esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, the inclusion of a large R\({}_{\text{B}}\) can significantly reduce I\({}_{\text{B}}\) since the gate bias V\({}_{\text{G}}\) = I\({}_{\text{B}}\)R\({}_{\text{B}}\) + V\({}_{\text{BS}}\), in which V\({}_{\text{BS}}\) is the forward-biased B-to-S voltage. In other words, I\({}_{\text{B}}\) reduces with an increase in R\({}_{\text{B}}\) in the on-state. In the off-state, the B-to-S junction of the DTMOS-R is reverse-biased, corresponding to a larger V\({}_{\text{th}}\), and hence, a higher R\({}_{\text{off}}\), i.e., a better off-state. This leads to a lower IL of the SPDT switch due to the better off-state of the parallel DTMOS-R. Moreover, the inclusion of a large R\({}_{\text{B}}\) can reduce I\({}_{\text{B}}\) since the gate bias V\({}_{\text{G}}\) = I\({}_{\text{B}}\)R\({}_{\text{B}}\) + V\({}_{\text{BS}}\), in which V\({}_{\text{BS}}\) is the reverse-biased B-to-S voltage. In other words, I\({}_{\text{B}}\) reduces with an increase in R\({}_{\text{B}}\) in the off-state. Furthermore, I\({}_{\text{B}}\) (suppression) simulation can be conducted using the function of I\({}_{\text{Probe}}\) in Advanced Design System (ADS), a piece of circuit design and simulation software provided by Agilent Technologies, Santa Clara, United States. That is, the simulated I\({}_{\text{B}}\) can be obtained through the insertion of the icon of an I\({}_{\text{Probe}}\) in the R\({}_{\text{B}}\) path between the control voltage (V\({}_{\text{sw1}}\) or V\({}_{\text{sw2}}\)) and gate/body nodes of a DTMOS-R in the schematic. Figure 1: Circuit diagram and chip photo of the SPDT switch in a 90 nm CMOS (SPDT SW1). In this work, we propose a CMOS SPDT switch with a low IL, high isolation, and a decent P1 dB for a 28 GHz 5G NR. The first SPDT switch (SPDT SW1) is designed and implemented in a 90 nm CMOS with a top metal thickness (TMT) of 3.4 \(\upmu\)m. A comparative SPDT switch (SPDT SW2) in a 0.18 \(\upmu\)m CMOS with a thinner TMT of 2.34 \(\upmu\)m is also designed and implemented. Figure 1 shows a circuit diagram and chip photo of SPDT SW1. Figure 2 shows a circuit diagram and chip photo of SPDT SW2. The transistor size and important component parameters are also labeled. The chip area, excluding the DC and RF pads, is 0.26 \(\times\) 0.26 mm\({}^{2}\), i.e., 0.068 mm\({}^{2}\). R\({}_{\rm B}\) values of 4.2 k\(\Omega\) and 2.4 k\(\Omega\) are used for SPDT SW1 and SPDT SW2, respectively. In the TX path, a parallel DTMOS-R, a series TL (equivalent \(\lambda\)/4 TL), and parallel capacitance, C\({}_{\rm ant}\), are included for a low IL in the TX mode and decent isolation in the RX mode. In the RX path, a series impedance and a parallel DTMOS-R are included for a high-P1 dB in the RX mode and decent isolation in the TX mode.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results
### Assessment Environment  
The proposed scheme was implemented in Python using a standard 64-bit Windows machine. The numerical data obtained from the analysis were subjected to MATLAB enriched with a data analytical toolbox for an extensive and dedicated study. As the proposed scheme must be benchmarked with the existing system, it is necessary to consider specific standard simulation parameters universally adopted in most existing approaches. The values of the simulation area, the quantity of mobile sensors, and its velocity, transmittance power, initialized energy, and energy to transmit data are chosen considering the frequently adopted research work discussed in Section 2. The channel capacities of MS, AP, and GN are selected based on conventional applications in WSNs. All the assigned values of the simulation parameters are highly flexible and can be easily amended based on particulars of applications. The details of the assessment environment concerning the simulation parameters are presented in Table 3.  
Figure 5: High-level diagram of the proposed framework.  
