esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 1 Introduction
In [24], the outage probability and throughput are investigated for cognitive two-way DF relay networks, under the effects of realistic relay transceiver structures are taken into consideration. It is evident that the research work in [23; 24] provides contributions on protocol design and outage performance analysis for energy harvesting DF relay networks with HI. However, [PERSON], et al. [23] analyzed OWR networks, and ignores the multiple-access constraint in TWR with TS and PS protocols in [24]. Meanwhile, the ergodic capacity and optimal energy harvesting design under considering the impact of hardware impairments is lack of exploration. In this paper, we focus on a two-way passive-relay network in which a power-splitting receiver is adopted at the relay to replenish energy under the consideration of hardware impairments at each node. The exact analytical expressions of the achievable sum rate and ergodic capacities in integral closed-form are derived for both DF and AF protocols and address the optimization problems allocated to energy harvesting and for both protocols to maximize the achievable sumrate, respectively. The main contributions of this paper can be described in more details as follows. * We have presented a self-powered TWR energy harvesting and signal transmission models for both DF and AF protocols suffered by hardware impairments considered at all nodes. * We obtained the new signal-to-noise-plus-distortion ratio (SNDR) expressions, and derived the exact analytical expressions of the achievable sum rate and ergodic capacities in integral closed-form for both DF and AF protocols respectively. * In order to obtain more engineering insights, we formulate and solve the optimal power splitting (OPS) ratio that maximizes the instantaneous achievable sum rate for both DF and AF protocols. * Simulation and numerical results are presented to verify our derivation and to assess the effects of various parameter settings on system performance. The achieved sum rate with the OPS design are compared that with the equal power splitting (EPS) design, and the performance of DF and AF protocols are also compared and discussed. The rest of this paper is organized as follows. In Section 2, we describes the system and signal models, and presents the new expressions of SNDRs and the instantaneous achievable sum rate expressions. The derivation of ergodic capacities of both DF and AF protocols are presented in Section 3. Section 4 formulates and develops solutions of OPS ratio for DF and AF protocols. Section 5 validates the analytical results and assesses the effects of various network parameter settings on the performance and optimal solutions via numerical results, followed by concluding remarks in Section 6.
## 3 Proposed Framework
### Offline Training Phase
#### 3.1.2 Odometry Sensor  
During the offline training phase, the odometry sensor plays a critical role in determining the position of the ego vehicle, which is necessary for localizing the ego vehicle  
Figure 3: Track positions relative to ego vehicle.  
Figure 2: Ego vehicle trajectory.  
during testing. Ego vehicle localization is enhanced by including the DBNs derived from the training phase as an additional switching variable. A critical point is that during the online testing phase, the ego vehicle localization is performed only using LiDAR data, leveraging the DBNs that have been trained with odometry sensor data.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 4 Numerical Results
### Different Seismic Intensities
#### 4.3.3 Internal Force  
Tables 14-16 show the maximum internal forces with different seismic intensities. The maximum impact rates of the fully prefabricated vault or bottom plate on the internal force were 5.23%, 2.96%, and 2.46% under the seismic peak accelerations of 0.1 g, 0.2 g, and 0.3 g, respectively, indicating that the peak acceleration had little influence on the joint distribution of the fabricated station structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Impact Rate (\%)**} \\ \cline{5-7}
**Peak** & **CC** & **QD** & **SZ** & **QD** & **SZ** & **SZ** \\  & & & & **Compared** & **with CC** & **with QD** & **with CC** \\ \hline
0.1 g & 25.10 & 24.89 & 24.87 & \(-\)0.84\% & \(-\)0.08\% & \(-\)0.92\% \\
0.2 g & 53.82 & 53.78 & 53.75 & \(-\)0.07\% & \(-\)0.06\% & \(-\)0.13\% \\
0.3 g & 87.03 & 87.03 & 87.11 & 0.00\% & 0.09\% & 0.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Maximum interlayer relative deformations of the structures (mm).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Impact Rate (\%)**} \\ \cline{5-7}
**Peak** & **CC** & **QD** & **SZ** & **QD** & **SZ** & **SZ** \\
**Acceleration** & **CC** & **QD** & **SZ** & **Compared** & **Compared** & **with CC** \\ \hline
0.1 g & 10.92 & 10.75 & 11.05 & \(-\)1.56\% & 2.79\% & 1.19\% \\
0.2 g & 14.81 & 14.43 & 14.38 & \(-\)2.57\% & \(-\)0.35\% & \(-\)2.90\% \\
0.3 g & 15.18 & 15.33 & 15.34 & 0.99\% & 0.07\% & 1.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Maximum axial forces (\(\times 10^{3}\) kN).  
