esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reinforcement Steel Corrosion Model
### Degree of Reinforcement Corrosion  
Generally, the reinforcement corrosion degree is described by the mass loss rate of reinforcement or the cross-sectional area loss rate, and the latter is used to represent the reinforcement corrosion degree in this paper, as shown in Equation (6):  
\[\rho(t)=\frac{A_{s}(t)}{A_{0}}\times 100\%, \tag{6}\]where \(A_{s}(t)\) is the residual cross-sectional area of generally corroded reinforcement at time \(t\), and \(A_{0}\) is the cross-sectional area of reinforcement before corrosion.  
Reinforcement corrosion can be divided into uniform corrosion and pit corrosion according to the shape types of reinforcement section corrosion. The residual diameter of uniformly corroded reinforcement at time \(t\) can be obtained by Equation (7):  
\[D_{u}(t)=D_{0}-2\int_{T_{i}}^{t}r(t)dt. \tag{7}\]  
Thus, the remaining cross-sectional area of uniform corrosion of reinforcement is:  
\[A_{u}(t)=\frac{\pi D_{u}(t)^{2}}{4}=\frac{\pi}{4}\Bigg{[}D_{0}-1.0499\Big{(} \frac{f_{cu,k}+3.445}{f_{cu,k}-13.78}\Big{)}^{1.64}\frac{(t-T_{i})^{0.71}}{C} \Bigg{]}^{2}. \tag{8}\]  
The corrosion depth of pit corrosion of reinforcement is presented as [34]:  
\[p(t)=R\int_{T_{i}}^{t}r(t)dt=2.2362\Big{(}\frac{f_{cu,k}+3.445}{f_{cu,k}-13.78} \Big{)}^{1.64}\frac{(t-T_{i})^{0.71}}{C}, \tag{9}\]  
where \(R\) is the nonuniformity coefficient of pit corrosion, whose value range is [4; 6], and it is adopted as 6 in this paper.  
The residual cross-sectional area of pit corrosion of reinforcement is expressed as [40]:  
\[A_{p}(t)=\left\{\begin{array}{ll}\pi D_{0}^{2}/4-A_{1}-A_{2}&\quad p(t)\leq D _{0}/\sqrt{2}\\ A_{1}-A_{2}&\quad D_{0}/\sqrt{2}{<p(t)<D_{0}}\\ 0&\quad p(t)\geq D_{0}\end{array}\right., \tag{10}\]  
where \(A_{1}\) and \(A_{2}\) are auxiliary surfaces \(A_{1}=\frac{1}{2}\bigg{[}\theta_{1}\big{(}\frac{D_{0}}{2}\big{)}^{2}-b(t) \bigg{|}\frac{D_{0}}{2}-\frac{p(t)^{2}}{D_{0}}\bigg{|}\bigg{]}\), and \(A_{2}=\frac{1}{2}\bigg{[}\theta_{2}p(t)^{2}-b(t)\frac{p(t)^{2}}{D_{0}}\bigg{]} ;\theta_{1}\text{ and }\theta_{2}\) are auxiliary angles \(\theta_{1}=2\text{arcsin}\bigg{[}\frac{b(t)}{D_{0}}\bigg{]}\) and \(\theta_{2}=2\text{arcsin}\bigg{[}\frac{b(t)}{2p(t)}\bigg{]}\); and \(b(t)=2p(t)\sqrt{1-\Big{(}\frac{p(t)}{D_{0}}\Big{)}^{2}}\).  
Reinforced concrete bridges generally have two forms of corrosion simultaneously in practical engineering. Therefore, the residual cross-sectional area of reinforcement under general corrosion conditions is  
\[A_{s}(t)=A_{u}(t)+A_{p}(t)-\pi D_{0}^{2}/4. \tag{11}\]
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Traditional Beam-Hoppping Model
Figure 1: Beam-Hopping working procedure.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 2 Generation of Sensors Data by AERMOD
### Introduction of Data  
The modeling scope is a region of 38 km \(\times\) 18 km which includes all the four factories and all the sensors. A total of 79 sources and 61 sensors are irregularly distributed in this area. The 79 potential sources are detected manually through on-the-spot investigation and the locations of the 61 sensors have been decided by the relevant departments. A random perturbation acting on the input data is experimented with to simulate the differences between the measured values and the real ones. Due to the short distances between the four factories there is some trouble in predicting the leaking sources, the key region occupies an area of 2 km \(\times\) 2 km. In this simulation, the assumption is made as follows:  
* The weather conditions and the state of the leaking source remains unchanged.