A specific dimension was considered for the simulation environment, which could be upscaled to fit any size of sensory application. The simulation area consisted of 500 users (or mobile sensors) with ten APs uniformly assigned within the simulation area, while two gateway nodes were appropriately positioned. The mobile sensors are randomly deployed within the simulation area, while the AP's coverage and service extensibility are optimal for all the sensors near the AP. Every AP was further connected to two GNs, where the coverage was sufficient to encapsulate the APs within the simulation area. The transmission range of each AP was uniform at 200 m, whereas the transmission range of each GN was maintained at 600 m. Although the values are lower than the original values of commercially available APs and GNs, they provide a scope for understanding the communication performance in the model. Various permutations and combinations of the location of the AP and GN have been explored to find that there is only a 0.76-1.87% fluctuation in the overall outcome. The simulation can be executed until all 500 mobile sensors deplete the energy while 5000 bytes of the test data packet is used to evaluate the communication performance. The experiment was also extended to evaluate peak traffic conditions by randomly varying the number of mobile sensors. The outcomes accomplished under varied assessment environments showed a satisfactory level of consistency.
## 2 Materials and Methods  
### PBF-LB/P System and Build Part Setup  
An EOS Formiga P 110 selective laser sintering machine was used to perform the experimental trials during this work. The PBF-LB/P system uses a CO\({}_{2}\) laser with 30 W of output power and a wavelength of 10.6 \(\upmu\)m to merge powder particles at the powder bed. PA12 (PA 2200) was used as high-performance nylon throughout this study. The system is capable of scanning speeds of up to 5 m/s within an exposed area (xy) of 200 mm \(\times\) 250 mm, merging a minimum of 50 \(\upmu\)m and a maximum of 200 \(\upmu\)m layer thickness. The maximum build part height (z) is 330 mm. The parameter setup included a layer thickness of 100 \(\upmu\)m and standardized non-adapted laser scan speed and hatch spacing by the EOS default exposure strategy. To ensure a constant process temperature according to the DSC curve, the process chambers at the upper installation space and lower unloading chamber are both heated by separate built-in process heaters.  
To create artificial curling defects, the default process temperature of 167 \({}^{\circ}\)C was manually reduced. A reduction of 3% to 162 \({}^{\circ}\)C revealed the first distortion effects. In addition, the intensity could then be slightly increased by manually interrupting the process and thus extending the layer cooling time. Further parameter adjustments during the ongoing sintering process are prevented by limited access to the operating software. As can be seen in Figure 5, a series of calibration parts were manufactured to define parameter settings as a basis for subsequent analysis. Figure 5 illustrates the visible appearance of distortion of the whole component (b) at the test geometry (a) compared to an accurate test part without visible warpage (c).  
After the definition of parameters, several test part geometries were defined as depicted in Figure 6a. The build parts taken as bases for further triangulation and thermal analysis contained cross-, triangular-, and circular-shaped geometries with outline measurements of 50 mm. The parts contained 6 identical stacked layers measuring 2 mm of material thickness for each layer, separated by a gap of 0.4 mm and connected by a central cylindrical pin of 8 mm in diameter as shown in Figure 6b. The total height of each part measured 14 mm. Stacking identical layers enabled systematic iterations of curling analysis without the in-between waiting time of process restart and heating phases.
###### Abstract  
With the widespread adoption of 5G telecommunication networks, to reduce construction costs, it has become necessary to add new equipment or antennas to existing 4G and 3G telecommunication towers. This significantly changes the original aerodynamic shape of the towers, leading to a substantial increase in the wind load, which may exceed the original structure's bearing capacity and pose a threat to the structure's safety. This study employed three-dimensional numerical simulation methods to systematically investigate the impact of various antenna arrangement parameters, such as the arrangement number, arrangement form, and arrangement layers, on the wind load characteristics of telecommunication towers. The findings revealed that the antenna arrangement form significantly affects the sensitivity of the telecommunication tower's wind load to the wind direction, with more uniform antenna arrangements resulting in less sensitivity. Compared to the drag coefficient and the windward base overturning moment coefficient, the tower's lateral force coefficient and the crosswind base overturning moment coefficient are more sensitive to changes in the wind direction. The change patterns in the tower's overturning force coefficient and overturning moment coefficient with the antenna arrangement number are essentially the same. However, as the antenna arrangement number increases, the growth rate of the tower's overturning moment coefficient is about twice that of the overturning force coefficient. The tower's overturning force coefficient increases approximately linearly with the increase in antenna arrangement layers, while the tower's overturning moment coefficient exhibits a nonlinear increase with the increase in antenna arrangement layers. The rate of increase in the wind load with the antenna arrangement layers is significantly greater than that with the antenna arrangement number. Thus, to reduce wind load, it is advisable in practical engineering applications to increase the antenna arrangement number per layer, thereby reducing the antenna arrangement layers. The study also summarized a calculation method for the structural wind load of telecommunication towers, taking into account the influence of antenna arrangement parameters, providing a reliable basis for the structural design and safety assessment of telecommunication towers in practical engineering.  