With the increase in the peak acceleration, the influence rates of different joint distributions on the seismic response of the fabricated station structure decreased gradually. In other words, with the increase in the seismic intensity, the seismic response of the fabricated station structure with different joint distributions tended to be consistent.
## 4 Case Study
### Data Processing

Table 3: _Cont._indicating that the two clusters, after clustering, have more obvious differences in the association relationship between the Pearson correlation and Spearman correlation, and the clustering is able to effectively differentiate between the different types of data, and the clustering effect is better. The clustering situation is shown in Figure 6.  
Bringing the correlation metrics into the equation yielded a weight ratio of 0.39285: 0.60715 for the two groups, which was approximated as 1:1.5. The screened eigenvalues were converted from low frequency to high frequency, using a quadratic interpolation  
Figure 5: Comparison of columns in clustered data. Figure 6: Grouping results. to convert the eigenvalue monthly data into daily data. Finally, a 3834 \(\times\) 31-sized data set consisting of 3834 days of data for 31 evaluation indexes, including the volume of the sea-rail intermodal container transport at Yingkou hub port, was obtained. The first and second sets of data were imported and the stacked diagrams of the imported data were plotted, as shown in Figures 7 and 8. The following figure shows the second set of stacked data, with the last indicator being the container throughput. Figure 8: Data stacking chart. Figure 7: Data stacking chart. To sum up, the pretreatment in this paper consisted of the following steps. First, 33 indicators were selected according to the classification criteria, and then the indicator data with more missing values were excluded through a missing value analysis, and 31 indicators were obtained. Subsequently, we used multiple imputation and outlier analysis methods many times until there were no outliers in the data. Next, Pearson and Spearman correlation analyses were conducted on the 31 indicators obtained and the container throughput indicators for sea-rail intermodal transport. According to the results obtained, two groups of categories were obtained using k-means clustering. Then, for each group we calculated the sum of the Pearson and Spearman correlations to assign weights. Finally, the monthly data were upsampled into daily data using the quadratic interpolation method to obtain the final input data.
## 4 Results and Discussion  
### Time-Frequency Analysis of Monitoring Points  
#### 4.1.1 Acceleration Time History  
We first compare the acceleration time history of the roof and floor of the immersed tunnel. The roof midpoint and floor midpoint are selected as monitoring points to analyze the acceleration time history (Figure 12).  
The horizontal peak acceleration of the roof midpoint is about 1.3 times that of the floor under the same incident angle, indicating that the seismic response of the roof of immersed tunnel is greater than that of the floor, which is mainly caused by the inconsistency of the constraint effect. The bottom of the immersed tunnel is greatly constrained by the surrounding medium, which is conducive to the stability of the tunnel structure, while the top of the tunnel has only a thin cover layer, which is more prone to slip and damage. When the SV wave changes from vertical incidence to oblique incidence, the horizontal peak acceleration of the monitoring point decreases, while the vertical peak acceleration increases significantly. The vertical peak acceleration at the roof midpoint increases from 0.34 m/s\({}^{2}\) (vertical incidence) to 1.04 m/s\({}^{2}\) (incident angle of 30\({}^{\circ}\)), which is approximately three times larger. That is, the oblique incident wave has a greater amplification effect on the vertical response of the tunnel structure, and the closer the incident angle is to the critical angle, the more significant the impact will be. The effect on the horizontal direction is relatively small, which is related to the change in horizontal and vertical component under oblique incidence. When the SV wave is incident at a certain angle, the horizontal component decreases compared with the vertical incident, while the vertical component  
Figure 12: Acceleration time history of monitoring point: (**a**) horizontal acceleration of roof midpoint; (**b**) vertical acceleration of roof midpoint; (**c**) horizontal acceleration of floor midpoint; (**d**) vertical acceleration of floor midpoint.  
