networks, because the real small network has a standard pattern that consists of backbone (core) part and edge part, and it is quite symmetric. Switches from S1 to S4 consist of a core backbone network with complete connections, whereas switches from S5 to S12 act as edge switches that provide connections to the hosts (H1 to H48). FloodLight, SDN controller used in evaluation part, provides only network utilization, and thus we have no choice to use metrics except the network utilization in this evaluation. All results are computed by averaging the values of 10 simulation runs to obtain statistical results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tools and Technologies** & **Name** & **Version** & **Description** \\ \hline Simulator & Mininet [46] & 2.2.1 & \begin{tabular}{c} Create a realistic virtual network, running real kernel, switch, and application code on a single machine (VM, cloud, or native). \\ \end{tabular} \\ \hline SDN Controller & FloodLight [47] & 1.2 & \begin{tabular}{c} An Open SDN Controller offered by Big Switch Networks that works with the OpenFlow protocol to orchestrate traffic flows in an SDN environment. \\ \end{tabular} \\ \hline IDS & Snort [27] & 2.9.8 & The most popular network intrusion detection system. \\ \hline Traffic Generator & Iperf [48] & 2.0 & \begin{tabular}{c} A popular network tool that was developed to measure TCP and UDP bandwidth performance. By tuning various parameters and characteristics of the TCP / UDP protocol, the user can perform several tests that provide an insight on the network’s bandwidth availability, delay, jitter, and data loss. \\ \end{tabular} \\ \hline Attack Tool & Hping [49] & 3.0 & \
\begin{tabular}{c} A network tool that can send custom TCP/IP packets and display target replies as a ping program does with ICMP \\ replies. Hping3 handles fragmentation as well as, arbitrary packets body and size, and can be used to transfer files encapsulated under supported protocols. It can thus be used as an attack tool. \\ \end{tabular} \\ \hline Development tools & Python & 2.0/3.0 &
\begin{tabular}{c} Python scripting language is used to develop the script for load balancing, routing, and preventing an attack. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tools and technologies.  
Figure 2: Mininet topology for simulation.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 3 Proposed Wind Power Curve Model
### Data Description  
Four wind datasets were taken to assess the performance of different power curve models. Dataset A and Dataset B were collected from China Xichang and Hunan wind farms, respectively. For Dataset C and Dataset D, they were collected from different wind turbines of the same wind farm. The whole dataset for each wind farm was divided into three separate parts: training set, validation set, and test set. The training set was used to train different power curve models, the validation set was employed to select the optimal model parameters, and the performances of different models were evaluated on the test set. The information of four datasets is presented in Table 2. In all datasets, only two variables, wind speed and wind power, were measured and reserved. So, the only input for all power curve models is wind speed.
## 2 Materials and Methods
### Reverse Transcription Polymerase Chain Reaction (RT-PCR)  
To measure the expression of tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)), Interleukin-6 (IL-6) and Interleukin-1\(\upbeta\) (IL-1\(\upbeta\)) in murine microglial cells, \(1\times 10^{5}\) cells/mL of BV-2 cells were cultured with or without varying concentrations of the _Spirulina_ along with 1 \(\upmu\)g/mL of LPS, following the same procedures in the NO and PGE2 measurement experiments. After incubation, RNA was isolated from BV-2 cells using the RNeasy mini kit (Qiagen, Hilden, Germany) according to the manufacturer's protocols. Then 1 \(\upmu\)g of total RNA was synthesized into cDNA by a cDNA synthesis kit (RevertAid First Strand Kit, Fermentas, ON, Canada) with incubations at 25 \({}^{\circ}\)C for 5 min., 42 \({}^{\circ}\)C for 60 min., and finally 85 \({}^{\circ}\)C for 5 min. Then, 40 \(\upmu\)l of cDNA was mixed with 0.5 \(\upmu\)l of each target gene primer for mouse TNF- \(\alpha\), IL-6, IL-1\(\upbeta\) and \(\upbeta\)-actin. Forward and reverse primers were provided by the manufacture (Bioneer, Inc., Seoul, Korea) as premade primers as follows: TNF- \(\alpha\) (N-4015, 300 bp), IL-6 (N-4013, 155 bp), IL-1\(\upbeta\) (N4009, 291 bp) and \(\upbeta\)-actin (N4021, 395 bp), respectively. The mixture was amplified with an RNA PCR kit (Takara, Shiga, Japan) at 94 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 1 min for 35 cycles using a PCR equipment (XP Thermal Cycler, TC-XP, BIOER Tech. Co., Hangzhou, China). Then, the PCR products were analyzed on ethidium bromide (EB, Sigma)-stained 1.2% agarose gels (BioRad co., Berkeley, CA, USA) through electrophoresis with 1-5 V/cm of the applied voltage. The amount of mRNA corresponding to each gene was quantified by Image Processing Analysis in Java (Image J, National Institute of Mental Health, Bethesda, MD, USA) by normalizing to the housekeeping gene \(\upbeta\)-actin; the values were expressed as the relative amounts (%).
