esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design
### Proposed Topology and Approach  
We started with a meander line planar monopole antenna, as shown in Figure 2. CST Microwave Studio (CST MWS) simulation software was used for all the simulations reported in this paper. The antenna consists of a planar monopole loaded with asymmetrical inverted slots and a truncated ground plane, which are made of ShieldIt(tm) and copper tape with a thickness of 0.17 mm and 0.035 mm, respectively, and it uses a felt substrate. The felt substrate has a relative permittivity (\(\epsilon_{\tau}\)) of 1.3, loss tangent (tan \(\delta\)) of 0.044, and a thickness of 1.5 mm. A denim layer of \(\epsilon_{\tau}=1.43\), tan \(\delta=0.02\), and a thickness of 0.7 mm was used under the ground plane to provide isolation between the antenna and the skin surface. The antenna was excited by a microstrip line connected to a monopole length (L\({}_{\text{mon}}\)) of 40.20 mm. By adding the slots and creating a proper loading the antenna's Q can be lowered, and therefore, the size can be miniaturized. The correct choice of slot dimensions can keep the bandwidth (BW) wide. In this design, the meander line antenna dimensions were calculated using transmission line equations [24; 25]. Table 2 shows the important dimensions of the proposed antenna.  
Figure 2: Design and dimensions of the planar meander line antenna, (dimensions are given in mm): (**a**) the top side; (**b**) the bottom side; (**c**) the insulation layer; and (**d**) the fabricated antenna.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 2 Materials and Methods
### FH Capacity Boost Signal Design
#### 2.2.2 FH Capacity Boost Signal Generation  
The FH capacity boost signal needs to meet the requirements of frequency-hopping point orthogonality, phase continuity, and code element alignment, as shown in Figure 3. In Figure 3, A denotes information A, B denotes information B.  
The frequency-hopping points are orthogonal and the carrier frequency points are \(\omega_{0}\), \(\omega_{1}=2\omega_{0}\), \(\ldots\), \(\omega_{\mathrm{M-1}}=\mathrm{M}\omega_{0}\). The carrier signal \(\cos\omega_{0}\), \(\cos\omega_{1}\), \(\ldots\), \(\cos\omega_{\mathrm{M-1}}\) is orthogonal in the period \(\cos\omega\). Quadrature frequency hopping is implemented in the baseband first, and then the frequency-hopped signal is up-converted to RF.  
In multi-symbol joint detection, to improve the BER performance of Information B without affecting the original anti-jamming performance of Information A, i.e., to maintain the frequency-hopping speed of Information A, multi-symbol joint detection is performed so that a single Information B code element controls one or more frequency-hopping points. At the same time, each set of frequency points is orthogonal to one code element period in the time domain.  
To achieve phase continuity--that is, to facilitate the demodulation of Information A at the receiving end--the phase of the signal at different frequency-hopping points should change continuously, to ensure that a symbol period is an integer multiple of the carrier period. To provide the end of a symbol period for the end of the carrier period, the next symbol starts just for the beginning of the carrier period, so that the phase changes continuously.  
The start and end moments of the code elements of the originating Information A and Information B are aligned with each other; the code element period of Information A corresponds to an integer number of frequency-hopping cycles; the code element period of Information B corresponds to an integer multiple of the code element period of Information A; and there are an integer number of carrier cycles within each frequency-hopping cycle.  
Figure 3: FH capacity boost signal generation.  
For baseband Information A, the number of frequency-hopping points is M. The frequency-hopping rate is \(\rm{r_{H}}\). The channel coding efficiency is \(\rm{\mu_{A}}\). The information rate is \(\rm{r_{A}}\). The code element rate is \(\rm{R_{A}}\). In general, the frequency-hopping period \(\rm{T_{H}}\) is equal to the code word period \(\rm{T_{A}}\), or an integer multiple of the code word period \(\rm{T_{A}}\).  
For baseband Information B, the code element cycle \(\rm{T_{B}}\) corresponds to j frequency-hopping cycles, \(\rm{T_{H}}\), where \(\rm{j\geq 1}\).
