esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation and Design of Three 5G Antennas
## 3 Design of 5G Microstrip Array Antennas
### Design and Simulation of 4 \(\times\) 4 Antenna Units
#### 3.2.3 Simulation of a 4 \(\times\) 4 Array Antenna  
Once the antenna elements and the feeding network have been determined, they can be combined to create the 4 \(\times\) 4 microstrip array antenna, as depicted in Figure 16.  
Figure 14: Schematic diagram of T-type power divider structure principles.  
Figure 15: T microstrip T-junction structure diagram.  
In this design, the feeding point for the entire array antenna is positioned at the center of the array, and coaxial feeding is utilized. The coaxial probe connects through small apertures to the radiating element of the antenna, enabling the feeding of the antenna. With the feeding point located inside the patch, it minimizes the impact on the antenna radiation. Figure 17 illustrates the coaxial feeding point of the array antenna.  
Figure 16: A 4 \(\times\) 4 microstrip array antenna.  
Figure 17: Coaxial feeding point of the array antenna.  
After performing HFSS simulations on the 4 \(\times\) 4 array antenna, the reflection coefficient S11 plot, as shown in Figure 18, is obtained. From the results in the graph, it can be observed that the array antenna provides full coverage of the LTE band 42 (3.4-3.6 GHz) frequency range. When the antenna operates at the central frequency of 3.5 GHz, the return loss reaches approximately \(-\)26.1 dB. With a return loss less than \(-\)6 dB, the achieved bandwidth is 373 MHz (3.321-3.694 GHz), corresponding to a relative bandwidth of 10.7%.  
Figure 19 represents the radiation pattern of the microstrip array antenna at a frequency of 3.5 GHz. From the 3D gain pattern in Figure 19a, it is evident that the maximum radiation direction is perpendicular to the antenna center, with a peak gain of 12.7 dB in the maximum radiation direction. Figure 19b presents the polar gain pattern of the array antenna.
## 4 Results and Discussions
### The Effect of Heating Mode  
The above figures show isothermals (on the left) and streamlines (on the right) for \(Ra=10^{5}\), \(Kr=10\), \(G=1.2\), \(Nr=10\), \(\phi=0.5\), \(Pr=0.71\) and \(\varepsilon=0.6\). In Figure 14, the isothermals in the cavity are almost horizontally distributed, which indicates that the heat transfer in the cavity is dominated by heat conduction and surface radiation. The reason for this is that when heating comes from the top, the air near the top has a lower density compared with that near the bottom, and it is difficult to form air circulation. The isothermals are convex in shape at the top and bottom walls of the cavity since the thermal diffusivity of the air is much larger than that of the solid walls. It can be also seen from the streamlines that the air flow is extremely slow, and thus the heat transfer in the cavity resulting from natural convection is not worth considering in this case. In Figure 15, the isothermals in the cavity are non-uniformly distributed and bend inwards, which indicates that the heat transfer in the cavity is dominated by natural convection and surface radiation. This is because when heating from the bottom, the air near the bottom has a lower density compared with that near the top, and it is easy to form air circulation. The streamlines indicate that the air flow is very strong, and heat transfer in the cavity occurs mainly via circulation of the air. Consequently, heating from the bottom makes it easy to cause stronger natural convection and, in this case, natural convection must be taken into consideration in the analysis of heat transfer.  
Figure 14: Streamlines and isothermals obtained when heating the top, \(W_{\max}=2.6317\).  
Figure 13: Variations of the average Nusselt number as a function of \(\phi\).
