esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.3 Blockchain Technology in 6G Networks  
Blockchain technology, propelled into the spotlight by the success of Bitcoin, garners significant focus in academia and industry [189]. Functioning as a distributed public ledger within a peer-to-peer network, a blockchain comprises a chain of blocks stemming from the genesis block. A hash value that is obtained from the contents of the parent block--which includes the block header and transaction data--is appended to every new block. Details such as block version are contained in the block header, parent block hash, timestamp, transaction count, and MerkelRoot concatenating transaction hash values. Miners record and bundle transactions through Proof of Work (PoW), solving a computational problem. The mined block undergoes network-wide broadcasting, engaging nodes in a consensus process to validate trust and update the chain.  
Blockchain boasts technological advantages, including immutability, decentralization, transparency, and security [190]. The unchangeable nature of transaction data, decentralized consensus mechanisms, and shared replication across the network enhance data persistence, security, and flexibility. Despite these merits, scalability proves a crucial chal lenge in widespread blockchain application, particularly concerning throughput, storage, and networking [191].  
The exploration of blockchain's potential in 5G and beyond has gained attention, show-casing applications in edge computing, network function virtualization (NFV), network slicing, and device-to-device communications [192]. In areas like smart cities, transportation, grid, healthcare, and UAVs, it improves services like spectrum and radio resource sharing, data storage, network virtualization, security, and privacy. Reciprocally, 5G network deployment can facilitate blockchain systems through mobile networks' connectivity, computing, and storage resources. These can aid local processing for blockchain systems on mobile devices, facilitating PoW problem-solving, hashing, encryption, and consensus execution. The integration of blockchain into the forthcoming 6G system is envisioned to fortify the information infrastructure, offering enhanced flexibility, security, and efficiency [193].
## 2 Related Research
### Sensor-Based Knee Joint Angle Estimation  
Non-camera-based methods estimate knee joint angles using inertial measurement units (IMUs) attached to the user's shins and knees or embedded in tightly fitting clothing [20; 21]. [PERSON] et al. developed a portable rehabilitation monitoring system using wearable IMUs to estimate knee joint range of motion, achieving a mean absolute error (MAE) of 6.0\({}^{\circ}\) to 8.5\({}^{\circ}\)[22]. [PERSON] et al. created the SKYRE platform for the remote assessment of patients undergoing knee rehabilitation, using nine-axis accelerometers and electromyography sensors, with MAEs ranging from 0.16\({}^{\circ}\) to 4.94\({}^{\circ}\), comparable to 3D motion analysis [23].  
Methods involving stretch sensors attached to clothing or bandages for knee joint coordinate estimation also exist [24; 25]. [PERSON] et al. proposed a system using stretch sensors on knee supporters to estimate knee flexion angles, achieving MAEs from 1.6\({}^{\circ}\) to 4.2\({}^{\circ}\)[26]. [PERSON] et al. developed a knee sleeve with 16 piezoresistive sensors to estimate knee flexion and external rotation angles, with root mean square errors of 12.6\({}^{\circ}\) and 3.5\({}^{\circ}\), respectively [27]. While methods for estimating knee flexion angles without cameras exist, methods for estimating knee valgus angles are lacking.
## 3 Results and Discussions
### SEM-EDS Sample Surface Analysis
#### 3.1.1 Morphology and Topography
Using four surface profiles taken from each of the five areas used previously for porosity analysis, the surface height parameters were determined, as defined in [36]:  
* standard deviation of the height distribution, or RMS surface roughness \[S_{q}=\sqrt{\frac{1}{A}\iint\limits_{A}z^{2}(x,y)dxdy}\] (1)
* mean surface roughness \[S_{a}=\frac{1}{A}\int_{A}|z(x,y)|dxdy\] (2)  
where \(A\) is the area of the investigated surface and \(z\)(\(x\),\(y\)) is the roughness profile in the \(x\) and \(y\) direction. The SEM height maps were generated in Hitachi Map 3D software by using each of the four channels of the BSE detector. An example of SEM topographic analysis is presented in Figure 4.  
In Figure 4, four directional backscatter images (Figure 4a) recorded for each investigated surface were converted into a 3D surface height map (Figure 4b). Using a Hitachi calibration standard, a 3D surface rendering was obtained from the height maps (Figure 4c). A 2D topographic profile (Figure 4d) illustrates the variation of the peak and pit heights on the \(x\) direction of the chosen cross-section (where zero level represents the mean plane). The results for surface roughness analysis, as defined in [36], are presented in Table 2. \begin{table}
\begin{tabular}{c c} \hline
**Sample Code** & **Porosity \(\pm\) SD (\%)** \\ \hline AZ63\_5m & 19.17 \(\pm\) 1.96 \\ \hline AZ63\_10m & 14.59 \(\pm\) 1.40 \\ \hline AZ63\_20m & 11.30 \(\pm\) 1.36 \\ \hline \end{tabular}
\end{table} 
Table 1: Mean porosity values of the PEO treated surfaces. Figure 3: Example of a used SEM image \(\times\)100 (**a**) and ImageJ pore identification (**b**) of the sample AZ63\_10m surface. The obtained results show similar roughness values for 5 and 10 min and an increase in surface roughness parameters for the 20 min PEO treated. In the early stages of PEO, well uniform distribution of the discharging channel led to lower values of surface roughness. As the processing time increased, the discharge channels were fewer and more localized, which could be the explanation for the increased roughness values of the AZ63_20m sample [37].
