esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Simulation Results of Intelligent Control Method for Sewage Treatment
The specific investigation results are shown in Figure 5.  
From the histogram in Figure 5, it can be concluded that after the optimization of the sewage treatment process, the weekly power consumption is much lower than before the optimization, and the gap between the power consumption is the largest in the fifth week. It also proves that the new method has a good effect at reducing power consumption. After using the new method, the monthly sewage treatment volume before and after the optimization of the sewage treatment in City A was investigated. The specific time was from January to June, and the survey results are shown in Figure 6.  
Figure 4: Adjustment and control results of the oxygen demand concentration in the sewage treatment process using the MOPSO algorithm. Figure 5: The electricity consumption of the sewage treatment in Factory A within 5 weeks. Figure 6: The monthly wastewater treatment volume before and after optimization of the wastewater treatment in Factory A. It can be seen that after optimization, the processing volume in the first month was basically the same as that before the optimization, and the processing volume in the other months was much higher than that before the optimization. This means that after using the new method, the processing capacity of the system was improved to be stronger. In the processing process, the processing efficiency is an important indicator reflecting the quality of the processing method. Figure 7 shows a comparison of the processing efficiency between the traditional method and the new method. The specific time is within one year, and the maximum processing efficiency is set at 100%. It can be concluded from the line chart that in January and February, the processing efficiency of the new method was lower than that of the traditional method, because the practice of the new method required an adaptation process. From March, the processing efficiency under the new method began to rise steadily, and the processing efficiency in the following months was higher than that of the traditional method. In contrast, the overall processing efficiency under the new method was 7.15% higher than that of the traditional method. From the above experiments, it can be concluded that the intelligent control optimization method of the sewage treatment based on multi-objective particle optimization algorithm can improve the sewage treatment capacity and treatment efficiency to a certain extent, and also reduce the treatment cost.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 2 System Description
### Sampling Distortion: Resolution Error  
Insufficient resolution due to sampling distortions can cause errors in signal phase estimation or resolution errors. In the time domain, the signal resolution depends on the sampling frequency and is numerically equal to the sampling interval. In the correlation domain, since the phase information distributed at different sampling points can be utilized, a higher phase resolution can usually be obtained, which is affected by the sampling frequency and the signal frequency.  
For a digital square wave signal, assuming that the coherent integration time of the correlator is an integer of the spread symbol width, and the correlation interval of the discriminator is an integer of the phase resolution of the correlator, the resolution \(P(k_{s},k)\) of the phase discriminator is [8; 12]:  
\[P(k_{s},k)=\frac{1}{F_{0}\cdot lcm(k_{s},k)}, \tag{6}\]  
where \(lcm(\cdot)\) represents the calculation of the least common multiple, \(F_{0}(=1/T_{coh})\) is the reference frequency, \(T_{coh}\) is the signal correlation time, \(k_{s}(=F_{s}/F_{0})\) is the sampling coefficient and \(k\) is the spreading factor.  
According to Formula (6), the noncommensurate sampling frequency of the BOC signal, \(\tilde{F}_{s}\), is defined as:  
\[\tilde{F}_{s}=\tilde{k}_{s}F_{0}, \tag{7}\]  
where \(\tilde{k}_{s}\) is a positive integer that is incomparable with both \(k_{c}\) and \(k_{sc}\), \(k_{c}(=F_{c}/F_{0})\) is the frequency coefficient of the code and \(k_{sc}(=F_{sc}/F_{0})\) is the frequency coefficient of the subcarrier.  
