esa-sceva/satcom-chunk-collection huggingface dataset:
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Traffic Data Prediction Model of the Intersection Entrance Section
#### 3.4.2 Model Construction  
In the model, two LSTM layers are first used to obtain the time correlation information in the sample fully, and the dropout layer is added after each LSTM layer. The dropout layer is used to randomly disconnect a certain percentage of neurons in the LSTM layer to prevent overfitting in model training. Finally, two dense layers are used, and the activation function is \"relu\" to ensure faster training speed. Two dense layers provide more nonlinear fitting capability for the model. The model optimizer is \"adam\", and the loss function is \"mse\" to ensure convergence. The structure of the prediction model is shown in Table 2.  
The traffic data prediction diagram of the intersection entrance section based on the LSTM neural network is shown in Figure 7.  
After the model structure is roughly built, the space mean speed and the vehicle steering ratio of the road entrance section are respectively sent into the model for training. Finally, different models are obtained to predict the corresponding type of traffic data. A randomly selected 70% portion of the samples is sent into the model for training, and the remaining 30% of the samples are used as test sets. In order to improve the prediction accuracy of the model, the parameters of the model need to be adjusted. Based on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Number** & **Layer** & **Layer Number** & **Layer** \\ \hline
1 & LSTM layer & 4 & Dropout layer \\
2 & Dropout layer & 5 & Dense layer \\
3 & LSTM layer & 6 & Dense layer \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure of prediction model.  
Figure 7: Schematic diagram of traffic data prediction based on LSTM neural network.  
structure of this model, the main adjusted parameters are the number of neurons in the LSTM layer and the dense layer and the proportion of disconnected neurons in the dropout layer. The proportion of disconnected neurons at the dropout layer increases from 0.1 to 0.9 in increments of 0.1, and the proportion at the lowest loss function value is selected as the model parameter. The selection range of the neuron numbers in the LSTM layer is [32, 64, 128, 256, 512]. The selection range of the number of neurons in the dense layer (the number of dense layer neurons in the last layer is 1) is [16, 32, 64]. The neuron number of the lowest loss function value was selected as the model parameter.
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 2 Background and Related Work
### ENF Applications in Digital Multimedia  
The ENF was initially introduced as a forensic verification technique for law enforcement applications to verify the authenticity of audio recordings [27]. Due to electromagnetic induction, the audio recorders directly connected to the power grid can also embed the ENF fluctuations in the audio recordings [28]. The applications were limited to devices connected directly to the power grid until the presence of the ENF was verified in battery-powered devices through the background hum generated by surrounding electrical appliances connected to the grid and increasing its range of devices [31].  
Along with audio, video recordings were also discovered to carry ENF fluctuations in the form of illumination frequency [33; 34]. The captured photons from artificial light have similar fluctuations, and the method estimation from the video recordings depended on the imaging sensor used in the capture device. Complementary Metal-Oxide Semiconductors (CMOSs) and Charge-Coupled Devices (CCDs) are the most commonly used imaging sensors with different shutter mechanisms [38]. In the case of CCD sensors, a global shutter mechanism is used where the whole sensor grid is exposed to photon capture at one instant, resulting in capturing the ENF samples equal to the number of frames per second. However, in CMOS, a rolling shutter mechanism captures the ENF sample per row in the sensor grid and vastly increases the captured samples [34]. Due to limited samples in the CCD sensor, an alternative aliasing frequency technique can be used to estimate the ENF fluctuations [33]; however, it is prone to signal noise. Most commercial-grade camera devices use CMOS sensors due to their cost-effective nature, resulting in an effective solution for ENF estimation through video recordings.  
The presence of the ENF signal in audio and video recordings has increased its viable applications in identifying the recording time due to its unique fluctuation nature. Although the fluctuations in the ENF are similar throughout the power grid interconnect, the propagation delay can be used to identify the geographical location of the recording within the grid, essentially enabling the ENF technology with the geotagging feature [53]. ENF presence in audio and video recordings can be used to synchronize the media recordings from multiple recorders in commercial applications [39]. Smart grid infrastructure relies on ENF fluctuations to analyze power consumption, create a feedback loop for power outages, and prevent grid-level blackouts [30].
## 4 Numerical Example
### Simulation of Training and Testing Samples  
The FE model was used to generate the training and testing samples. In this study, early bolt looseness conditions were considered, with the assumption that at most two out of four rows of bolts could be loosened. The local bolt looseness severity for the training samples ranged from 0 to 0.8 with an interval of 0.1, resulting in 486 (\(C_{4}^{2}\times 9\times 9\)) samples. Four levels of local bolt looseness severities with fi of 0.35, 0.45, 0.55, and 0.65 were utilized for the testing samples, yielding 96 (\(C_{4}^{2}\times 4\times 4\)) testing samples. An automatic sample generation program was developed by integrating ABAQUS 6.14, Python 3.9.6, and MATLAB 2023b, eliminating the need for manual GUI operations. This approach enables the collection of a large number of samples and greatly improves the computational  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Damage Cases** & \(\{f\}=\{f_{1,}f_{2,}f_{3,}f_{4}\}\) \\ \hline Case 1 & \(\{0,0.3,0.3,0\}\) \\ Case 2 & \(\{0,0.45,0.45,0\}\) \\ Case 3 & \(\{0,0.6,0.6,0\}\) \\ Case 4 & \(\{0,0.75,0.75,0\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of damage severity for four example cases.  