increases. This amplification effect may increase the damage of structural members and increase the risk of tunnel structures during earthquakes.
## 2 Related Work
### Prior-Based Methods  
Traditional image-dehazing methods rely on prior assumptions to estimate the transmission map and atmospheric light. [PERSON] et al.'s [11] Dark Channel Prior (DCP) assumes that the minimum value of one color channel in natural images is near zero, while in foggy images, this value increases, reflecting haze intensity. The DCP method estimates the atmospheric light and transmission map, then restores the image through a recovery formula. [PERSON] et al. [32] introduced a fast dehazing method based on the Color Attenuation Prior (CAP), which assumes the attenuation of the blue channel correlates with haze intensity. By calculating the color attenuation and transmission maps, this method quickly restores image clarity. [PERSON] et al. [12] assumed haze exhibits spatial consistency across the image, with uniform local impact despite varying intensity. They also converted the image to HSV color space, allowing for more efficient haze removal by separately processing luminance. [PERSON] et al. [33] proposed a non-local prior, suggesting that clean images consist of color clusters in a non-local space. These clusters help recover the transmission map and restore image details by removing haze. [PERSON] et al. [34] introduced boundary-constraint priors combined with context regularization, forming an optimization model for accurate transmission map estimation, enhancing image clarity in haze-affected areas. [PERSON] et al. [35] proposed an enhancement method based on color-channel compensation (3C), assuming different color channels are distorted to varying degrees by haze. By recovering lost color information and adjusting channel contrast, this method improves color reproduction and image contrast. However, all these methods are highly dependent on specific priors, which may not be universally applicable. When the assumed prior fails to represent hazy images accurately, it can result in poor transmission-map estimation and color distortion.
## 2 Results and Discussion
### Evaluation of Catalytic Performance
#### 2.2.2 Effects of Reaction Conditions on CO\({}_{2}\) Cycloaddition Performance  
The influence of reaction conditions on CO\({}_{2}\) cycloaddition performance in the presence of P-BVIMCl was studied. The GLC yield increased from 81.1% to 93.4% with temperature elevation from 70 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. However, a further increase in temperature to 110 \({}^{\circ}\)C resulted in only marginal improvement, indicating reaction equilibrium was essentially achieved at 100 \({}^{\circ}\)C. This temperature was therefore selected as optimal (Figure 6a). Figure 6b demonstrates the pressure dependence under 0.5-2.5 MPa. The yield improved remarkably from 75.9% to 93.4% with pressure increasing from 0.5 MPa to 2.0 MPa, attributed to the enhancement of CO\({}_{2}\) dissolution in GO and interfacial contact efficiency. Beyond 2.0 MPa, the yield dropped slightly, likely attributed to GO molecular escaping into the gas phase under excessive CO\({}_{2}\) pressure. Thus, 2.0 MPa was identified as the optimal pressure [40; 41].  
Increasing the catalyst loading from 4.50 wt% to 6.70 wt%, the GLC yield was boosted from 80.9% to 93.4%, suggesting that the availability of the active site had increased. Further increasing the catalyst loading showed negligible enhancement, possibly caused by the catalyst deposition-induced contact blockage between GO and active sites [40; 42] (Figure 6c). Time-dependent studies (Figure 6d) revealed rapid conversion kinetics, achieving 93.4% GLC yield within 3.0 h. Prolonged reaction times (4.0-5.0 h) maintained identical yields, confirming reaction completion at 3.0 h. The systematic optimization established 100 \({}^{\circ}\)C, 2.0 MPa, 6.7 wt% catalyst loading, and 3.0 h as optimal parameters, delivering the GLC yield of 93.4% and the selectivity of 99.6%.  
