esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this article, we analyze the RACH success probability under the CE mechanism in the LEO satellite-based IoT network based on stochastic geometry theory. On the basis of a power consumption model of NB-IoT terminals, an ACE method is proposed to meet the requirement of different applications. Numerical results show that the maximum repetition number of the preamble and back-off window size have a great influence on the system performance, and their value ranges should be set within [4; 18] and [0; 2048] to meet different requirements in LEO satellite IoT networks. The ACE method can effectively enhance the RACH success probability while concurrently reducing power consumption when NB-IoT terminals are engaged in diverse demands. The related conclusions are of practical guidance for the future deployment of NB-IoT terminals in NTN scenarios. However, in the random access procedure, the damage caused by the LEO satellite channel to Msg3 and Msg4 was not considered. The contention resolution results of Msg3 and Msg4 for the random access process are equally important. Furthermore, the resource allocation and scheduling process in the RA procedure need to be studied in detail. Further works on the influence of Msg3 and Msg4 on the RA procedure need to be discussed such as the problem of outdated resource allocation in LEO satellite-based IoT networks.  
Figure 11: Throughput performance with different NB-IoT terminal parameter configurations [22; 24].  
**Author Contributions:** [PERSON] and [PERSON] led the main contribution and writing of the manuscript. [PERSON], [PERSON] and [PERSON] supervised the findings of this work. All authors contributed to the manuscript and approved the submitted version. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (No. U21A20450, No. 62171234, and No. 61971440), Jiangsu Province Basic Research Project on Leading Technologies (No. BK20192002), and the National Key R&D Program of China (No. 2022 YFB2902600).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 4 IBC Time Analysis Method  
### Kernel Density Estimation  
Kernel density estimation (KDE) is a statistical method that uses a small sample of data to estimate a population's underlying probability density function. It is a non-parametric technique in which the data is not assumed to have a specific distributional form. By applying a kernel function to each data point, KDE generates a smooth, bell-shaped curve  
Figure 12: Time difference between Cosmos Hub and Measurement Node.  
(typically a Gaussian or normal distribution). The bandwidth parameter governs the smoothness of the estimate.  
The seaborn.kdeplot() [27] method in Python can be used to generate kernel density estimation charts. From the observed data, this function builds a histogram, normalizes it to form a probability density function, and applies the kernel function (\(K_{h}\)) to each data point. Equation (1) gives the equation for estimating kernel density:  
\[\hat{f}_{h}(x)=\frac{1}{n}{\sum_{i=1}^{n}K_{h}(x-x_{i})}=\frac{1}{nh}{\sum_{i=1 }^{n}k(\frac{x-x_{i}}{h})} \tag{1}\]  
\(n\) is the total number of observations, \(x_{1},\ x_{2},\ \ldots,\ x_{n}\) are the observed sample data, \(K_{h}\) is the kernel function, \(x\) is the variable of interest, and \(h\) is the bandwidth parameter.  
Choosing the proper kernel function and accurately choosing the bandwidth value is critical for practical kernel density estimation. The kernel function is used to determine the shape of the density estimate. In contrast, the smoothness of the estimate is controlled by the bandwidth parameter. Although a larger bandwidth offers a smoother estimate, it may over smooth the data. A lower bandwidth, on the other hand, produces a more detailed estimate, but it may bring noise into the estimate.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Terahertz Antennas for 6G Applications
#### 3.7.3 Antennas Selection  
Among many innovative ideas regarding antennas transmitting at terahertz frequencies, Graphene-Based Antennas, Photoconductive Antennas, Metamaterial-Based Antennas, and Horn Antennas (ref. [133]) stand out because of the advantages they offer. Moreover, four types of antennas that are prevalent in the scientific community are briefly analyzed below and in Table 12.
