esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### 5G Optimization Algorithms
5G Optimization--Future Challenges and Research Issues  
5G key parameters such as improved spectral efficiency, low latency, high data rates, and extended coverage capacity are achieved using the optimization algorithms. There are still some challenges and issues in the implementation of AI-based optimization algorithms that need to be addressed. The key issues that are to be explored in the future are:  
* ML and RL Algorithms: New ML and RL based optimization algorithms are required to enhance the 5G network coverage and capacity and also to bridge the gap between intelligent algorithms and 5G technologies;
* Scheduling Algorithms: Scheduling algorithms need to be proposed for 5G networks to optimize the throughput and to enhance the capacity of cellular networks;
* Optimization of the Antenna Parameter: Optimization of antenna tilt angle, antenna height, and number of antennas is required to enhance the coverage and capacity and to reduce cell edge interference, deployment cost, and complexity. 5G key parameters such as coverage capacity, latency, throughput, spectral efficiency, data rate, outage probability, interference management, and power consumption can be enhanced by using different algorithms and methods in the literature. Table 17, lists the various key performance indicators and the articles in which they were used for performance evaluation. Statistical analysis of 5G key parameters is shown in Figure 14. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No** & **Performance Indicator** & **List of Research Articles** \\ \hline \multirow{4}{*}{1} & Coverage Capacity [177] \\  & \(C=\sum_{u=1}^{N_{u}}Blog_{2}\bigg{(}1+\frac{|h_{u}b_{u}|^{2}P_{u}}{P_{u}+\sum_ {i=1,j\
eq 4}^{N_{u}}|h_{u}b_{i}|^{2}P_{i}}\bigg{)}\) & [50,52,55,56,65,66,68,81,86,103,105,107,120,146,177,178,185,189,190,191,192] \\ \hline \multirow{4}{*}{2} & Latency [90] \\  & \(E[d]=E[T_{q}]+E[c]+E[ans]\) & [89, 90, 91,185] \\ \hline \multirow{4}{*}{3} & Throughput [57] \\  & \(TP_{u,r,k}=BR_{r}(1-BLER(r,\gamma_{m(p),u,k})\) & [52,56,57,67,69,81,82,84,85,89,96,99,101-104,106,120,122,139-141,186,187] \\ \hline \multirow{4}{*}{4} & Spectral Efficiency [181] \\  & \(SE=log_{2}\bigg{[}\frac{1}{(1+H^{H}H)^{-1}}\bigg{]}\) & [54,56,67,84,86,99,101,108,120,150,179,181] \\ \hline \multirow{4}{*}{5} & Data Rate [140] \\  & \(R_{u}(t+1)=\bigg{(}1-\frac{1}{\tau}\bigg{)}+\frac{r_{u}(t)}{T},u\in U\) & [69,99,100,121,134,140,143,146,176] \\ \hline \multirow{4}{*}{6} & Outage Probability [168] \\  & \(P(R\leq R_{tt}|B)=P(\sum_{i=1}^{B}R_{i}\leq R_{tt})\) & [50,54,68,83,155,156,162,167] \\ \hline \multirow{4}{*}{7} & Interference Management [107] \\  & \(I=\sum_{j=1}^{N_{0}}P_{j,j}d_{j,r}^{-s}+\sum_{k=1}^{N_{u}}P_{k}h_{k,r}d_{k,r}^ {-s}+N_{0}\) & [57,78,79,87,98,106,107,110] \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summary of research articles based on performance indicators.
