esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Tooth Gear Impact-Echo System for Repeated and Rapid Data Acquisition
## 2 Theoretical Background
### Air-Coupled Sensing Technology  
The IE frequency components generated from concrete are measurable in air without touching the surface of the concrete. [PERSON] and [PERSON] theoretically and experimentally demonstrated that such vibration modes including the S1-ZGV mode and flexural mode could be measured by microphone [5]. In a half-space (air-concrete), the vibration modes were leaked into the air and an air-coupled sensor measured the same frequency components.  
To confirm the theoretical background of the air-coupled IE method, wave propagation in the half-space was simulated using the k-wave toolbox in Matlab [20; 21]. A half sine wave simulating a hammer-type impact was applied to the surface of concrete, and the corresponding stress wave field of air and concrete was solved. Forty sensors were continuously placed in air 20 mm above the surface of the concrete, 5 mm apart. The in-plane normal stress was analyzed as the output data. The details of the simulation are shown in Table 1. Figure 1a shows the wave field image of the air-coupled IE test, where the Von Mises stress is shown. From the image, the non-propagating S1-ZGV mode (thickness mode) was generated inside the concrete after the impact source was applied at the center of the concrete slab. In air, the leaky portion of the propagating waves (mainly Rayleigh waves) exhibited much greater velocity compared to the direct acoustics (semi-circle shape) generated by the impactor. Inside the semi-circle, the leaky portion of the thickness mode, measurable using air-coupled sensors, is shown. The signals obtained, as shown in Figure 1b, conveyed that the majority of frequencies were from the thickness mode vibration (S1-ZGV) while the propagating waves (including the leaky waves and direct acoustics) had a much higher amplitude, which were noise components of the air-coupled IE test. In fact, acoustic system noise has been reported during air-coupled sensing [11; 12; 16] so that efforts have been made to carefully measure the appropriate frequencies from the IE testing. Therefore, the impactor was required not to generate system noise itself while propagating leaky waves were quickly damped.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation Parameters** & **Value** & **Simulation Parameters** & **Value** \\ \hline CFL & 0.1 & Type & Half sine wave \\ Number of grids (N\({}_{\text{x}}\)) & 500 & Input source & Range of frequency & 30 kHz \\ Number of grids (N\({}_{\text{y}}\)) & 1000 & & Location & \((290,500)\) \\ \hline Mesh size (dx) & 1 mm & & Number of sensors & 40 \\ Mesh size (dy) & 1 mm & & Spacing & 5 mm \\ \hline Length of grid & Concrete & \(190\times 1000\) mm & & \\ Air & \(310\times 1000\) mm & Concrete & Elastic modulus & 30 GPa \\ Operation time & 1.5 ms & & Density & 2400 kg/m\({}^{3}\) \\ \hline dt & 5 0 ns & Air & Density & 240 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical simulation details.
## 3 Antenna Testing and Result Analysis
### Mode 4 (Both \(D_{1}\) and \(D_{2}\) Are On)  
Mode 4 corresponds to both diodes being in the on state. In this mode, the antenna offers a tri-band mode, where the simulated results show the S11 as being less than \(-10\) dB with impedance bandwidth ranges of 2.55-2.72 GHz, 3.5-3.85 GHz, and 4.99-7.15 GHz, as shown in Figure 10a. The measured results also offer the tri-band mode, where the two narrow bands are observed across the resonance of 2.65 GHz and 3.7 GHz with respective  
Figure 9: Mode 3 performance parameter comparison of (**a**) return loss, (**b**) surface current, and (c) peak gain.  
bandwidths of 2.51-2.7 GHz and 3.47-3.9 GHz. On the other hand, the wideband ranges from 5.05 GHz to 7 GHz, as depicted in Figure 9(a). The current distribution graphs are also presented for both lower band resonances of 2.65 GHz and 3.7 GHz, as shown in Figure 9(b). It is clear from the current distribution analysis that the middle band of 3.7 GHz is generated due to the inverted L-shaped stub, as most of the current is observed across it. Meanwhile, the lower resonance of 2.65 GHz is due to the inner meandered line stub, which corresponds to a larger electrical size, as depicted in Figure 9(b).  