## 4 Results
### Comparison with Groundwater Level  
Variations in the groundwater level may trigger ground surface deformation for multiple reasons [17]. One of the most effective reasons is that the decrease in groundwater level causes changes in the stress level on deformable soils that result in subsidence. Therefore, the groundwater level in the region is an important factor that may trigger ground surface deformation. Groundwater level data are obtained from the Directorate of Turkish State Hydraulic Work. Location of the well no 28372 is shown in Figure 4c,d. Cumulative ground surface deformation measured in LOS is compared with groundwater level in Figure 9 for ascending and descending datasets, respectively. There is quite a good correlation between groundwater level changes and LOS deformation trends both in long term and short term. In the long term, both data show a generic decline trend due to overexploitation of groundwater. In the short term, with the effect of rainfall and irrigation, seasonal changes are observed.  
Irrigation and precipitation are two main reasons for groundwater variations. Groundwater has been the primary source of water for irrigation in the region. Extensive use of groundwater for agriculture results in the decrease of groundwater level throughout the years. It can be observed in Figure 9 that there is a generic decline trend in groundwater levels that causes subsidence phenomenon in the long term. Together with irrigation, another important factor of groundwater level variation is the rainfall. The long-term decline trend line is removed from the groundwater level and compared with monthly average rainfall in Figure 8a. As precipitation increases in spring, the groundwater level also increases. As a result of the groundwater level increase, short term uplift is observed on the ground. Towards the end of the summer, the opposite effect is observed with decreasing rainfall. Hence, subsidence starts to be effective in the region. The increase in irrigation in the summer period also contributes to the aforementioned process.
###### Abstract  
In Indonesia, there is still a disparity in telecommunications access, with most rural areas experiencing \"no signal\" or \"blank spots.\" In contrast, urban areas enjoy modern and societally-beneficial technologies. A comprehensive framework is needed to address the disparity in telecommunications access between \"rich\" and \"poor\" groups in urban and rural/remote areas, respectively. This paper proposes a framework, built by the mathematical model, that can be used as a reference for the Indonesian government in constructing the nation's telecommunications infrastructure. The framework categorizes Indonesian administrative regions into four grids: Grid #1: \"fostered\" districts; Grid #2: \"developing\" districts; Grid #3: \"developed\" districts; and Grid #4: \"independent-advanced\" districts. To determine where each district falls in these grids, we propose a novel statistical approach using 17 indicators involving a telecommunications network and socioeconomic factors. The proposed framework results in a grid visualization of 7232 districts in Indonesia. Finally, as this paper is replete with academic research approaches and mathematical model perspectives, it is expected that the results may be a valuable input to the development of the country's telecommunications policy.  
technomications; policy; regulation; mathematical model; network; socioeconomic; Indonesia; framework; infrastructure Article
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 3 Results
### Developed Internal Network
#### 3.4.2 Batch Server  
TriBell Labs, a manufacturer of the VP-100, developed an algorithm that detects R-peaks and differentiates certain types of arrhythmia with the VP-100 [24; 25]. Specifically, an adaptive median filter was used to detect R peaks from the band-pass-filtered ECG data, regardless of their sampling rates. The performance of the R-peaks' detection was verified with the MIT-BIH arrhythmia database at 360 Hz. A total of eight types of arrhythmia can be identified with the following signal patterns: bradycardia, tachycardia, PVC, bigeminy, trigeminy, ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). Although bradycardia, tachycardia, and PVC were detected early with the VP-100, an improved performance can be achieved with a band-pass filter and other biometric parameters. For PVC, the exact Q onset and S offset points can be identified using discrete wavelet transforms [26]. Several biometric parameters were utilized to detect other types of arrhythmia. For instance, average P areas, HRs, HR variability triangular index, and spectrogram were obtained to detect AFs. The performance in detecting arrhythmias was evaluated with an ECG simulator (MS400, Contec Medical Systems, Qinhuangdao, China) and the VP-100. The sensitivity of detecting bigeminy, trigeminy, VT, and SVT were 0.903, 0.873, 0.993, and 0.990, respectively [25]. This morphology-based algorithm was imported into the batch server as a dynamic-linking library function. The algorithm was operated every 90 s, and the results were transmitted to the API server. In addition, alarm push-notification data were created in this server unit and transmitted to the push server in the DMZ.