## Appendix A  
After defining \(\hat{z}=\frac{z}{\hat{\gamma}}\), the components of the \(N_{\rm P}\times N_{\rm P}\) matrices in Equation (2) are as follows:  
\[\begin{array}{ll}M({\rm m},{\rm n})=&\int_{0}^{1}\rho(\hat{z})A(\hat{z})\,\hat{ z}^{{\rm n}+{\rm m}-2}dz+\int_{0}^{1}\ \ ({\rm n}-1)({\rm m}-1)I_{D}\,\hat{z}^{{\rm n}+{\rm m}-4}{\rm d}\hat{z}\\ &+\sum_{i=1}^{N_{\rm d}}\left[m_{\rm i}^{4}(z_{\rm i}^{4})^{{\rm n}+{\rm m}-2}+ ({\rm n}-1)({\rm m}-1)\ I_{\rm Di}^{\rm d}(z_{\rm i}^{4})^{{\rm n}+{\rm m}-4} \right]\end{array}, \tag{11}\]  
\[G({\rm m},{\rm n})=\int_{0}^{1}\ ({\rm n}-1)({\rm m}-1)I_{\rm P}\,\hat{z}^{{\rm n }+{\rm m}-4}{\rm d}\hat{z}+\sum_{i=1}^{N_{\rm d}}\left[({\rm n}-1)({\rm m}-1) \ I_{\rm Pi}^{\rm d}(z_{\rm i}^{4})^{{\rm n}+{\rm m}-4}\right], \tag{12}\]  
\[C_{\rm xx}({\rm m},{\rm n})=\sum_{i=1}^{N_{\rm b}}\left[C_{\rm xgi}^{\rm b}(z _{\rm i}^{4})^{{\rm n}+{\rm m}-2}\right], \tag{13}\]  
\[C_{\rm xy}({\rm m},{\rm n})=\sum_{i=1}^{N_{\rm b}}\left[C_{\rm xyi}^{\rm b}(z _{\rm i}^{4})^{{\rm n}+{\rm m}-2}\right], \tag{14}\]  
\[C_{\rm yy}({\rm m},{\rm n})=\sum_{i=1}^{N_{\rm b}}\left[C_{\rm yyi}^{\rm b}(z _{\rm i}^{4})^{{\rm n}+{\rm m}-2}\right], \tag{15}\]  
\[K_{\rm s}({\rm m},{\rm n})=\int_{0}^{1}\ ({\rm n}-1)({\rm n}-2)({\rm m}-1)({\rm m }-2)\ E(\hat{z})\,I(\hat{z})\,\hat{z}^{{\rm n}+{\rm m}-6}{\rm d}\hat{z}, \tag{16}\]  
\[K_{\rm xx}({\rm m},{\rm n})=\sum_{i=1}^{N_{\rm b}}\left[K_{\rm xgi}^{\rm b}(z _{\rm i}^{4})^{{\rm n}+{\rm m}-2}\right], \tag{17}\]  
\[K_{\rm xy}({\rm m},{\rm n})=\sum_{i=1}^{N_{\rm b}}\left[K_{\rm xyi}^{\rm b}(z _{\rm i}^{4})^{{\rm n}+{\rm m}-2}\right], \tag{18}\]  
\[K_{\rm yy}({\rm m},{\rm n})=\sum_{i=1}^{N_{\rm b}}\left[K_{\rm xyi}^{\rm b}(z _{\rm i}^{4})^{{\rm n}+{\rm m}-2}\right] \tag{19}\]  
where \(l\) and \(z\) represent the shaft length and axial coordinate, respectively. \(N_{\rm P}\), \(N_{\rm d}\), and \(N_{\rm b}\) denote the total number of polynomial, disk, and bearing, respectively. \(\rho(\hat{z})\) and \(A(\hat{z})\) represent the shaft density and cross-sectional area, respectively. \(I_{\rm D}\) and \(I_{\rm P}\) represent the shaft diametral and polar mass moment of inertia, respectively. \(m_{\rm i}^{4}\) represents the i-th disk mass. \(I_{\rm Dk}^{\rm d}\) and \(I_{\rm Pi}^{\rm d}\) represent the i-th disk diametral and polar mass moment of inertia, respectively. \(C_{\rm xyi}^{\rm b}\), \(C_{\rm xyi}^{\rm b}\), and \(C_{\rm yyi}^{\rm b}\) represent the j-th bearing damping coefficients. \(E(\hat{z})\) and \(I(\hat{z})\) represent the shaft elastic modulus and cross-sectional moment of inertia, respectively. \(K_{\rm xgi}^{\rm b}\), \(K_{\rm xyj}^{\rm b}\), and \(K_{\rm yyj}^{\rm b}\) represent the j-th bearing elastic constant.
