esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation  
### Model Setup  
We validated the proposed OSP objective functions by using the DMOPSO algorithm on the Canton Tower, which has a height of 610 m. This building was previously studied by [PERSON] et al. [27,28] and [PERSON] et al. [6]. The method proposed in this paper was compared with the Immune Monkey Algorithm presented in [26] and the discrete Artificial Bee Colony algorithm presented in [6]. Canton Tower comprises two parts, namely a 454-m-high main tower and 156-m-high steel antenna mast. The main tower is a tube-in-tube structure consisting of a steel lattice outer structure and reinforced concrete inner structure. Detailed descriptions and photos of this structure can be found in [28]. The present study extended a preliminary study on FEM analysis and a simplified 3D beam model with lumped mass that was proposed by [PERSON] et al. [29]. The simplified model consisted of 37 beam elements, with 27 elements for the main tower and 10 elements for the antenna mast. Each beam node had five DOFs (two horizontal translations and three rotations) when neglecting the vertical deformation. Therefore, the beam model comprised 185 DOFs. As in previous studies, we only considered the horizontal translation DOFs for OSP. We applied the IIRS technique to the open-access data provided by [PERSON] [29] to reduce the 3D beam model. Consequently, a reduced model that only considers horizontal translation DOFs was obtained. The obtained reduced model had 74 DOFs, which were considered as candidate sensor locations. This part of the present study is the same as the work presented in [6,27,28] for a fair comparison of the same model. We used the same settings (e.g., the number of sensors) for the OSP framework and proposed MODM strategy.  
Given the stiffness and mass matrices of the aforementioned finite-element model of Canton Tower, the mode shape matrix \(\Phi\) was computed. The optimization algorithm was then implemented in MATLAB, and the order of modes \(m\) (=15) as well as the number of sensors \(s\) (=20) were fed into the algorithm, whose parameters were configured as follows:  
PSO: Particle swarm population size \(N\) = 210 and maximum iteration cycle \(MaxIter=1000\).  
DMOPSO: Neighborhood size \(T\) = 21; number of subobjectives = 210 (equal to the population size); number of solution sets on the Pareto front = 100; and decomposition penalty value \(\theta=5\).
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### Second Stage: Path Planning
#### 3.2.3 Determine Navigation Route from Reference Points
```
\(ListPoints=SequencePoints\{CurrentPosition,MiddlePaths,Vertexes\}\) \(Speed=CalculateSpeed(Path)\) \(StartingPoint=CurrentPosition\) fori=1 to NumberOfIntermediatePoints do \(IntermediatePoint=ListPoints(i)\) \(PathPoints=NavigationPt2 Pt\{\)\(StartingPoint\), IntermediatePoint, Speed \(\}\)  Add \(PathPoints\) to \(FinalPath\) \(StartingPoint=IntermediatePoint\) endfor Return: FinalPath
```