Figure 11: Changes in the end-reflected wave packet under different bolt looseness severities (\(l=200\) mm). (a) \(S_{1}\)-\(S_{3}\); (b) \(S_{1}\)-\(S_{4}\).  
Figure 10: Energy distribution during wave propagation (\(l=200\) mm).  
efficiency. Details can be found in reference [44]. The received signal from each wave path underwent wavelet transform, from which the effective time-frequency region was extracted. The effective frequency range was adopted from 40 kHz to 120 kHz, considering the excitation frequency (80 kHz). The combined effective time-frequency regions from the four wave paths formed the training pattern. Figure 12 shows the combined training patterns for several bolt looseness cases, which were then converted to grayscale with dimensions of \(68\times 350\times 1\).
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 4 Discussions  
This research concerned both the water circulation structure and water quality effects in the Paldang Reservoir. In the period from May 12 to June 17, 2016, research on hydraulic parameters, temperature, and meteorological parameters in the Paldang Reservoir was carried out. The results presented hydraulic data, water temperature data, and meteorological data. These parameters were compared by analyzing the characteristics of water stability and circulation in the Paldang Reservoir in order to study the relationship between water velocity and flow from the studied period and to  
Figure 12: Time series of dissolved oxygen (DO) by water depth for each monitoring station.  
develop a three-dimensional water circulation structure, which was verified with the use of DO data from the period of 2012-2018.  
In order to identify the variability of water quality in the Paldang Reservoir, the survey period was considered to be an important period of the year because it was the time when algae began to grow due to rising temperatures. The survey period was short, so the results of this study cannot represent all phenomena in the reservoir for a year. However, they could be used as basic data to show the water circulation characteristics inside the reservoir.  
Algal blooms occur frequently in the middle and lower reaches of the North Han River, where a convergence zone is formed by a countercurrent flow occurring on the surface layer (Figure 12). Such a convergence zone is considered responsible for algal blooms in the region. The upstream flow occurring in the surface layer in the middle and lower reaches increases the water residence time in the surface layer, thus leading to an extended residence time for the organic matter in the convergence zone and a higher vertical stability in the surface layer, both of which can create favorable conditions for algal blooms. Especially during the zero-discharge of the CPD, the flow velocity below 0.1 m/s at PC1 affected the growth of algal bloom. The change of hydrodynamic conditions and the decrease of flow velocity showed that the increase of the flow velocity would accelerate the growth of algae and the occurrence of bloom [48].  
This study is expected to serve as a foundation for research aiming to determine the primary cause of algal blooms based on the complex circulation characteristics of the Paldang Reservoir. Moreover, further studies are needed to accomplish calibration and validation work for multidimensional numerical models through comparisons with field survey data, as well as to develop water quality management strategies for the Paldang Reservoir under a variety of hydrological conditions.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 2 Development Approach: Design Goals, Materials, and Methods
### Methods  
For the design, optimization, and precise evaluation of the antenna parameters and characteristics, we selected the finite-difference time-domain (FDTD) method due to its proven ability to effectively model complex antenna structures made of inhomogeneous materials across a wide frequency range and in the time domain [31]. We performed numerical simulations using the full-wave electromagnetic solver Remcom xFDTD (Remcom Inc., State College, PA, USA) on a uniform FDTD mesh with a cell size of 0.1 mm to provide a detailed representation of the antenna geometry and ensure high numerical accuracy. To build the numerical model of the substrate, we used the electromagnetic parameters presented in Table 2, measured using the cavity perturbation method as described in [32] at a frequency of 2.565 GHz. Moreover, we modeled all metallic elements incorporated into the numerical model of the rectenna as perfect electric conductors to reduce the computational time. This modeling approach was further justified by our previous investigations into the influence of non-ideal electrical conductivity on antenna performance [33]. Specifically, a comparison between an antenna model employing perfect electrical conductors (PECs) and one with a finite DC conductivity of \(2.5\times 10^{5}\) S/m--corresponding to the measured conductivity of the conductive textile used in the prototype--revealed no change in antenna gain and an insignificant reduction in radiation efficiency of approximately 0.5%.
