esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 4 5G Enhancement Techniques--Key Technologies, Advantages, Limitations, and Future Challenges  
In this section, 5G key parameter enhancement techniques, their advantages, limitations, and future challenges are explored. In Section 3, all 5G enhancements techniques were discussed. Their advantages, limitations, and future challenges have been summarised in Table 18.  
5G key parameters can be enhanced using small cells, CA, D2D, NOMA, MIMO, and optimization techniques. Each technique's pros and cons and their future challenges are listed in Table 17. In small cells, the future challenges can be achieved by developing path loss models using ML, AI, and deep learning techniques. These path loss models will enhance the coverage radii, mobility, handovers, etc. Future challenges in CA can be met by developing dynamic frequency carrier aggregation techniques and heuristic optimization algorithms to maximise battery life. In D2D, the future challenges are achieved by proposing ML and AI-based algorithms for device discovery, synchronisation, mode selection, etc. In NOMA, significant optimization techniques are required to enhance the power efficiency, user pairing, security, and receiver complexity. In MIMO, ANN and CNN algorithms need to be proposed to detect the signals and estimate the channel information. The 5G optimization algorithms are required to enhance the coverage capacity, data rates, and latency. Figure 14: Statistical analysis of 5G key parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No** & **Performance Indicator** & **List of Research Articles** \\ \hline
8 & Power Consumption [150] & \\  & \(p^{total}(s,\gamma)=\sum\limits_{i=1}^{N_{b}}p_{i}^{total}(s,\gamma)\) & [50,82,150,151,153,178] \\ \hline
9 & Overall System Performance [105] & \\  & \(SINR_{D2 DR}=\dfrac{|h_{TR}|^{2}p_{D}d_{TR}^{-\alpha}}{|h_{CR}|^{2}p_{CR}d_{CR}^{ -\alpha}+N_{0}}\) & [55,56,57,69,80,104,105,138,140,163,164,164,165,166,170] \\ \hline \hline \end{tabular} Note: Only one mathematical expression is included for the 5G key parameters present in Table 17. However, different authors follow different techniques to enhance these parameters, such as path loss techniques, SINR, optimization algorithms, etc.  
\end{table} 
Table 17: _Cont._
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 3 Simulation Results and Discussion
### Influence of \(AR\) on the Uplift Bearing Capacity of Helical Anchor Foundations  
After the installation of the helical anchor foundation is completed, a vertical displacement load with a speed of \(2\times 10^{-3}\) m/s is applied to the reference point at the top of the anchor rod until 20% of the anchor plate diameter is pulled out. Figure 12 shows the load-displacement curves under various \(AR\)s during the uplifting process, with the vertical axis representing the uplift load \(F_{\text{T}}\) and the horizontal axis representing the uplift displacement \(h\).  
From Figure 12, it can be observed that as the uplift displacement increases, the uplift load initially increases rapidly, then slowly decreases, and gradually stabilizes. The initial slope of the curve can be used to characterize the stiffness of the soil. According to the characteristics of the curve, it can be found that as the \(AR\) increases, the initial slope of the curve tends to decrease. This indicates that an increase in the \(AR\) also increases the degree of soil disturbance.  
To accurately assess the uplift performance of the helical anchor, the peak point of the load-displacement curve is determined as the ultimate uplift capacity \(Q_{u}\) of the helical anchor foundation. From Figure 12, it can be observed that when the \(AR\) = 0.8, the ultimate uplift capacity is the highest, about 5% higher than that when \(AR\) = 1.0. However, the displacement corresponding to the ultimate uplift capacity is essentially the same as that when \(AR\) = 1.0. As the \(AR\) increases, the ultimate uplift capacity gradually decreases, but the displacement corresponding to the ultimate uplift capacity increases. Taking the ultimate uplift capacity when \(AR\) = 1 as the basis, the ultimate uplift capacity under different \(AR\)s is normalized. The normalized ultimate uplift capacity \(Q_{u,\text{AR}}/Q_{u,1.0}\) of each \(AR\) is shown in Figure 13.  