**Algorithm 13** CalculatePath\([Position,Path,MiddlePaths,Vertexes]\)```
Extract current position for robot 1: \(CurrentPositions_{robot1}\) Extract current position for robot 2: \(CurrentPositions_{robot2}\) Extract corners from \(Vertexes\): \(Corners=[Vertexes(1),Vertexes(2),Vertexes(Penultimate),Vertexes(Last)]\) Detect closest corner and the distance for the two robots \(Corner_{robot1}=getClosestCorner(CurrentPositions_{robot1},Corners)\) \(distance_{robot1}=getPixelDistance(CurrentPositions_{robot1},Corner_{robot1})\) \(Corner_{robot2}=getClosestCorner(CurrentPositions_{robot2},Corners)\) \(distance_{robot2}=getPixelDistance(CurrentPositions_{robot2},Corner_{robot2})\) if\(distance_{robot1}<distance_{robot2}\)then Add \(Corner_{robot1}\) to \(ListIntermediatePoint_{robot1}\)\(ListPoints_{robot1}=SequencePoints\{Corner_{robot1},MiddlePaths,Vertexes\}\) Add \(ListPoints_{robot1}\) to \(ListIntermediatePoint_{robot1}\) Detect opposite corner to \(Corner_{robot1}\) closest to robot 2: \(Opposite_{robot1}\) Add \(Opposite_{robot2}\) to \(ListIntermediatePoint_{robot2}\)\(ListPoints_{robot2}=SequencePoints\{Opposite_{robot2},MiddlePaths,Vertexes\}\) Add \(ListPoints_{robot2}\) to \(ListIntermediatePoint_{robot2}\) else Add \(Corner_{robot2}\) to \(ListIntermediatePoint_{robot2}\)\(ListPoints_{robot2}=SequencePoints\{Corner_{robot2},MiddlePaths,Vertexes\}\) Add \(ListPoints_{robot2}\) to \(ListIntermediatePoint_{robot2}\) Detect opposite corner to \(Corner_{robot2}\) closest to robot 1: \(Opposite_{robot1}\) Add \(Opposite_{robot1}\) to \(ListIntermediatePoint_{robot1}\)\(ListPoints_{robot1}=SequencePoints\{Opposite_{robot1},MiddlePaths,Vertexes\}\) Add \(ListPoints_{robot1}\) to \(ListIntermediatePoint_{robot1}\) endif \(Speed=CalculateSpeed(ParcelMask)\)\(StartingPoint_{robot1}=CurrentPosition_{robot1}\) for i = 1 to NumberOfIntermediatePoints do \(IntermediatePoint=ListIntermediatePoints(i)\)\(Path=NavigationPoint2 Point\{\)\(StartingPoint\), \(IntermediatePoint\), \(Speed\) \(\}\) Add \(Path\) to \(FinalPath_{robot1}\)\(StartingPoint=IntermediatePoint\) endfor \(StartingPoint_{robot2}=CurrentPosition_{robot2}\) for i = 1 to NumberOfIntermediatePoints do \(IntermediatePoint=ListIntermediatePoints(i)\) \(Path=NavigationPoint2 Point\{\)\(StartingPoint\), \(IntermediatePoint\), \(Speed\) \(\}\) Add \(Path\) to \(FinalPath_{robot2}\)\(StartingPoint=IntermediatePoint\) endfor  Store \(FinalPath\)
```  
**Algorithm 14** CalculatePath[\(Positions,Path,MiddlePaths,Vertexes\)]
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 3 Results and Discussion
### Parallel Pipeline Performance and Scalability  
To validate the timing measurements, logs from the Spark History Server were examined, focusing on job durations, executor tasks, and shuffle overhead. The collected results, summarized in Table 4, highlight execution times, speedup, efficiency, throughput, memory usage, and I/O performance across different processing stages.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Process**} & **Single** & \multirow{2}{*}{
\begin{tabular}{c} **Core** \\ **Execution** \\ **Time (s)** \\ \end{tabular} } & **8-Core** & **8-Core Pre-** & **Speedup** & **(8 Cores vs.