Noncommensurate sampling can remarkably improve the phase resolution of a digital discriminator. For a BOCs (1, 1) signal with a spreading code rate \(F_{c}\) of 1.023 MHz, a subcarrier symbol rate \(F_{sc}\) of 2.046 MHz, a coherent integration time \(T_{coh}\) of 1 ms, and a corresponding reference frequency \(F_{0}\) of 1 KHz, and the noncommensurate sampling frequency is designed to be 4.999 MHz, the resolution of the code phase discriminator, \(P_{c}\), is 0.2 ns (\(2.0\times 10^{-4}\) chips), while the resolution of the subcarrier phase discriminator, \(P_{sc}\), is 0.1 ns (\(1.0\times 10^{-4}\) chips), which is obviously higher than the time domain resolution of the signal \(T_{s}\) (200 ns).
## 4 Analysis of the Relationship Between the Number of Input Ground Motions and Seismic Fragility
### Example Facility: Six-Story Commercial Building

Table 8: Seismic fragility curve parameters range of the six-story building (N = 30). Figure 6: EDS spectrum analysis of 17-4 PH (**a**), 316L (**b**), and 304 (**c**). Figure 8 illustrates the difference between the mean values of each fragility input variable and the reference values for the three performance levels. When 30 or more ground motions were considered, the difference in the median value from the reference value was about 0.47%, 0.47%, and 0.44% and about 0.50%, 0.50%, and 0.47% for the log-standard deviation at each performance level. When 50 or more ground motions were considered, the difference in the median decreased to about 0.34%, 0.32%, and 0.31% for each performance level, and when 100 or more were considered, it decreased to 0.20%, 0.19%, and 0.19% for each performance level. For the log-standard deviation, the difference decreased to 0.37%, 0.35%, and 0.35% for each performance level with 50 ground motions and to 0.23%, 0.19%, 0.19%, and 0.26% for each performance level with 100 ground motions. The values converge toward the reference values as the number of considered ground motions increases. This difference between the two fragility parameters indicates that using few ground motions when generating fragility curves can lead to overestimation during subsequent analyses, such as vulnerability assessments, cost evaluations, or seismic performance assessments. Thus, the number of ground motions considered in fragility curve generation is critical. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Level**} & \multicolumn{4}{c}{**Median (g)**} & \multirow{2}{*}{**Difference**} \\ \cline{2-2} \cline{4-6}  & **Ref** & **Min** & **Max** & **Avg** & **(\%)** \\ \hline
1—IO & 0.3000 & 0.2265 & 0.4294 & 0.3007 & 0.2449 \\
2—LS & 0.9638 & 0.7248 & 1.3300 & 0.9663 & 0.2605 \\
3—CP & 1.0148 & 0.7718 & 1.3920 & 1.0175 & 0.2599 \\ \hline \multirow{2}{*}{Performance Level} & \multicolumn{4}{c}{Log-standard deviation} & \multirow{2}{*}{Difference} \\ \cline{2-2} \cline{4-6}  & Ref & Min & Max & Avg & (\%) \\ \hline
1—IO & 0.7237 & 0.4670 & 0.9978 & 0.7169 & 0.9379 \\
2—LS & 0.7430 & 0.4886 & 1.0060 & 0.7383 & 0.6331 \\
3—CP & 0.7471 & 0.4903 & 1.0074 & 0.7425 & 0.6123 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Seismic fragility curve parameter range of the six-story building (N = 50). Figure 7: Seismic fragility curves and histograms of the six-story building (N = 350). (**a**) Performance Level 1; (**b**) Performance Level 2; (**c**) Performance Level 3. Figure 8: No. of ground motions vs. Difference of the six-story building. (**a**) Performance Level 1; (**b**) Performance Level 2; (**c**) Performance Level 3.
