esa-sceva/satcom-chunk-collection huggingface dataset:
# Airwave Noise Identification from Seismic Data Using YOLOv5
###### Abstract  
Airwave interference presents a major source of noise in seismic exploration, posing significant challenges to the quality control of raw seismic data. With the increasing data volume in 3D seismic exploration, manual identification methods fall short of meeting the demands of high-density 3D seismic surveys. This study employs the YOLOv5 model, a widely used tool in object detection, to achieve rapid identification of airway noise in seismic profiles. Initially, the model was pre-trained on the COCO dataset--a large-scale dataset designed for object detection--and subsequently fine-tuned using a training set specifically labeled for airway noise data. The fine-tuned model achieved an accuracy and recall rate of approximately 85% on the test dataset, successfully identifying not only the presence of noise but also its location, confidence levels, and range. To evaluate the model's effectiveness, we applied the YOLOv5 model trained on 2D data to seismic records from two regions: 2D seismic data from Ningqiang, Shanxi, and 3D seismic data from Xiushui, Sichuan. The overall prediction accuracy in both regions exceeded 90%, with the accuracy and recall rates for airway noise surpassing 83% and 90%, respectively. The evaluation time for single-shot 3D seismic data (over 8000 traces) was less than 2 s, highlighting the model's exceptional transferability, generalization ability, and efficiency. These results demonstrate that the YOLOv5 model is highly effective for detecting airway noise in raw seismic data across different regions, marking the first successful attempt at computer recognition of airwaves in seismic exploration.  
seismic acquisition; deep learning; quality control; single-trace classification +
Footnote †: journal: applied sciences
## 3 Method  
The main framework for calculating the HVR is shown in Figure 2. The detailed steps are as follows:  
1. Pre-processing of the Luojia-1 and POI data. Geometric correction, radiation correction, and mask processing were used to preprocess the Luojia-1 data, and kernel density analysis was performed to obtain the same resolution as the Luojia-1.
2. Extraction of built-up areas. The LJ&POI index was constructed by using the treated Luojia-1 and POI data, and then the threshold was determined according to the method of Li [36] to extract the built-up area of the main urban area of Xi'an city.
3. Estimation HVR. It was necessary to remove the influence of roads and nonresidential areas on the basis of built-up areas to obtain the actual DN value of residential areas because the main research object was residential areas. The DN value in the case of a full residential area was determined. The result was calculated from the ratio of the DN value between a residential area and a full residential area.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Calculating the Node Location
#### 3.4.2 The Calculation Process of Localization  
The equation of the trajectory can be expressed as  
\[y=k\times x+b \tag{44}\]  
\(k\) and \(b\) can be obtained by bringing the coordinates of two broadcast points into Equation (44). The coordinates of point \(C\) can be computed by solving  
\[\begin{cases}d_{BC}=\dfrac{|k\times x_{C}-y_{C}+b|}{\sqrt{k^{2}+1}}\\ \dfrac{y_{C}-y_{B}}{x_{C}-x_{B}}=-\dfrac{1}{k}\end{cases} \tag{45}\]  
However, solving Equation (45) will get two candidate positions, as shown in Figure 10. To determine the true position of point C, when the anchor node reaches the end of the trajectory, it continues to travel a distance along the right perpendicular line of the trajectory. If RSSI values received by the node become larger relative to the RSSI value of the end point, point C in Figure 10 is the true position. If RSSI values received by the node become smaller relative to the RSSI value of the end point, then point \(C^{{}^{\prime}}\) in Figure 10 is the true position. After obtaining the true coordinates of point C, the coordinates of point A can be determined as \((x_{C},y_{C},z_{A})\).
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 2 Development Approach: Design Goals, Materials, and Methods
### Statistical Analysis of Experimental Data  
To ensure statistical reliability and minimize experimental uncertainty, we repeated each measurement ten times under identical conditions. Moreover, we conducted all experiments within an anechoic chamber to eliminate external electromagnetic interference and multipath reflections, thereby ensuring a controlled and reproducible environment. The arithmetic mean of the ten repeated measurements was computed and reported as the representative value for each data point. To quantify the measurement variability, we calculated the standard deviation and depicted it as error bars in the corresponding figures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Real Part of the Relative Permititivity**} & **Electrical Conductivity** & **Density** \\  & **(S/m)** & **(kg/m\({}^{3}\))** \\ \hline
2.8687 & 0.004926 & 700 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electromagnetic parameters of the substrate numerical model.  