** & **Efficiency** & **Throughput** & **Avg. Task** & **Memory** & **Shuffle** \\  & **Execution** & **Time (s)** & **Execution** & **Single** & **(\%)** & **(\%)** & **(Tasks/s)** & **Latency (s)** & **(MiB)** & **(MiB)** \\ \hline Data & \multirow{2}{*}{538} & \multirow{2}{*}{175} & \multirow{2}{*}{175} & \multirow{2}{*}{3.07} & \multirow{2}{*}{38.4} & \multirow{2}{*}{n/a} & \multirow{2}{*}{n/a} & \multirow{2}{*}{1024} & \multirow{2}{*}{n/a} \\ Preprocessing & & & & & & & & & \\ Outlier & \multirow{2}{*}{102} & \multirow{2}{*}{60} & \multirow{2}{*}{60} & \multirow{2}{*}{1.7} & \multirow{2}{*}{21.25} & \multirow{2}{*}{40.8} & \multirow{2}{*}{0.196} & \multirow{2}{*}{1024} & \multirow{2}{*}{57} \\ Detection & & & & & & & & & \\ Bi-LSTM & \multirow{2}{*}{13,320} & \multirow{2}{*}{2340} & \multirow{2}{*}{n/a} & \multirow{2}{*}{5.69} & \multirow{2}{*}{71.12} & \multirow{2}{*}{1.86} & \multirow{2}{*}{32.29} & \multirow{2}{*}{434.4} & \multirow{2}{*}{30.8} \\ Optimization & & & & & & & & & \\ Bi-LSTM & \multirow{2}{*}{7920} & \multirow{2}{*}{2460} & \multirow{2}{*}{50} & \multirow{2}{*}{3.22} & \multirow{2}{*}{40.25} & \multirow{2}{*}{0.078} & \multirow{2}{*}{58} & \multirow{2}{*}{0} & \multirow{2}{*}{9.1} \\ Imputation & & & & & & & & & \\ F-LSTM & \multirow{2}{*}{4680} & \multirow{2}{*}{1620} & \multirow{2}{*}{50} & \multirow{2}{*}{2.89} & \multirow{2}{*}{36.13} & \multirow{2}{*}{0.089} & \multirow{2}{*}{50} & \multirow{2}{*}{0} & \multirow{2}{*}{9.1} \\ Forecast & & & & & & & & & \\ Random & \multirow{2}{*}{4800} & \multirow{2}{*}{900} & \multirow{2}{*}{200} & \multirow{2}{*}{5.33} & \multirow{2}{*}{66.63} & \multirow{2}{*}{0.16} & \multirow{2}{*}{50} & \multirow{2}{*}{512} & \multirow{2}{*}{300} \\ Forest & & & & & & & & & & \\ Forecast & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time measurements and logs from Spark History Server.
## 3 Results  
### Demographic and Clinical Data  
The demographic and clinical characteristics of the three groups are summarized in Table 1. There were no significant differences in age, sex, years of education, TIV, and vascular risk factors (e.g., hypertension, diabetes, hyperlipidemia, and smoking status) among the CSVD-GS, CSVD-PS, and HC groups. Compared with the CSVD-GS and HC groups, the CSVD-PS group displayed higher PSQI and HAMD scores. Both CSVD subgroups demonstrated more severe WMH, EPVS burden, and higher risk of lacunes and CMBs than those in the HC group. In contrast, there were no statistically significant differences in WMH, EPVS burden, lacunes, or CMBs between the CSVD-GS and CSVD-PS groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **HC** & **CSVD-GS** & **CSVD-PS** & \multirow{2}{*}{**Overall \(p\) Value**} \\  & **(_u = 40_)** & **(_u = 23_)** & **(_u = 36_)** & \\ \hline Age (years) & 61.53 \(\pm\) 6.07 & 64.91 \(\pm\) 6.40 & 64.31 \(\pm\) 7.09 & 0.079 \\ Male, \(n\) (\%) & 25 (62.5\%) & 15 (65.2\%) & 16 (44.4\%) & 0.180 \\ Education (years) & 9.73 \(\pm\) 3.87 & 9.65 \(\pm\) 4.58 & 9.03 \(\pm\) 4.16 & 0.740 \\ Hypertension, \(n\) (\%) & 17 (42.5\%) & 14 (60.9\%) & 23 (63.9\%) & 0.137 \\ Diabetes, \(n\) (\%) & 7 (17.5\%) & 5 (21.7\%) & 8 (22.2\%) & 0.854 \\ Hyperlipidemia, \(n\) (\%) & 8 (20.0\%) & 8 (34.8\%) & 12 (33.3\%) & 0.319 \\ Smoking status, \(n\) (\%) & 11 (27.5\%) & 7 (30.4\%) & 8 (22.2\%) & 0.840 \\ PSQI & 3.88 \(\pm\) 1.28 & 3.61 \(\pm\) 1.16 & 12.36 \(\pm\) 2.97 & \textless{}0.001 \({}^{\text{a,b}}\) \\ HAMD & 4.60 \(\pm\) 2.81 & 3.43 \(\pm\) 3.22 & 6 \(\pm\) 2.11 & 0.002 \({}^{\text{a,b}}\) \\ TIV (cm\({}^{3}\)) & 1524.14 \(\pm\) 138.74 & 1528.61 \(\pm\) 115.41 & 1520.06 \(\pm\) 160.02 & 0.975 \\ Total Fazekas score & 1.08 \(\pm\) 0.83 & 4.39 \(\pm\) 1.12 & 4.64 \(\pm\) 1.20 & \textless{}0.001 \({}^{\text{ac}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic and clinical characteristics.