Surface current distributions of the antenna working in mode 4 are depicted in Figure 9(c). For all of the selected frequencies of 2.65 GHz, 3.7 GHz, and 5.8 GHz, the antenna offers a monopole-like dual-beam radiation pattern in the principal E-plane (phi = 0\({}^{\circ}\)). On the other hand, for the principal H-plane (phi = 90\({}^{\circ}\)), the antenna offers a nearly omni-directional pattern. Moreover, in all cases, a good comparison among simulated and measured results is observed, with a slight deviation being due to the presence of biasing wires and other biasing components. Furthermore, the antenna offers the measured peak gain of 1.83 dBi at 2.65 GHz, while for 3.7 GHz, the peak gain of 2.11 dBi is observed along with gain of more than 2.5 dBi for the wideband range. Mode 4 covers the globally allocated band spectrum for LTE-Band-7, 5g-sub-6 GHz, C-band, ISM-upper-band, Wi-Fi-6E, and weather radar system applications.  
For all diode switching modes, the strong comparison among simulated and measured results validates the performance stability as well as the significance of the work. Moreover, the switching option among the wideband, dual-band, and tri-band modes, while covering  
Figure 10: Mode 4 performance parameter comparison of (**a**) return loss, (**b**) surface current, and (c) peak gain.  
the globally allocated band spectrum for various applications, raises the potential of the proposed antenna to be used in present and future compact smart electronics. Moreover, the simplified structure will minimize the fabrication tolerance while the measurement using the whole biasing structure strengthens its potential for practical application where multi-mode frequency reconfigurability is required.
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 1 Introduction
According to the literature cited above, the most research carried out mainly revolves around regulating the speed of conveyor belts, e.g., [4; 5; 10; 20], ensuring the quality [1; 11], measuring the quantity [4; 12; 14], tracking the movement [7] or size of objects [17; 19; 25] etc., However, it's important to note that many existing methods depend on the use of high-end devices, including stereo cameras, lasers, speed monitors, and charged-coupled devices, to carry out the necessary experimentation. Dependency on additional devices limits the usage of a solution, given the aspects of availability and affordability of components used. Moreover, the environmental factors also add to the usability of the solution. Through this research, our primary objective is to address a practical challenge encountered by one of the Soda-Ash production plants: specifically, the accurate determination of the flow rate of a mixture on an outdoor conveyor belt. Building upon insights from existing literature, our approach involves automating this process through the application of Computer Visiontechniques. By doing so, we aim to achieve several significant advantages, including the ability to conduct experiments more frequently, the elimination of the need for human intervention, and a reduction in the likelihood of errors, ultimately enhancing overall efficiency. Furthermore, our plan is to implement this solution utilizing an easily accessible and simple RGB camera, thereby ensuring a cost-effective and broadly applicable approach. The rest of the paper is organized as follows. Section 2 explains the whole industrial process and how the data is captured for experimental evaluation. The proposed framework is presented under Methodology in Section 3, which is followed by the Results & Discussion Section 4. Lastly, the Conclusion Section 5 wraps it up by summarizing the findings.
###### Abstract  
This research presents a geometric analysis of Sun-assisted low-energy lunar transfers and several convenient tools that enable the systematic trajectory design in the framework of the planar bicircular restricted four-body problem. By analogy with the patched conic approximation approach for high-energy transfers, a Sun-assisted low-energy trajectory is divided into three legs. Two interior legs, departing and arriving, are located inside the Earth-Moon region of prevalence and designed in the Earth-Moon circular restricted three-body problem, whereas the exterior leg lies outside the region of prevalence and is calculated in the Earth-Moon-Sun bicircular restricted four-body model. The whole trajectory is obtained by smoothly patching the three legs on the boundary of the region of prevalence. The arrival conditions are met by targeting a specific point in the \(L_{2}\) lunar gateway. The interior legs are easily adjustable to the four-body dynamics. The database of planar lunar transfer trajectories can be used to select an initial guess for the multiple-shooting procedure of designing a three-dimensional Sun-assisted lunar transfer in high-fidelity dynamical models.  
low-energy transfer; weak stability boundary transfer; ballistic lunar transfer; bicircular restricted four-body problem; circular restricted three-body problem; region of prevalence +
Footnote †: journal: Applied  
Acticle
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
## 2 Circuit Design
### Absorptive SPST Switch Design  
In many applications, reflective switches are not suitable due to the significant standing waves introduced between components [10]. In this MFC, an absorptive SPST switch has been designed to control array calibration of channel; the structure is shown in Figure 2. In this design, when V\({}_{\text{T}}\) is 0 V, the SPST switch is in the on-state. A two-stage series-shunt structure is adopted to achieve high isolation performance. When V\({}_{\text{T}}\) is \(-3\) V, the SPST switch is in the off-state. Two 50 \(\Omega\) absorptive resistors are used to decrease the return loss. A 45 dB isolation and a 19 dB return loss have been achieved in these two ways. The parameters of SPST switch design are shown in Table 1.  