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 3 Related Work
Two points are considered: (i) whether the proposal has been validated in any way and (ii) whether the validated proposal uses a 5 GC core. In this sense, only one related work hashad its proposal validated, which is the work [23]. In this study, validation was conducted in a test environment using different machines as edge servers and an OAI network core. However, when analyzing this study, it is possible to notice elements of the Evolved Packet Core (EPC) architecture of the network cores, such as the Mobility Management Entity (MME) and Home Subscriber Server (HSS). Thus, the study does not employ an autonomous (SA) 5G network core despite the validation. The validation focused on the ability of the proposed architecture to create network slices for the MEC in the context of 5G, facilitating communication between the two technologies. Thus, from the analysis of the conducted studies, it is evident that our proposal stands out both for its integrative approach and its validation. The proposed integration will be implemented through a service and validated within a test environment utilizing a 5G SA network core. Furthermore, it is notable that only one of the papers with architectural-level proposals has undergone validation, whereas the others remain at the theoretical and conceptual level. [PERSON] et al. discuss the possibility of deployment as an application offering a service [16]. This possibility could represent a commercial advantage, considering the launch time and the potential for the service to evolve into an MEC platform as the technology and business models stabilize. This reinforces the proposal presented, providing an academic scenario with an approach different from the others. To the best of our knowledge, there are no open-source solutions in the literature to compare the implementation.
(e.g., determining an organ's hardness) [6; 7]. Further, the development of fully or partly automated robotic surgical procedures based on machine learning has become a trend in the field of surgical robotics [8]. To pursue these highly integrated systems, along with vision and position data, additional information such as force interaction is required in most cases [9].  
Despite these expected benefits, force sensing in surgical robots remains challenging for the following reasons. First, the force sensors must be implemented at the tip of instruments to avoid friction (which often occurs between the insertion port and the instrument or because of the internal structure of the instrument). To do so, the sensors must be small enough to be installed at the instrument tip. Because surgical instruments for MIS are generally 5 mm or smaller in diameter, such an implementation is challenging, because the internal mechanical elements (e.g., forceps drive shaft) should be intact but not influence the sensor output during motion. Second, the force sensor must be composed of biocompatible material and should be able to be sterilized. Last, the force sensor must be electrically safe for the patient and surgeon, but also tolerant of electric noise. This means that not only should there be no noise emitted from the sensor, the sensor should also be tolerant to the high levels of noise emitted from surgical energy devices, such as an electric knife.  
In this study, we propose a new optical-based force sensor to fulfill the abovementioned requirements. For this purpose, we implemented the sensor as a modular sensor. Thus, similar to strain gauges, which are widely employed in the industry but do not meet the above restrictions, the sensor can be simply attached to a surgical robot or conventional instrument to provide the force information. In this paper, related work is described in the second section; the proposed concept and its implementation are described in the third section; experimental methods and results are described in the fourth section; and the discussion, conclusion, and future work are described in the last section.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 1 Introduction
In this work, the philosophy is to bring metrological principles to Earth Observation data comparison, facilitated by the availability of per-pixel uncertainty components for SLSTR radiometric and SST data products. Crucially within this philosophy, a comparison exercise should be viewed as being primarily aimed at validating uncertainty, as this is ultimately the claimed measurement performance. In Section 2, the SLSTR tandem data archive is described in more detail, including the available uncertainty information, as well as the methods used for the tandem comparison. Sections 3 and 4 then provide the radiometric and SST comparison results, respectively. Finally, a discussion of the findings is in Section 5, including describing the advantages of the tandem phase.
# Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method
## 1 Introduction
Similarly, radiographic and acoustic inspections can assess concrete conditions, but give no direct information related to rebar conditions [28]. Some active magnetic-based methods such as magnetic flux leakage (MFL) can provide information directly related to the rebar corrosion condition [29]. Such methods need an external source such as electromagnets to properly magnetize objects during inspection [30], which increases assessment time and energy costs. These methods are challenging to perform on structures with complicated geometries [31], and complex rebar geometries can hamper clear interpretation of different data sets collected over time. With the intent of providing a better measure that is quantitative and consistent, we introduce the passive magnetic method, which takes advantage of the Earth's natural magnetic field in order to inspect ferromagnetic structures [32]. Passive magnetic methods require no special preparation [33] or artificial magnetic source [34], and use anomalies in the passive magnetic flux density to locate defects [35]. This method can detect rebar defects such as corrosion sites or cracks [33], and stress changes that impact the crystalline ferromagnetic structure [36]. We built a Passive Magnetic Inspection (PMI) tool to exploit the passive magnetic concept and examine the corrosion condition of embedded rebar by scanning from the external concrete surface [8]. Preliminary successes have been described [37] in which solid rebar was sketched in COMSOLR software version 5.3a (COMSOL Group, Stockholm, Sweden). based on a real rebar's geometry. It was then magnetized, assuming a certain value of magnetic field. Next, the passive magnetic behavior was investigated at a fixed distance from the rebar. Building on that work, in this current paper, the same ferromagnetic steel rebar with artificial defects is scanned with a three-dimensional (3D) laser scanner to generate a detailed point cloud of the structure. This point cloud then serves as the geometry basis for the finite element method software (COMSOLR software), in studying how the Earth's magnetic field affects the rebar. Different magnetic properties of the object are extracted and interpreted at several distances from the rebar, and the parameters influencing them are investigated. Additionally, a statistical detection method is presented as a new development in passive magnetic data processing and interpretation.
## 2 Mobility data
### Testing Procedure
#### 5.4.1 Variation of Data Loads and Query Types  
We first vary workloads by running various queries described in Section 5.3.1, and capture the queries running times, including time imposed by the cost of creating the unified view. To account for machines stochastic behaviours, we repeat the queries tests 10 times and captured the 95 th percentile of the measurements to delineate an accurate measure of the system's query run times.  
We specifically vary the size of mobility data to understand system behaviours with varying data load, specifically we vary loads with the following: 20k, 100k, 250k and 500k tuples.  
For the brute-force baseline, we measure the total running time as in Equation (2).  
\[\text{Total running time for baseline}=\text{tb1}+\text{tb2}+\text{tb3}. \tag{2}\]  
where tb1 is the time required to brute-force join mobility data with the neighbourhoods represented in polygons, tb2 the time required to brute-force join pollution data with the neighbourhoods represented in polygons, tb3 is the time required to perform the Cartesian product join between the two intermediate datasets, thus ultimately generating the unified view.  
As a way of contrast, we measure the running time for our novel system EMDI as in Equation (3).  
\[\text{Time for EMDI}=\text{te1}+\text{te2}+\text{te3}+\text{te4}. \tag{3}\]where \(\mathrm{te1}\) is the time required for applying the filter-refine to join mobility data with polygons, whereas \(\mathrm{te2}\) is the time required for applying the filter-refine to join pollution data with polygons, \(\mathrm{te3}\) is the time required to calculate the sketches for the pollution data, \(\mathrm{te4}\) is the time required to perform the equijoin on the resulting intermediate datasets.
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 5 Simulation and Result Analysis  
### Simulation Experiment  
In the simulation experiment, OPNET simulation software was used to simulate the Space-Integrated Ground Information Intelligent Network environment to obtain the data. A satellite network composed of six satellites was used to simulate the data flow formed by the ground communication traffic through this network. The simulated time was one week and the traffic data information of a node was randomly collected for analysis and learning. The ground communication data are uploaded to the satellite network through the currently covered satellite nodes and sent to the receiving end through the network. The position of each satellite changes with time, and the traffic transmission is not stable to simulate the burst and periodicity of the traffic data in the intelligent network of the Space-Integrated Ground Information Intelligent Network. Simultaneously, we designed a ground network with the same number of communication nodes to simulate the data flow of ground nodes and collect their traffic data under the same conditions. We calculated the variances of the two networks to represent and compare their volatility. The experiment showed that the variance of the traffic of the Space-Integrated Ground Information Intelligent Network was 1,319,308.70, and the variance of the traffic of the ground network was 47,820.93, proving that the traffic volatility of the Space-Integrated Ground Information Intelligent Network was much higher than the ground network.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 6 Parameter Sensitivity Analysis
### Effect of Rotation Angle on Resultant Forces  
When the rotation angle was zero, the wall was at rest, as shown in Figure 7. The resultant force dropped sharply once the rigid wall began rotating. With the wall rotation increasing to approximately 20 \(\times\) 10\({}^{-4}\) rad, the rate of reduction of the combined force slowed until the magnitude of the resultant force stabilized at approximately 100 \(\times\) 10\({}^{-4}\) rad. In this case, the rise in angle had a smaller effect on the combined earth pressure because, at larger angles, most of the soil displacement behind the wall reached a critical value and the shear strength was fully developed.