## 2 General Structure of the Methodology
### Fuzzy Fusion Estimation Module
#### 2.3.1 Fuzzy Controller Design Ideas for Unintegrated Rail Surface Conditions  
The real-time calculated creep-slip velocity and the adhesion coefficient estimated by the observer serve as the two input variables, while the similarity between the current rail surface and the four predefined standard rail surfaces \(x_{1}\), \(x_{2}\), \(x_{3}\), and \(x_{4}\) constitute the four output variables. To establish a structured classification, the adhesion characteristic curves of the four standard rail surfaces are analyzed, and the creep-slip velocity is categorized into three levels based on the density distribution of the curves: large creep, medium creep, and small creep. Similarly, the adhesion coefficient is classified into four fuzzy subsets, denoted as 1, 2, 3, and 4, each corresponding to a specific standard rail surface. The similarity measure between the current rail surface and each standard rail surface is also divided into four fuzzy subsets: very similar, similar, generally similar, and not similar. These fuzzy values are then converted into precise numerical values using the center of area defuzzification method. The rules governing the fuzzy controller are systematically organized and presented in Table 1.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 2 Related Work
The contributions of this paper are summarized as follows:  
* The problem of task offloading optimization is modeled as a minimum average total energy consumption each time by combining each member in platooning with the MEC server. * To meet the requirements of the execution deadline and energy consumption, an optimization algorithm based on Lyapunov function is proposed, and a greedy algorithm is adopted to approximate the sub-optimal decision. * The proposed algorithm can significantly reduce the energy consumption of platooning members (PMs) compared with the shortest queue waiting time algorithm and the full offloading to an MEC algorithm.
# A Review and Experimental Evaluation on Split Learning
## 2 Taxonomy and Review of SL Paradigms  
In this section, we provide a taxonomy and review of SL paradigms. To better distinguish them from FL, the SL paradigms that we consider generally involve a model split between the server and clients. However, with advances in SL and FL, several FL paradigms have incorporated some SL semantics within the FL framework, such as dynamically adapting the model size through pruning during training [85]. Therefore, we also include some FL paradigms that use submodels during training in our discussion. To comprehensively introduce these paradigms, we categorize them into three types based on whether intermediate data (e.g., smashed data or gradients) or model weights are aggregated during the training process. The first type, i.e., model split-only approach, refers to the SL paradigms where the model architecture is partitioned across different participants, but no data or model weight aggregation is performed during training. The second type, referred to as weight aggregation-based approach, utilizes techniques such as FedAvg [12] to aggregate model weights from different client devices to obtain a global model. The third type, called intermediate data aggregation-based approach, aggregates intermediate data from different client devices, such as gradients or smashed data. By categorizing SL paradigms into these three types, our goal is to provide a clear and concise understanding of different approaches to their design ideas.  
According to the above classification, we conduct a comprehensive comparison of the SL paradigms in Table 3, which lists the main SL paradigms and compares them along four dimensions. Here, \"Commun. Cost Reduction\" represents whether a paradigm reduces communication costs between clients and the server; \"Label Protection\" indicates whether a paradigm can prevent label leakage; \"Data Heterogeneity\" refers to whether a paradigm mitigates model performance degradation due to data heterogeneity between different clients; and \"Commun. or Comput. Heterogeneity\" indicates whether a paradigm considers training time variations among clients caused by differences in communication bandwidth or computation resources. Next, we review the three types of SL paradigm in more detail.
## 4 Results
### Pore Structure Characteristics  
The Taiyuan Formation shale exhibits a highly intricate and varied pore structure characterized by a broad range of PSD. According to the classification system proposed by IUPAC, we can classify these pores into three distinct categories: micropores (\(\lambda<2\) nm), mesopores (\(\lambda\) = 2-50 nm), and macropores (\(\lambda>50\) nm) [49]. Based on the categorization of adsorption curves by IUPAC and the characteristics of hysteresis loops, the nitrogen adsorption curves of shale exhibit similarities with type IV adsorption curves while predominantly featuring H\({}_{3}\) and H\({}_{4}\) hysteresis loops (Figure 9). First, the H\({}_{4}\) hysteresis loop includes \(Z\)-11 and \(Z\)-20. The adsorption curve slowly increases. The adsorption and desorption curves overlap when P/P\({}_{0}\) is less than 0.5. When P/P\({}_{0}\) is between 0.5 and 1.0, the hysteresis loops are narrow with a slight inflection point at P/P\({}_{0}\) = 0.5, indicating the presence of fine bottlenecks and slit/wedge pores in the shales.  
