esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Antimicrobial Activity  
The substance was tested for its antimicrobial effectiveness against harmful organisms. _Streptococcus pyogenes_ exhibited the largest inhibitory zones (25 mm), followed by _Staphylococcus aureus_ (23 mm), _Escherichia coli_ (23 mm), _Pseudomonas aeroginosa_ (22 mm), _Candida albicans_ (19 mm), and _Apergillus niger_ (17 mm). With the use of the agar well diffusion technique, a considerable zone of inhibition (ZOI) was discovered, as shown in Figure 2a. Table 1 shows that the rate of microbiological growth decreased as the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticle concentration was increased. The results for the synthesized nanoparticles' antimicrobial efficacy against our chosen microorganisms showed that they possessed strong antimicrobial activity.  
Similarly, [PERSON] et al. [21] demonstrated that biosynthesized iron-oxide nanoparticles can inhibit the growth of _Staphylococcus aureus_, _Escherichia coli_, _P. aeruginosa_, and _Streptococcus mutans_ and have potential bactericidal activity. The strong antimicrobial activity of the synthesized nanoparticles was likely brought about by the presence of the plant extract on their surfaces. Even though these were extremely positive results, additional research is required to determine whether iron-oxide nanoparticles are a viable alternative to silver nanoparticles for the treatment of bacterial infections. Although the precise mechanism of the antimicrobial action of the \(\alpha\)-Fe\({}_{3}\)O\({}_{4}\) nanoparticles that causes damage to bacterial proteins and DNA has not yet been identified, it was speculated that it may involve oxidative stress brought about by reactive oxygen species, such superoxide radicals (O\({}_{2}\)), singlet oxygen (\({}^{1}\)O\({}_{2}\)), hydroxyl radicals (-OH), or hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). The process that produces ROS can readily diffuse into the cytoplasm of the cell, causing ROS-induced release in the mitochondria and causing death [22].
## Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems  
**[PERSON] 1,4, [PERSON] 2,*, [PERSON] 3, [PERSON] 2 and [PERSON] 1**  
1 College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China  
2 State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China  
3 School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210042, China  
4 Faculty of Information Technology, Queensland University of Technology, Brisbane QLD 4001, Australia  
* Author to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (F.X.); [EMAIL_ADDRESS] (L.M.)  
_Received: 31 May 2008; in revised form: 7 July 2008 / Accepted: 8 July 2008 / Published: 15 July 2008_  
**Abstract:** There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.  
**Keywords:** wireless control systems, feedback scheduling, cross-layer, event-triggered
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Analysis of Wireless Bandwidth Consumption
#### 5.2.3 Confirmation of the R Node Status in the Domain Description after Power up  
The procedure to confirm the status of node R must be done once after turning on the power supply of this node. Table 3 presents the transmission requirements for the procedure of confirming the status of the R node.  
If, from the moment of the last activity of the enabled R node in the domain, there were changes, and if, currently, the Master role is performed by another node, the MAD address of the node that acts presently as Master will be sent in the _status_confirm_ans_ frame in the ADR field. In this case, the enabled R node must retry the procedure using the newly received MAD address.  
The study focused on the assessment of the feasibility of the proposed solution and its scalability. The main conclusion resulting from the obtained results (presented in the Tables 1-3, and Figure 24, Figure 25 is the linear dependence of the domain deployment time on the number of its nodes. The same conclusion can be transferred to energy consumption. Energy consumption for domain preparation also increases linearly with the increase in the number of domain nodes. Thus, it showed that the proposed solution is scalable.
## 6 Future Work, Challenges, and Limitations
### Availability of Training Data  
Both supervised and unsupervised ML algorithms are data dependent, requiring adequate data for training robust models. The amount and quality of the collected training data significantly affect the performance of ML algorithms. Achieving high accuracy localization becomes challenging when the training data are imbalanced, particularly when there are only a few NLoS as compared to LoS components in the data samples and vice versa. In situations where there is an imbalance in the dataset, existing ML algorithms face difficulties in training a robust classifier to classify the NLoS signal. To solve this problem, it is important to develop standard ML methods for training and predicting data that are independent, such as GD, GGD, and WNB algorithms [17; 78; 79].
# Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
## 4 Results and Discussion
### Weld Quality Prediction Based on the CGO-MLSSVR Model
To verify the accuracy of the CGO-MLSSVR model, four sets of test data were selected for prediction; the relative errors of the prediction results and the experimental results are shown in Table 5, and the comparison of the results is shown in Table 7.  