###### Abstract  
In order to reduce the amount of abandoned clean energy, the complementary characterization of wind power plants (WPPs), photovoltaic power plants (PVs), hydropower stations (HSs), and thermal power plants (TPPs) combined with energy storage devices (ESDs) is considered, and they are integrated into a multi-energy complementary system (MECS). Firstly, a scenario-generating technique is proposed for uncertainty factors using the Wasserstein method and the improved K-medoids theory. Then, a multi-objective model and solution algorithm are constructed under the objectives of attaining the maximum operation revenue, the minimum abandoned energy cost, and the minimum output fluctuations. Meanwhile, the influence of different ESD operation modes on MECS operation is discussed, specifically, the longest life cycle (LLC) and the optimum economic efficiency (OEE). Thirdly, in order to solve the multi-objective model, a solution algorithm is proposed by using the rough set method to convert the multi-objective model into a single objective model based on the payoff table. Moreover, the complementary features of the MECS are evaluated in terms of the load tracking degree, HS secondary peaking capacity, and units of coal consumption. Finally, the improved IEEE 14-bus system is chosen for the simulation analysis. The results show that (1) the proposed uncertainty simulation method can efficiently generate the most representative scenarios; (2) MECSs can utilize complementary power sources, the OEE mode can better optimize MECS scheduling, and the LLC mode can ensure the ESDs' life cycles; (3) the scheduling scheme of MECS operation reach the optimal level when the capacity ratio of ESD:WPP-PV iso [0.62, 0.77] in the OEE mode and [1, 1.08] in the LLC mode on a typical summer day, and the ratio is [0.92, 1] in the OEE mode and [1.23, 1.31] in the LLC mode on a typical winter day. Therefore, the proposed model provides effective decision-making support for designing the optimal plan for MECS operation.  
multi-energy; multi-objective; uncertainty; wind power; energy storage 2018 8 2293; doi:10.3390/app8112293
## 5 Influence of Geometric Parameter
### Inflection Angle  
The interference results of the BWB aircraft with different inflection angles are shown in Figure 12 and Table 9. The difference of lift interference was less than 0.001 when the P \(\leq\) 17\({}^{\circ}\), while the amount increased to 0.0016-0.003 at P = 20\({}^{\circ}\). The difference of drag interference was less than 0.0001, indicating that drag was insensitive to the changing of the inflection angle. The pitching moment interference decreased as the inflection angle increased, and a significant difference was observed. In the calculation range of the current research, the influence of the inflection angle on the pitching moment interference was -78%-77%. When the attack angle was 0\({}^{\circ}\), the pitching moment interference was -0.0065 at  
\begin{table}
\begin{tabular}{c c c c} \hline
**L/mm** & _Cp(A)_ & _Cp(B)_ & _Cp(C)_ \\ \hline
200 & \(-0.091\) & \(-0.278\) & 0.026 \\
300 & \(-0.089\) & \(-0.269\) & 0.061 \\
400 & \(-0.104\) & \(-0.291\) & 0.049 \\ \hline \end{tabular}
\end{table}
Table 7: Typical pressure coefficient of the BWB aircraft with different straight-section lengths (L = 200 mm/300 mm/400 mm, \(Ma\) = 0.75, \(\alpha\) = 0\({}^{\circ}\). The position of point A/B/C is shown in Figure 10a).  
Figure 11: Support interference results of the BWB aircraft with different first-section lengths (H = L2 - 100 mm/L2/L2 + 100 mm, \(Ma\) = 0.75): (**a**) \(C_{L}\)-\(\alpha\) curves; (**b**) \(C_{D}\)-\(\alpha\) curves; (**c**) \(C_{m}\)-\(\alpha\) curves.  
\begin{table}
\begin{tabular}{c c c c} \hline
**H/mm** & \(\Delta C_{L}\) & \(\Delta C_{D}\) & \(\Delta C_{m}\) \\ \hline L2 - 100 & 0.01480 & \(-0.00100\) & \(-0.00410\) \\ L2 & 0.01573 & \(-0.00090\) & \(-0.00441\) \\ L2 + 100 & 0.01490 & -0.00080 & \(-0.00400\) \\ \hline \end{tabular}
\end{table}
Table 8: Interference results of the BWB aircraft with different first-section lengths (H = L2 - 100 mm/ L2/L2 + 100 mm, \(Ma\) = 0.75, \(\alpha\) = 0\({}^{\circ}\)).  
P = 13\({}^{\circ}\), which is four times \(-\)0.0016 at P = 20\({}^{\circ}\). The interference of pitching moment was quite sensitive to the changing of the inflection angle.  
The pressure distribution near the cavity of the BWB aircraft with different inflection angles is shown in Figure 13 and Table 10. The pressure coefficient of the sensitive area decreased as the inflection angle increased, as shown in Figure 13b and Table 10, so that the BWB aircraft was influenced by a positive pitching moment and the negative interference of \(C_{m}\) was reduced (as shown in Figure 12c).  