Figure 5: Catalyst synthesis conditions: (**a**) the mass percentage of AIBN; (**b**) polymerization time; (**c**) polymerization temperature; (**d**) the ratio of solvents; (**e**) the type of solvents; and (**f**) the copolymerization reaction with DVB. Catalytic capabilities of catalysts: GO 5 mmol, temperature 100 \({}^{\circ}\)C, CO\({}_{2}\) pressure 2.0 MPa, and time 3.0 h.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Wearables and Health  
Using different IoT communication protocols, it is simple to observe and work on a patient's health-related parameters, operate connected medical environments, healthcare wearables, patient surveillance, telemedicine, fall detection, athlete care, track chronic diseases, and track mosquito and other similar insect populations [13]. Most systems require low latency, diversity, range, and QoS, making NB-IoT a great fit for these applications. The healthcare business is tremendously benefiting from IIoT applications. They save money by allowing remote control of medical equipment, home-bound patient care, modeling, and monitoring [128]. As a result, hospitals benefit from innovative equipment that reduces patient wait times and enhances equipment performance. The growing popularity of mobile internet connections has accelerated the expansion of IIoT-powered in-home healthcare (IHH) services [114]. Patient-centered medical home (PCMH) care is a straightforward answer to numerous problems in the healthcare sector, such as chronic disease management, misuse of emergency departments, patient satisfaction, excessive medical expenses, and accessibility. Sensor devices provide significant information about patient health and aid in diagnosing disease [129]. Furthermore, the IIoT application domain provides telemedicine solutions, such as notifying patients about their wellbeing and monitoring their health with advanced medical equipment [130]. Healthcare wearables, patient monitoring, indoor remote health, PCMH care, and wellbeing monitoring are examples of applications that can be operated with shorter-range IoT protocols like Sigfox and LoRa [50; 131]. A case study in Finland showed that LoRaWAN technology is suitable for wellbeing monitoring, location tracking, pet monitoring, and staff management in indoor communication [132]. The case study showed that LoRa technology is better suited for applications that tolerate delays and losses than dependent applications with stringent quality-of-service requirements. On the other hand, wearables like smartwatches require high data rates and low latency, which can be met by RedCap. RedCap can offer a peak bit rate of 50-150 Mbps with one to two weeks of battery life [34]. This allows RedCap to be used in wearable health monitoring and medical devices. Low-end augmented reality (AR), virtual reality (VR) glasses, and smart power grids are also able to utilize RedCap's low latency and high data rate IoT services [22].
## 4 The Settlement Monitoring Results of the ELH Bridge
### Comparative Analysis of the GB-RAR Settlement Results and Leveling Data  
This section compared and analyzed the leveling results with the GB-RAR results to prove the reliability of the settlement time series results obtained using the GB-RAR technology. Considering that GB-RAR obtained high-frequency radar data, and the sampling interval of leveling was 2 h, the data should be processed as follows before the comparative analysis: (1) Select the monitoring data with overlapping time of the two methods; (2) Extract the same settlement as the leveling sampling time from the settlement time series obtained by GB-RAR.  
Figure 6a,b show the settlement time series of No. 7 and 8 piers, respectively. The results obtained by leveling and GB-RAR technology had good consistency. During the shield tunnel of the Metro Line 11 crossing the bridge, the settlement changes of No. 7 and  
Figure 5: GB-RAR-derived settlement time series of No. 7 and 8 piers.  