## 5 Experimental Results and Discussion
### Generalized Zero-Shot Cross-Modal Retrieval Results
First, in the case of the truck image as input Table 3 (a), the HSL model did not retrieve the true label but retrieved the other labels in seen classes. In contrast, the HSL+UCE model retrieved true label in unseen classes as top-1. The other retrieved results including both seen and unseen classes are also semantically similar to each other in that they are person-made objects related to transportation. In this way, we can see a practical example of generalized zero-shot learning complemented by the UCE, which is difficult to solve with the HSL model only. Next, in the second row with input Table 3 (b), while the HSL model could not properly predict the true label, the HSL+UCE model predicted the true label as top-4. This is an improvement over the results of the existing HSL model only, but the UCE is less influential than the example in Table 3 (a) and the true label is not adequately retrieved as top-1. The example of Table 3 (c) shows the result of correctly retrieving the true label of the unseen classes as top-1 through the HSL+UCE model. In contrast to the example of Table 3 (b), since all retrieved labels from top-1 to top-5 are in the unseen classes, we can surmise that the influence of UCE is over-applied, which shows the limitation that the seen classes cannot be retrieved properly. So, even though this is not the usual generalized zero-shot learning evaluation setting, we need to check the degradation of general cross-modal retrieval for seen class data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Query** & \multicolumn{2}{c}{**HSL**} & \multicolumn{2}{c}{**HSL+UCE**} \\ \hline  & \(\bullet\) & train & ✓ & **truck (unseen)** \\  & \(\bullet\) & streetcar & \(\bullet\) & train \\  & \(\bullet\) & bus & \(\bullet\) & airplane (unseen) \\  & \(\bullet\) & house & \(\bullet\) & streetcar \\  & \(\bullet\) & tractor & \(\bullet\) & automobile (unseen) \\ \hline  & \(\bullet\) & cattle & \(\bullet\) & cattle \\  & \(\bullet\) & fox & \(\bullet\) & fox \\  & \(\bullet\) & lion & \(\bullet\) & lion \\  & \(\bullet\) & kangaroo & ✓ & **horse (unseen)** \\ (b) horse & \(\bullet\) & camel & \(\bullet\) & kangaroo \\ \hline  & \(\bullet\) & castle & ✓ & **ship (unseen)** \\  & \(\checkmark\) & **ship (unseen)** & \(\bullet\) & airplane (unseen) \\  & \(\bullet\) & bridge & \(\bullet\) & bird(unseen) \\  & \(\bullet\) & skyscraper & \(\bullet\) & automobile (unseen) \\ (c) ship & \(\bullet\) & rocket & \(\bullet\) & horse (unseen) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Retrieved semantic labels for unseen class image query.
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 4 Results and Discussion
### Benchmark  
After ablation experiments, to evaluate and improve the performance of the model, we made comparisons between the VLSM-Net model and other segmentation methods in the LiTS and LUNA dataset.  