* The wind speed is set to more than 0.5 m/s since AERMOD cannot handle the situation that the wind speed is less than 0.5 m/s.
* There is no more than one leakage source leaking during the forward diffusion process.  
Figure 1 shows a schematic diagram of the key region. The light blue points represent the potential leaking sources and tracking these sources is the main task of this issue. The small yellow '+'s represent the sensors distributed in the region. Different from some previous studies, the distribution of the sensors is tailored to the needs of urban residents' living quality rather than to making monitoring data fit more easily. The buildings circled by a dark blue bounding box are taken into consideration during the modeling procedure, including their widths, lengths, and heights.  
The input of AERMOD contains geographic data (digital elevation model), meteorological data, and pollution source data. Several atmospheric features including wind direction, wind speed, temperature, total cloud cover, and low cloud cover help to model the diffusion procedure in AERMOD. The unit of wind direction is degrees, ranging from 10 degrees to 360 degrees, with intervals of 10. The unit of wind speed is meter per second, ranging from 0.5 to 13 m/s (strong breeze), with intervals of 0.5. The unit of temperature is degrees Celsius, ranging from \(-\)5 to 45 \({}^{\circ}\)C, with intervals of 5. Total cloud cover and low cloud cover are treated as constant, with values of 7 and 3, respectively. There are 79 potential leaking sources and 61 sensors in the region of diffusion simulation. Thus, in total, the data table contains 36 (wind directions) \(\times\) 26 (wind speeds) \(\times\) 11 (temperature) = 10,296 rows and 79 (number of potential leaking sources) \(\times\) 61 (number of sensors) = 4819 columns.  
Figure 1: Schematic diagram of the key region. The small yellow ‘+’s indicate the locations of the sensors. The light blue points represent the potential leaking sources.  
Figure 2 is an example of the diffusion result when one of the 79 sources is leaking. The gas concentration is represented in the form of contour lines. The bold character 'X' represents the position of the highest concentration.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Secure Routing Scheme Based on ECC  
In order to adapt to the limited resources in UAV communication network, this paper studies a secure and efficient digital signature scheme based on SAODV routing protocol. Compared with other public key systems such as RSA, DSA, elliptic curve cryptosystem (ECC) can provide better encryption strength, faster execution speed, and shorter key length.
## Materials and Methods  
### Developed Model for WTGSs  
In this study, it is aimed to model the CFSIG based WTGS for unbalanced load flow analysis of distribution power systems. It has a delta configured induction generator directly connected to the bus. The mechanical input power (\(P_{T}\)) of a WTGS can be calculated by regarding the air density (\(\rho\)), the area swept by the rotor (\(A\)), the power coefficient (\(C_{P}\)) and wind speed (\(u\)) [12]:  
\[P_{T}=\frac{1}{2}.\rho\cdot A\cdot u^{3}C_{P}(\lambda). \tag{1}\]  
The power coefficient is subject to the tip speed ratio (\(\lambda\)) expressed as follows:  
\[\lambda=\frac{\omega R}{u}, \tag{2}\]  
where \(\omega\) and \(R\) denote the angular velocity and turbine rotor radius, respectively. To provide the model of WTGS, firstly, the positive- and negative-sequence equivalent circuits of the induction generator shown in Figure 1 are taken into consideration. Figure 1: (**a**) positive-sequence and (**b**) negative sequence equivalent circuits of an induction generator referred to its stator side. In the same figure, \(V_{sp}\), \(V_{sn}\), \(V_{rp}\) and \(V_{m}\) indicate the magnitudes of the positive- and negative-sequence voltages for the stator and referred rotor sides, respectively. For the equivalent circuits, \(R_{sc}\) is the short-circuit equivalent resistance, which is sum of the stator winding (\(R_{s}\)) and referred rotor winding resistance (\(R_{r}\)), and \(X_{sc}\) is the short circuit equivalent reactance, which is sum of the stator winding (\(X_{s}\)) and referred rotor winding reactance (\(X_{r}\)). \(R_{m}\) and \(X_{m}\) stand for the resistance and the magnetizing reactance of the core for the positive-sequence equivalent circuit. On the other hand, by looking at Ref. [13], in the negative-sequence equivalent circuit, \(R_{m}\) and \(X_{m}\) are neglected for the simplification. \(P_{sp}\) and \(P_{sn}\) stand for the positive- and negative-sequence active powers absorbed by the stator. The sum of the positive- and negative-sequence rotor active powers, denoted as \(P_{rp}\) and \(P_{m}\), gives the mechanical input power of the WTGS:  