8 piers showed a slight downward trend. Figure 6c,d show the residual distribution of the GB-RAR results relative to the leveling results. The residual was basically distributed in the range of \(-\)0.5-0.5 mm. The settlement time series obtained by the GB-RAR technology and leveling have good consistency. The difference between the two methods was statistically analyzed. In the No. 7 pier, the average error between the GB-RAR and the leveling results was \(-\)0.05 mm, the root mean square error was 0.20 mm, the maximum difference was 0.40 mm, and the minimum difference was 0.01 mm. The average error, root mean square error, maximum difference, and minimum difference of the No. 8 pier were \(-\)0.12, 0.27, 0.55, and 0.01 mm, respectively. In conclusion, the settlement results obtained by using the GB-RAR technology consistent with the leveling results. In addition, the safety monitoring requirements of the ELH Bridge can be met by the use of GB-RAR and the leveling technology under the shield tunnel of Metro Line 11.
### Suggested Solutions in DSM Implementation  
The following suggested solutions are viable for implementation and for driving grid integration programs in a more effective and coordinated way to deal with the above concerns and obstacles faced during the implementation of various policies concerning DSM using DGs systems:  
* An adequately designed pricing structure will result in a flexible electricity system, allowing residential customers and utilities to achieve their goals.
* Time-of-day (ToD) pricing can incentivize large-scale residential and commercial users to conserve energy.
* The load profile forecast mechanism can serve as a transitive feedback signal, and the tariff associated with it can serve as a transitive incentive signal.
* A stochastic and multi-objective optimization technique for the optimal scheduling of various domestic appliances utilizing model predictive control (MPC) optimization.
* The transitive energy concept is a viable coordination paradigm for maximizing the importance placed on prosumers and operators at the utility level and their overall participation in the market structure.
* From the perspective of trading entities present in the market and their involvement and market-based signaling, extensive changes in government laws consider both energy providers and customers.
* A variety of sophisticated methodologies can factor in individual residential prosumer overhead and comfort levels, optimize individual consumption schedules, and offer positive DSM impacts [7].
* To allow the electricity markets to generate higher revenues, an incentive-based program can change conventional consumers into new era prosumers by modifying their behavior and habits of use [8].
* For the improved functioning of DR ideas in residential utilities, measurement and verification protocols and an automated procedure are required [9].  
* Complete service-oriented topology and structure is a necessity to allow for provisions of appropriate infrastructure oriented toward dynamic integration techniques and for a more flexible operation to bring out the best in the power system scenario [10].
* DSM contributors can consume or generate energy in a coordinated operational state as cooperative agents or virtual power plant models, which can simulate the performance of an aggregated virtual single power source indirectly incorporated into the power system [11].
* The introduction of generation systems such as solar photovoltaics (SPV) and energy storage system combinations for usage during peak hours.
## 3 Related Works
They classified optimization approaches in aquatic monitoring mediums as follows: (1) Intelligent Optimization Algorithms: these algorithms, such as the Chemical Reaction Optimization (CRO) algorithm, combine elements from genetic algorithms, simulated annealing, and ant colony optimization to determine the best node deployment strategies. (2) Robot Collaboration: Robots are employed to deploy nodes more efficiently and precisely in the harsh underwater environment. Their adaptability and autonomy allow them to deal with specific problems like uneven terrain and water currents. They also pointed out that the combination of intelligent algorithms and robot collaboration considerably enhances UWSN coverage. For instance, the CRO algorithm has been shown to achieve an average coverage rate of 95.66%, outperforming traditional methods. Also, optimized node deployment enhances the accuracy and efficiency of environmental monitoring, which is crucial for applications like marine resource exploration and scientific research [13]. [PERSON] et al. demonstrated the Reliable and Efficient Routing for Water Quality Monitoring (REWQ) mechanism, which employs the Cuckoo Search (CS) method. This strategy focuses on optimizing routing paths to provide consistent data transfer while reducing energy consumption. This technique attempts to improve data transmission reliability and energy efficiency in UWSNs by tackling typical issues, including restricted bandwidth, high energy consumption, and node mobility [14]. The rest of the current paper was planned as follows: Section 2 shows the problem's preliminary stages, details, proposal model, and suggested algorithms. Section 3 presents the simulation results and evaluations of the proposed model.