esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Synthesis Method and Application of NaP
## Results and Discussion
### The NaP Zeolite to Remove Ammonia Nitrogen
#### 3.7.1 Influence of Adsorbent Dosage  
The relationship between the amount of NaP zeolite and the ammonia nitrogen removal rate and adsorption amount is shown in Figure 7. When the amount of zeolite was increased from 0.5 g/L to 40 g/L, the ammonia nitrogen removal rate increased from 13.55% to 93.83%, and the adsorption amount decreased from 21.11 mg/g to 2.35 mg/g. It can be seen that in the wastewater with an ammonia nitrogen concentration of 100 mg/L, increasing the amount of zeolite can significantly increase the removal rate of ammonia nitrogen. However, when the dosage of the adsorbent is more than 20 g/L, the ammonia nitrogen available for ion exchange decreases due to the decrease in the ammonia nitrogen concentration, and the agglomeration of the adsorbent increases, resulting in a slow increase in the ammonia nitrogen removal rate. Therefore, in the subsequent experiments, 20 g/L was selected as the optimal dosage of adsorbent [46; 47; 48].  
Figure 6: N\({}_{2}\) adsorption-desorption isotherms (**a**) and DFT pore size distribution curve (**b**) of zeolite NaP.
# Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences
## 1 Introduction
### Overview  
To summarize, this paper proposes a pose-recovery approach of motion capture for recovering 3D human poses from unmarked RGB video sequences. This approach mainly includes three parts: end-to-end 3D human pose estimation based on single frame images, human-body pose generation based on video streams, and motion capture experiment verification based on 3D human pose recovery. The main contributions of this paper are as follows:  
* The end-to-end method for 3D human pose estimation based on a single image is combined with 2D/3D skeleton point constraints and human height constraints to generate the 3D human model with higher precision. Meanwhile, the traditional method of manually defining rules is replaced by GAN constraints, which is used to generate a more human-like 3D human model;
* Combining the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos for smoothing to generate a more stable 3D human pose;
* Using two Kinect devices and iPi Soft series software to build a platform for motion capture experiments, an approach of using RGB-D video sequences is compared with the proposed approach of using RGB video sequences to verify the effectiveness of the proposed solution. In addition, experimental datasets are available to the public for related academic research.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Based Localization Techniques
#### 2.2.3. Angulation  
The angulation process aims to determine the unknown location of the receiving node by estimating the angles from the corresponding known transmitting nodes. The triangulation localization technique mainly utilizes the AoA estimations from at least two known transmitting nodes and provides a moderate precision. At least two transmitting  
Figure 5: Hyperbolic localization technique, reproduced with permission from [PERSON], A Survey of Indoor Localization Systems and Technologies; published by IEEE, 2019.  
nodes are required, and the LoS conditions and the position of the receiving node \((x,y)\) can be directly estimated, as follows:  
\[x=\frac{d\tan(\theta_{2})}{\tan(\theta_{2})-\tan(\theta_{1})} \tag{16}\]  
\[y=\frac{d\tan(\theta_{1})\tan(\theta_{2})}{\tan(\theta_{2})-\tan(\theta_{1})}, \tag{17}\]  
where \(\theta_{1}\) and \(\theta_{2}\) are the angles formed by the two transmitting nodes, and the distance \(d\) between the two transmitting nodes given by:  
\[d=\sqrt{(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}}, \tag{18}\]  
where \((x_{1},y_{1})\) and \((x_{2},y_{2})\) are the transmitting nodes' coordinates.  
Its accuracy depends on the precision of the AoA estimation; therefore, its localization performance is proportional to the known transmitting nodes' density [27, 44]. For the angulation technique, directional antennas can be utilized for identifying the intersection of the two line of bearings, at the expense of increased system's cost. Moreover, angulation is affected by multipath under NLoS propagation conditions, which can lead to localization errors [22]. Similar to trilateration, triangulation is based on an ideal model of the wireless propagation. Therefore, these techniques may be unreliable in the presence of effects that are not included in the models, for instance, important information may be lost, since it is not practically feasible to measure the calibrated antenna pattern for every single node [24].