The observed standard deviations were consistently low across all measurement points, confirming excellent reproducibility.
## 2 Problem Description  
### Routing Problem in the LEO Network  
The routing problem in LEO satellite networks refers to the series of problem of determining the path for each satellite to transmit data to the other satellites in a network consisting of multiple satellites. In this paper, Markov decision processes (MDP) are used to represent this problem mathematically, in particular, grid Markov decision processes [21]. The advantage of using a Grid MDP is that the underlying theory of reinforcement learning can be easily expressed mathematically. In addition, since the state space is organized in the form of a grid, the computation of state transitions and rewards is simpler than in a general MDP. In this paper, a deep reinforcement learning-based routing algorithm that can operate in the Grid MDP environment is used.  
The Grid MDP is based on an assumption called the first-order Markov assumption, which states that the probability of a state at time n is only affected by the state at the previous time, n - 1. In a Grid MDP, the problem can be modeled in four terms: state, action, reward, and state transition probability. Here, state represents the location of each satellite and the data in the packet stored on that satellite, and action refers to the direction in which the satellite sends data. The reward is the score obtained for sending data in a certain direction, and the state transition probability represents the change in position due to packet transmission.  
Using a reinforcement learning algorithm and applying it to a grid MDP eliminates the need to define a global state. In a conventional grid MDP environment, the state, actions, rewards, and state transition probabilities must be modeled in advance to be applied successfully; however, with reinforcement learning, they can be calculated and derived directly without any prior modeling [22; 23]. In addition, information about states and rewards is collected in real time through learning, and based on this, the reinforcement learning algorithm helps determine the optimal routing path. Due to the characteristics of reinforcement learning, the initial performance is inferior, because the training data do not exist; however, the performance improves as the training is repeated. In summary, an MDP allows the learning processor to use dynamic programming algorithms such as value iteration or policy iteration to find the optimal value function or policy, and reinforcement learning allows the agent to gain direct experience through state observation and reward to learn the value function or policy. In conclusion, applying a reinforcement learning-based algorithm to low-orbit satellite network routing will require additional time due to the learning time required, but it will eliminate the modeling process of predefining the entire state. A Grid MDP can be used to find a robust policy, and reinforcement learning can be used to improve the results through learning, although the initial results may be unstable; so, it is suitable as a routing algorithm in this environment due to its high adaptability to the dynamically changing satellite network environment.
## 7 Risk Analysis
#### 7.1.1. Ecu  
In both architectural frameworks, the ECUs represent tangible physical electronic components, often configured as systems-on-chips (SoCs). These components are inherently susceptible to a range of security threats since they become integral parts of printed circuit boards and are in charge of centralizing the data processing.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 1 Introduction
By analyzing abnormal beam operation scenarios for various ions, including uranium and oxygen, as well as the melting time of accelerator components like cavities and vacuum chambers, the _FPS_ requirements can be accurately determined [10]. In most cases, a response time of approximately 50 us is sufficient to prevent beam damage on the beam line components, considering the incident beam's grazing angle. Moreover, the melting time of beam line components made of materials like SUS304 for vacuum chambers or niobium for superconducting cavities has been calculated as ~46 us and ~69 us, respectively, when it is hit by a uranium beam energy of 18.9 MeV/u in a right angle, which is the fastest loss case, providing crucial insights into protection time requirements. The _FPS_ and _SIS_ operate at microsecond and a millisecond scales, respectively. Abnormal signals from most of the collected devices go through the _SIS_ to reach the _FPS_, and some of the other collected devices directly send their abnormal signals to _FPS_. Upon receiving abnormal signals, the _MPS_ must fully execute the entire process within 50 us to protect the accelerator from sudden beam losses by alerting the mitigation devices, such as _LEBT_ chopper and _RFQ_ amp to shut down the beam. Equations (1)-(3) show the calculation of beam shut-off latency using the parameters used for the equation, as presented in Table 1.  