Figure 1: The diagram of the actual application.
## 6 Simulation Results
### Convergence Analysis  
The convergence performance of MAPPO and IPPO with different network architectures is compared in the subsection. The learning curves which represent the episode rewards, average observation rate \(\alpha\) and average exploration rate \(\beta\) with respect to training timesteps are respectively depicted in Figure 4. The algorithms using basic network architectures are referred to as MAPPO and IPPO, while those utilizing network with GRU module are labeled as rMAPPO and rIPPO. We train each method for five times with \(10^{5}\) timesteps. The mean is represented by dark lines and the standard deviation over five different random seeds is represented by the shaded area. The results of Figure 4 are obtained from the cooperative search mission of 5 UAVs for 7 moving targets in an area of 20 \(\times\) 20 cells. And all the hyperparameters used in training process are listed in Table 3.  
As depicted in Figure 4a, MAPPO and IPPO have shown better performance. The episode rewards obtained by MAPPO and IPPO converge to about 110, while rMAPPO and rIPPO converge to about 80. Moreover, we can see that the convergence of MAPPO and rMAPPO is faster and more stable compared to IPPO and rIPPO. As depicted in Figure 4b, the average observation rate \(\alpha\) of MAPPO and IPPO converge to about 0.28, while rMAPPO and rIPPO converge to about 0.27. In Figure 4c, the average exploration rate \(\beta\) of MAPPO and IPPO converge to about 0.81, while rMAPPO and rIPPO converge to about 0.75.
## 5 Time Difference and Source Location  
Figure 10 shows the time difference between each SA and AE sensor during the PLB test. The data is sampled with a sampling frequency of 10 MHz. The point of PLB is the center of the plate, thus the time difference of each SA and AE sensor ideally becomes zero. From this result, the deviation of time difference shown in the AE sensor result is smaller than the SA result. However, with regard to the velocity of the s\({}_{0}\) and a\({}_{0}\) modes of about 5000 m/s and 3300 m/s respectively, 1 \(\upmu\)s time difference could simply reflect a 2.5 mm or 1.6 mm location error. This location error is only 0.8% and 0.5% length of the actual sensor distance. From these result, the SA sensor has enough potential to be used as the sensor for source location of a PLB test.  
Figure 8: Waveform of SA sensor (**a**) ch.1 and (**b**) ch.2.  
Figure 9: Wavelet transform of SA sensor (**a**) ch.1 and (**b**) ch.2.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface  
The \"ICCore\" silicon photonics micro-transceiver chip operates in the 1310-nanometer wavelength regime (O-band) with on-off keying (OOK) modulation data rates per lane of 25, 32 and 50 Gbps. An IOCore chip incorporates four optical transmission and four optical receiver channels under a proprietary multimode optical waveguide or \"optical pin\" array interface [1] on a 5 \(\times\) 5 mm footprint (Figure 3).  
The IOCore transceiver serves as a transceiver building block that can be assembled into a variety of different standard or bespoke packages to address diverse high-volume, high-temperature and space-restricted applications including co-packaged optics in 5G/6G, hyperscale data centers, HPCs and automotive systems.  
We will now introduce the overall structure of the IOCore transceiver, before going on to describe its individual components in more detail.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
###### Abstract  
Software Defined Networking (SDN) has proved itself to be a backbone in the new network design and is quickly becoming an industry standard. The idea of separation of control plane and data plane is the key concept behind SDN. SDN not only allows us to program and monitor our networks but it also helps in mitigating some key network problems. Distributed denial of service (DDoS) attack is among them. In this paper we propose a collaborative DDoS attack mitigation scheme using SDN. We design a secure controller-to-controller (C-to-C) protocol that allows SDN-controllers lying in different autonomous systems (AS) to securely communicate and transfer attack information with each other. This enables efficient notification along the path of an ongoing attack and effective filtering of traffic near the source of attack, thus saving valuable time and network resources. We also introduced three different deployment approaches i.e., linear, central and mesh in our testbed. Based on the experimental results we demonstrate that our SDN based collaborative scheme is fast and reliable in efficiently mitigating DDoS attacks in real time with very small computational footprints.  