## 2 Materials and Methods
### Farm Management System Integration  
Farm Management Systems (FMS) represent a technological evolution in agriculture, providing farmers with advanced tools to optimize the management of their farms. These systems integrate agro-meteorological data, soil and crop information, and predictive models to guide agronomic decisions in real time.  
The benefits are substantial: increased production efficiency, reduced operational costs, and minimized environmental impact through targeted use of resources such as water and agrochemicals. Moreover, FMS can enhance product traceability and quality, two aspects that are increasingly demanded by environmentally and socially conscious consumers.
# Recognizing and Recovering Ball Motion Based on Low-Frame-Rate Monocular Camera
## 2 Related Work
### Trajectory Reconstruction  
The information of a ball trajectory contains ball position, movement direction, and velocity. After obtaining the projection of a ball, a trajectory can be reconstructed with camera imaging principles. In the method proposed in [10], [PERSON] et al. used deblurred circles to recover sphere positions in space and determined speeds by sphere movement distances between two adjacent frames. In the method proposed by [8], [PERSON] et al. estimated ball positions using a simple triangulation and predicted follow-up positions of moving balls using the Kalman filter. These methods work well when projections are circle-like but fail in other cases. On the other hand, these methods cost more time since the information on directions and velocities is based on the analysis of multiple frames.  
To deal with non-circle-like projections, researchers have devised models based on the pixel intensity of streaks. Streaks in long-exposure photographs of a moving sphere contain more information than circle-like projections. In this case, only one image frame may be enough to reconstruct a trajectory. A motion-blurred streak is the image of a canal surface. In [15], [PERSON] et al. operated on properties of canal surface contours. They reconstructed a canal surface with an ordered sequence of coupled point pairs. Then in [12], they proposed a model to obtain speed information with the pixel intensity of streaks. However, due to the change of ambient light and non-uniform background in long-exposure photographs, streaks can easily become incomplete. In this case, this model fails to build out a complete trajectory.
## 5 FPGA Implementation
### FPGA Implementation of the Code Tracking Module  
The FPGA module of the code tracking loop is shown in Figure 30. The main code tracking loop contains the module for the absolute value operation, along with the correlator, integrator, error detector and the loop filter modules. yihuo_mult in Figure 30 is the correlator module, integer_track is the integrator module, jueduizhi is the operation module for obtaining the absolute values, while error_detect is the error detection module. The main function of the error detector module is to calculate the corresponding phase difference according to the correlation cumulative sum of the anticipated code and lag code between received code and local PN code. In addition, codeloopfilter in Figure 30 is the loop filter module. The function of this module is to control and adjust the clock of the local PN code generator according to the output error value of the error detection module.  
After Verilog coding, a timing simulation of the code tracking module was conducted, the results of which are shown in Figure 31. m1 and m2 are two PN code signals which have different phase. DF is the output value of the loop filter. Code tracking is successful when the error of the phase difference between the two signal channels is very small. At that moment, the output value of the loop filter is zero.  
The result of the system debugging of the code tracking module is shown in Figure 32. filter_out is the synchronized version of the received signal. cos_out signal is the adjusted filter_out signal, while m2 is the local PN code signal. filter_loop signal is the output value of the loop filter. From Figure 32, we can see that the output of the loop filter is zero when the local PN code signal tracks the received PN code signal, which verifies the design of the code tracking module.  
In summary, the code synchronization system meets the requirements of the digital transceiver.  
Figure 30: The top-level design of the code tracking module.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 4 Network Traffic Anomaly Detection Scheme Based on LNN-RLSTM Neural Network  
Our proposed multi-neural network fusion model is a combination of Labeling Neural Networks and Long Short-Term Memory (LSTM) networks. The essence of the labeling neural network is a BP neural network. The labeling neural network is proposed to train a neural network that can automatically identify and label outliers in a sliding window, which can automatically label individual data points according to the data distribution in a local range. The training set for the labeling neural network is derived from three sources.  