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 3 Methods
### Optimization of the Yolox-Tiny Model  
In the original Yolox-tiny model, the backbone feature extraction network processes input images to produce three distinct feature layers: feat1, feat2, and feat3, each sensitive to large, medium, and small objects, respectively. However, the diminutive size of some pests and the complexity of image backgrounds complicate feature extraction. Moreover, multiple downsampling operations may result in the loss of information concerning small target objects. To address these challenges, two optimizations have been introduced to the Yolox-tiny model: limiting the number of downsampling operations to minimize information loss about small target pests and incorporating the convolutional attention module CBAM to enhance feature extraction for small targets and reduce the impact of complex backgrounds.  
Figure 2: Yolo Head Structure Diagram.  
CBAM represents a convolutional attention mechanism designed for feed-forward convolutional neural networks. Upon receiving a feature layer, CBAM independently infers attention maps along the channel and spatial dimensions sequentially. These attention maps are subsequently multiplied with the input feature layer to enable adaptive feature refinement.  
Figure 3 illustrates the improved Yolox-tiny network structure. This revised structure eliminates one Resblock_body module from the CSPDarknet network and reduces downsampling in the backbone feature extraction network, thereby minimizing feature information loss for small objects and more effectively preserving features of all detectable objects in the image. A CBAM convolutional attention mechanism has been incorporated into the remaining Resblock_body modules to allow adaptive feature refinement during feature extraction. This mechanism strengthens the network's ability to focus on pests of varying sizes and mitigates the effects of complex backgrounds on object detection. It also simplifies feature fusion and enhances the classification and regression processes in the FPN and Yolo Head components. The CSPDarknet backbone extracts initial features, the CBAM enhances feature refinement, the FPN constructs a feature pyramid, and the Yolo Head generates predictions for classification, regression, and objectness.
## 2 Materials and Methods
### Array Setup and Data Acquisition Process  
The actual experimental system is presented in Figure 4a, while a drawing of the setup is given in Figure 4b. The setup consists of two concentric cylindrical tanks with 100 and 200 mm diameters, respectively. A target of 16 mm diameter can be placed inside the inner tank to emulate the discontinuity in the homogeneous background medium. We surrounded the outer periphery of the larger tank with an absorber (ECCOSORB MCS) and covered it with a metallic shield to reduce surface waves propagating along the periphery of the immersion tank, as well as any external interference. Our eight-antenna configuration forms a circular ring inside the outer acrylic tank of Figure 4a, as also shown in more detail in the schematic of Figure 4b. We note that the choice of eight antennas was made to simplify data acquisition; from a theoretical point of view, this number should be equal to \(2\beta\alpha\), where \(\beta\) is the wave number and \(\alpha\) is the radius of the imaging domain [34]. Vertical and horizontal mounts allow us to control the antenna positions with good precision and vary their height as well as the array diameter, which was kept at 130 mm in the present experiments.  
To overcome the absence of a switch matrix in the present setup, we collected bistatic data from a two-port vector network analyzer (VNA) from Keysight, and acquired synthetic data for the array by switching manually the receiver to each of the seven positions in Figure 4. The measurement process included the following steps: First, we assembled the setup and calibrated the VNA set to 201 data points with an averaging factor of 5 and IF bandwidth 200 Hz. Then, we filled up the immersion liquid in the tanks, and we connected the transmitter and receiver to the VNA's Port1 and Port2, respectively. We then changed the receiving antenna at Port2, and after a complete sweep for the seven positions, we moved the transmitting antenna to the next position and repeated the process. We then introduced the target inside the imaging domain and repeated the process for the 'with target' case, which produces the scattered signal information to be used by the algorithm for reconstruction.
## 3 Method
### Crop Growth Model
#### 3.1.2 Calibration of Crop Growth Model
Field validation showed that the relative errors of the CERES-Wheat model-simulated anthesis, maturity and yield were 0.97 days, 1.81 days and 131 kg\(\cdot\)ha\({}^{-1}\), respectively. The calibration results indicated that simulation with the CERES-Wheat model agreed well with the measured phenological and yield data at the field scale.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 2 Security and Usability Concerns for Spatial Files within Enterprise GIS
Such a solution has the potential to be applied in other domains; however, the main relevance to enterprise GIS is addressing data silos and the resulting security vulnerabilities through consolidating spatial files into a single repository.