As can been seen in Table 3 in LiTS, VLSM-Net achieves better \(DSC\) compared to many other models, such as SegNet, U-Net and Net01 [27; 28; 29]. This is due to the change of structure from 2D to 3D, and the added shortcut to connect different convolutional  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(DSC\)**\%** & **Sensitivity/\%** & **Precision/\%** \\ \hline LSM & 57.53 & 75.27 & 72.85 \\ V-Net & 79.34 & 89.11 & 85.53 \\ VLSM-Net & 82.27 & 89.91 & 86.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the different algorithm models in LUNA16.  
Figure 7: This is a top view display of the predicted images generated by different segmentation models in the LiTS dataset. (**A**) The original input image. (**B**) The label image. (**C**) The predicted image of the LSM. (**D**) The predicted image of the V-Net model. (**E**) The predicted image of the VLSM-Net.  
layers. The result in LUNA is similar to the analysis of LiTS. Compared to the U-Net, U-Det, InceptionV3, and RUNet models, better performance is achieved by our VLSM-Net model.  
Although VLSM-Net has been shown to obtain good results in previous experiments, there are many ways to enhance it. First, more effective steps can be taken in the pre-processing stage, such as the normalization of inputs and clipping of HU values. Then, there are plenty of ways to modify the structure, by adding more short and long skip connections, adjusting as weighted cross-entropy loss functions, such as Jaccard distance, and using some ensemble learning methods which can combine the benefits of multiple models.  
However, it is worth stating that our model is a universal structure, which may have fewer advantages in specific segmentation tasks. In addition, our goal at this stage is to prove that our model can effectively achieve the segmentation of 3D CT images, and adding the LSM into the V-Net model can improve its performance. In future research, we will use more tricks in pre-processing, structure, and training to improve performance and try to combine more models with LSM.
## 3 Methods and Materials
### Model Validation and Accuracy  
Model validation was performed by the Root mean square error (RMSE) approach. RMSE is calculated using Equation (43) [5; 26].  
\[\text{RMSE}\ =\ \sqrt{\frac{\sum_{\text{i}=1}^{\text{M}}\left(\text{x}_{\text{i, cal}}-\text{x}_{\text{i,exp}}\right)^{2}}{\text{M}}} \tag{43}\]  
where \(\text{x}_{\text{i,cal}}\) is the data from numerical simulation computation, \(\text{x}_{\text{i,exp}}\) is the experimental data obtained from the secondary data, and M is the number of samples. Considering the experimental gas volumes of CO, CO2, CH4, H2, and N2 were in %vol, the simulation results were also converted to %mol. Therefore, \(\text{x}_{\text{i,cal}}\) is defined as follows [11].  
\[\text{x}_{\text{i,cal}}(\%)\ =\ \frac{\text{n}_{\text{i}}}{\text{N}_{\text{t}}} \times 100 \tag{44}\]  
were,  
\[\text{N}_{\text{t}}\ =\ \text{n}_{1}+\text{n}_{2}+\text{n}_{3}+\text{n}_{4}+ \text{n}_{6}+\text{n}_{8}\]  
The total tar content (g/Nm3) in \"\(n_{8}\)\" is calculated as follows [11].  
\[\text{C}_{6}\text{H}_{6}\left(\text{g/Nm3}\right)\ =\ \frac{M_{BM}\times\text{n}_{8}}{V_{ gas}} \tag{45}\]  
A paired data mean test was applied to evaluate the accuracy of models M1A-M6D in simulating compositions of CO, CO2, CH4, H2, and N2 obtained from gasification, regardless of the types of reactors. A paired \(t\)-test with a significance level of \(\alpha=0.05\) was applied. The developed models were deemed to be accurate if the models correctly predicted at least three partial hypothesis tests. The hypothesis states that the mean composition of producer gas (%mol) predicted by the models is the same as the mean composition of the gas obtained from the experiments.
## 3 Materials and Methods
### Antenna Radiation Model
The concept of sagitta (\(s\)) of a circular arc is widely used in architecture and optics. The sagitta of a circular arc is the distance from the center of the arc to the center of its base; see Figure 3.  