Figure 11: Results of the influence of \(AR\) on the normalized installation torque for different values of the depth of installation \(z\). (**a**) \(z\) = 0.40 m. (**b**) \(z\) = 0.80 m. (**c**) \(z\) = 1.20 m.  
From Figure 13, it can be observed that the normalized ultimate uplift capacity shows a linear negative correlation with the \(AR\). The slope of the fitted line is defined as the influence coefficient of \(AR\) on the ultimate uplift capacity. The fitting results show that the influence coefficient of \(AR\) on the ultimate uplift capacity is only 0.17, indicating that the influence of \(AR\) on the ultimate uplift capacity is relatively small. However, this linear relationship is specific to the range of \(AR\) values studied. The goodness-of-fit index, \(\chi^{2}\), for the linear model is 0.852, providing a quantitative assessment of how well the model fits the data. It is important to note that while the linear fit may appear reasonable in small graphs, the actual fit may have limitations.
## 1 Introduction
The \"14 th Five-Year Plan\" for the maritime system clearly states that by 2025, the NO\({}_{\chi}\) and SO\({}_{\chi}\) emissions from operating ships will drop by 7% and 6%, respectively, compared with 2020 [8]. At the two sessions in 2021, \"carbon peaking\" and \"carbon neutrality\" were written into the government report for the first time. China solenmly pledged to achieve \"carbon peaking\" by 2030 and strive to achieve \"carbon neutrality\" by 2060. \"Carbon peaking\" and \"carbon neutrality\" are not only related to the responsibility relationship between countries to reduce emissions but they are also closely related to China's sustainable development goals [9]. It can be seen that the country and the government have paid more and more attention to the influence of the atmospheric environment on the health of the people, and it is imperative to strengthen the control of air pollution and improve the atmospheric environment. At present, the research on ship pollution discharge in China is in the stage of rapid development. The research on ship pollution discharge mainly focuses on ocean shipping and coastal cities [10; 11; 12; 13], and there are not many studies on inland shipping. Therefore, it is urgent to establish a high-precision ship air pollution emission inventory for a more refined emission calculation model in the inland river area [14]. The traditional power method model does not consider the matching of the propeller of the ship and is not applicable to the actual navigation of the ship in the inland river area. Engine propeller matching greatly affects the efficiency of diesel engine energy conversion. When the engine-propeller matching is unreasonable or even poor, the diesel engine will be damaged during the ship's sailing process, and the fuel consumption rate will increase. It is directly related to the air pollution emissions of ships. After conducting the tail shaft power test of representative ships in the region, it was found that the matching coefficient of the propellers affects the accuracy of the ship's pollution emission inventory. Therefore, this paper corrects the traditional power method emission inventory calculation model by introducing the engine propeller matching coefficient, which can optimize the pollution emission inventory of inland ships, and also provide suggestions and thinking for the Chinese government to formulate energy saving and emission reduction measures in inland river areas.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 2 Problem Description
### Measures for Train Delays  
When dealing with train delays, the requirement of real-time becomes crucial. In situations with longer delays, flexible measures like train service cancellations or route adjustments can be implemented to mitigate the overall impact of delays on the system. However, in situations with short-term delays, the timely rescheduling of the train timetable becomes essential as it directly affects the operational efficiency and safety of the metro system.  
Without staff adjustments, the metro system would continue operating based on the original plan, resulting in subsequent trains experiencing the same delay as the first train. This cumulative effect would lead to significantly longer overall delay times. To prevent this accumulation, staff members, following the metro's emergency plan, assess the delay situation of the first train and progressively retain subsequent trains to ensure safe operation and to address timetable adjustment issues. However, manual adjustment often requires a long dwelling time and may lead to uneven adjustments across multiple trains and physical stations. In addition, the First-In-First-Out (FIFO) algorithm is commonly  
Figure 2: Train delay with train dwell and train optimization adjustment.  
used for rescheduling. This algorithm arranges departures based on the order in which trains enter the physical station, maintaining the original entry order. However, the FIFO algorithm does not consider factors such as train priority or the degree of delay, which might result in suboptimal rescheduling plans.  