## 3 Optimization Design Strategy
### Non-Dominated Sorting Genetic Algorithm  
NSGA-II was invoked for multi-objective optimization. This algorithm employs the concept of Pareto dominance to solve the multi-objective problem simultaneously [33]. The four important concepts in this optimization algorithm are \"non-dominated sorting\", \"crowding distance\", \"mutation\", and \"crossover\". \"Non-dominated sorting\" and \"crowding distance\" are used to improve the elitism of the strategy and to reduce the process complexity. \"Crossover\" and \"mutation\" are used to efficiently perform the search and to avoid local minima.
## 2 Cloud Radar Prototype
### Radar Hardware  
The block diagram of the radar system is presented in Figure 3. The _transmitter_ continuously feeds the transmitter antenna with an up-chirp waveform within the Ka-band, produced by modulating a Gunn oscillator with a saw-tooth reference signal. The receiver antenna delivers the echo-signal to the _receiver_, which down-converts the input signal by multiplying it with the currently transmitting signal, and directs the resulting intermediate frequency (IF) signal to a digital acquisition system (DAS). The DAS converts the IF signal and transmitter reference voltage (see scheme in Figure 3) into digital signals to compute reflectivity and Doppler profiles by using a MATLAB script in a desktop computer.  
The ideal transmitted chirp waveform is repeated with a frequency of 150 kHz, yielding a theoretical non-ambiguous range of 500 m (see Equation (1), with \(F_{d}=B\)). The chirp bandwidth is set to 10 MHz, thus producing, at best, a range resolution of 15 m (see Equation (2)).  
The DAS analog-to-digital-converters (ADC) acquire \(10^{7}\) samples, every 10 s for cloud measurements or every 3 s during the drone experiment, with a sample-rate of 20 MHz, and with a range of \(\pm 1\) V.  
Figure 3: FMCW Radar Block diagram.  
Figure 2: Transmitted up-chirp radar waveform and the corresponding echo signal arriving at the receiver from a single stationary point target. If multiple targets are present at different ranges, \(F_{d}\) is the linear combination of the response for each point target.  
Implementation specific details of the transmitter chain, antennas, receiver chain, and Data Acquisition System can be found in Appendix A.  
Figure 4 shows the actual implementation of the transmitter chain, receiver chain, and power supply of the radar system.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 4 Algorithm of OTPP
### Path Planning in OTPP  
The MAN must visit all the positions of the beacon points if the beacon points \(c_{1}\) and \(c_{4}\) were designated as its first (starting) point and final (ending) point positions for visitation. In the obstacle-free environment, the visit order of the beacon points for MAN is \(c_{1}\), \(c_{2}\), \(c_{0}\), \(c_{3}\), and \(c_{4}\).  
1. Basic square containing two-covered areas  
When the basic square contains two-covered areas, the MAN first visits \(c_{1}\) and \(c_{2}\). From the four corners of an obstacle, the proximate location from \(c_{2}\) is selected, which is an additional beacon point \(m_{1}\), and its diagonal position is set as \(m_{2}\). Thereafter, the MAN visits \(m_{1}\) and \(m_{2}\). Lastly, the MAN visits \(c_{3}\) and \(c_{4}\), i.e., the MAN completes the travel on the entire basic square, as shown in Figure 8.  
Figure 8: The trajectories of OTPP with basic square containing two-covered area. The red arrows are the trajectories while the shadow area is the obstacle.  
Figure 7: The results of beacon point simplification in scenarios where (**a**) the basic square contains two-covered areas and (**b**) the basic square contains zero-covered and one-covered areas.  