\[T_{Diag}+T_{FPS}+T_{MD}\ \leq 50\ \mu sec, \tag{1}\]  
\[T_{Diag}=(T_{BPM}\ \text{or}\ T_{BLM}\ \text{or}\ T_{BCM})+T_{DFN}, \tag{2}\]  
\[T_{FPS}=\sum_{n=1}^{7}\bigl{(}T_{L_{n}N_{n}}+T_{N_{n}N_{n-1}}\bigr{)}+T_{MN} \tag{3}\]  
In summary, the _FPS_ and _SIS_ play vital roles in protecting the accelerator from sudden beam losses. The _FPS_ operates at the microsecond scale and ensures swift response times for abnormal signals, while the _SIS_ operates at the millisecond scale, facilitating the transmission of abnormal signals to the _FPS_. To effectively protect the accelerator, the entire _MPS_ process, including signal detection and mitigation device activation, must be completed within a specified time frame. In this article, we present a detailed account of the design, development, and testing of the _RAON_-optimized custom _FPS_, utilizing the Xilinx ZYNQ system-on-chip (_SoC_), in order to meet the fast shut-off requirement within 20 \(\upmu\)s.
# Mercury Uptake and Transport by Plants in Aquatic Environments: A Meta-Analysis
## 3 Results and Discussion
### BAF of Hg in Roots  
The root BAF of the plants was divided into two categories, which corresponded to Hg uptake from water and that from sediments. Based on the existing data, the effect sizes (In BAF) of plants belonging to the same family were combined for water (13 families, \(n=62\)) and sediment (14 families, \(n=61\)) to independently calculate the mean and 95% CI values. The total effect size of the plants for Hg absorption from water via the roots (mean = 4.999, CI: 4.186-5.772) and the average effect size of the plants in each family were significantly greater than zero. Additionally, the effect size of the Symodoeaceae root system was the highest (mean = 8.709, CI: 8.389-9.028), whereas among the 13 families in the water uptake category, only Onagraceae and Plantaginaceae roots did not significantly accumulate Hg from the water (Figure 3a). Regarding Hg absorption from sediments, the total effect size of the plants was less than zero (mean = \(-\)0.591, CI: \(-\)1.248-0.014), and only the average effect sizes of Symodoeaceae (mean = 0.556, CI: 0.201-0.916), Hydrodraitaceae (mean = 0.622, CI: 0.550-0.693), and Posidoniaceae (mean = 0.372, CI: 0.182-0.560) were greater than zero, indicating that these families significantly absorbed Hg from sediments (Figure 3b).  
Figure 2: Biological concentration factors (BCFs) of plants based on their families, presented as the mean effect size and 95% CI. The numerals after the family names represent the number of observations. “Mean” indicates the overall effect considering all families.  
In this analysis, R (In BAF) represents the ability of plant roots to absorb Hg from the aquatic environment. During the process of plant growth, root exudates (e.g., macromolecular compound iron carriers, complexin, low-molecular-weight compounds, H2 CO3, and organic acids) affect the solubility and bioavailability of heavy metal ions, owing to their influence on rhizosphere acidification, precipitation, chelation, and redox reactions [61]. Furthermore, owing to natural selection, plants that grow in polluted waters show a certain level of tolerance toward heavy metals in the environment. As the plant organ that is primarily responsible for nutrient absorption, the roots play an important role in the absorption and enrichment of heavy metals (Figure 3a). However, numerous heavy metals accumulate in sediments via precipitation and absorption [62]. Therefore, this analysis can only be used as a reference to compare the Hg uptake ability of different plant roots from the same environment.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 4 Experimental Results and Discussion
### Experimental Results  
The measurements are stored at 50 Hz, but we resampled it to 5 Hz to estimate the location. Considering the speed of a typical vehicle in the tunnel and the spatial resolution of the magnetic field, it was possible to accurately estimate the vehicle's position even  
Figure 12: Attitude estimation results of all scenarios. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4.  