From Table 5 and Figure 7, the average relative errors of the positive electrode nugget diameter, negative electrode nugget diameter, and tensile shear load obtained by the CGO-MLSSVR prediction model are 1.72%, 3.51%, and 2.34%, respectively. This indicates that the CGO-MLSSVR prediction model predicts the test samples as very close to the experimental values, and the model has high prediction accuracy and generalisation capability. There are three main reasons for the error. (1) The nugget diameter was measured by the WEIMI-322 ES micrometer, and there was a certain measurement error between the measured value and the actual value, thus affecting the accuracy of the model training. (2) The MLSSVR model uses absolute error to represent the sample global error, and there was calculation error in the training model for the output variables. (3) In the small sample experimental space based on the orthogonal experimental method, the data information between the process parameters was ignored due to the large difference in values between the process parameters, thus affecting the training accuracy of the CGO-MLSSVR model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multicolumn{3}{c}{**Positive Electrode Nugget Diameter**} & \multicolumn{3}{c}{**Negative Electrode Nugget Diameter**} & \multicolumn{3}{c}{**Tensile Shear Load**} \\ \cline{2-10}  & **Experimental Values (mm)** & **Predicted Vales (mm)** & **Relative Error (\%)** & **Experimental Values (mm)** & **Predicted Vales (mm)** & **Relative Error (\%)** & **Experimental Values (mm)** & **Predicted Vales (mm)** & **Relative Error (\%)** \\ \hline
1 & \(0.9883\) & 0.9984 & 1.02 & 0.9047 & 0.9668 & 6.86 & 175.763 & 182.332 & 3.74 \\ \hline
2 & \(0.9296\) & 0.9414 & 1.27 & 0.9328 & 0.9641 & 3.36 & 186.237 & 190.073 & 2.06 \\ \hline
3 & \(1.2957\) & 1.2475 & 3.72 & 0.9002 & 0.8404 & 6.64 & 217.112 & 225.852 & 4.03 \\ \hline
4 & \(1.0382\) & 1.0168 & 2.06 & 0.8896 & 0.8872 & 0.27 & 205.117 & 207.158 & 1.00 \\ \hline
5 & \(1.0218\) & 1.0177 & 0.40 & 0.8821 & 0.9116 & 3.34 & 191.333 & 197.258 & 3.10 \\ \hline
6 & \(1.0649\) & 1.0447 & 1.90 & 0.9404 & 0.9349 & 0.58 & 206.242 & 206.518 & 0.13 \\ \hline Average Error (\%) & & & 1.72 & & & 3.51 & & 2.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The accuracy of the established CGO–MLSSVR model.
# Real-Time Cloth Simulation Using Compute Shader in Unity3D for AR/VR Contents
## 2 Related Work  
Previous approaches that have focused on cloth simulation in real-time have been studied to represent relatively simple models, which concentrated on faster algorithms. The finite element method (FEM) has been proven to be an accurate approach to simulate deformable models [10]. FEM is a time-consuming method, due to the method of dividing an object into a large number of elements, and solving a large system of equations. However, many approaches have improved FEM techniques to be used in real-timesimulation [11; 12; 13]. Mesh simplification approaches have been studied to simplify the detail of the 3D object and maintain the quality of the mesh [14].  
[PERSON] et al. [15] applied a position-based dynamic (PBD) method to simulate the deformable cloth object. The position-based method is fast, stable, and robust, but is not an accurate method. Hence, the velocity and force properties are ignored in PBD, which directly change the position of each node using project position to satisfy all constraints.  
Many force-based methods have been researched and applied to the real-time simulation of deformable cloth objects. The explicit and implicit methods are used to obtaining a numerical approximation to the solution of time-dependent equations [16]. The explicit Euler method is a first-order method of linear approximation, which uses a discrete time-step to approximate the solution of the motion equation. Since the explicit method is relatively inaccurate compared to the implicit method, it requires less operation, and is simple to implement. The position and velocity of a node are affected by the internal force and external force, and the acceleration can be computed based on [PERSON]'s second law of motion. The mass-spring system method of deformable cloth simulation was first introduced by [PERSON] [17]. [PERSON] et al. [18] proposed a GPU structure to solve a numerical equation of the mass-spring method with an efficient memory access pattern using OpenGL architecture. Similarly, [PERSON] et al. [19] accelerated a spring-mass system for a surgical simulator that was implemented on the OpenGL platform.  
In the mass-spring system method, a spring's force is used to maintain the distance of the spring at each given time-step. However, the distance of each spring that can be stretched or compressed causes a numerical problem. [PERSON] [17] applied a dynamic inverse constraint on springs that were over-elongated to prevent the \"super-elastic\" effect. As the result, this method could help a model perform realistically, but the displacement of each node directly ignored the physical consequence of the dynamic behavior of motion. Consequently, an implicit constraint was used to solve the elongated springs beyond 10%, and to prevent the excessive elongation of springs [20]. [PERSON] et al. used a smaller threshold of (1 and 0.1)% to simulate a piece of cloth that was quite stiff [21].