## 1 Introduction
In this figure, the number of signaling packets considering both the original flooding and chase packets is plotted against the hop-count separating source and destination nodes. As a reference, the number of packets generated by traditional flooding is shown as a constant number, indicating all nodes in the network always participated in the flooding process. As Figure 1 shows, repeal-based protocols are efficient only when the source and destination nodes are somewhat near to each other; otherwise, they can generate even more signaling packets than the original flooding. The main idea behind F&C is to have a mechanism that generates an overhead similar to repeal-based flooding schemes for near destinations, and behaves like traditional flooding for distant destinations. The critical point to achieve such a behavior is to allow each node to establish its optimized repeal-flooding boundary beyond which there is no attempt to chase and stop propagating the original flooding. Establishing and finding such an optimized repeal-flooding boundary is the key contribution of F&C and what sets this protocol apart from other repeal-based schemes. The main features of the F&C algorithm are as follows:  
1. F&C reduces the number of control messages over the whole network, thus saving energy and making the network more resistant to some Vampire Energy Depletion Attacks [8]. 2. F&C protocol establishes a repeal-flooding boundary that delimits if the flooding is worth attempting to stop, allowing each node to decide whether it should initiate a chase flooding independently. Experiments demonstrate that F&C achieves an optimized performance with as few as just one previous chase flooding, and its performance is relatively immune to variations in the network shape, node density, and mobility patterns. 4. F&C's signaling overhead is proportional to the distance separating source and target nodes before the repeal-flooding boundary. This behavior makes F&C appeal to applications where the target node is located close to the source node, as in some IoT applications [1; 2]. 5. Considering all possible locations of the source and target nodes within the network, F&C generates up to 35 percent fewer signaling packets on average, compared with traditional flooding. Other repeal-based schemes might transmit even more signaling packets on average than traditional flooding. 6. F&C maintains a lower than or equal delay, compared with other repeal-based schemes. That is, it behaves at least as good as them while reducing the delay when the target node is far from the source node in terms of hops. The rest of this paper is organized as follows. Section 2 presents relevant research related to reducing the overhead of flooding, particularly protocols based on chasing the original flooding. Section 3 presents a detailed description of the F&C algorithm. Section 4 describes how F&C allows each node to estimate its optimized repeal-flooding boundary. Section 5 describes the experiments conducted on the F&C algorithm as well as on other repeal-based schemes, and Section 6 presents the conclusions.
## 4 Results and Discussion
### Limitations and Future Directions
Furthermore, examining different sections of patent documents, such as claims and descriptions, can provide a richer and more comprehensive dataset for analysis. Expanding the variety of databases explored in research, from well-established patent databases to newer or less utilized sources, can also enhance the depth and breadth of retrieval results. By embracing a wider array of information sources, researchers can develop more robust systems for patent analysis that better reflect the complexities and nuances of innovation across different industries and regions. Further, embracing interdisciplinary approaches that utilize different technological advances can significantly enrich the field of patent retrieval. Techniques from data analytics, information retrieval, and artificial intelligence could be synergistically combined to develop more robust systems for managing and analyzing patent information. These advancements aim to make patent retrieval more comprehensive, inclusive, and effective, enabling researchers to navigate the complexities of global innovations more efficiently. This forward-looking approach is designed not only to address the identified limitations but also to harness cutting-edge technologies and methodologies to expand the boundaries of what is currently possible in patent retrieval research. By adapting to and integrating these innovations, the field can evolve to meet the challenges of an increasingly complex intellectual property landscape.
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 5 User Study
### Observations and Discussions  
The paper-based instructions yielded a NASA-TLX score relatively lower than the other forms, although there was no statistically significant difference among them. The result was similar to the finding of a previous study [16]. This result may indicate that people are still accustomed to the paper presentation. Note that the usability of the paper-based instructions had been re-designed and improved based on the preliminary experimental result. The improvements included clearer illustrations for the scale and shape of the components, the component orientation while assembling, and color-coded arrows indicating the assembly features. Few subjects implied that they experienced difficulties in determining the part orientation and position using the paper-based instructions.  
As for the 3D viewer system, some subjects expressed concern that differentiating between part models and identifying the actual part corresponding to their virtual model were difficult. This may be the reason that the 3D viewer system showed the highest score in mental load. All the subjects had no prior experience of using a similar assisted system. They might thus need additional time and effort to learn how to operate the system functions.  