Figure 13: Vehicle speed of all scenarios. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4.  
using a measurement value of 5 Hz. The core objective of this study is position estimation by accumulating the magnetic sensor value for a certain period of time, and thereafter comparing it with the DB.  
The magnetic field values obtained in the fourth scenario are presented in Figure 14. Blue lines represent the magnetic fingerprint DB values. Red lines in Figure 14a,c,e present the x, y, and x axes, respectively. In the fourth scenario, since the attitude of the smartphone is different from the DB frame, it can be seen that the magnetic field pattern of user data is also quite different with that of DB data like Figure 14a,c,e. Figure 14b,d,f show user magnetic field data after axis transformation and calibration. Finally, the user data patterns have been changed very similarly to the DB.  
Figure 15 shows the localization error of proposed system in the entire tunnel for fourth scenario. The magnetic field data was accumulated in the user buffer for 25 s. As a result of various experiments, it was confirmed that stable results were obtained when the magnetic sensor data was accumulated for more than 15 s. Also, through the results of scenario 4, the position estimation results validated that both the axis transformation and calibration of the smartphone were conducted adequately. An analysis of the overall positioning error is presented in Table 2. Table 2 presents the mean, root mean square  
Figure 14: User data before and after applying axes transformation and calibration for scenario 4. Blue lines and red lines indicate the magnetic fingerprint DB data and user magnetic field data, respectively. (**a**) Before calibration of x axis. (**b**) After calibration of x axis. (**c**) Before calibration of y axis. (**d**) After calibration of y axis. (**e**) Before calibration of z axis. (**f**) After calibration of z axis.  
The computational time for position calculation is approximately 10-ms. The experimental data were analyzed on a personal computer with an Intel Core i7-1165G7 processor. Considering the amount of computation of the proposed system, the proposed method could effortlessly estimate the position in real time.
## 2 Methodology  
The task addressed in this study is depicted in Figure 1. Based on this task, the framework of the proposed MAE model is illustrated in Figure 2. It consists of three core components: the AdaptiveWeight Module, the SFT Module, and the HTFLoss. In the model, sEMG signals and joint angles are first downsampled and preprocessed before being used as inputs. The inputs are then processed through a multi-branch approach, where the prepared sEMG signals, joint angles, and their combination are directed to the AdaptiveWeight Module for weight extraction. Subsequently, weights are assigned to each branch, and temporal features are extracted by the SFT Module, followed by a weighted averaging and concatenation. Finally, during the training phase, the output predictions are optimized through the HTFLoss. Each module is described in detail below.
## 3 Results
### Influence of Process Conditions on Adsorption Performance of rGOA-1  
Inlet concentration and bed temperature are two important process parameters affecting adsorption in industrial applications [49]. Figure 8 demonstrates the parameter \(t_{\text{B,th}}\) as a function of \(C_{\text{in}}\). The curve exhibits an exponentially descending tendency, illustrating that a low inlet concentration is beneficial to the L2 adsorption. It is likely that these could be owing to the increased adsorption rate at higher initial concentration. Empirical Equation (3) obtained by regression analysis of experimental data can describe the trend of the curve, with the correlation coefficient values (\(R^{2}\)) greater than 0.999. This provides a theoretical reference for industrial applications of L2 adsorption.  