## 3 Characteristic Mode Analysis of the MTS
In contrast, the current distributions of \(\mathbf{J}_{3}\) and \(\mathbf{J}_{4}\) exhibit central symmetry. Due to the out-of-phase currents, radiation nulls emerge in the +Z direction, as demonstrated in Figure 4c,d. The consistency between the modal current distributions and the modal radiation patterns indicates that Mode 1 and Mode 2 are ideal directional radiation modes, capable of supporting the formation of high gain. Exciting either of these modes will result in a linearly polarized antenna. Given the inherent orthogonality between the two modes, simultaneously exciting them with a 90\({}^{\circ}\) phase difference will lead to the formation of a CP antenna. Nevertheless, Mode 3 and Mode 4 are considered undesirable modes in this context. The modal significance values of the first four characteristic modes are shown in Figure 5. It can be observed that Mode 1 and Mode 2 form a pair of degenerate modes with the same modal significance across the entire frequency band. A pair of degenerate modes can achieve CP radiation through a specific feeding structure. However, the frequency bands of Mode 1 and Mode 2 overlap with those of the undesirable Mode 3 and Mode 4. This overlap may impede the realization of CP radiation. To address this problem, we have made two improvements to the MTS: Firstly, four parasitic patches are added at the center of each edge of the MTS. The currents on these parasitic patches are in phase with those of the adjacent unit cells, effectively broadening the resonance frequency bands of Mode 1 and Mode 2, as shown in Figure 5b. Secondly, mode-suppression patches are arranged in an inverted triangular pattern around the four corners of the dielectric substrate. These mode-suppression patches are precisely positioned near the regions where the currents \(\mathbf{J}_{3}\) and \(\mathbf{J}_{4}\) reach their maximum amplitudes. When excited, they indirectly weaken the currents on the edge patches through coupling effects between the metallic patches, thereby achieving mode suppression. After loading these mode-suppression  
Figure 4: Modal radiation pattern of the first four modes at 6.9 GHz. (**a**) Mode 1; (**b**) Mode 2; (**c**) Mode 3; (**d**) Mode 4. Figure 1:0 Modal current distribution of the first four modes at 6.9 GHz. (**a**) Mode 1; (**b**) Mode 2; (**c**) Mode 3; (**d**) Mode 4.  
patches, the overlapping frequency bands between the desired modes and the undesirable modes have been significantly reduced, as depicted in Figure 5c.
# Rapid 3D-Imaging of Semiconductor Chips Using THz Time-of-Flight Technique
## 4 Conclusions  
In conclusion, we developed a rapid 3D TOF imaging tool for the inspection of packaged semiconductor chips using the time-domain THz spectroscopy technique. The OSCAT system was incorporated with a 2-axis galvano scanner to deliver a scanning speed of more than 100 Hz/pixel with a signal-to-noise ratio larger than 20 dB. Through the use of the Hilbert transformation, we could clearly reconstruct the 3D structure of the packaged chip in a nondestructive manner. Conversely, frequency-selective imaging allowed us to manipulate the resolution of the images, and we found that higher resolution was obtained when monitored with the higher frequency component. Importantly, phase information could be used to reveal the presence and types of defects in the packaged chip, such as delamination and the presence of epoxy-rich regions. Our work will contribute to the development of in-line inspection methodology in the industry, such as semiconducting, biomedical, and novel functional material characterization.  
Not applicable.  
Data availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effects of Gas Components
#### 3.3.3 Effects of SO\({}_{2}\)  
With 600 ppm SO\({}_{2}\) added into the reaction system, Hg\({}^{0}\) removal efficiency declined from 73.4% to 56.1% in the absence of O\({}_{2}\). Notably, the level of Hg\({}^{0}\) removal efficiency in the presence of SO\({}_{2}\) and O\({}_{2}\) remained lower than in the flue gas without SO\({}_{2}\), which implies that SO\({}_{2}\) negatively affects Hg\({}^{0}\) removal. Immediately after the increase of concentration of SO\({}_{2}\), such an inhibitory effect was enhanced, for which there were two reasons. On the one hand, SO\({}_{2}\) and Hg share the same adsorption sites on the adsorbent surface site, with competitive adsorption occurring between Hg\({}^{0}\) and SO\({}_{2}\)[49]. On the other hand, SO\({}_{2}\) reacts with surface-active components to form metal sulfate salts, which reduces the number of available surface-active sites, thus interfering with the Hg\({}^{0}\) adsorption process [50]. After the addition of 5% O\({}_{2}\), this inhibitory effect diminishes, which is accompanied by a slight increase in the level of Hg\({}^{0}\) removal efficiency. This is suspected to result from the capacity of O\({}_{2}\) to compensate for the consumed lattice oxygen. Eventually, the oxidation of Hg\({}^{0}\) to HgO as deposited onto adsorbent surface subsequently is enhanced [42].