DDoS; SDN; Software Defined Networking; Software Defined Security; DDoS mitigation 2018 10.3390/610030023
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 6 Results and Discussion
### Mesh Quality Improvement by Geometry Simplification
Considering the nonlinear relationship between the mesh size and quality, the square term of the mesh size (\(s^{2}\)) was added as an independent variable. In the case of maximum non-orthogonality, the effect of the characteristic length was not statistically significant (\(p\)-value = 0.350), and it was excluded from the analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Multiple R\({}^{2}\)** & **Adjusted R\({}^{2}\)** & **F-Statistic** & \(p\)**-Value** \\ \hline
0.3806 & 0.3784 & 169.7 & \textless{}2.2e-16 \\ \hline
**Variable** & **Coefficient** & **Standard Error** & \(p\)**-Value** \\ \hline (Intercept) & 0.706601 & 0.014671 & \textless{}2e-16 \\ \(d_{threshold}/L_{C0}\) & 0.012601 & 0.001138 & \textless{}2e-16 \\ \(s\) & \(-0.174158\) & 0.012044 & \textless{}2e-16 \\ \(s^{2}\) & 0.030040 & 0.002733 & \textless{}2e-16 \\ \(C_{ITC0}\) & 0.320294 & 0.026096 & \textless{}2e-16 \\ \(L_{C0}\) & 0.234317 & 0.016826 & \textless{}2e-16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression analysis of hexahedron cell ratio. Figure 10: Mesh quality according to simplification threshold and mesh size. All of the linear regression models were evaluated as being statistically significant (\(p\)-value \(<2.2\times 10^{-16}\)). For all the metrics, the mesh quality improved with the increasing simplification threshold (\(d_{threshold}/L_{\text{CO}}\)) and the geometry characteristic metrics (\(C_{ITC0}\) and \(L_{\text{CO}}\))). Considering that the adjusted R\({}^{2}\) of all linear models was less than 0.4, it is estimated that some factors that affected the mesh quality were not considered in this study. However, considering the statistical significance of the individual variables, there is an effect in improving the mesh quality for FVM analysis with the geometry simplification process proposed in this study. In particular, the maximum skewness decreased exponentially according to the distance threshold (\(e=\exp\left(3.02-0.118(d_{threshold}/L_{\text{CO}})-1.20s+0.213s^{2}-4.64C_{ITC0}-0.720L_{ \text{CO}}\right)\)), and the corresponding quality improvement effect was expected to be high. From the regression models, it is expected that the mesh quality can be predicted, and an appropriate simplification threshold (\(d_{threshold}\)) can be selected before designing a building FVM simulation model.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 4 Experiments and Results
### Experimental Settings  
**Dataset.** We used the Nyu-depth v2 dataset [29] as our training images. Nyu-depth V2 dataset consists of 1449 pairs of indoor color images with dense markers of ground truth depth information, with sizes of \(640\times 480\). For each image in the training set, we extract \(40\times 40\) patches with a stride number of 30, resulting in 466,435 training patches generated in total.  
We used three test sets, namely, O-HAZE [30], I-HAZE [31] and SOTS (Synthesis Object Testing Set). I-HAZE and O-HAZE contain 35 and 45 pairs of images consisting of hazy images and clear images (ground truth), where the smoke is produced by a professional haze-generation machine, and SOTS belongs to RESIDE [32], a synthetic outdoor dataset, setting the atmospheric light a of each channel between [0.7, 1.0], and uniformly randomly selecting the beta between [0.6, 1.8].  
**Training details.** We adopted the ADAM optimizer with a batch size of 1. The learning rate was set to 0.0002 and the exponential decay rates were (\(\beta 1\), \(\beta 2\)) = (0.5, 0.999). We took \(\mu\) as 0.001. We implemented our method with PyTorch framework and nvidia 1080 Ti GPU on the ubuntu 16.04 system and used the PyCharm software.  
**The guided filtering layer.** The smoothing filter radii were set to {2, 4, 8, 16, 32}, and the regularization coefficients were set to {0.001, 0.0001}.