The mathematical equations of this problem are Equations (1)-(7). The \(s\) can be calculated as the difference between the radius (\(r\)) and the aptheum (\(ap\)); see Equation (1). According to the Pythagorean Theorem, \(s\) can also be calculated as a function of \(r\) and chord (\(c\)); see Equation (2). Moreover, it is possible to apply the verseine function to obtain the value of \(s\) according to Equation (3) where \(\alpha\) is the angle formed by the two \(r\), which forms the triangle with the \(c\). Alternatively, there is one last method to calculate the \(s\); see Equation (4). \[s=r-ap \tag{1}\]  
\[s=r-\sqrt{r^{2}-\left(\frac{c}{2}\right)^{2}} \tag{2}\]  
\[s=r\,x\,\,\text{\it{versin}}\frac{\alpha}{2}=r\,x\left(1-cos\left(\frac{\alpha }{2}\right)\right) \tag{3}\]  
Figure 3: Sagitta of the circular arch described by the coverage of the drone. Figure 2: Area covered by the sensor antenna located on the drone: (**a**) Omnidirectional antenna case, (**b**) Directional antenna case. \[s=\ \frac{c}{2}\ x\tan\frac{\alpha}{2} \tag{4}\]  
In our problem, the _ap_ represents the flying height (_fh_), \(r\) is the radius of the drone coverage (_dc_), and the chord represents the coverage in the soil. The known variables are _fh_ and _dc_, and our objective is to calculate the value of \(c\). Therefore, the employed equations in our calculations will be Equations (1)-(2). Combining them, we can obtain Equation (5), which can be expressed as Equation (6) to obtain the value of \(c\). Finally, considering the flying velocity (_fv_), we can calculate the time (_t_) during which a single node has coverage from the gateway mounted on the drone (Equation (7)). \[dc-fh=dc-\sqrt{dc^{2}-\left(\frac{c}{2}\right)^{2}} \tag{5}\]  
\[c=2\sqrt{dc^{2}-\left(-fh\right)^{2}} \tag{6}\]  
Figure 3.0  
Now, we can use Equation (7) to calculate the time during which a node has coverage for different scenarios. In our calculations, we include values of _dc_, _fh_, and _fv_, which can be found in real applications. The selected values are _dc_ from 200 to 25 m, _fh_ from 5 to 100 m, and _fv_ from 5 to 20 m/s.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Heterogeneous Networks
#### 4.1.2 Types of Cell and Scenarios of the Communication of HetNets  
Figure 5 shows a perfect HetNet with various SCs. The network types can be classified into four categories based on their various coverage areas and operation scenarios [51; 52; 53] as follows.  
1. Macro-cell Networks: A macro-cell network can supply extensive coverage by utilizing a high-power BS, which is usually utilized in cellular networks. The macro-cell network characteristics include: (i) being permanently located in a high area, such as skyscrapers or summits of mountains which can provide a line of sight over the neighboring buildings and obstructions; (ii) having a high transmission space and a massive coverage region, where the radius of the cell ranges from 1 to 25 km. Moreover, the space between adjacent MBSs is large; (iii) shadowing, fading, and interference of multipath have a significant impact on the cell-edge user QoS; and (iv) due to the existence of uncovered or hot areas because of unevenly distributed serving demands, the indoor users' QoS is much lower when serviced by the MBS [54].
2. Microcell Networks: A low-power BS is used to serve the micro-cell network that is always established in highly populated metropolitan areas, such as shopping malls [55]. This network's coverage radius ranges from 200 m to 1 km, which is significantly less than that of the macro-cell network. Meanwhile, with low-power BSs, the frequency reuse distance decreases, while the number of channels and the density of traffic both increase substantially [56].
3. Pico-cell Networks: A pico-cell network spans a significantly lower area (between 100 m and 200 m) when compared to a micro-cell network, such as training buildings. Typically, pico-cells are utilized to increase the coverage of indoor regions. As a result, they have the potential to minimize the uncovered areas of indoor communications [57].