Basic square containing zero- and one-covered areas  
When the basic square contains zero-covered and one-covered areas, the MAN verifies whether the path from \(c_{1}\) to \(c_{2}\) is obstructed by an obstacle or if \(c_{1}\) to \(c_{2}\) has a straight path without being obstructed by an obstacle. The MAN first visits \(c_{1}\) and \(c_{2}\). Among the additional beacon points \(m_{q}\) (\(q=1,2,\,\ldots,6\)), the additional beacon point proximate to \(c_{2}\) is selected as the next beacon point to be visited following the visit to \(m_{1}\)-\(m_{6}\). Lastly, the MAN visits \(c_{3}\) and \(c_{4}\), as shown in Figure 9.  
In another case, if the straight-line path from \(c_{1}\) to \(c_{2}\) is blocked by an obstacle, then, the positions of \(c_{2}\) and \(c_{3}\) are interchanged. The MAN first visits \(c_{1}\) and \(c_{2}\). Thereafter, additional beacon points \(m_{q}\), proximate to \(c_{2}\) are selected as the beacon points to be visited next. The sequential visits to the additional beacon points follow the order \(m_{5}\), \(m_{6}\), \(m_{4}\), \(m_{3}\), \(m_{1}\), and \(m_{2}\). Lastly, the MAN visits \(c_{3}\) and \(c_{4}\), as shown in Figure 10. Figure 10 is considered as the result of the rotation of Figure 9 by \(90^{\circ}\).
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
## 1 Introduction
By analyzing the problems of some traditional routing algorithms of OppNet, combining the small-world effect and the relationship between the nodes, this paper considers how to choose the right neighbor node as the next hop from the perspective of social attributes between nodes. Practice has proved that the cosine similarity algorithm can accurately calculate the similarity between two texts [19]. In this paper, the cosSim routing algorithm based on cosine similarity is proposed to measure the strength of the social relations between nodes by calculating the cosine similarity between data packets of nodes, so as to select the next-hop nodes. The paper is organized as follows: Section 2 describes the related work. Section 3 describes the proposed routing algorithm in detail. Experimentation and results are shown in Section 4 and, finally, in Section 5 some conclusions are drawn. Figure 1: Schematic diagram of opportunistic network transmission.
## 2 Materials and Methods  
### Animal Model and Plasma Flux Generator  
C57 BL/6 mice, purchased from Nomura Siam International (Pathumwan, Bangkok, Thailand), were used following an approved animal protocol from the Institutional Animal Care and Use Committee of the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. The burn wound mouse models with and without infection were performed in accordance with the previous publications [26; 27]. Briefly, in burn wound models without infection, an aluminum rod 1 cm in diameter was heated to 100 \({}^{\circ}\)C, using a dry block heater (Thermo fisher Scientific, Waltham, MA, USA) for 5 min before placing on a shaved area at the dorsal part of the mice under isoflurane anesthesia. After that, mice with the heated rod attached to the back were turned ventral upward to allow their own body weight to function as a controlled pressure on the heated rod for 20 s. In the infected wound model, the heated rod was placed with the same procedures of the non-infected wound model. Then, 1 \(\times\) 10\({}^{7}\) CFU of _Staphylococcus aureus_ (ATCC 25923, the American Type Culture Collection, Manassas, VA, USA) in 0.1 mL normal saline (NSS) was directly spread onto the wound 30 min after the injury, in accordance with a previous publication [26]. After 24 h, necrotic tissue at the burn wounds was removed in both wound models. Subsequently, treatment by non-thermal plasma flux for 30 s along with a visual evaluation of the wound was performed daily until the 7 th day of experiments, when the mice were sacrificed via cardiac puncture under isoflurane anesthesia with sample collection (blood and tissue at the wound). For the in-house plasma generator, the machine was designed following the principle of atmospheric pressure plasma with alternating current (AC) electricity at 13 kHz in frequency, using argon gas flow to release the ionized, enriched gas [28] as demonstrated in the diagram (Figure 1A,B). In brief, the plasma jet was designed with two electrodes. The inner electrode was a tungsten rod with a diameter of 1 mm, while the outer electrode was a thin copper sheet in the shape of a ring, surrounding the borosilicate glass syringe as the dielectric material. To prevent arc generation during operation, the outer electrode was wrapped with a rubber band. The distance between the plasma source and substrate was 5 mm. The current of the output was approximately 0.5 mA with the square waveform. Argon flow was used during operation at a speed of 1.5 L/min.