The pitching moment interference of the V-shaped support on the BWB aircraft was reduced effectively by increasing
## 2 Materials and Methods
### Test of Dependence on Mobile Devices and Sociodemographic Data  
Previous to the application of the instruments, a pilot test was conducted with 36 students to verify comprehension of the questions, make adjustment to wording when necessary, organize the order of the questions, and determine the real-time for completing the instruments. The rest of the sampling continued once the adjustments were made. The Mobile Dependence Test (MDT) described by [PERSON] [12] was used to determine the level of dependence on mobile devices, and its application lasted 10 min. This instrument comprises 22 items on a Likert-type scale subdivided into the following three factors, with scores ranging from 0 (zero) as the minimum value up to 88 as the maximum value:  
1. tolerance and abstinence, represented by items 11-22;
2. abuse and difficulty in controlling the impulse, corresponding to items 2, 4, 5, 6, 8, 12, and 20;
3. problems caused by excessive use, represented by items 1, 3, 7, 9, and 10.  
This questionnaire was validated and linguistically adapted in 2016 for university students from public and private universities, with reliability for abstinence and tolerance (\(\alpha=0.901\)), for abuse and difficulty controlling impulse (\(\alpha=0.8553\)), and for problems caused by excessive use (\(\alpha=0.762\)). The MDT detects low, medium, and high levels of dependency as well as its absence. This questionnaire was validated and linguistically adapted to Spanish in 2016 for university students from public and private universities [13]. Sociodemographic data such as sex, age, study program, and years completed were recorded in different forms.
## 2 Study Areas and Datasets
### Study Areas and In Situ Measurements
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Study Region** & **Longitude** & **Latitude** & **In Situ Data Acquisition Date** & **N** & \multicolumn{2}{c}{\(Z_{sd}\) (m)**} & \\  & & & & **Acquisition Date** & **Mean** & **Min** & **Max** \\ \hline
1 & Guanting Reservoir & 115.73 E & 40.35 N & 5/22/2019 & 18 & 1.16 & 0.30 & 2.15 \\ \hline
2 & Lake Baiyangdian & 116.01 E & 38.82 N & 5/21/2019 & 16 & 1.13 & 0.70 & 1.60 \\  & & & & 5/22/2019 & 13 & 1.13 & 0.55 & 1.70 \\ \hline
3 & Panjiakou Reservoir & 118.29 E & 40.43 N & 9/24/2019 & 25 & 3.41 & 1.20 & 4.50 \\ \hline
4 & Daheiting Reservoir & 118.31 E & 40.28 N & 9/25/2019 & 12 & 1.38 & 0.85 & 2.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Waterbody names, the central longitude and latitude of the study areas, in situ data acquisition date, number of samplings (N), and \(Z_{sd}\) values for the field measurements.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 6 Experiment and Analysis  
To evaluate the effectiveness of the proposed method, a series of experiments are carried out to realize the self-organized coverage of the unknown areas by swarm robots. We used multi-agent simulation software Netlogo to experiment. The operating system is Win10, and the hardware resources include 4G memory and I5 CPU processor. The experimental parameters are set as shown in Table 2.  
To calculate the coverage rate, the target area is divided into a 100 \(\times\) 100 pixel grid. Coverage rate is represented by the ratio of the number of pixels covered to the total number of pixels in the target area.
## 1 Introduction  
Theabrownins (TB) are macromolecular compounds with many hydroxyl, carboxyl, and amino groups [1]. They are soluble in water but insoluble in chloroform, \(n\)-butanol, ethyl-acetate, and other organic solvents [2]. Theabrownins are abundant in Pu-erh tea. Theabrownins are mainly derived from the solid-state fermentation of Pu-erh tea. Theabrownins have diverse bioactivities, such as lowering hypertension and hyperglycemia [3, 4, 5, 6], and have antioxidant [5], anti-cancer [7, 8, 9, 10, 11], and antimicrobial properties [12]. They also help with weight loss [6, 13]. The hydroxyl, carboxyl, and other functional groups in the structure of theabrownins can scavenge DPPH free radicals and have strong antioxidant properties for both hydroxyl radicals and superoxide radicals [14, 15, 16]. Therefore, obtaining theabrownins quickly and efficiently is a popular research topic in tea scientific research.  
A series of problems such as a long production cycle, low production efficiency, high consumption of human and material resources, high costs, and poor safety have affected the development and utilization of theabrownins obtained from the solid fermentation of Pu-erh tea. Many researchers have increased the theabrownins content of Pu-erh tea [17]by attempting to inoculate it with different microorganisms, supplementing the fermentation with exogenous enzymes, and improving the fermentation conditions [18]. The production of theabrownins by liquid fermentation with specific microorganisms provides a high-efficiency method to control the production environment and can be easily automated [19; 20]. _Aspergillus niger_ is the dominant strain in the solid-state fermentation of Pu-erh tea, and when inoculated with _Aspergillus niger_ solid-state fermented tea, the tea sample aroma is strong and pure, with a special mushroom fragrance, and the soup color is dark brown [21].  
However, the large amount of waste from green tea production is not reasonably utilized, which affects the economic value of the tea industry. Therefore, in this study, in order to produce theabrownins quickly and efficiently, we report a new, rapid, and highly controlled process for consistently producing theabrownins from infusions of crushed green tea via the liquid fermentation of _Aspergillus niger_.