\[P_{T}=3\cdot\big{(}P_{rp}+P_{m}\big{)}. \tag{3}\]  
It is seen from the equivalent circuits that the induction generator draws the positive- and negative-sequence reactive powers (\(Q_{sp}\) and \(Q_{sn}\)) from the grid. In addition, the positive- and negative-sequence reactive powers (\(Q_{rp}\) and \(Q_{m}\)), which are measured at the rotor (\(r\)) bus, should be nil.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 3 Wind Speed Characteristics
### Wind Speed Fluctuation  
The accuracy of wind speed predictions typically depends on how much the wind is fluctuating. It is advantageous to employ a stable wind speed for forecasting; nevertheless, a wind speed with significant changes indicates that the wind is more non-stationary and has higher needs for the nonlinear capabilities of the prediction model. A site's wind speed fluctuation can be roughly represented by a fluctuating wind speed ratio \(R_{>3\text{m/s}}\) of \(>\)3 m/s. \(R_{>3\text{m/s}}\) can be calculated as  
\[R_{>3\text{m/s}}=\frac{len(diff(\overline{U}_{T})>3\text{m/s})}{len(\overline{ U}_{T})}\times 100\%, \tag{1}\]  
\(diff(\cdot)\) represents difference, \(len(\cdot)\) represents the length of sequence, and \(\overline{U}_{T}\) is the average wind speed on time scale \(T\).  
The \(R_{>3\text{m/s}}\) of the four RSs' wind speed and the WSs' wind speed are depicted in Figure 4. The \(T\) of RS and WS \(R_{>3\text{m/s}}\) are \([1\text{s},60\text{min}]\) and \([5\text{min},60\text{min}]\), respectively. \(R_{>3\text{m/s}}\) of RS displayed an increase-decrease-increase pattern. When the time scale is less than \(10^{2}\) s, the amplitude of the pulsating wind speed of K1066 is the highest, and the HSR station reaches its apex at the \(10^{1}\) s scale. When the time scale is more than \(10^{2}\) s, the RS \(R_{>3\text{m/s}}\) gradually grows until it reaches its maximum at the 60 min scale, with K1005 being the most noticeable. The WS \(R_{>3\text{m/s}}\), unlike the RS \(R_{>3\text{m/s}}\), achieves its peak at the 5 min time scale and exhibits a rebound tendency at the 30 min and 60 min time scales. In general, RSs and WSs function differently in terms of fluctuations. While WSs see the greatest swings during the shortest time periods, wind speed fluctuations at high-speed train stations steadily rise on a minute scale. Therefore, it is advised to anticipate of high-speed rail wind speed using a time scale of \(10^{2}\) s to 5 min.  
Figure 3: Correlations between various RSs and WSs at different time scales.
## 5 Results and Discussion
### Ablation Analysis
#### 5.1.2 Ablation Analysis of CTC Search Space Reduction  
Row 8 and 9 investigate the impact of the decoding block (Section 3.4) separately. The average differences between the proposed R-CTC and the standard original CTC across the four tested benchmarks were:  
\[\Delta_{Decoding}(9-8)=(\overline{\Delta_{F.Acc}}=9.8\pm 1.25,\overline{ \Delta_{mAP}}=6.53\pm 2.29,\overline{\Delta_{Edit}}=4.23\pm 1.58)\]  
Generally, original CTC performed quite competitively at the order level, despite its rather poor performance at frame-level (the finer the evaluation metric applied, the larger the gap grew in the rightmost column of Rows 8 and 9, highlighted as brown background cell color, growing darker as the cell value grows greater), which is consistent with its underlying principle to learn order distributions rather than alignments. The performance improvement was statistically significant in all 12 dataset/measurement combinations tested, indicating that the proposed decoding procedure is effective.  