## 3 Review of PLC Systems
### Evolution of PLC Standardization
#### 3.2.2 BB PLC Standardization  
The different BB PLC systems were developed and improved from the last part of the decade in the 1990s and in the first part of the decade of the 2000s. However, it was not until 2008 that the first formal standard appeared (ANSI, TIA-1113 [71]). This standard is mainly based on the HomePlug 1.0 specifications [72], one of the very first industry standards for BB PLC, with a great impact in the worldwide adoption of PLC systems for in-home environments.  
However, the wider impact standards for the evolution of BB PLC systems have been prepared in IEEE and ITU. These standardization bodies inherited much of the PLC R&D of the first decade of the 21 st century. An outstanding sample of R&D was the collaborative OPERA project [41]. OPERA was a multi-year project (developed in two stages) funded by the European Community that engaged a large and relevant community of PLC stakeholders. OPERA focused both on BB PLC technology and its applications.  
The IEEE 1901 Working Group was created in 2005 to develop a standard for high-speed communication (over 100 Mbps), in both in-home and access environments [73, 74, 75, 76]. Its efforts were completed in September 2010 with the definition of two BB PLC technologies [47, 77, 78] (it was not possible to get to a consensus on one single implementation) together with a mandatory coexistence mechanism allowing IEEE 1901 devices to coexist among them and with other BB PLC standards (_i.e._, ITU G.hn).  
The ITU-T G.hn Home Networking framework was established in 2006 to develop a recommendation for an in-home transceiver capable of operating over different types of in-home physical media (\(<\)1 Gbps [73, 76, 79]). Recommendation G.9960 [80] (PHY (PHYsical) layer) was issued in October 2009 and Recommendation G.9961 [81] (Data Link Layer) in June 2010. These recommendations do not target access applications.
# Gamma Ray Shielding Properties of Yb\({}^{3+}\)-Doped Calcium Borotellurite Glasses
###### Abstract  
This research work aims to investigate the radiation shielding ability of a Yb\({}^{3+}\)-doped calcium borotellurite glass system. The system has the basic composition of CaF\({}_{2}\)-CaO-B\({}_{2}\)O\({}_{3}\)-TeO\({}_{2}\)-Yb\({}_{2}\)O\({}_{3}\) but is denoted as TeBYbn for simplicity. The effect of increasing the TeO\({}_{2}\) content in the glasses from 10 to 54 mol\(\%\) was investigated, with five different chosen compositions and densities. The Phy-X/PSD program was used to investigate the mass attenuation coefficient (\(\mu/\varrho\)) of the samples. The mass attenuation coefficients were theoretically determined by using an online software for the calculation of shielding parameters. Other parameters were then calculated and analyzed, such as the linear attenuation coefficient (\(\mu\)), transmission factor (TF), radiation protection efficiency (RPE), effective atomic number (Z\({}_{\text{eff}}\)), and mean free path (MFP). TeBYb5, the glass with the greatest TeO\({}_{2}\) content, was shown to have the greatest \(\mu/\varrho\); however, at greater energies, the differences between the values are practically negligible. \(\mu\) was shown to increase with density, such as from 0.386 cm\({}^{-1}\) to 0.687 cm\({}^{-1}\) for TeBYb1 and TeBYb5 at 0.284 MeV, respectively. The least TF was found for samples with a thickness of 1.5 cm, proving an inverse correlation between the thickness of the sample and the TF. The HVL and TVL of the glasses decreased as the density of the samples increased, which means that TeBYb1 is the least effective out of the investigated glasses. The five samples proved to have a lower MFP than some other shielding glasses, demonstrating their capabilities as radiation shields. Based on the calculated parameters, TeBYb5 indicated the greatest photon attenuation ability.  