## 3 Results and Discussion
### Validation of Natural Frequency Parameter and Acoustic Power Calculation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Type of** \\ **Stiffener** \\ **Patches** \\ \end{tabular} & \begin{tabular}{c} **Uniform** \\ **Unloaded** \\ **Plate** \\ \end{tabular} & \begin{tabular}{c} **Plate with 1** \\ **Stiffener** \\ **Patch** \\ \end{tabular} & \begin{tabular}{c} **\% of** \\ **Negative** \\ **Variation** \\ **in \(\lambda^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **Plate with 2** \\ **Stiffener** \\ **Variation** \\ **in \(\lambda^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **\% of** \\ **Negative** \\ **Variation** \\ **in \(\lambda^{2}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Plate with 4** \\ **Stiffener** \\ **Variation** \\ **in \(\lambda^{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **\% of** \\ **Negative** \\ **Variation** \\ **in \(\lambda^{2}\)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Rectangular  Stiffener} & (0,0) & 13.4929 & 13.4819 & \(-\)0.0815 & 13.4862 & \(-\)0.04960 & 13.487 & \(-\)0.0385 \\  & (0,1) & 13.4946 & 13.4942 & \(-\)0.00296 & 13.4937 & \(-\)0.0069 & 13.4914 & \(-\)0.0237 \\  & (0,2) & 14.1185 & 14.1148 & \(-\)0.0262 & 14.1099 & \(-\)0.0690 & 14.1053 & \(-\)0.0934 \\  & (0,3) & 16.6681 & 16.6638 & \(-\)0.0258 & 16.6638 & \(-\)0.02580 & 16.6658 & \(-\)0.0258 \\ \hline \multirow{4}{*}{Concentric  Stiffener} & (0,0) & 13.4915 & 13.4223 & \(-\)0.518 & 13.3615 & \(-\)0.964 & 13.3242 & \(-\)1.260 \\  & (0,1) & 13.5023 & 13.4842 & \(-\)0.148 & 13.4427 & \(-\)0.444 & 13.3614 & \(-\)1.037 \\ \cline{1-1}  & (0,2) & 14.1214 & 14.0815 & \(-\)0.283 & 14.0235 & \(-\)0.708 & 13.9212 & \(-\)1.416 \\ \cline{1-1}  & (0,3) & 16.6762 & 16.6014 & \(-\)0.419 & 16.5056 & \(-\)1.019 & 16.3843 & \(-\)1.740 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical comparison of first four natural frequency parameter \(\lambda^{2}\) of uniform unloaded plate with different attachment of rectangular and concentric stiffener patches for taper ratio, \(\mathrm{T_{x}}=0.00\). Figure 6: Comparison of % variation of natural frequency parameter with different modes for a uniform plate with taper ratio, T\({}_{\rm x}\) = 0.00 and with different attachment of concentric stiffener patches. Figure 7: Comparison of variation of different frequency parameter with different modes for an unloaded plate and for a plate with 4 rectangular and 4 concentric stiffener patches.
# The Impact of Orthodontic Treatment on Pre-Existing Gingival Recessions: A Retrospective Study
###### Abstract  
(1) Background: This retrospective study aimed to examine the change of gingival recessions (GR) before and after orthodontic treatment (OT). (2) Methods: Patients who had at least one GR on the vestibular surface of maxillary/mandibular teeth mesial to first molars were included. GR was measured on good quality plaster models before and after OT. (3) Results: Significant improvement of mean GR at patient level was found between T0 and T1 (0.45 mm, 95% CI: 0.28, 0.62) (\(p<0.001\)). In 51.4% of patients GRs became better, in 37.8% GRs did not change. and in 10.8% GRs became worse. At tooth level the mean GR improvement was 0.54 mm (CI: 0.42, 0.65; median 0.55 mm QI; Q3: 0.12, 0.96) (\(p<0.001\)). GR improved in 67 teeth (58.7%), did not change in 41 (36.0%), and worsened in 6 (5.3%). Full healing was observed in 15 teeth. A reduced GR improvement was found in cases with pre-treatment open bite (OR 3.89; CI: 1.35, 11.16; \(p=0.008\)) and dental Class III patients (OR 2.6; CI: 1.11, 6.0; \(p=0.026\)). (4) Conclusions: There was an improvement of GR after OT in more than half of the patients. Class III malocclusion and reduced pre-treatment overbite had a negative impact on GR improvement.  
[PERSON] _et al._ 2021, 11, 9036. [[https://doi.org/10.3390/app11199036](https://doi.org/10.3390/app11199036)]([https://doi.org/10.3390/app11199036](https://doi.org/10.3390/app11199036))
## 3 Experimental Work
### Tests and Methods
#### 3.3.2 Ultrasonic Pulse Velocity (UPV)  
In line with [35], measurements of ultrasonic pulse velocity (UPV) were made to assess the homogeneity and structural integrity of diverse compacted soils. The longitudinal wave velocities were calculated using the pulse transmission method, which entails connecting two piezoelectric sensors positioned on opposite faces of a sample with a diameter of 6 cm and a length of 12 cm. The velocity of the ultrasonic waves was determined using a standard meter (Matset Company located in Italy). A viscoelastic gel was added to the sample's flat surface. The samples were positioned between the transmitter and receiver, and until a constant velocity was measured, the faces of the transducers were firmly pressed against the compacted dirt samples. The UPV is measured using the pulse transmission method using a Panametrics Pulser-Receiver (Model 5058 PR) and an Agilent DSO-X-2014A Digital Storage Oscilloscope (100 MHz) manufacturerered by Matset Company, Treviolo, Bergamo, Italy. Triple specimens were used and tested under each condition, and the average of the results was reported.