The SSA of Taiyuan Formation shale in SNCB ranges from 9.47 to 22.14 m\({}^{2}\)/g, with an average of 14.51 m\({}^{2}\)/g, according to the BET method calculation results. The PV and PSD characteristics of shale samples are analyzed based on the calculation results of BJH theory (Table 3, Figure 10). The total PV varies between 0.0098 and 0.022 cm\({}^{3}\)/g, with an average of 0.017 cm\({}^{3}\)/g. The APS varies between 3.59 and 6.30 nm, with an average of 4.84 nm. The PV and SSA distribution of shale is unimodal, with a peak value at 2-5 nm, indicating  
Figure 9: LTNA curves of the Taiyuan Formation shale in the SNC.  
that the predominant micropores are mesoporous, and the main contribution to SSA comes from pores sized between 2 and 5 nm. The development of nanoscale pores within shale facilitates efficient storage of shale gas by providing sufficient space for gas adsorption.
## 3 Results  
### Spatial and Temporal Dynamics in Forest and Grassland Ecosystems  
#### 3.1.1 Divergent Trends in Forest and Grassland  
In 2020, grassland was the predominant land cover in the Altai Mountains National Park Candidate Area, comprising 59.09% of the total area (7907.88 km\({}^{2}\)), whereas forest accounted for 12.3% (1646.55 km\({}^{2}\)). The analysis of the proportions of forest and grassland from 2000 to 2020 (Figure 3) indicates that 2010 marked a pivotal turning point in their evolution. Forest cover increased from 10.8% to 12.1% by 2010 and has continued to rise steadily since then. In contrast, grassland, which had been gradually declining but remained above 65% in the preceding decade, experienced a significant reduction after 2010, decreasing from 65.8% to 59.1%.  
Compared to 2000, the forested area increased by 197.76 km\({}^{2}\) in 2020, reflecting a 13.65% rise. Conversely, the grassland area decreased by 1047.15 km\({}^{2}\), representing an 11.69% decline, indicating that the extent of grassland degradation was approximately five times greater than forest growth. To more effectively illustrate the dynamics between forest and grassland, an ecosystem transfer chord diagram and spatial transfer distribution were  
Figure 3: (**a**) Annual distribution of forest and grassland percentages in the Altai Mountains National Park Candidate Area from 2000 to 2020. (**b**) Trends in forest and grassland percentages over time.  
constructed (Figure 4). The results indicate that the primary transitions involved grassland being converted to bare land, cropland, and forest, with transfer rates of 11.32%, 3.18%, and 2.86%, respectively. Overall, grassland experienced substantial loss, with the largest net conversion of 681.13 km2 to bare ground, followed by a conversion of 215.35 km2 to forest.
## 4 Conclusions  
In this paper, the dynamic characteristics and crowd load response of the stadium stands in Suzhou Industrial Park were tested on-site, combined with the numerical calculation of the corresponding finite-element model, and the following conclusions were obtained:  
(1) The test results of the dynamic characteristics of the structure were consistent with the calculated values, indicating that the finite-element modeling of the structure was accurate and reasonable.  
(2) The measured vertical fundamental frequency of structural modal test area 1 was 3.750 Hz, and that of modal test area 2 was 3.950 Hz, which was greater than the limit of 3 Hz given by Technical standard for human comfort of the floor vibration [7]. However, its second-order resonance frequency was still within the range of pedestrian load frequency, which may still cause vibration comfort problems. When the surface layer is added, the decoration is completed, and when the stadium holds an event, the constant and live load increase, especially the increase in the crowd load caused by the audience, and the vertical vibration frequency of the floor structure decreases. Therefore, from the perspective of vertical vibration frequency, the problem of vibration comfort of the structure should still be paid attention.  
(3) Comparing the structure finite-element model and the obtained acceleration response peak with the test results showed that the presence of people increased the damping effect of the structure.  
(4) Experience and data references for the platform construction of large stadiums should be provided in the future. In design and construction, the structural frequency should be avoided as far as possible from the pedestrian step frequency and the double frequency of the step frequency; the measured damping is small, and other methods should be used to increase the structural damping.  
Conceptualization, W.X. and H.L.; methodology, Y.Z.; software, W.X.; validation, H.L., W.X. and P.W.; formal analysis, H.L. and Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, W.X.; writing--original draft preparation, W.X.; writing--review and editing, W.X.; visualization, W.X.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (grant no. 51278106).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors confirm that the data supporting the findings of this study are available within the article.  
The authors thank Southeast University for providing the test instrument.  
The authors declare no conflict of interest.