# Mechanically Reconfigurable, Beam-Scanning Reflectarray and Transmitarray Antennas: A Review
## 2 Element Movement  
A mechanically reconfigurable RA can be realized by creating reconfigurable constituting elements. This requires a dedicated mechanical mechanism to each element and a method for distributed control. For large arrays with many elements, this can result in a complex overall system. On the other hand, individually tunable elements mostly offer full 0-360\({}^{\circ}\) reflected phase compensation. Therefore, this is a promising approach for applications requiring fine scanning resolution over a broad scanning range. The proposed methods in the literature to mechanically reconfigure elements in a RA can be categorized into two groups: (i) reconfigurable resonant elements and (ii) reconfigurable non-resonant elements.
## 3 Results and Discussions
### Effect of A. secundiramea Essential Oils on Biofilm Formation  
Biofilm formation is a quorum-sensing regulated differentiation system used by different bacteria on plant surfaces [60], but also in the human body environment, as a protection against other microorganisms and antibacterial substances [61]. To study the leaf and flower oil effect on bacterial biofilm formation, we used _P. aeruginosa PAOI_, a biofilm producer, as the indicator strain. Bacterial cells were grown with and without the addition of leaf and flower oil at different concentrations (25, 50, and 100 \(\upmu\)g/mL), not inhibiting the planktonic growth. As shown in Figure 6, a progressive increase of  
Figure 4: **(A)** Inhibition halo of _A. secundiramea_ flowers and leaves essential oils against _E. coli_\(DH5\alpha\) (1), _P. aeruginosa PAOI_ (2) and _S. aureus ATCC_\(6538\)P (3). Positive and negative control is represented by antibiotics (2 Colistine, 1 and 3 Ampicillin), and dimethyl sulfoxide (DMSO 2%), respectively. **(B)** The inhibition halos, shown in panel A, are expressed in AU/mL Values are expressed as the average of three different experiments; standard deviations were always less than 10%.  
Figure 5: Antibacterial activity of _A. secundiramea_ essential oils from flowers and leaves, evaluated by colony count assay, against _E. coli_\(DH5\alpha\), _P. aeruginosa PAOI_, and _S. aureus_ ATCC 6538P at a fixed concentration of 500 \(\upmu\)g/mL. Untreated cells represented control because negative controls are bacterial cells with dimethyl sulfoxide (DMSO 2%). Each bar is the average of three different experiments. \(p\) value is <0.05.  
_A. secundiramea_ oil concentration corresponds to a decrease of biofilm formation. In particular, 100 \(\mu\)g/mL of flowers extract oil inhibited the biofilm formation for about 65%. To exclude any antimicrobial effect on planktonic growth, we followed the bacterial growth curve with and without both extracts at maximum concentration, used in the previous experiment. In this condition, PAOI growth rate did not change when compared to the untreated cells (data not shown). Biofilm formation of microorganisms causes persistent tissue and foreign body infections resistant to treatment with antimicrobial agents, such as _Staphylococcus epidermidis, P. aeruginosa, S. aureus_, and _E. coli_[62]. Our oil, containing a single compound that possesses antibioticfilm activity, represents an important resource to counteract the biofilm formation.
## 2 Related Activities
### 5G Exposure Patterns  
5G New Radio (NR) differs from earlier generations of wireless technologies (e.g., frame duration, bandwidth, and power allocation) in its use of massive multiple input multiple output (Ma-MIMO) beam-forming and in the use of millimeter waves (i.e., FR2) to support multiple usage scenarios [6; 7]. The current deterministic approaches in exposure distribution for passive antennas used in earlier generations of mobile communication are different from the actual exposure to Ma-MIMO 5G antennas. Ma-MIMO antenna technology has introduced a degree of freedom in the management of exposure distribution. Based on that, the possibility to shape and direct emissions only in the direction where service is required allows optimization of the coverage and power usage with a consequent reduction of total exposure. Under these conditions, the exposure is present and distributed per the requested services. This makes power distribution estimation more challenging, necessitating creative developments of statistical features suitable for power distribution estimates in time and space. NextGEM accomplishes necessary and much sought-after improvements in field-level assessment in real scenarios by developing a maximum power extrapolation technique and a suitable statistical model in both single user (SU) MIMO and multiple user (MU) MIMO cases.