## 2 Materials and Methods
### Determination of Cell Surface Hydrophobicity  
The cell surface hydrophobicity (CSH) of the bacteria was determined by the microbial adhesion to solvents (MATS) test based on their affinity to non-polar solvents [30]. Xylene was used as the hydrocarbon phase. _Listeria_ cells were grown in TSB for 24 h at 37 \({}^{\circ}\)C. The cells were harvested twice by centrifuging (3200\(\times\)\(g\), 10 min, 4 \({}^{\circ}\)C) and washed with sterile PBS (Merck KGaA, Darmstadt, Germany). The cells were re-suspended in TSB at an initial concentration of 10\({}^{5}\) cfu/mL. After 24 h at 37 \({}^{\circ}\)C, the cells were centrifuged, washed twice with PBS, and re-suspended in 150 mM NaCl at a concentration of 10\({}^{8}\) cfu/mL. An aqueous-phase sample (0.4 mL) was obtained, and the optical density at 405 nm (OD\({}_{405}\)) was determined (Bioscreen C MBR, Oy Growth Curves Ab Ltd., Helsinki, Finland). The cell suspension (2.0 mL) was vortexed with 0.33 mL of xylene (Merck KGaA, Darmstadt, Germany) for 60 s and then allowed to stand for 15 min at room temperature, thus completely separating the two phases. The percentage of cells present in the solvent was calculated using the following equation: % affinity to xylene = \(100\times[1-(A/A0)]\), where \(A0\) is the OD\({}_{405}\) of the original suspension prior to mixing, and \(A\) is the OD\({}_{405}\) of the aqueous phase. The cell surface hydrophobicity was grouped into three categories: weak (\(<\)21%), moderate (21% to 50%), and strong (\(>\)50%) affinities for xylene [30]. All the determinations were carried out eight times over four separate days (twice on each day).
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 3 Results and Discussion
### Calibration and Validation Results of WEAP Model  
In this study, the WEAP model was manually calibrated via trial and error [37], and was validated to ensure the simulated monthly discharge of years 2000-2008 and 2009-2017 satisfactorily fit the observed data at station E.68A. Firstly, a graphical technique was used for visual comparisons for both discharges, and for an initial overview of model performance [38]. It was noted that both compared discharges during both periods were in close agreement, however, there was a slight under/over estimation of peaks (see Figure 6). In addition, the robustness and performance of the WEAP model was also found to be good with both calibration and validation statistics of R\({}^{2}\) and NSE values equal to 0.76, 0.76 and 0.70, 0.69, respectively. These obtained results indicate the acceptable efficiency of the WEAP model in simulating hydrologic behavior of the Lam Phaniang River Basin, as well as representing river basin processes accurately and predicting their responses to different outputs.
# CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements
## 3 Results
### Actuator Line Large-Eddy Simulation Results
The percent differences obtained for the horizontal streamwise velocity at 30-, 50- and 70-m heights on masts M1 and M3 are similar to the case without synthetic inflow turbulence. The percent differences obtained for the power and RPM are also similar. The percent differences obtained for power and RPM vary between 0.44% and 11.42%. The mean percent differences from the four flow cases for power and rotor speed are 6.77% and 1.47%, respectively. Figures 13-15 are the results in Table 6 displayed in a bar-chart format identical to Figures 9-11. All comparisons in Figures 13-15 are very similar to Figures 9-11. The turbulence intensities based on the horizontal streamwise velocity in Table 6 are shown in bar-chart format in Figures 16 and 17. It was not possible to attain very good agreement for the turbulence intensities. The highest percent difference is 55.23% at 30 m on M1 for the 4 m/s and 180 degrees case. The turbulence intensities obtained from the AL-LES largely depended on the chosen input parameters for the surface roughness (=0.4) when generating the Mann turbulence box and the turbulence scaling factor (=1.5), see Section 2.3. Figure 12: Iso-surface of vorticity for the actuator line large-eddy simulation of wind turbine 14 with synthetic inflow turbulence for 7 m/s wind speed and 180 degrees wind direction (south to north). The domain shown is the same as in Figure 4b. The synthetic inflow turbulence input is seen by the rectangular plane 2.5 rotor diameters upstream of the wind turbine. _Appl. Sci._**2018**, \(8\), 788
## 5 Discussion  
As expected by the determination algorithm for the distances shown in Figure 7, for the desired end effector pose, Figure 8 shows all of the distance combinations that meet the constraints described by Equations (10)-(16). Each set of combinations corresponds to a set of inverse kinematics solutions. By using the proposed closed-form inverse kinematics in Equations (6), (9), and (19), Figure 9 shows different manipulator configurations and joint angles corresponding to different distance combinations. The end-effector poses of these configurations remain unchanged. Simultaneously, the calculated joint angles are within the given joint limit \"not more than 0.4363 rad (25 \({}^{\circ}\)) and not less than \(-0.4363\) rad (\(-25\) \({}^{\circ}\))\". These results verify the correctness of the proposed inverse kinematics model. Moreover, compared with the inverse kinematics based on backbone curve in [10], the proposed model is completely closed-form. Compared with the algorithm based on Jacobian analysis in [23], the proposed method can solve all joint angles at once. Compared with the inverse kinematics model proposed in [22] for three universal joints and one rotary joint manipulator, the proposed algorithm has stronger applicability.  
Using a set of constant distance combinations, Figure 10 shows the inverse kinematics solution when the end-effector moves linearly along the \(x\) axis. Figures 11 and 12 show the corresponding results for the \(y\)- and \(z\)-axes, respectively. Figure 13 shows the inverse kinematics solution when the end effector performs circular motion. These joint angles are all within the given joint limit. These results further verify the correctness of the proposed  
Figure 13: Solution for a circle path: (**a**) configuration of the manipulator along the circle path; (**b**) corresponding joint angles.  
inverse kinematics model. Simultaneously, these results also indicate that the distance combination has the following property: a set of distance combinations can be used to solve the inverse kinematics of different end-effector poses. These distances have geometric meanings as shown in Figure 6, so this property has certain guiding significance for path planning and obstacle avoidance of the \(2n\)-DOF hyper-redundant manipulator.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 5 Performance Evaluation
### End-to-End Delay  
Delay is considered when selecting the best next hop according to \(mOFQS\). To evaluate the End-to-End delay, we calculated the difference in time between sending a packet by the client and the reception by the server. We actually ran several tests in order to check the synchronization of the clock, and we realized that clock drift is negligible. Figure 7 shows the end-to-end variation throughout the experience time for both \(MRHOF/OF0\) and \(OFQS\). We can see that \(OFQS\) end-to-end delay is always below \(MRHOF/OF0\) with an improvement ranging from 6% to 10%. Even though \(HC\) chooses paths with the fewer hops from the sink, these paths are generally slower with a higher potential of loss since \(HC\) is not aware of links congestion and saturation. On the other hand, \(ETX\) is not also aware of the delays due to interference on the links and queuing in the nodes as long as the packets are transmitted; therefore, sending a packet with less re-transmissions does not necessarily mean sending it on a faster link. In \(OFQS\), the \(d\) factor takes into account the delay of sending a packet between two adjacent nodes in the metric computation. In this way and mainly in instance 1, the metric will foster faster routes with less interference and congestion that \(HC\) and \(ETX\) are not aware of. Moreover, we can see that the delay variations for \(OFQS\) are minimal between 20 and 40 min. This is due to the variation of the battery levels (\(PS\) passing to a smaller value) which affects the choice of routes with low delays. Finally, and starting from the 40 th min until the end of the experiment, we can notice that the end-to-end delay starts to increase. This is due to the depletion of the batteries of some nodes that switch to a lower \(PS\), which means that the metric will switch from these nodes to other ones and foster sometimes longer routes in order to increase the network lifetime. We note that the experience stops after 44 min for \(MRHOF/OF0\) compared to 58 min for \(OFQS\) as we can see on the graph. This extension of the network lifetime will be discussed in detail in Section 5.2.