4. Femto-cell Networks: A femto-cell network (also known as a Home e-Node B) is a network with a small and low-power BS that is formed to increase the quality of communication in a home or small company. Using the home BS improves the QoS for indoor users [58]. Furthermore, femto-cells are significantly easier and more cost-effective to deploy than other types of cells. Besides that, femto-cells can be used to fill in the gaps between pico-cells and prevent the loss of signal via buildings. The  
Figure 5: The two-tier HetNet with multiple SCs.  
fundamental distinction between femto-cells and pico-cells is that the users' number in femto-cells is less than in pico-cells [59].  
Based on the above explanation, the properties of various networks are briefly described in Table 1. The term \"radius\" refers to the BS's transmission radius. While the term \"power\" refers to the BS's maximum transmitting power. Moreover, the term \"scenario\" represents the application context in which each cell operates.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 1 Introduction
However, the results shown do not present a clear picture of the multi-hop operation on the energy consumed, only the number of hops in the available path. The paper is structured as follows: Section 2 includes the system model, describing the 3D network model, mathematical model of the propagation, and the energy consumption for undersea environment. Section 3 explains the working methodology of the proposed protocol. Section 4 describes the performance metrics, analysis, and parameters used for evaluation. After that, Section 5 discusses in detail the obtained results in terms of random topology, fault tolerance and scalability. Finally, conclusions are given in Section 6.
###### Abstract  
Introduction: Cone-beam computed tomography (CBCT) has been applied to implant dentistry. The increasing use of this technology produces a critical number of images that can be used for training artificial intelligence (AI). Objectives: To investigate the learning curve of the developed AI for dental implant planning in the posterior maxillary region. Methods: A total of 184 CBCT image sets of patients receiving posterior maxillary implants were processed with software (DentiPlan Pro version 3.7; NECTEC, NSTDA, Thailand) to acquire 316 implant position images. The planning software image interfaces were anonymously captured with full-screen resolution. Three hundred images were randomly sorted to create six data sets, including 1-50, 1-100, 1-150, 1-200, 1-250, and 1-300. The data sets were used to develop AI for dental implant planning through the IBM PowerAI Vision platform (IBM Thailand Co., Ltd., Bangkok, Thailand) by using a faster R-CNN algorithm. Four data augmentation algorithms, including blur, sharpen, color, and noise, were also integrated to observe the improvement of the model. After the testing process with 16 images that were not included in the training set, the recorded data were analyzed for detection and accuracy to generate the learning curve of the model. Results: The learning curve revealed some similar patterns. The curve trend of the original and blurred augmented models was in a similar pattern in the panoramic image. In the last training set, the blurred augmented model improved the detection by 12.50%, but showed less accuracy than the original model by 18.34%, whereas the other three augmented models had different patterns. They were continuously increasing in both detection and accuracy. However, their detection dropped in the last training set. The colored augmented model demonstrated the best improvement with 40% for the panoramic image and 18.59% for the cross-sectional image. Conclusions: Within the limitation of the study, it may be concluded that the number of images used in AI development is positively related to the AI interpretation. The data augmentation techniques to improve the ability of AI are still questionable.  
artificial intelligence; deep learning; dental implant
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 3 Analysis of the Experimental Results for Lamb Wave FDM-PPM
### Analysis and Discussion of Bit Error Rate
The ASK method takes the complete waveform of the direct S\({}_{0}\) mode Lamb wave signal as the analysis object and does not need to consider the phase information of the signal. Therefore, the signal waveform distortion caused by Lamb wave dispersion and equalization compensation errors will not significantly affect the demodulation results. However, PPM and FDM-PPM signals carry phase information, and the FDM-PPM signal has a broader frequency band, further complicating the signal's phase discrimination. To address the high error rates caused by Lamb wave dispersion, channel coding techniques such as Hamming and convolutional codes can enhance the error correction capability of the transmitted information. The FDM-PPM method proposed in this study slightly reduces demodulation accuracy compared with the ASK, PPM, and FDM methods. However, it achieves a significant increase in transmission rate, with the excitation signal's energy being only half that of the other methods.