To address train delays, this paper proposes a new method that comprehensively considers multiple factors, including train priority and delay conditions. The decision variables are the physical station dwelling times of subsequent trains, and the section running times with different block running time levels. When delays occur, strategies such as increasing the dwelling time of subsequent trains at physical stations or modifying trains' block running time level can be adopted. The goal is to provide a more practical solution for real-time train adjustments.
## 3 Approach
### Generation Dynamic Encryption Key  
The Devicesensor and Devicegateway that have the same TransactionPool independently generate the Nth dynamic encryption key. Figure 10 shows how to generate dynamic encryption key. The TransactionPool hash value is encrypted with Encrypt (DeviceKey\({}_{n-1}\),TransactionPool(Hash)). The reason for encryption is to prevent exposure as the TransactionPool (Hash) value has already been sent TargetValue. The Block\({}_{n-1}\) hash value is added to this encrypted value and the result is set to BlockInfo\({}_{\text{N}}\). One more hashing of BlockInfo\({}_{\text{N}}\) becomes the hash value of Nth block.  
The hash value of Block\({}_{n}\) is used as a seed value to generate the key for the new symmetric key AES. This newly generated encryption key is the Nth DeviceKey\({}_{n}\). Both Device\({}_{\text{sensor}}\) and Devicegateway will generate the same symmetric encryption key. The pseudo code is as Algorithm 4.  
```
1:procedureDeviceKeyGeneration
2:DeviceKeyGeneration
3:(Block\({}_{n-1}\) (Hash),Encrypt(DeviceKey\({}_{n-1}\),TransactionPool(Hash)) -> Blockinfo\({}_{n}\)
4:Seed\({}_{n}\) = SHA256(Blockinfo\({}_{n}\))
5:Call module AESKeyGenerator(Seed\({}_{n}\)) -> DeviceKey\({}_{n}\)
6:Devicegateway executes
7:(Block\({}_{n-1}\) (Hash),Encrypt(DeviceKey\({}_{n-1}\),TransactionPool(Hash)) -> Blockinfo\({}_{n}\)
8:Seed\({}_{n}\) = SHA256(Blockinfo\({}^{n}\))
9:Call module AESKeyGenerator(Seed\({}_{n}\)) -> DeviceKey\({}_{n}\)
10:endprocedure
```  
**Algorithm 4**DeviceKeyGeneration
## 3 Key Technologies Achieved by the System
### Duplicated Data Filtering and Missing Data On-line Filling  
Repetition and missing occur to the acquisition data received by the PC management software of the field monitoring subsystem. For data repetition, a duplicated data filtering method based on time identification is proposed. The time field of the acquired data is compared with that of the latest record of related nodes in the database. If time interval is less than half of the sampling cycle, the acquired data are considered duplicated and are discarded. Therefore, duplicated data are effectively filtered and system reliability is raised. For data missing, a self-decision and on-line filling method is proposed. Estimation strategies are chosen based on the strength of the spatial correction and temporal correlation of the missing data, and weights of the various estimation algorithms are determined based on the principle of minimum sum of squared errors (SSE). Technologies based on multithread programming are used for on-line identification of missing data and the self-decision and on-line filling method is used for missing data estimation and storage in database. Therefore, continuity and integrity of data acquisition by the system are guaranteed and the negative effects of data missing on the follow-up data utilization are reduced.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam
### Analysis of Parameters Affecting the Fire Resistance of Prestressed Concrete T-Beams
#### 3.3.2 Effect of Different Ratios of Prestressing Bar on Fire Resistance  
Under the conditions of load ratio \(n=0.4\), \(n=0.6\), and \(n=0.8\), the law of change of fire resistance of prestressed concrete T-beams with different ratios of the prestressing bar was compared and analyzed, as shown in Figure 11. When the ratios of the prestressing bar increase from 0% to 0.0658%, there is a 25-30% increase in the fire resistance of prestressed concrete T-beams. As prestressing bars play an important role in prestressed concrete T-beams, damage to prestressing bar performance can directly affect fire resistance. At the same fire time, there is little difference in the amount of reduction in prestressing performance for different ratios of the prestressing bar. Therefore, increasing the ratio of prestressing bar will increase the structure's fire resistance.
# Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
###### Abstract  
Phase unwrapping of high phase noise and steep phase gradient has always been a challenging problem in interferometric synthetic aperture radar (InSAR), in which case the least squares (LS) phase unwrapping method often suffers from significant unwrapping errors. Therefore, this paper proposes an improved LS phase unwrapping method based on the Chebyshev filter, which solves the problem of incomplete unwrapping and errors under high phase noise and steep phase gradient. Firstly, the steep gradient phase is transformed into multiple flat gradient phases using the Chebyshev filter. Then the flat gradient phases are unwrapped using the LS unwrapping method. Finally, the final unwrapped phase is obtained by iteratively adding the unwrapping results of the flat gradient phases. The simulation results show that the proposed method has the best accuracy and stability compared to LS, PCUA, and RPUA. In the real InSAR phase unwrapping experiment, the RMSE of the proposed method is reduced by 63.91%, 35.38%, and 54.39% compared to LS, PCUA, and RPUA. The phase unwrapping time is reduced by 62.86% and 11.64% compared to PCUA and RPUA.  
least squares; phase gradient; phase unwrapping; Chebyshev filter +
Footnote †: journal: Applied  
1
Footnote 1: College of Mechanical Electrical Engineering, Fujian Agriculture and Forestry University,  
Fuzhou 350100, China; [EMAIL_ADDRESS]  
2
Footnote 2: Quanzhou Institute of Equipment Manufacturing Haixi Institute, Chinese Academy of Sciences,  
Quanzhou 362200, China  
3
Footnote 3: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China; [EMAIL_ADDRESS]  
4
Footnote 4: Correspondence: [EMAIL_ADDRESS]; Tel.: +86-135-9975-1683
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 5 Lectin-ADA Conjugation
### Lectin Mediated Drug Delivery  
A lectin-modified polymer-based drug carrier has been intensively studied to achieve a high local drug concentration [10]. In this case, the lectin direct targeting method is a valuable strategy for oral or other routes, controlled release, and diagnostic tools. These lectin-mediated drug systems for local delivery into the colon via a gastrointestinal tract (GIT) have been found to be more effective than time-pH-based systems, enzyme-triggered systems, or pressure-based systems [109]. The side specificity of drug targeting improves drug pharmacokinetics, prevents side effects on healthy tissue, and increases drug uptake by targeted cells [110]. Other research has also pointed out that the lectin grafted liposomes are a potential drug delivery system for a sustained release [111].  
The binding and uptake ability of bovine serum albumin-wheat germ agglutinin (BSA-WGA) conjugates have been studied using the Caco-2 cell model and were found to overcome the mucosal barrier [7]. Later, a poly(lactic-co-glycolic acid) microcarrier grafted with wheat germ agglutinin was successfully synthesized [8]. The resulting polymer can be used to deliver a fluorescent model drug. It has been found that the system is able to be utilized for fluorescence detection and microscopic imaging for cellular interaction. The binding affinity of Ricinus communis agglutinin (RCA 120)-conjugated gold nanoparticles have been analyzed using HeLa, 293, and 293T cell lines [112]. The study suggested that the lectin-conjugate nanoparticles detect glycosyl complex differential expression on the cellular surface, especially when strongly accumulated onto HeLa (cervical cells).  