## 5 Challenges and Limitations in Carotenoids Analysis
### Challenges in Regulatory and Standardization  
Standardizing carotenoid analysis is essential for ensuring data comparability and method reliability. However, several regulatory gaps remain:  
* Lack of official standardized methods for all carotenoids: While regulatory agencies such as AOAC International and the European Food Safety Authority (EFSA) provide validated methods for \(\beta\)-carotene analysis, official methods covering a broader spectrum of carotenoids are still lacking [127]. This gap affects the accuracy and reproducibility of carotenoid quantification across different laboratories. Standardization efforts should focus on harmonizing protocols for food safety testing and nutritional labeling.
* Limited availability of analytical standards: Certified reference materials and analytical standards are not available for all carotenoids, especially for minor and newly identified carotenoids, limiting the ability to perform accurate quantification [128]. This challenge is particularly relevant for minor carotenoids and newly discovered derivatives, where standard synthesis and commercial availability remain constrained. Expanding the availability of commercially synthesized carotenoids could support regulatory compliance and method validation.
* High determination limits (low sensitivity) and incomplete validation: Many existing methods exhibit high limits of detection (LODs) and limits of quantification (LOQs), restricting their applicability to trace-level carotenoid analysis in specific food matrices [129]. The adoption of high-resolution MS and isotope dilution methods has enhanced precision, particularly in regulatory food testing laboratories. Furthermore, incomplete analytical validation of carotenoid determination methods, including interlaboratory reproducibility studies, hinders regulatory approval and method harmonization.  
* Alignment with food safety regulations: Chromatographic techniques must align with food industry regulations, particularly in regions with strict labeling requirements. For example, HPLC and MS-based methods are widely accepted in FDA and EFSA regulations for carotenoid content verification in fortified foods [130,131]. Further regulatory updates may be needed to accommodate emerging techniques, such as SFC and novel solvent-free extractions.
## 4 Experimental Evaluation
### Dataset and Experimental Environments  
The Microsoft Malware Classification Challenge (BIG 2015) dataset was used to verify the proposed method [29; 30]. This dataset comprises approximately 500 GB of data divided into training data and test data. These data consist of ASM and byte files extracted using the Interactive DisAssembler (IDA) tool. Because the test data in the dataset had no label information, all the training data were used. Table 1 shows the total number of obfuscated and unobfuscated malware, segregated by family, used in the experiments. The family index is the index of each malware family, and the family name is the real name of the malware of each family. The total number of obfuscated malware is 605, and that of unobfuscated malware is 10,263. As is obvious, the number of obfuscated malware is lower than the number of unobfuscated malware. The total number of obfuscated and unobfuscated malware is 10,868.  
Table 2 shows the parameters of the experiments. Batchsize is the number of images entered at one time. Imageshape is the image size that is input to the GAN and CNN. Because of the limitation of experimental environments, the parameter Imagshape of GAN was set to (32,32,1). Epoch is the number of trainings. Filter_size is the filter size of the convolution. G_h0, G_h1, and G_h2 are the sizes of the convolutional layer of the generator of GAN and G_h3 is the size of the FC layer of the generator of GAN. D_h0, D_h1, and D_h2 are the sizes of the deconvolutional layer of the discriminator of GAN,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Family Index** & **Family Name** & **Obfuscated Malware** & **Unobfuscated Malware** & **Total Number** \\ \hline
1 & Ramnit & 28 & 1513 & 1541 \\
2 & Lollipop & 8 & 2470 & 2478 \\
3 & Kelihos\_ver3 & 6 & 2936 & 2942 \\
4 & Vundo & 28 & 447 & 475 \\
5 & Simda & 8 & 34 & 42 \\
6 & Tracur & 457 & 294 & 751 \\
7 & Kelihos\_ver1 & 11 & 387 & 398 \\
8 & Obfuscator.ACY & 58 & 1170 & 1228 \\
9 & Gatak & 1 & 1012 & 1013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total number of obfuscated and unobfuscated malware, by family, used in the experiments.  
Figure 5: Convolutional neural network (CNN) training process.  
and D_h3 is the size of the FC layer of the discriminator of GAN. Conv1, conv2, and conv3 are the sizes of the convolutional layer of the CNN. Fc1 and Fc2 are the sizes of the FC layer of the CNN.