**Source 1:** Anomalous isolated points after initial screening.  
**Source 2:** Anomalous cycles after decompression.  
**Source 3:** Anomalous isolated points or anomalous cycles selected manually.  
Then, the labeling neural network is applied to label the brand-new time series by continuously iterating the sliding window and then substitute the labeled dataset into the RLSTM neural network for training, and finally obtain an optimal RLSTM network that can perform anomaly detection at the global level. Our proposed dual network model has the following advantages:  
1. Scientific automatic machine labeling (mainly in the integration of business knowledge and data feature detection).
2. Greatly reduces the workload of manual labeling (only manual click anomaly cycle).
3. Support for big data anomaly detection and update, network update suitable for big data environment.
# Assembly Tolerance Design Based on Skin Model Shapes Considering Processing Feature Degradation
## 4 Case Study  
### Description of the Tolerance Allocation Problem  
The proposed method is applied to solve an assembly tolerance design problem in the rotary feed system of a VTM200F5 five-axis turning and milling complex center. General tolerance indices of the rotary feed system have a significant influence on the cutting precision and are extremely sensitive to geometric deviation in the cutting process due to its direct link to the cutting tool. Considering assembly tolerance, the rotary feed system is simplified to two key parts: the hole part describing devices fixing rotary bearings, and a shaft part depicting the motor spindle component. The shaft part is an outsourced part made by Ti-Al thermostable alloy with its surface specially heat-treated. So, the key accuracy indices such as location accuracy, shape accuracy, and surface accuracy should not be modified in order to protecting the special surface coating. The hole part is a self-made part made by common bearing steel. Its key accuracy indices are to be designed, as shown in Figure 5. This is a typical component containing observable parts (with the same pattern of processing features and from different batches) and predictable parts (to-be-designed parts).  
In this component, the runout accuracy of the distal end of the shaft part is set as the target accuracy index due to its direct influence on the cutting accuracy. In the shaft part, the nominal flatness of the distal end surface is 0.010 mm. The nominal total runout of the distal end surface is 0.020 mm. The nominal total runout of the proximal end surface is 0.010 mm. The nominal verticality between the distal end surface and axis C is 0.010 mm. The nominal flatness of the shoulder surface is 0.010 mm. In the hole part, surface A is the assembly positioning surface. The key accuracy indices to be designed in this tolerance allocation problem are listed in Table 1. The flatness of the shoulder surface is \(FL_{F}\). The total runout of inner hole is \(TR_{D}\). The verticality between axis D and surface A is \(V_{DA}\). The parallelism of the shoulder surface and surface A is \(PL_{EA}\).  
Figure 5: An assembly tolerance design example in designing the rotary feed system of a five-axis turning and milling complex center (VTM200F5).
## 3 Results and Discussion
### Ambient Photoluminescence Characterization
distance away from the irradiation spot, the A\({}^{-}\) radiation was dominant with the average spectral fraction of A\({}^{-}\) peak of ~71.5%, well consistently with that measured before the FLI process of ~74.9%. However, once the laser-treated region was reached with increasing \(d\), the spectral fraction of A peak increased abruptly, indicating the transition of A\({}^{-}\) to A. The average spectral fraction of A in the irradiated region (\(d\geq\) 2.5 \(\upmu\)m) was found to be ~87.6%, much larger than that of the pristine ML-MoS\({}_{2}\) of ~18.4%. On the contrary, A\({}^{-}\) peak shows an average spectral fraction of only ~12.1% in the irradiated region, while ~62.0% in the pristine ML-MoS\({}_{2}\). These results verify that the contribution of A recombination became overwhelming as the irradiation center was approached. The point defects, such as S vacancies, may interact with O\({}_{2}\) and H\({}_{2}\)O molecules in the air resulting in the electron transfer from ML-MoS\({}_{2}\) to the gas molecules [7; 25; 26; 37]. Therefore, the increase in the amount of S vacancies by the FLI process leads to the augmentation of p-type doping effects accompanied by the active conversion from A\({}^{-}\) to A, consistently with previous defect engineering studies on ML-MoS\({}_{2}\)[7; 25; 26; 32; 33; 36; 37; 38]. The slight shift of the PL peaks to the lower energy is observed in the highly defective region of ML-MoS\({}_{2}\), such as micro-cracks [26]. As shown in Figure 3e, the slight red-shift of the ambient PL peaks by ~0.04 eV was observed, indicating that the highly defective area was created area-selectively by the FLI process.