Studies have been conducted to investigate the bioadhesion of _Lycopersicon esculentum_ agglutinin (LEA). The lectin was extracted from tomato and specifically bound tetra-(N-acetylglucosamine), (GluNAc)\({}_{4}\). Their bioadhesion towards Caco2 cells and fixed pig enterocytes after conjugation to coated polystyrene microspheres have been demonstrated [113]. An overview of different carrier-lection conjugates used in targeting delivery, along with the method for the conjugation, is provided in Table 1.
# A Site-Specific Response Analysis: A Case Study in Hanoi, Vietnam
## 6 Conclusions  
This study investigates the applicability of the Vietnamese seismic design code TCVN 9386:2012 using one-dimensional equivalent linear (EQL) and nonlinear (NL) site response approaches and discusses the relative differences between the two methods. A total of six soil profiles from three districts in Hanoi (Vietnam) and fifteen ground motions compatible with the rock design spectrum of the Vietnam design code were used as input data to conduct 1-D site response analyses. The following conclusions are achieved based on numerical analyses.  
(1) The TCVN 9386:2012 underestimates the response amplification for soil profiles falling to site class C in the range of period from 0.6 to 2 s and it overestimates the response across the whole period range for site class D (except in the case of the EQL method). It is therefore suggested that the seismic design code of Vietnam needs to be modified for both stiff profiles (i.e., site class C) and soft profiles (i.e., site class D).  
(2) It is well-known that the NL approach is appropriate to capture the actual response rather than the EQL approach, which is recommended to be used only in the case of shear strain level of less than 0.4% [31; 32]. In this study, the spectra shapes from the NL analyses are higher than those of the EQL analyses, especially in soft soil (i.e., site class D). Overall, the shear strain and PGA obtained from the EQL method can adequately approximate the NL below the threshold shear strain levels. The difference between the NL and EQL results are greater for soft soil. For the PGA, the difference occurs at the ground surface, the PGA of the EQL analysis is higher than that of the NL. Based on the  
Figure 11: Maximum PGA of soil profiles: (**a–f**) P1–P6.  
results of the NL analyses, the maximum shear strain is less than 0.5%, except soil profile P6 with 1%, and PGA is lower than 0.16 g.  
(3) The NL results revealed the occurrence of deamplification in the soft soils of Hanoi. It is recommended that the site-specific response analysis should be considered to perform the seismic design and response of above structures in site class D.  
Conceptualization and methodology, M.A. and D.P.; formal analysis, resources, and writing--original draft, V.-Q.N.; review and editing, D.-D.N. and N.-V.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST), grant number NRF-2019 RIA2C1011323.  
The authors would like to express their thanks to all participants.  
The authors declare no conflicts of interest.
## 3 InSAR Historical Review (ERS, ENVISAT, Sentinel-1)
The processing results of the three constellations are shown in Figure 4. It is remarkable the spectacular improvement in coverage from ERS to Sentinel-1. Within the ROI, we could determine 3586 velocity values for the ERS constellation, 5507 values for ENVISAT, and 14,091 values for Sentinel-1. In the case of ERS and ENVISAT, the gap in data coverage could be explained by the large baselines. When the temporal or spatial baselines between two images are too far apart, signal decorrelation may be locally high, and the results from these areas are eliminated during the processing by low coherence filtering. The poor coverage of the ERS dataset is especially remarkable, preventing the identification of any deformation pattern in the scene. The ENVISAT dataset shows better coverage, and three zones of deformation in tailing zones can be identified. Finally, the Sentinel-1 dataset shows dense coverage along the scene providing information from almost all non-dense vegetated areas and water bodies. The very small Sentinel-1 baselines preserve the coherence between images and provide a spectacular improvement in measurement coverage. The deformation areas unveiled by ENVISAT are now well mapped in extension and value. Besides this, it  
Figure 2: Workflow of the CPT algorithm for time series calculation. One of the main differences between the CPT technique with other coherence-based techniques is the initial estimation of the linear term of the displacement (Linear block). uncovered a new deformation zone, indeed, the most conspicuous deformation zone in the area of the east wall of the Atalaya pit (Zone 4). This area is being monitored by the mining company and will be discussed in detail in the following sections.