# The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
###### Abstract  
Bone drilling is a common surgery procedure. The drill bit shape directly affects the drilling force. Excessive drilling force may cause bone damage. In this work, on the premise of analyzing and comparing several finite element method (FEM) simulation results for drill bit of 5 mm in diameter commonly used in medical practice, a combination of drilling speed and feed rates which can minimize the drilling force for drilling parameters is determined. Then, the effects of the drill bit shape parameters including helix angle, point angle and edge radius on the drilling force are simulated by using the obtained drilling parameters, and after validation the FEM analysis results show that their variation trend is the same as the experimental one. Then, the optimum bit structure parameters are obtained through the following research: (1) the prediction model of the relationship between drill edge parameters and drilling force is established based on the result of FEM of the drilling process; (2) A particle swarm optimization algorithm is used to obtain the optimal matching parameters of the bit structure; (3) The priority order of the influence of the parameters of the bit on the drilling force is analyzed. The results show that the order of the influence is: the edge radius is the largest, the point angle is the second, and the helix angle is the smallest. The optimum combination of bit structure is that point angle, helix angle and edge radius are \(95^{\circ},35^{\circ}\), and \(0.02\) mm, respectively.  
b 2022 August 2022  
**The Influence of Bit Edge Shape Parameters on Bone Drilling**  
**Analysis. _Appl. Sci._**2022**, 12, 8551. [[https://doi.org/10.3390/app1278551](https://doi.org/10.3390/app1278551)]([https://doi.org/10.3390/app1278551](https://doi.org/10.3390/app1278551))  
Academic Editor [PERSON]  
Received: 25 June 2022  
Accepted: 22 August 2022  
Published: 26 August 2022  
**Published:**  
**Subfileware's Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Keywords**:** Deep drilling; bit structure; drilling force; particle swarm optimization
## 1 Introduction
Second, the performance of the existing connectivity-based multipolling MAC protocol can be enhanced by the proposed node insertion algorithm, which derives a near-optimal number of serially connected multipolling sequences in real time. Finally, the proposed node insertion algorithm can accelerate the migration of Wi-Fi networks to wireless sensor networks. The contributions are summarized as follows:  
* The node insertion algorithm almost eliminates the computational time uncertainty from the backtracking algorithm for deriving the minimal number of serially connected multipolling sequences. * The performance of the connectivity-based multipolling MAC protocol is greatly enhanced by the node insertion algorithm. * The node insertion algorithm can accelerate the migration of Wi-Fi networks to wireless sensor networks. As can be seen in the next section, where the MAC protocols for wireless sensor networks in the literature [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38] are surveyed, most of the MAC protocols employed in wireless sensor networks based on Wi-Fi networks are contention-based. However, to resolve the immense interference among many sensors, it is necessary to develop an efficient contention-free MAC protocol for wireless sensor networks based on Wi-Fi networks. The problem tackled in this paper is stated as follows:  
* Developing a contention-free MAC protocol for wireless sensor networks based on Wi-Fi networks that allows Wi-Fi sensors to transmit their data with minimal transmission overhead.
###### Abstract  
This paper presents the design, simulation, and experimental validation of a dual-Circularly Polarized (CP) array antenna to be used as single element for a bistatic radar system, aimed at detecting and tracking objects in Low Earth Orbit (LEO). The antenna operates at 412 MHz in reception mode and consists of an array of 19 slotted-patch radiating elements with a cavity-based metallic superstrate, designed to support dual circular polarization. These elements are arranged in a hexagonal configuration, enabling the array structure to achieve a maximum realized gain of 17 dBi and a Side Lobe Level (SLL) below \(-17\) dB while maintaining high polarization purity. Two identical analog feeding networks enable the precise control of phase and amplitude, allowing the independent reception of Right-Hand and Left-Hand Circularly Polarized (RHCP and LHCP) signals. Full-wave simulations and experimental measurements confirm the high performance and robustness of the system, demonstrating its suitability for integration into large-scale Space Situational Awareness (SSA) sensor networks.  
space surveillance; debris detection; bistatic radar; hexagonal array configuration; slotted-patch antenna Article
# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 2 The Profile of the Thermal Power Plant  
This study is focused on a 1030 MW ultra-supercritical coal-fired power generation unit, which is a Spiral Wound Universal Pressure boiler (SWUP) featuring balanced ventilation, ultra-supercritical parameters, one reheating, and a spiral furnace. The unit is equipped with a solid slag discharge method and an open layout. A medium-speed coal mill, a positive pressure direct blowing cold primary air pulverizing system, front and rear wall opposing combustion mode, and low NOx dual-adjustable swirl burners and low NOx nozzles are employed in the system. The furnace chamber has a cross-sectional width of 33,128.7 mm, a depth of 16,308.7 mm, and a height of 64,500 mm. Boiler pulverization and the boiler system are crucial parts of a power plant, directly affecting power generation efficiency and energy utilization (Figure 1).  