Training Time  
Figure 7 compares learning efficiency. The non-CTC model converged the fastest but the final loss on validation was the highest. CTC converged slower than non-CTC baseline because of the increased depth in the gradient propagation process. Although the gradient depth was further increased in reduced-CTC compared to the original CTC, its convergence process did not deteriorate but even accelerated, thanks to the significantly reduced search space of potential alignments.
## 2 State of the Art
### Routing Protocols
#### 2.2.1 Topology-Based Routing Protocols
There are other hybrid protocols designed or modified for FANETs, such as the Temporarily Ordered Routing Algorithm (TORA) [38], Rapid-reestablish TORA (RTORA) [39], Hybrid Wireless Mesh Protocol (HWMP) [40], Sharp Hybrid Adaptive Routing Protocol (SHARP) [41], and Hybrid Routing Protocol (HRP) [42].
## 3 Results and Discussion
### Models Performance  
The Table 4 shows the results in terms of the evaluation metrics that were achieved for each of the architectures on the test set. In the following paragraphs, a comparison of the performance of the architectures will be discussed.  
It is easy to see that YOLOv8 obtained the best performance among the architectures evaluated, achieving an mAP of 89.10%. This model also had the highest recall (80.9%), indicating its superior ability to detect a large proportion of the objects present, as well as good o overall performance in correctly detecting layout regions. Its optimized structure combines computational efficiency with good generalization power, which may explain its superior performance.  
Although it has lower recall (0.712) and precision (0.754) values than YOLOv8, YOLOv9 achieves an mAP50 of 0.757, which can still be considered acceptable in certain applications. However, its relatively lower performance suggests that it is less effective at correctly detecting document layout elements than YOLOv8. The model introduced refinements in the anchoring strategy and improvements in feature extraction, but it may not be as optimized for the dataset used.  
The YOLOv10 architecture, although it has the lowest recall (0.495) and the worst mAP50 (0.692), it has the highest precision (0.949), outperforming the other architectures in this metric. This suggests that YOLOv10 is highly selective, resulting in high accuracy at the expense of low sensitivity. This configuration may be unsuitable for detection tasks where it is necessary to identify all relevant objects, since a large number of them are ignored. Structurally, YOLOv10 has been modified to reduce computational costs and improve energy efficiency, which may have had an impact on its detection capacity.  
Finally, YOLOv11 had the second best mAP50 (0.871), almost matching the overall performance of YOLOv8. In addition, this architecture achieved balanced recall (0.801) and precision (0.854) values. This architecture could be a good alternative for applications that require a balance between recall and precision, despite not achieving an mAP50 as high as the YOLOv8. The structural changes in version 11 focused on integrating attention mechanisms and optimizing parameters, which may explain the improvement in Precision over YOLOv8.  
In view of the above, YOLOv8 and YOLOv11 are the most balanced models and are preferable for layout analysis. YOLOv10, despite its high precision, has low recovery capacity, which could be a problem for complete detection. YOLOv9 underperformed, suggesting that its structural modifications may not be ideal for this specific task.
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
## 1 Introduction  
The semiconductor industry is driven by the continuous device scaling of the metal-oxide-semiconductor field-effect transistor (MOSFET) according to Moore's Law. However, with the feature size stepping into the nano-scaled regime, the traditional planar device is confronted with great challenges, such as a weaker electrostatic controllability and larger body leakage. Recently, enormous attention has been paid to gate-all-around silicon nanowire transistors (GAA SNWT) due to their extraordinary electrostatic controllability, and they are considered as one of the most promising candidates in the sub-10 nm technology node.  
However, GAA SNWT still has some issues relating to its performance and process which need to be addressed before it is used in commercial applications. Firstly, the short channel effect (SCE) is a critical factor affecting the device's static power consumption and driving performance. In principle, it can be effectively suppressed by continuously reducing the line width of nanowires. Many research institutions have already done a lot of work in this area [1; 2; 3; 4; 5]. However, some issues still emerge, such as the limitation of the lithography patterning and the geometry variation from process instability [6]. Therefore, it is necessary to suppress SCE by optimizing the structure besides the line width, such as the cross-sectional shape, thereby improving the device performance.  