## 2 Materials and Methods
### Forecasting Error  
Forecasting errors are used to quantify the accuracy of prediction. The mean absolute percentage error (MAPE) is frequently used in reporting the accuracy of electrical load forecasts [43]. MAPE quantifies the discrepancy between the real observed value and the forecasted value and is calculated as follows  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Y_{i}-\hat{Y}_{i}}{Y_{i}}\right| \times 100 \tag{7}\]  
where \(n\) is the number of observations, and \(Y_{i}\) and \(\hat{Y}_{i}\) are the observed and predicted values for the \(i\)th observation. The MAPE value reveals the average percentage error for all observations. A good prediction is reflected in a low MAPE value.
## 2 Materials and Methods
### Heart Rate Variability (HRV)
#### Resting Muscle Oxygen Consumption Data  
1. \(\mathrm{SmO_{2}}\) Dynamics: The analysis of \(\mathrm{SmO_{2}}\) dynamics involved examining the following curves: desaturation (the difference between \(\mathrm{SmO_{2}}\) at the beginning and end of occlusion), resaturation (the difference between \(\mathrm{SmO_{2}}\) stabilized at the end of recovery and \(\mathrm{SmO_{2}}\) at the end of occlusion), and reactive hyperemia (the area under the \(\mathrm{SmO_{2}}\) curve from the end of recovery to the beginning of occlusion) [6; 13; 14; 15] (See Figure 2); 2. Reoyxgenation kinetics: The post-occlusion reoxygenation kinetics were derived from oxyhemoglobin [oxy (Hb+Mb)] values taken at 1 s intervals and plotted against time [17; 34; 35]. The curve was fitted to an exponential model using the least squares method to describe the oxyhemoglobin response following arterial occlusion. The equation used was as follows: \[\mathrm{Y(t)=Y0+A\times(1-e^{-(t-TD)/\tau})}\] where \(\mathrm{Y0}\) represents the baseline value (i.e., the last 20 s before the end of the 3 min arterial occlusion), \(\mathrm{A}\) is the amplitude of the oxy[Hb+Mb] response, TD is the time delay immediately after the occlusion is released, and \(\tau\) (tau) is the time constant associated with the slope recovery.  
This fitting was performed using a combined filtering and correlation method [36; 37]. The point at which the correlation reached its maximum was considered the starting point of the mono-exponential curve, while the endpoint of the mono-exponential curve was determined by identifying the highest value. Finally, the mean response time (MRT) was calculated as the sum of TD and \(\tau\) constant. The goodness of fit (R\({}^{2}\); coefficient of determination) was deemed satisfactory if it exceeded 0.85 [36]. All curve fitting and reoxygenation kinetics data analysis was performed using SigmaPlot 10 and SigmaStat 3.5 software (Systat Software Inc., San Jose, CA, USA).
## 5 Analysis of Computational Complexity
### Implications for Underwater Networks  
* **Energy Efficiency**: DAWPC-MAC's low complexity reduces energy consumption, critical for battery-operated nodes.
* **Scalability**: DAWPC-MAC's \(O(n^{1.2})\) complexity ensures efficient performance in large-scale networks.
* **Latency and Reliability**: DAWPC-MAC's adaptive mechanisms reduce latency and improve reliability, making it suitable for time-sensitive applications.  
Summarizing, the proposed **DAWPC-MAC** protocol outperforms the benchmark techniques in computational complexity, scalability, and energy efficiency. Its use of Deep  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Protocol** & **Complexity Order** & **Scalability** & **Key Strengths** \\ \hline DAWPC-MAC & \(O(n^{1.2})\) & Excellent & Adaptive, energy-efficient \\ TDTSPC-MAC & \(O(n^{2})\) & Poor & Time synchronization \\ DC-MAC & \(O(n^{1.8})\) & Moderate & Game-theoretic optimization \\ UW-ALOHA-QM & \(O(n^{1.9})\) & Moderate & Reinforcement learning \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of MAC Protocols.  
Figure 15: Computational complexity of the proposed DAWPC-MAC protocol with benchmark techniques: TDTSPC-MAC, DC-MAC, and UW-ALOHA-QM.  
Q-Learning enables adaptive and intelligent decision-making, making it highly suitable for dynamic and large-scale underwater networks. In contrast, the benchmark protocols exhibit higher complexity and are less scalable, limiting their effectiveness in real-world applications.