telecommunication tower; antenna; arrangement parameter; numerical simulation; wind load +
Footnote †: journal: Applied Sciences
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 3 Results and Analysis
### Comprehensive Risk Assessment of Urban Floods  
Based on the results obtained by analyzing the severity of disaster-causing factors, the exposure of the disaster-prone environment, and the vulnerability of the disaster-bearing body, the comprehensive assessment model was used to carry out a weighted integrated evaluation, and the final results were graded using the natural breakpoint method, i.e., very low comprehensive risk, low comprehensive risk, medium comprehensive risk, high comprehensive risk, and very high comprehensive risk. The comprehensive risk distribution of flood disasters in Liaocheng City was obtained, as shown in Table 11.  
As shown in Figure 5, the spatial distribution of the comprehensive risk of flooding in Liaocheng City was characterized by high risk in the central and western parts and relatively low risk in the southern and northern parts. Comprehensive risk was dominated by medium and low risk, with medium-risk and low-risk areas being distributed in Liaqing  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Return Periods** & **Very Low** & **Low** & **Medium** & **High** & **Very High** \\ \hline
10a & 21.14\% & 35.12\% & 24.29\% & 15.27\% & 4.18\% \\
50a & 20.76\% & 34.80\% & 24.04\% & 15.20\% & 5.20\% \\
100a & 20.54\% & 36.57\% & 23.19\% & 13.41\% & 6.29\% \\
200a & 23.39\% & 34.03\% & 22.00\% & 13.42\% & 7.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Area share of comprehensive urban flood risk.  
Figure 4: Zoning map shows the vulnerability of the disaster-bearing body.  
With the gradual increase in return periods, the overall spatial distribution of medium-to-very-high-risk areas in risk zoning gradually shrinks, and the spatial distribution of very-high-risk areas gradually moves south but maintains a stable distribution law. The high-risk areas of Liaocheng City, in terms of comprehensive flood risk, are mainly distributed in Guanxian County and tend to be concentrated with the increase in return periods. Very-high-risk areas increase with the increase in the recurrence period, expanding from 4.18% to 7.16%, which are concentrated in Dongchangfu District. Due to the frequent and heavy rainfall in Dongchangfu District and Guanxian County, the precipitation variability was high. This, coupled with the dense population and rural population, dense road network, and rapid economic development, resulted in high flood severity and the high vulnerability of the disaster-bearing bodies in Dongchangfu District and Guanxian County. At the same time, Dongchangfu District has many rivers, a gentle terrain, and low vegetation cover, resulting in a high level of exposure. Therefore, Dongchangfu District and Guanxian County have a higher likelihood of flooding and are the key areas for future forecasting and prediction as well as flood prevention in weather characterized by rainfall.  
Figure 5: Zoning maps show the comprehensive risk of urban floods.
## 3 Results
### Effects of Self-Selected Music on RPE and HR  
Figure 2 illustrates the RPE and HR values recorded after the Wingate test. There were no significant differences in RPE (M: 17.76 \(\pm\) 0.75, NM: 17.71 \(\pm\) 1.21; [PERSON]'s \(d\) = 0.06; 95% CI: \(-\)0.60 to 0.72; \(t_{16}\) = 0.187, \(p\) = 0.854) and HR (M: 182.24 \(\pm\) 8.79, NM: 178.53 \(\pm\) 10.51; [PERSON]'s \(d\) = 0.38; 95% CI: \(-\)2.44 to 9.88; \(t_{16}\) = 1.271, \(p\) = 0.222) between the music intervention and no music intervention after the Wingate test.  
Figure 1: Listening to self-selected music during the warm-up had no significant effect on RPE (\(p\) = 0.854, (**A**)) and HR (\(p\) = 0.222, (**B**)) after the Wingate test. RPE, rating of perceived exertion; HR, heart rate; M, music intervention; NM, no music intervention; n.s., no significance. The dots represent the raw data.