borotellurite; radiation shielding; radiation glasses; gamma-rays; Phy-X/PSD +
Footnote †: journal: Applied Sciences
# Advanced Scanning Technology for Volume Change Measurement of Residual Soil
## 5 Testing on Residual Soil
The specimen is equilibrated with the headspace of a sealed chamber, containing a mirror. At equilibrium, the water potential of the air in the chamber and the water potential of the specimen become the same. The mirror temperature is controlled by a thermoelectric (Peltier) cooler. A photoelectric cell detects the exact point at which condensation occurs on the mirror. The device directs a beam of light onto the mirror reflecting into a photodetector, which senses the change in reflectance when condensation occurs on the mirror. A thermocouple, attached to the mirror, records the temperature at which condensation occurs. Values start to be displayed, and this indicates that initial measurements are being recorded. A green LED is flashed with a beeping sound when it reaches the final value. The final water potential and temperature of the specimen are displayed on the screen of the device. The device uses an internal fan that circulates the air within the specimen chamber to reduce the equilibrating time. The WP4C Dewpoint Potentiometer measures the total suction (sum of the osmotic and matric suctions) in the soil specimen. Soils bind water mainly through matric forces and therefore dominate the matric component, especially at low water content. The shrinkage test was carried out on soil specimen with 5 cm diameter and 3 cm height. The soil specimen was exposed to air evaporation for a certain period of time. 10 sets of 3D scan tests on different soil specimens from the same soil type were conducted to ensure the reasonableness of shrinkage curve. Soil volume change measurement using 3D scanning involves capturing high-resolution data (following procedures explained in Section 4) was \begin{table}
\begin{tabular}{c c} \hline
**Index Properties Test** & **Testing Standards** \\ \hline Specific gravity & ASTM D854-14 [54] \\ Grain-size distribution & ASTM D422-63 [55] \\ Afterberg limits & ASTM D4318-17e1 [56] \\ Soil Classification (Unified Soil Classification System) & ASTM D2487-17 [57] \\ \hline \end{tabular}
\end{table} 
Table 1: Index properties testing standards. carried out to monitor changes in soil dimensions over time. The measurement of soil volume and water volume was conducted three times a day until the soil volume and water volume was considered constant. The measurement data of soil volume change was converted into void ratio, whereas the measurement data of water volume change was converted into gravimetric water content. The void ratio was plotted against gravimetric water content to generate the shrinkage curve. The discrete data of shrinkage curve was best fitted using Equation (1). The discrete data from SWCC test using Tempe cell, pressure plate, and WP4C were best fitted using Equation (2) (for unimodal SWCC) or Equation (3) (for bimodal SWCC). Then, the fitting parameters of Equation (1) were used to calculate the void ratio for every gravimetric water content in the SWCC plot generated using either Equation (2) or Equation (3). Finally, SWCC in terms of degree of saturation against soil suction can be plotted.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 3 Enabling Technologies  
Virtualization can be achieved by using enabling technologies such as SDN and NFV and it helps in implementing network slicing in 5G. In this section, we discuss the key network slicing enabling technologies.
## 2 Methodology
### Meshing Strategy  
The mesh configuration is consistent for center running and offset running cases. The mesh layout is presented here using the offset running case. The topology of the volume mesh is the prism layer mesh and trimmer mesh in STAR-CCM+ 18.02 software, which has been proven to be accurate in studying train aerodynamic issues [47; 48]. As illustrated in Figure 5, according to the mesh strategy of [PERSON] et al. [49] and considering the requirements for overset mesh, multi-level refinement blocks are applied around the train and wake region. The minimum grid size around the train is set to 0.05 m. To solve for the boundary layers on the train surface and the tunnel wall, 16 layers of prism layer mesh are applied to the train surface with a stretching ratio of 1.2, resulting in a total thickness of 118.12 mm. Similarly, the tunnel wall is stretched with 16 layers and a stretching ratio of 1.2, resulting in a total thickness of 78.7 mm. To reduce discretization errors, it is necessary to ensure that the grid cell sizes in the overlapping regions of the two sets of meshes remain identical. The total number of mesh cells in the computational model is 32.84 million, with 5.45 million cells in the background region and 27.39 million cells in the overset region.