The thermal power unit comprises various components, including a coal feeder, coal mill, coal-fired boiler, air supply, steam piping, turbine, generator, and auxiliary equipment.  
Figure 1: Process flow diagram of thermal power plant.  
Raw coal is delivered to a raw coal hopper via a coal conveying belt, weighed by using a weighing belt, and then passed through the coal feeder to the coal mill, where it is ground into suitable pulverized coal. Given the huge amount of pulverized coal and the difficulty in keeping the grinding degree consistent, unqualified pulverized coal is returned to the mill for further grinding to ensure moderate diameter of the pulverized coal particles, while magazines that cannot be ground are collected in the stone coal hopper. In preventing coal powder leakage, the pulverizing system is equipped with a sealed air system, consisting of centrifugal fans that supply air to the rotary separator and other equipment after pressurization. Then, the treated pulverized coal is introduced into the boiler for combustion via the powder pipe. The main equipment of the boiler system includes a burner, boiler, turbine, generator, condenser, and auxiliary equipment. The burner converts the chemical energy of the pulverized coal into heat energy through combustion, generating a large amount of steam in the boiler. The steam pushes the turbine, converting heat energy into mechanical energy, which drives the generator to produce electricity. Thus, based on the processed data of the coal-fired boiler, the online identification of coal type, timely detection of abnormal changes in coal quality, and timely adjustment of boiler optimization control parameters are important to improve the efficiency of coal-fired power generation.
###### Abstract  
Natural gas hydrate is widely distributed, shallow-burried, clean, and pollution-free and has enormous reserves, it is regarded as the alternative clean energy source in the oil and gas field with the most potential. Pressure coring is the only way to drill for gas hydrate core on the surface under in situ conditions, which is of great value for analyzing its occurrence conditions and reserves comprehensively. Based on this, a new wireline pressure coring system (WPCS) with a ball valve seal was designed and developed in this paper; it was applied in the deep sea for the first time in the South China Sea hydrate survey voyage of the Guangzhou Marine Geological Survey (GMGS). A total of 15 runs of deep-sea gas hydrate drilling and coring applications were carried out, and they tested well. The experimental water depth was 1700-1800 m, and the coring depth below the seafloor was about 100-150 m. The formation consisted of sandy hydrate and argillaceous hydrate. The results showed the following. (1) The success rate of ball valve turn-over could reach almost 100% in the argillaceous hydrate reservoir, although there are some isolated cases of pressure relief. Meanwhile, drilling in the sandy hydrate reservoir, the success rate was only 54.55%. (2) When drilling in the argillaceous hydrate reservoir, the core recovery rate could reach 80%, while in the sandy hydrate reservoir, it was almost 0%. In practice, the sandy formation with gas hydrate is stiff to drill compared to the performance in argillaceous formations. After our analysis, it was believed that the ball valve and core tube could be easily plugged by sand debris during the sampling of sandy hydrate formation. Moreover, the sandy core is easily plugged into the core liner because of the high friction of sand grains in clearance. (3) The pressure-holding effect of the core drilling tool was related to the formation of hydrate, the sealing form of the ball valve, and the environmental pressure. Sandy hydrate formations often caused the ball valve to jam, while the muddy hydrate formation did not. The research results of this paper have reference value for the further optimization of the WPCS structure, the optimization of drilling parameters, and the design parameters of the ball valve structure, which could be better used for the pressure coring of gas hydrate and subsequent research work in the future.  
Keywords:natural gas hydrate; pressure coring; structure design; ball valve seal; core recovery rate +
Footnote †: journal: Applied Sciences
## 1 Introduction
Our experiments confirm that attention models are very effective when positioned between the last dense module and the final feature fusion module, meaning that the attention module plays a key role in aggregating low-level local features with long-term dependency. Hence, the final feature fusion module can easily exploit both global and local information. And the best attention module is different, depending on the spatiotemporal characteristics of the dataset. Through extensive experiments, we validate that choosing dataset-specific attention mechanisms and configurations leads to optimal performance. The experimental results show that, compared to STDenseNet, our ADNet improved RMSE performance by 3.27%, 3.84%, and 9.52% in call service (Call), short message service (SMS), and Internet access (Internet) sub-datasets, respectively. Our contributions are as follows:  
1. We propose ADNet, based on STDenseNet, which is designed through searches for optimal types and positions of attention modules. 2. We conduct comprehensive experiments and several ablation experiments to validate effectiveness. By applying our method, we can achieve remarkable prediction performance compared with existing methods. The remainder of this paper is organized as follows. Section 2 reviews related work about network traffic prediction using convolutional networks and attention mechanisms. Section 3 analyzes the dataset used in this paper. Section 4 describes the architecture of the proposed method, ADNet. Section 5 presents the experimental settings and results. Section 6 concludes the paper and discusses potential directions for future work.