## 4 Experiment and Result Analysis
### Eevaluation Criteria  
In order to evaluate the performance of the different models in all aspects, in this paper, we employ the accuracy, precision, recall, F1, and inference time of the model classification as the evaluation criteria. In particular, it should be pointed out that due to the rapid spread of modern networks and the large scale of Internet users, the harm caused by misreporting bad images as normal images is much greater than that caused by identifying normal images as bad images. Based on this, this paper further introduces the false alarm rate of bad images as the evaluation standard of this experiment on the basis of classification accuracy, precision rate, recall rate, F1 value, and reasoning time.  
The calculation formula of classification accuracy in this paper is as follows:  
\[Accuracy=\frac{P_{C}}{P_{A}}\times 100\% \tag{8}\]  
Here, \(P_{C}\) represents the number of correct images predicted by the model in the validation dataset, and \(P_{A}\) represents the total number of validation images incorporated in the experiment.  
In order to comprehensively evaluate the classification performance of the model, we used the \(F1\) value to reconcile the precision rate and the recall rate. The formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
Figure 8: The proportions of pictures in various categories.  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{11}\]  
Here, \(TP\) is the number of samples in which the model predicts positive classes as positive classes, \(FN\) is the number of samples in which the model predicts positive classes as negative classes, and \(FP\) is the number of samples in which the model predicts negative classes as positive classes.  
We set up multiple sets of experimental data, with each group containing a different number of images, and then input each set of data into the inference model to record the respective running times. The calculation formula for inference time is as follows:  
\[Time_{i}=Tend_{i}-Tstart_{i} \tag{12}\]  
Here, \(Tend_{i}\) represents the end time of group \(i\), and \(Tstart_{i}\) represents the beginning time of group \(i\).  
In the real network environment, if bad images are misreported as normal pictures, discord will be sown in society following their dispersal in the network environment. We classify pornographic images and violent images as bad images, and the false positive rate of bad images can be expressed as  
\[FAR(False alarm rate)=\frac{P_{FAR}}{P_{A}}\times 100\% \tag{13}\]  
where \(P_{FAR}\) represents the number of bad images misreported as normal images, and \(P_{A}\) represents the total number of images in the validation set.  
In order to ensure the accuracy of the experimental data, all the experimental data were obtained by averaging the same hardware equipment after conducting training many times in the same training environment.
###### Abstract  
The fast growth experienced by the telecommunications field during the last few decades has been motivating the academy and the industry to invest in the design, testing and deployment of new evolutions of wireless communication systems. Terahertz (THz) communication represents one of the possible technologies to explore in order to achieve the desired achievable rates above 100 Gbps and the extremely low latency required in many envisioned applications. Despite the potentialities, it requires proper system design, since working in the THz band brings a set of challenges, such as the reflection and scattering losses through the transmission path, the high dependency with distance and the severe hardware constraints. One key approach for overcoming some of these challenges relies on the use of massive/ultramassive antenna arrays combined with hybrid precoders on fully connected phase-shifter architectures or partially connected architectures, such as arrays of subarrays (AoSs) or dynamic AoSAs (DAoSs). Through this strategy, it is possible to obtain very high-performance gains while drastically simplifying the practical implementation and reducing the overall power consumption of the system when compared to a fully digital approach. Although these types of solutions have been previously proposed to address some of the limitations of mmWave/THz communications, a lack between link-level and system-level analysis is commonly verified. In this paper, we present a thorough system-level assessment of a cloud radio access network (C-RAN) for beyond 5G (B5G) systems where the access points (APs) operate in the mmWave/THz bands, supporting multi-user MIMO (MU-MIMO) transmission with massive/ultra-massive antenna arrays combined with low-complexity hybrid precoding architectures. Results showed that the C-RAN deployments in two indoor office scenarios for the THz were capable of achieving good throughput and coverage performances, with only a small compromise in terms of gains when adopting reduced complexity hybrid precoders. Furthermore, we observed that the indoor-mixed office scenario can provide higher throughput and coverage performances independently of the cluster size when compared to the indoor-open office scenario.  