Most of the subjects in the experiments considered the part confirmation and assembly demonstration function highly useful. They indicated that the AR animation was effective in helping them understand the assembly task and estimating the dimensions of the actual components. Most users emphasized that it was more intuitive than the 3D viewer system that only displays virtual models. For the drawbacks of the AR-based system, some subjects expressed that placing the parts and assemblies under the camera fixed in the test environment caused them physical fatigue. They felt physically and mentally tired by repetitively holding parts under the camera for confirmation purposes. The highest physical demand in the NASA-TLX questionnaire somehow reflects this problem. The usage of HMD goggles frees the hands of the user and may, therefore, solve the aforementioned problem. The slow recognition speed sometimes frustrated the subjects. This also contributed to the longer total assembly time than the other two forms.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 1 Introduction
### Importance of the Research  
The data produced by urban vehicles continue in nature, which predicts road safety and traffic control. It is a challenging process to predict [12]. The speeds of the urban area's vehicles are fast, so pre-time predicts road conditions and accidents. If this happens, it can save us from disasters [13]. Thus, designing an efficient data pre-cache prediction is one of the more efficient techniques to resolve cache problems in VANET. The proposed methodology informs vehicles about interesting safety events, such as pre-decision in disastrous situations, and efficiently overcomes disasters through early prediction results [14].  
In this paper, the Geocast-based technique is the most efficient data dissemination approach for VANET applications, especially for safety applications, because \"security events only concern vehicles in the area close to the event's location\". Moreover, this paper improves the prediction results for early road safety conditions through urban VANET based on machine learning. We improved the cache hit ratio, prediction accuracy, throughput, and average delay. The main idea is to secure applications to improve drivers' and passengers' road safety. Urban vehicle safety is one of the main reasons for the early prediction of road and accident conditions. These applications are based on data dissemination which is periodic or not enabling the state of the road and surrounding vehicles. The main benefit of this prediction is securing road conditions and other related operations in urban areas. The main idea is to deliver data about prediction with secure road conditions. In Geocast, a data dissemination message is sent only to the vehicle  
Figure 2: Machine learning models for VANET [11].  
inside a specific geographical area called a zone of relevance (ZoR). This study handles the content pre-caching using machine learning under the secure parameter of blockchain technology to enhance network content throughput, minimize average delay, and minimize the hope count.
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Communication Technologies Integration
#### 4.2.2 CIRS Communications  
Although the research on CIRS communications is by far not so abundant as in the SR communications described previously, in this section, the technologies related to CIRS, which have been gathered from this systematic review, are identified. They have been grouped by the corresponding main technology, but in some works, several communication technologies will be identified.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 2 Materials and Methods
### Transfer Function Analysis of Vehicle Dynamics Model
The Bode diagram results of the transfer function are shown in Figure 5.  
Figure 4: The Bode diagrams of two methods: (**a**) The Bode diagram of steering angle and sideslip angle; (**b**) The Bode diagram of steering torque and sideslip angle. It can be seen from Figure 5 that in the range of \(10^{-2}\) to \(10^{0}\) Hz, the 2-DOF model of the vehicle will produce a large phase overshoot, with the phase changing from \(178^{\circ}\) to \(-22^{\circ}\), which is the main source of phase overshoot of the system. The EHPS transfer function based on the steering angle in Figure 5a has a phase lag of almost 0 in the range of \(10^{-2}\) to \(10^{0}\) Hz, while the torque-based transfer function in Figure 5b has a phase lag of \(180^{\circ}\). The EHPS model transfer function based on steering torque can rectify phase lead better than the transfer function based on steering angle. Therefore, the estimation of sideslip angle based on steering torque can more accurately reflect the real status of the vehicle.