## 3 Results and Discussion
### Comparitive Study of TA Degradation under Various Processes  
An initial comparative study on the degradation of TA aqueous solution by six different processes including chemical oxidation by S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) alone, chemical oxidation by H\({}_{2}\)O\({}_{2}\) alone, direct UV irradiation, UV/H\({}_{2}\)O\({}_{2}\), UV/S\({}_{2}\)O\({}_{8}\)\({}^{2-}\), and UV/H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\) was performed at the same operating conditions (temperature, pH and initial concentrations). The experimental results were compared in Figure 2. According to Figure 2, only 4.22%, 5.35%, and 9.86% of aromatics were reduced by S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) alone, H\({}_{2}\)O\({}_{2}\) alone and direct UV irradiation, respectively during 120 min. These results indicate that the TA degradation by S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) alone, H\({}_{2}\)O\({}_{2}\) alone and direct photolysis irradiation was very inefficient, which reveals the direct reactions of persulfate or H\({}_{2}\)O\({}_{2}\) with TA in water are generally slow oxidative kinetics. However, combining UV irradiation with H\({}_{2}\)O\({}_{2}\) oxidation (UV/H\({}_{2}\)O\({}_{2}\)) achieved more than 29% of aromatics removal after 120 min.  
This indicates that the combination of hydrogen peroxide with UV radiation enhanced TA degradation. This is because of the HO\({}^{\bullet}\) radicals produced from UV photolysis of H\({}_{2}\)O\({}_{2}\). The above results implied that UV/H\({}_{2}\)O\({}_{2}\) process is more efficient than direct UV irradiation and H\({}_{2}\)O\({}_{2}\) oxidation alone, but it was not very effective to reach complete removal of TA and its degradation intermediates.  
As can be observed from Figure 2, high TA degradation yields can be reached by UV/PS and photo-Fenton oxidation processes, respectively. For a reaction time of 120 min, the highest aromatics removal efficiency was measured as 80.78% for UV/PS and 94.27% for photo-Fenton. This result indicates that TA degradation could be improved by incorporating persulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) or H\({}_{2}\)O\({}_{2}\) and ferrous iron (H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)) to TA aqueous solutions under UV radiation. This enhancement is due to the generated radical species from UV photolysis of persulfate (Equations (1)) or from UV photolysis of the H\({}_{2}\)O\({}_{2}\) and from the Fenton reaction (Equations (5) and (6)). In this study, UV/PS and photo-Fenton processes have a higher efficiency to degrade TA than the other four processes (S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) alone, H\({}_{2}\)O\({}_{2}\) alone, UV irradiation, and UV/H\({}_{2}\)O\({}_{2}\)).
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Industrial Applications
#### Robotics  
Nowadays there are many applications that combine WSNs and robots. Robots can cooperate and combat some of the major problems of WSNs, such as sensor node mobility, node redeployment, travelling salesman, etc. Typical WSN applications of this kind are presented below.  
A robotic navigation method proposed in [40] provides road maps for the robot to traverse. It uses a WSN with sensors, designed to provide sophisticated maps of their sensing areas. Specifically, each sensor constructs a map, based on the traversable area sensed. Then, all sensor maps are combined to create one large map. Once the road maps are generated, the sensors are used to sense areas of interest for the robot to travel to. The robot then considers all possible roads to take and selects the most efficient path available in the network. If an area becomes hazardous for the robot, the network can reconfigure the road map, and remove this hazardous area from the list of available paths.  
One of the most difficult tasks concerning WSNs is the maintenance of a projected network. A combination of WSNs and robotics was researched in [41], where a robotic network servicing system, named Randomized Robot assisted Relocation of Static Sensors (R3S2) was developed. In R3S2, robots move around a network which is contained within a virtual grid. The robot moves to the least recently visited grid point, searching for sensing holes in the network. When an area that is not being covered by a sensor is detected, the robot will find a node which has overlapping coverage with other nodes and move it to the uncovered area. In addition, if the robot discovers redundant sensors, it will move the nodes to cover a greater area.  