## 7 Proposed Architecture for Cellular System Based on SDN, NFV, and C-RAN
### Interaction Between SDN Controller, NFV MANO, and C-RAN
* Deployment and Orchestration: The SDN controller, in collaboration with NFV orchestration tools, can deploy and manage the lifecycle of VNFs, such as scaling out or in, based on the network's requirements. As depicted in Figure 19, the network orchestration function is utilized to establish network service chaining policies. These policies are also shared with the SDN controller within the NFVI networking layer through the NFV MANO framework. This collaboration provides efficient traffic routing and enhances overall network services [104]. * Policy Enforcement: The SDN controller can enforce network policies at a granular level, directing specific types of traffic to pass through certain VNFs for processing, such as firewalls and load balancers. * **Integration of SDN with C-RAN and VNF:*
* Flexibility and Scalability: By integrating SDN with C-RAN and VNFs, the network gains flexibility and scalability, allowing it to support a wide range of services and adapt to changes in traffic patterns or network conditions. Resource Utilization: The SDN controller enhances resource utilization by matching computing and radio resources with network demands in real-time, which is critical for the efficiency of both C-RANs and VNFs. In summary, NFV and SDN both help make networks more flexible and adaptable, but they do different jobs that work well together. NFV focuses on making network functions virtual, which means they are more flexible and efficient. SDN, on the other hand, provides the tools to manage and organize these virtual functions across the network. The SDN controller acts as the brain of the network. It makes decisions to make sure everything runs smoothly, including both the C-RAN and the VNFs. This teamwork between NFV and SDN is crucial for delivering fast, reliable, and adaptable service, especially with the demands of advanced cellular networks like 5G and beyond.
## 2 Performance of IWP with Steel Anchor Beam
### Experimental Results  
#### 2.2.1 Stress of the Steel Plate  
To study the stress distribution in the steel structure, the stress state of the concrete, and the load transfer mechanism at the steel-concrete interface in the pylon anchorage zone, stress and strain measurement points were arranged at key positions on the expected load path from the stay cable through the concrete to the IWP components to capture the local stress response during loading. The vertical stress on the end plates reflects the transfer pattern of the vertical component of the cable force at the interface. Figure 7 shows the variation in Y-direction stress at certain measurement points on the end plate with increasing load. At points p-10, p-18, and p-22--located near the connection between the end plate and the steel bracket--the Y-direction stress increases linearly with the applied load before entering the plastic stage. These points exhibit consistently higher stress levels throughout the loading process compared to other locations. The top plate of the bracket is subjected to tensile stress, while the lower part of the support plate experiences concentrated compressive stress.  
Figure 8 show the measured Mises stress distribution on the perforated plate under a 2.5P load. From these figures, the Mises stress at the measurement points on the perforated plate varies significantly with height. From the first row of holes downwards, the Mises stress gradually increases, reaching a maximum near the sixth row of holes; further down, the Mises stress decreases and then increases again, with a second peak near the twelfth row of holes, and then it decreases again. Under the 2.5P load, the maximum Mises stress at effective measurement points on the perforated plate is 406.9 MPa, occurring between the 12 th and 13 th rows of holes on plate C. Comparing the different plates, plate C experiences the highest stress, while plate A experiences the lowest stress.  
Figure 6: Triaxial strain gauges.  
Figure 9 shows the relationship of Mises stress with load at certain measurement points on the perforated plate. The Mises stress at these measurement points increases as the load increases. At measurement point C-13, the Mises stress reaches 430 MPa at approximately 1.8P, indicating that the local steel plate has yielded.  
Figure 8: Mises stress distribution on the perforated plate of the test model under 2.5P: (**a**) plate A; (**b**) plate B; (**c**) plate C; (**d**) plate D (unit: MPa).  
Figure 7: Y-directional stress on the end plate.  
Figure 9: Mises stress in the perforated plates: (**a**) same position on different plates; (**b**) different positions on plate C.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 1 Introduction
To demonstrate the antenna performance near the human body in a practical environment, the power density was simulated to estimate the electromagnetic exposure with a simplified skin phantom at 28 GHz.