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 4 Oscillation Suppression of PSVS
### Verification of Oscillation Suppression Method
#### 4.2.2 Stability Margin Analysis
Therefore, the linearized flow gain coefficient of the main valve spool can be taken as  
\[k_{q}\widetilde{x}_{s}-k_{c}\widetilde{p}_{c}=A_{c}s\widetilde{x}_{c}+A_{\
u}s \widetilde{x}_{\
u}+\frac{V_{cv}}{\beta}s\widetilde{p}_{c} \tag{29}\]7  
\[k_{q}\widetilde{x}_{s}-k_{c}\widetilde{p}_{c}=A_{c}s\widetilde{x}_{c}+A_{\
u}s \widetilde{x}_{\
u}+\frac{V_{cv}}{\beta}s\widetilde{p}_{c} \tag{29}\]8  
In order to simplify the block diagram some measures are adopted. \[k_{q}\widetilde{x}_{s}-k_{c}\widetilde{p}_{c}=A_{c}s\widetilde{x}_{c}+A_{\
u}s \widetilde{x}_{\
u}+\frac{V_{cv}}{\beta}s\widetilde{p}_{c} \tag{29}\]9 Power stage control block diagram. Figure 19.0 Bode diagram of the pre-stage. (1) The cylinder is a load with large stiffness, which has a spring force much larger than the inertia and damping forces. Therefore, only the spring stiffness is considered in the analysis. (2) The fixed backpressure is an interfering term which does not affect the transmission function and can be ignored in the simplification. The simplified block diagram of power stage can be obtained as shown in Figure 19.1  
The transfer function can be obtained in Formula (41). When taking Formula (39) or Formula (40) into Formula (41), the open-loop gain can be obtained in Formula (42). Figure 19.2  
Figure 19.3  
It can be seen that the open-loop gain of the power stage is related to the operating point of the pressure servo valve. It means that the open-loop gain of the power stage is changing with different load differential pressure. According to Formula (41), the Bode diagram of the power stage is shown in Figure 19.4  
The stability margin of the power stage is shown in Figure 19.5  
Figure 19.6 Simplified control block diagram of power stage. Figure 19.7 Bode diagram of pressure servo valve power stage. It can be seen that the power stage is a stable system and has a sufficient stability margin to resist external disturbances. Furthermore, it can be seen that the system has a higher stability margin when \(x_{v}>0\). Therefore, it can be judged that the PSVS is a stable dynamical system and has sufficient stability margin to maintain stability under certain external disturbances. This is also consistent with the time domain analysis results of the PSVS in Section 4.2.1.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
###### Abstract  
Coiled tubing (CT) plays a pivotal role in oil and gas well intervention operations due to its advantages, such as flexibility, fast mobilization, safety, low cost, and its wide range of applications, including well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject to fatigue and mechanical damage caused by repeated bending cycles, internal pressure, and environmental factors, which can lead to premature failure, high operational costs, and production downtime. With the development of CT properties and modes of application, traditional fatigue life prediction methods based on analytical models integrated in the tracking process showed, in some cases, an underestimate or overestimate of the actual fatigue life of CT, particularly when complex factors like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a dataset derived from both lab-scale and full-scale fatigue tests. We incorporated the impact of different parameters such as CT grades, wall thickness, CT diameter, internal pressure, and welding types. By using advanced machine learning techniques such as artificial neural networks (ANNs) and Gradient Boosting Regressor, we obtained a more precise estimation of the number of cycles to failure than traditional models. The results from our machine learning analysis demonstrated that CatBoost and XGBoost are the most suitable models for fatigue life prediction. These models exhibited high predictive accuracy, with R\({}^{2}\) values exceeding 0.94 on the test set, alongside relatively low error metrics (MSE, MAE and MAPE), indicating strong generalization capability. The results of this study show the importance of the integration of machine learning for CT fatigue life analysis and demonstrate its capacity to enhance prediction accuracy and reduce uncertainty. A detailed machine learning model is presented, emphasizing the capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable CT management and safer, more cost-efficient well intervention operations.  
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