A different approach is proposed in [42], where a mobile robot is used to transfer data from a widespread network between nodes that are out of reach from one another for various reasons. This is the so-called travelling salesman problem (TSP)--within a WSN. By having a robot traveling among nodes which are out of each other's wireless communication range, it allows the network to be widespread while also saving power by using the robot for data muling.
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 2 System Model and Problem Planning
### Problem Planning  
In this paper, the macrocell user and microcell channel resource allocation have been determined, and the macrocell user and microcell user communication service quality is guaranteed, and the D2D user is allocated a channel by maximizing the total capacity of the system. Assuming that two D2D users can be multiplexed on each channel, according to the Shannon formula, the objective function and constraints for the optimization problem can be obtained as Equations (6)-(11).  
\[X_{c}^{n},X_{j}^{n},X_{d}^{n},(c\in M,j\in W,d\in H)\quad T\Big{(}X_{c}^{n}, X_{j}^{n},X_{d}^{n}\Big{)}=\]  
\[X_{c}^{n},X_{j}^{n},X_{d}^{n},(c\in M,j\in W,d\in H)\quad\sum\limits_{n=1}^{N }\left[\begin{array}{c}\sum\limits_{c\in M}X_{c}^{n}\log_{2}(1+SINR_{c}^{n })+\sum\limits_{j\in W}X_{j}^{n}\log_{2}\!\!\left(1+SINR_{j}^{n}\right)+\\ \sum\limits_{d\in H}X_{d}^{n}\log_{2}\!\!\left(1+SINR_{d}^{n}\right)\end{array}\right] \tag{6}\]  
\[SINR_{c}^{n}\geq SINR_{c,\ threshold}^{n},\ \forall\ c\in M \tag{7}\]  
\[SINR_{j}^{n}\geq SINR_{j,\ threshold}^{n},\ \forall\ j\in W \tag{8}\]\[SINR_{d}^{n}\geq SINR_{d,\ threshold^{\prime}}^{n}\ \forall\ d\in H \tag{9}\] \[\sum_{n=1}^{N}X_{j}^{n}\leq 1,\ \sum_{j\in W}X_{j}^{n}\leq 1,\ \forall\ j\in W\] (10) \[\sum_{n=1}^{N}X_{d}^{n}\leq 1,\ \sum_{d\in H}X_{d}^{n}\leq 2,\ \forall\ d\in H \tag{11}\]  
Equations (7)-(9) ensure that the macrocell users, microcell users, and D2D users have signal-to-noise ratios greater than the threshold. Equation (10) ensures that one microcell user multiplexes up to one channel resource and allows up to one microcell user to communicate on one channel. Equation (11) ensures that one D2D user multiplexes at most one channel resource and one channel resource can be multiplexed by at most two D2D users.
## 2 Schematic and Set-Up of the Frequency Synthesis Chain
In Figure 2a, PLL1 employs a narrow-loop bandwidth of 150 Hz to preserve the frequency error of the reference signal while suppressing noise from higher offset frequencies. The 200 MHz OCXO has an output power of approximately +10 dBm, a tuning sensitivity of around 50 Hz/V, and a voltage tuning range of 0 V to 8 V. Similarly, the DRO operates at an output power of approximately +15 dBm, a tuning sensitivity of about 2.2 MHz/V, and a voltage tuning range of 0 V to 9 V. It is important to note that all input and output signals are matched to 50 \(\Omega\) impedance. Given the limitations in frequency error of the 10 MHz reference source, it is essential for the final microwave signal to be precisely adjustable within a range of approximately \(\pm\)1 kHz, with a high frequency resolution of less than 0.1 Hz. To achieve this level of precision, the DDS module assumes a critical role within the frequency synthesis chain. In this structure, the DDS module is integrated to generate a 7.368 MHz clock with a remarkable frequency resolution of up to 0.7 \(\upmu\)Hz [19; 21]. Accurate frequency matching holds significant importance in the design of highly stable atomic clocks [23; 24].