## 5 Conclusions  
This paper designs a mixed harmonic detection method based on DFT and DWT using the Bartlett-Hann window function. The improved method realizes the separation and extraction of steady-state harmonics and attenuation harmonics in the high frequency part, which makes up for the shortcomings of the original method. The detection accuracy of the high frequency steady-state is about 95.9%. At the same time, the accuracy of the steady state harmonic detection in the low frequency part is guaranteed. The relative error between the detection accuracy and the theoretical value is less than 5%, and the accuracy is 95.3375%, which is 1.5175% higher than the existing method. Simulation results show that the harmonic detection method designed in this paper not only improves the detection accuracy, but also can more effectively detect and restore each harmonic component in the harmonic signal, which is helpful to improve the ability of harmonic analysis in the power grid. The method proposed in this paper can separate the steady-state harmonics from the high frequency non-steady-state part and can be applied to the analysis of complex harmonics in the power grid.  
The data in this article illustrate the effectiveness and feasibility of the proposed method, not an absolute value. Applying the Monte Carlo simulation algorithm to improve and perfect the proposed method will give us a future research direction.  
[PERSON] was responsible for theoretical development; [PERSON] was responsible for simulation and verification; [PERSON] helped to collect data as required; [PERSON] drafted the manuscript and responsible for paper revision; and [PERSON] was responsible for submission. Each author has contributed to the research approach development. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.  
We gratefully acknowledge the technical assistance of DL850E ScopeCorder.  
The authors declare no conflict of interest.
## 1 Introduction
In addition, several studies have investigated the change of EOR performance and storage capacity when impurities and CO\({}_{2}\) are co-injected in the aquifer [10; 11]. The presence of contaminants will affect the trapping mechanism since it changes the wettability, interfacial tension, and buoyancy of CO\({}_{2}\)[23; 24]. However, studies on CO\({}_{2}\)-EOR performance coupled with CO\({}_{2}\) storage considering the composition of the actual impure CO\({}_{2}\) stream from carbon sources are insufficient. Previous studies have focused on laboratory investigation, and discussions on reservoir-scale studies considering the composition of actual CO\({}_{2}\) are insufficient. Therefore, it is important to investigate the impact of impurities on CO\({}_{2}\) properties combined with the performance of EOR and carbon storage. In this study, compositional simulation was used to investigate the impact of impurities on the phase behavior of CO\({}_{2}\), CO\({}_{2}\) displacement mechanism, and carbon storage when injected without removing impurities from the oxy-fuel combustion stream.
## 1 Introduction  
The Pohang Accelerator Laboratory X-ray Free-Electron Laser (PAL-XFEL) project was started in 2011 for the generation of X-ray FEL radiation in a range of 0.1 to 10 nm for users. The Korean government launched the project on 1 April 2011 with a budget of 400 billion Won (\(\sim\)400 million USD). The facility has the capacity for five undulator lines in total; three hard X-ray (HX) undulator lines and two soft X-ray (SX) undulator lines. However, the budget was limited to two undulator lines; one for a hard, and the other for a soft X-ray line. Since the PAL is the host institution carrying out the project, the project budget was able to avoid significant costs; for example, there was no need to purchase the land for the building site and a significant portion of the necessary infrastructures, such as power transmission lines and substations, were already in place. A total of 75 members were involved during the project. Among them, there were 35 newly hired members and 39 experienced members from an existing Pohang Light Source-II (PLS-II) team. A yearly budget is shown in Table 1.  
PAL-XFEL includes a 10-GeV S-band (2856 MHz) normal-conducting LINAC, which is about 700 m long. The LINAC consists of a photocathode RF gun, 176 S-band accelerating structures with 50 klystrons and matching modulators, one X-band RF system for linearization, and three bunch compressors in the HX line and one more for the SX line [1]. We also chose out-vacuum/variable-gap undulators for the easy change of beam parameters, and the fast development and manufacturing of undulators. Beyond the 10-GeV LINAC, a 250-m long hard X-ray undulator hall follows. An experimental hall, which is 60 m long and 16 m wide, is located at the end of the facility. The total length of the building is 1110 m, and the entire floor is 36,764 m\({}^{2}\). The building can withstand a maximum wind load of 63 m/s (US building code) and a seismic intensity of 0.19-g [2]. The facility suffered no damage from the earthquake of a 5.8 magnitude on 12 September 2016, nor from the typhoon Chava on 5 October 2016. The facility is shown in Figure 1.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Year** & **2011** & **2012** & **2013** & **2014** & **2015** & **Total** \\ \hline Budget in billion Won & 20 & 45 & 105 & 120 & 113.8 & 403.8 \\ \hline \end{tabular}
\end{table}
Table 1: Budget of PAL-XFEL Project.  
Figure 1: Overview of Pohang Accelerator Laboratory. The Pohang Light Source (PLS) is shown in the middle (circular building) and PAL-XFEL is shown above the PLS.