\[t_{\text{B,th}} =20.942\times\text{e}^{(-\frac{C_{\text{in}}}{15.225})}+1.378 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Absorbent**} & \multicolumn{3}{c}{**Experimental**} & \multicolumn{3}{c}{**Model**} \\ \cline{2-9}  & \(t_{\text{B}}\) & \(Q_{\text{B}}\) & \(t_{\text{B,th}}\) & \(Q_{\text{B,th}}\) & \(K_{\text{YN}}\) & \(\tau\) & \(R^{2}\) \\  & **(min)** & **(mg g\({}^{-1}\))** & **(min)** & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(min)** & **(min)** & **(\(R^{2}\))** \\ \hline IGGO & 1.16 & 8.1 & 0.89 & 7.1 & 3.0263 & 1.86 & 0.9901 \\ rGOA-0 & 1.45 & 10.6 & 1.20 & 9.5 & 2.2160 & 2.53 & 0.9942 \\ rGOA-0.5 & 7.96 & 63.6 & 8.06 & 64.8 & 1.6900 & 9.80 & 0.9969 \\ rGOA-1 & 9.82 & 80.3 & 9.66 & 77.7 & 2.2066 & 10.99 & 0.9985 \\ rGOA-2 & 7.18 & 57.9 & 7.17 & 57.6 & 1.8042 & 8.80 & 0.9995 \\ \hline \hline \end{tabular}  
* \(m\approx 0.10\) g, \(C_{\text{in}}=14.62\) mg L\({}^{-1}\), \(V_{g}=50\) mL min\({}^{-1}\).  
\end{table}
Table 2: Adsorption parameters of different adsorbents for L2 \({}^{\text{a}}\).  
Figure 7: Breakthrough curves of different adsorbents for L2.  
Figure 9 shows the adsorption breakthrough curves for rGOA-1 at different bed temperature (0-35 \({}^{\circ}\)C) by the Yoon-Nelson model, with \(m\) of 0.10 g, \(C_{\rm in}\) of 14.62 mg L\({}^{-1}\), and \(V_{\rm g}\) of 50 mL min\({}^{-1}\), respectively. In addition, the calculated parameters (\(K_{\rm YN}\), \(\tau\)) by the Yoon-Nelson model \(t_{\rm B,th}\) and \(Q_{\rm B,th}\) are listed in Table 3. The bed temperature decreases while the breakthrough time and adsorption capacity increase, which could be explained by the hydrophobic interaction between the rGOA-1 surface and L2 molecules and the exothermic micropore-filling adsorption of the rGOA-1 for L2 adsorbate [21,49].
## 3 Results
### State-of-the-Art CNN Models for Automatic Screening of Athletes Osteopenia and Osteoporosis
#### 3.1.3 EfficientNet  
EfficientNet is a family of deep neural network architectures. These architectures are constructed using a compound scaling method that simultaneously scales up the depth, width, and resolution of the network. Among the variants of EfficientNet, EfficientNet-B0 is the smallest, possessing fewer parameters and lower computational complexity compared to its larger counterparts. Figure 6 illustrates the division of EfficientNet-B0 into nine stages. The initial stage consists of a standard convolutional layer with a kernel size of \(3\times 3\), including batch normalization (BN) and the Swish activation function. Stages 2 to 8 involve the repetition of MBConv structures, while stage 9 comprises an ordinary \(1\times 1\) convolutional layer (including BN and Swish), an average pooling layer, a fully connected layer, and a softmax function layer. The MBConv structure itself consists of a \(1\times 1\) ordinary convolutional operation (which raises the dimensionality, including BN and Swish), a k \(\times\) k (\(3\times 3\) or \(5\times 5\)) depthwise convolution (including BN and Swish), an SE module, a \(1\times 1\) ordinary convolution (which reduces dimensionality, including BN), and a dropout layer.  
EfficientNet has been utilized in various studies for the automatic screening of osteoporosis using radiographs. For instance, [PERSON] et al. evaluated the predictive ability of EfficientNet on hip radiographs and achieved a sensitivity of 82.26%, specificity of 92.16%, and an AUC of 92.19% [37]. [PERSON] et al. employed EfficientNet with fine-tuning techniques to classify osteoporosis in knee radiographs, achieving a sensitivity and specificity of 86% [39]. EfficientNet effectively manages model complexity and computational costs through composite scaling factors, while still maintaining high performance. It can achieve performance comparable to larger models with smaller model sizes. However, it is important to note that EfficientNet's high performance often relies on the availability of large training datasets and ample computing resources. In the context of  
Figure 5: The backbone of DenseNet-169.  
osteoporosis classification, if the training dataset is limited or computational resources are constrained, it may have an impact on the training and performance of the model.