## 3 Principle of Operation of RIS
### RIS-Assisted Smart Radio Environment  
The RIS-based smart radio environment is discussed in [32]. The advantage of this smart radio solution can be enhanced by utilizing one or more RISs in the case of wireless propagation. For example, to encourage spatial multiplexing by the received power via beamforming or by affecting the channel. In essence, a RIS can be named any passive surface that can be dynamically reconfigured to control electromagnetic incident waves and modify the channel's conditions.  
Limited Phase Shifts Problem and RIS Assisted Communication Model  
In such a smart environment scenario, sometimes the line of sight (LOS) propagation link may not be stable, or even fall into a complete outage due to the varying wireless environmental circumstances resulting in unexpected fading and possible obstacles. As a result, RIS has gained a great deal of worldwide attention to tackle this problem [58]. RIS is adopted to reflect the signal to enhance the QoS to establish a perfect condition for propagation. For example, a cellular uplink network using M\(\times\)N electrically controlled RIS elements is shown in Figure 2. There is a deep fading of the direct connection between the base station (BS) and the user. RIS with minimal phase shifts is considered to enhance the QoS at the BS to reflect the signal from the consumer to the BS in the presented scenario.  
Figure 1: Schematic of RIS system illustrating basic principle [57].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Performance Parameters** & **Values** \\ \hline
[59] & Outage probability & \(3\times 10^{-3}\), M = 100 \\  & & \(3\times 10^{-1}\), M = 1600 \\
[60] & Ergodic rate & 3.8, M = 100, K = 5 \\  & & 4.5, M = 1600, K = 20 \\
[61] & Network Spectral efficiency (bps/hz) & 85, M = 100 (T = 50 and t = K) \\
[61] & Ergodic System Spectral efficiency (bps/hz) & 65, M = 100 (L = 0.5, K = 20) \\
[60] & Variance of data rate & 0.2, M = 1600 (L = 0.5, K = 20) \\
[62] & Achievable Data rate (bps/hz) & 21, d = 200 m \\
[16] & Average EE (Kbit/Joule) & 130 at Pmax (dBm = 40) M = 32, K = 16, N \\  & & = 12 \\
[16] & Average SE (bps/hz) & 145 at Pmax (dBm = 40) M = 32, K = 16, N \\  & & = 12 \\
[42] & Received SNR (dB) & 17, N = 100 \\
[63] & Secrecy outage probability & \(10^{-1}\), N = 50 \\
[64] & Coverage distance (km) & 12, N = 8 \\
[64] & Probability of SNR gain (dB) & 0.9, N = 4 \\
[64] & Delay outage rate & \(10^{-1}\), B = 2 MHz \\ \hline \hline \end{tabular} Where, N = Number of ILSs, K = Number of LUs units in each LIS (equal to the number of devices) = 0.5, M = Number of antennas on each LIS unit = 100, 1000, L = Half length of each LIS unit = 0.5 m.  
\end{table}
Table 3: RIS performance parameters comparison.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Calculation Process of the Prestress Loss for Steel Strands
#### 4.1.4. Prestress Loss from the Relaxation of Prestressed Steel Strands  
For steel strands, the prestress loss caused by relaxation of prestress tendons can be determined on the basis of the relationship between the tension control stress \(\sigma_{\text{con}}\) and the standard tensile strength \(f_{\text{ptk}}\) of the prestressed steel strands, as presented in Equation (9). Normally, the tension control stress is around 75% of the standard tensile strength.  