terahertz (THz); multiuser ultra-massive-MIMO; hybrid precoder design; system-level simulation; cloud radio access network (C-RAN); beyond 5G (B5G); 6G +
Footnote †: journal: Applied Sciences
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 7 Discussion
### Applicability to Average and Variance of AoI  
This work is also readily extensible to other key AoI metrics, such as the average and variance of AoI. Specifically, the optimal group size \(K^{*}\) and blocklengths \((L_{1}^{*},L_{2}^{*})\) that minimize either the average AoI or its variance can be obtained by applying the analysis and optimization framework presented in this work. For the analysis, the average AoI of sensor \((g,k)\), denoted by \(\overline{\Delta}_{(g,k)}\), and its second moment, denoted by \(\overline{\Delta^{2}}_{(g,k)}\), are expressed as follows:  
\[\overline{\Delta}_{(g,k)} =\lim_{T\to\infty}\frac{1}{T}\int_{0}^{T}\Delta_{(g,k)}(t)\, \mathrm{d}t,\] \[\overline{\Delta^{2}}_{(g,k)} =\lim_{T\to\infty}\frac{1}{T}\int_{0}^{T}\Bigl{(}\Delta_{(g,k)}(t )\Bigr{)}^{2}\,\mathrm{d}t.\]  
These expressions are derived in ([5], Equations (7) and (9)) for a two-hop status update system with joint packet coding, as illustrated in Figure 1. The system-wide average AoI \(\overline{\Delta}\) and variance \(\sigma^{2}\) are given by  
\[\overline{\Delta} =\frac{1}{N}\sum_{g=1}^{G}\sum_{k=1}^{K}\overline{\Delta}_{(g,k)},\] \[\sigma^{2} =\frac{1}{N}\sum_{g=1}^{G}\sum_{k=1}^{K}\biggl{\{}\overline{ \Delta^{2}}_{(g,k)}-\Bigl{(}\overline{\Delta}_{(g,k)}\Bigr{)}^{2}\biggr{\}}.\]  
The optimization procedure described in Section 5 can then be applied to the functions \(\overline{\Delta}=\overline{\Delta}(K,L_{1},L_{2})\) and \(\sigma^{2}=\sigma^{2}(K,L_{1},L_{2})\). Since the variance is associated with a second-order moment, its minimization can involve additional technical challenges.
## 1 Introduction  
Wide energy bandgap materials such as silicon carbide (SiC) and gallium nitride (GaN) are recognized as promising candidates for high-power devices due to a superior inherent property. They cause high critical electric field strength and high drift saturation velocity, which results in a high blocking capability with low on-resistance compared with Si devices. Especially, 4H-SiC trench-gate MOSFETs (TMOSFETs) have the benefit of low on-resistance due to their high cell density and absence of JFET regions [1; 2; 3; 4; 5]. It is possible to create a miniaturization application, such as an electric power converter, due to high-power operation, high frequency, and thermal capability. However, the TMOSFETs suffer reliability issues due to electric field crowding at the gate oxide in the blocking mode. To suppress electric field crowding, various technologies such as bottom protection p-wells (BPWs), bottom thick oxides, double trench structures, and double p-base structures were investigated [6; 7; 8; 9; 10; 11; 12].  
BPW doping concentrations influence the electric field at the gate oxide, the effective thickness of the drift layer, and the parasitic capacitances. As the BPW doping concentration (N\({}_{\text{BPW}}\)) increases, the electric field at the oxide decreases under 4 MV/cm by spreading the trench corner, so that the reliability of the gate oxide can be ensured [13; 14]. However, this can decrease blocking characteristic because the effective thickness of the drift layer decreases when N\({}_{\text{BPW}}\) exceeds the optimal condition. Thus, it is important to determine the optimal Al doping concentration in order to improve electrical characteristics and oxide reliability.  
In previous studies on numerical TCAD simulations to determine the optimal value of the electrical characteristics, the effects of the BPW junction profile on the switching property, specific on-resistance, and blocking voltage were analyzed by varying the BPWprocess conditions, as presented in Refs. [15; 16; 17; 18; 19; 20]. Even though this is useful to obtain the optimal points, considerable computational costs are incurred due to the enormous data set. On the other hand, an analytical model makes it possible to calculate the optimal N\({}_{\text{BPW}}\) value by using Poisson's equation with consideration of the depletion width, effective thickness of the drift layer, and electric field distribution.  
In this study, an analytical model is proposed to investigate and optimize the electrical characteristics of 4H-SiC TMOSFETs, which are compared with numerical TCAD simulations. In addition, the reverse transfer capacitance (C\({}_{\text{gd}}\)) was extracted using an analytical method for comparison with the C\({}_{\text{gd}}\) of the numerical TCAD simulation results.