The second challenge comes from the lack of a feasible method for fabricating three-dimension suspended silicon nanowire with an accurately controlled size and cross-sectional shape [7], which is beneficial to the device variability suppression and mobility enhancement. Meanwhile, this siliconnanowire process flow should be highly compatible with the traditional CMOS technology considering its manufacturability.  
For the first challenge, we proposed a triangular shape to be the optimized cross section for the nanowire and to confirm its advantages in scalability and performance by TCAD simulation. For the second issue, we adopted the anisotropic TMAH wet etching process to form the suspended nanowire channel with a stable, smooth, and precisely-size-controlled inverted triangular cross section shape. The low-temperature mobility characterization was then carried out to evaluate the benefit to the suppression of surface roughness scattering by the self-limited structure.
# Study on Vibration Characteristics of Multi-Beam Structures with Stick and Slip at Joints
## 3 Result Analysis and Discussion
### Validation  
In this section, the accuracy of the model is initially verified through experiments. For a unified quantitative comparison, the connection conditions of the beam structure are designed as bolted fastening connections, while the model presented in this paper can also easily realize the degradation of connection conditions through external force virtual work terms. The experimental system is shown in Figure 2, where DH 1A102E acceleration sensors (resolution factor: 0.002 g, measurement range: \(\pm\)500 g, frequency response (\(\pm\)10%): 1-10,000 Hz, weight: 5.5 g, and axial sensitivity (23 \(\pm\) 5 \({}^{\circ}\)C): 1 mV/m\(\cdot\)s\({}^{-2}\) (9.8 mV/g)) are used to acquire vibration signals, which are then sampled through a DH5922D data acquisition system (frequency response range: DC-100 kHz, input impedance: 10 M\(\Omega\) + 10 M\(\Omega\)) and analyzed using a real-time data measurement and analysis software system (Dong-Hua Test DHDAS). The geometric parameters of the two composite beam structures are shown in Figures 2b and 2c, respectively, and the beams have a thickness of \(H\) = 8 mm and a width of \(B\) = 50 mm. The material density is \(\rho\) = 7850 kg/m\({}^{3}\) and the elastic modulus is \(E\) = 200 GPa. The bolts used are M2 standard threaded, with an equivalent transitional stiffness of 10\({}^{6}\) N/m and a rotational stiffness of 10\({}^{3}\) N\(\cdot\)m/rad. Additionally, in the model used in this paper, the theoretical results converge when the truncation number N exceeds 15, which is from multiple verification analyses. As shown in Table 1, the comparison of the first three natural frequencies under simply supported and clamped boundary conditions shows that the analysis results of the model in this paper are in good agreement with the experimental results, which can verify the accuracy of the model.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & \multicolumn{3}{c}{**Case I**} & \multicolumn{3}{c}{**Case II**} \\ \hline  & & **Experiment** & **Present** & **Error** & **Experiment** & **Present** & **Error** \\ \hline \multirow{3}{*}{Simply supported} & 1 st & 38.156 & 36.736 & 3.8\% & 37.109 & 35.969 & 3.1\% \\  & 2 nd & 154.297 & 155.771 & 1.0\% & 146.484 & 149.145 & 1.8\% \\  & 3 rd & 322.326 & 326.375 & 1.3\% & 332.031 & 333.604 & 0.5\% \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of natural frequencies of two composite beams.  
Figure 2: The experimental test system and two composite beam structures are presented. (**a**) The experimental setup; (**b**) Case I, beam structure dimensions: L\({}_{1}\) = 340 mm, L\({}_{2}\) = 340 mm; (**c**) Case II, beam structure dimensions: L\({}_{1}\) = 425 mm, L\({}_{2}\) = 255 mm.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 5 The Influence of Constellation on Space Environment  
The launch of a large number of constellations will significantly increase the number of space objects, increase the risk of collision between satellites, and have an irreversible impact on the space environment. According to the Delta model [19] developed by the ESA to simulate the evolution of future space objects, considering the effects of the explosion, collision, disintegration and other events as the main sources of debris, it is calculated that the introduced constellation consists of 4452 satellites deployed in phase i and ii of the Starlink constellation. The design life of a single satellite is 5 years, it was first launched in May 2019, and the deployment of all constellations will be complete in 2026. Using the Monte Carlo method [4; 20] to calculate the number of space objects above 10 cm under different conditions in the next 20 years.