\[\sigma_{I4}=\left\{\begin{array}{cl}0.20\bigg{(}\frac{\sigma_{\text{con}}}{f _{\text{ptk}}}-0.575\bigg{)}\sigma_{\text{con}}&0.7f_{\text{ptk}}<\sigma_{\text {con}}\leq 0.8f_{\text{ptk}}\\ \\ 0.125\bigg{(}\frac{\sigma_{\text{con}}}{f_{\text{ptk}}}-0.5\bigg{)}\sigma_{ \text{con}}&0.5f_{\text{ptk}}<\sigma_{\text{con}}\leq 0.7f_{\text{ptk}}\\ \\ 0&\sigma_{\text{con}}\leq 0.5f_{\text{ptk}}\end{array}\right. \tag{9}\]
## 2 Concept and Principles
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \(\mathbf{l_{p}}\) & \(\mathbf{w_{p}}\) & \(\mathbf{p}\) & \(\mathbf{w_{1}}\) & \(\mathbf{l_{1}}\) & \(\mathbf{l_{2}}\) & \(\mathbf{l_{3}}\) & \(\mathbf{l_{4}}\) \\ \hline Element 2 & 0.303 & 0.392 & 1 & 0.143 & 0.04 & 0.09 & 0.14 & 0.02 \\ Element 3 & 0.303 & 0.392 & 1 & 0.143 & 0.09 & 0.19 & 0.29 & 0.02 \\ Element 4 & 0.303 & 0.392 & 1 & 0.143 & 0.14 & 0.29 & 0.09 & 0.02 \\ Element 5 & 0.303 & 0.392 & 1 & 0.143 & 0.19 & 0.04 & 0.24 & 0.02 \\ Element 6 & 0.303 & 0.392 & 1 & 0.143 & 0.24 & 0.14 & 0.04 & 0.02 \\ Element 7 & 0.303 & 0.392 & 1 & 0.143 & 0.29 & 0.24 & 0.19 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the element and phase shifter (unit: mm). Figure 1: (a) Geometrical construction of the element. (b) Geometrical construction of the phase shifter. Figure 2: S-parameters of radiation patch obtained from each arm of feeding line in (a) element 2 (b) element 3 (c) element 4 (d) element 5 (e) element 6 (f) element 7. \(5\times 2\pi\)/7 rad, \(6\times 2\pi\)/7 in element 2, 3, 4, 5, 6, 7, respectively, by switching these arms. Each element had three phase orders. Moreover, a phase shift of 2\(\pi\)/7, 4\(\pi\)/7, and 6\(\pi\)/7 between adjacent elements was achieved when the arms 2, 3, and 4 were on, covering a complete phase range of 2\(\pi\), 4\(\pi\) and 6\(\pi\), respectively. These results show that the proposed tunable radiation elements are good candidates for active wavefront manipulation, which will be discussed in the following.
## 5 Experiment
### Experiment Setup  
#### 5.1.1 Datasets  
In this work, we utilize three widely used datasets, all based on Wikidata: LC-QuAD 2.0 [18], QALD-9 plus [19], and QALD-10 [20].  
LC-QuAD 2.0 provides labeling in both DBpedia 2018 and Wikidata; it consists of a mixture of simple and complex questions that were verbalized by human workers on Amazon Mechanical Turk. It is a large and varied dataset comprising 24,180 train questions and 6046 test questions, which contains a wide range of question types, such as single fact, multi fact, boolean, dual intentions, count, ranking, and so on. Since most of the DBpedia queries could not be executed in the current version, we use the Wikidata query as the target label, wherein similar steps have been performed by [11; 46]. Our experimental approach is consistent with the methodologies outlined in references [11; 46], encompassing three primary phases. Initially, we systematically rename all variables within the SPARQL queries based on their sequence of appearance. For instance, a typical SPARQL query such as  
SELECT (COUNT(?sub) AS?value) {?sub wdt:P1433 wd:Q324878} is transformed to  
SELECT (COUNT(?var0) AS?var1) {?var0 wdt:P1433 wd:Q324878} to standardize variable names according to their occurrence order. Subsequently, we encode each element of the SPARQL syntax (e.g., SELECT,COUNT, DISTINCT, etc.) as a unique sentinel token, assigning a distinct ID to each keyword, a method detailed in Section 4.2. Lastly, we employ a serialized input format aligned with the one described in [11], as detailed in Section 4.3, to maintain consistency with established practices.  
In addition, we leverage the QALD-9 plus and QALD-10 datasets, both integral components of the Question Answering over Linked Data (QALD) challenge series. The QALD-9 plus dataset enriches the original QALD-9 by introducing high-quality translations of questions into eight languages and transferring SPARQL queries from DBpedia to Wikidata. This not only bolsters the dataset's usability and relevance but also facilitates the training and testing of KBQA systems over DBpedia and Wikidata in multiple languages. In contrast, QALD-10 seeks to rectify several deficiencies in previous benchmarks, such as insufficient translation quality for non-English languages and the lower complexity of gold standard SPARQL queries. Consequently, QALD-10 stands out as of the most complex and practically applicable datasets in the QALD challenge series.