esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Comparison of Adsorbent Performance  
The performance of different adsorbents reported so far for the CFA adsorption in aqueous solution are compared in Table 6. The pure silica SBA-15 (entry 1) exhibited the lowest adsorption capacity (0.07 mg g\({}^{-1}\)) due to its hydrophilic surface and lack of specific active sites for CFA adsorption. The functionalization of mesoporous silica with aminopropyl groups (entry 2) enhanced the capacity of the CFA adsorption via electrostatic and hydrogen-bonding interactions between the protonated amine groups (pK\({}_{\text{b}}\approx\) 3.4) and the negatively charged carboxyl groups of the CFA molecules (pK\({}_{\text{a}}\) = 2.9) at neutral pH (-7) [3; 17; 36]. The coconut shell-derived activated carbon (entry 3) provided higher CFA adsorption capacity than the amine-functionalized mesoporous silica (entry 2), which is owed to not only its high surface area and porosity but also the hydrophobicity of its porous surface. The enhanced affinity for CFA molecules by hydrophobic carbonaceous materials originates from \(\pi\)-\(\pi\) and Van der Waals interactions [44]. The pillared bentonite modified with a cationic surfactant (entry 4) exhibited a high capacity for CFA adsorption due to the improved hydrophobic characteristics provided by the aliphatic chain of surfactant molecules [45]. In the case of NR/WMS-NH\({}_{2}\) (entry 5), the presence of amine groups and rubber moieties as hydrophobicity improves resulted in the highest adsorption performance (7.62 mg g\({}^{-1}\)). Moreover, the mesostructured framework, with the large mesopore volume and surface area of the adsorbents, encourages the intraparticle diffusion of adsorbate molecules [17; 33].  
Figure 11: Reusability test of NR/WMS\(-\)0.05 in the CFA removal from aqueous solution. (\(\bullet\)) Fresh, (\(\blacktriangle\)) 1 st Cycle, (\(\blacklozenge\)) 2 nd Cycle and (\(\blacksquare\)) 3 rd Cycle.
## 4 Python-Based MPI Collective Communication Library
### Wrapping Memkind  
This subsection describes how we use the KNL processor's MCDRAM in a Python environment. Numpy [39; 40; 41] and Memkind libraries are used to perform Python MPI communication using MCDRAM. Numpy is a Python library for mathematical and scientific operations that makes it easy to process matrices or large multidimensional arrays. Wrapping is implemented in Python using Cython [13; 14] to use Memkind library in Python environments. Figure 4 shows the Wrapping method with Cython is written in the following four steps.  
* Step 1. Implement C code that allocates memory using Memkind.
* Step 2. The Cython file has.pyx extension. Wrap the C code implemented in Step 1 using Cython code.
* Step 3. The Cython code must be compiled differently from Python. Implement a setup file to perform compilation.
* Step 4. Import the C module defined in Cython.pyx and setup.py into the Python MPI code.  
In the first step, hbw_malloc.c implements the code that allocates memory using the hbw_malloc function from the Memkind library. In the second step, Cython.pyx is defined to wrap the array allocated memory with the hbw_malloc function in the first step into an array of Numpy. To receive C code from a Python object, Cython.pyx needs to get a pointer to the PyObject structure. This step defines the arrays used to exchange data. We use PyArrayObject when defining an array. PyArrayObject is a C API provided by Numpy. Numpy provides a C API to access array objects for system extensions and use in other routines. PyArrayObject contains all the information necessary for a C structured array. We use the PyArray_SimpleNewFromData function on PyArrayObject and create a Python array wrapper for C [42]. In the third step, the Python code setup.py is implemented using the distutils package provided by Cython to compile the Cython.pyx file. The distutils package supports building and installing additional modules in Python. To compile the Cython.pyx file, setup.py contains all compile-related information. The setup.py uses Cython compiler to compile Cython.pyx file into C extension file. After that, the C extension file is converted to the.so or.pyd extension file by the C  
Figure 3: Architecture difference between general memory allocation and Multi-Channel Dynamic Random Access Memory (MCDRAM) memory allocation using Memkind.  
compiler, and.so file is used as a Python module. Finally, the fourth step is to implement the Python MPI code to exchange the actual data. Implementing the MPI code defines the Numpy used for data communication. In defining Numpy, the hbw_malloc module defined in Cython.pyx is used for the array data part of Numpy. MPI communication is performed with Python wrapped with Memkind and Numpy. Therefore, through this process, data exchange is performed using MCDRAM.
## 4 DT Use Cases and Application in Healthcare
### Advanced Diagnosis and Preventive Treatment  
DT technology holds the potential to revolutionize patient care by simulating a wide range of patient-specific characteristics and replicating their responses in various medical scenarios. This advanced simulation capability can be instrumental in tracking patient health, facilitating more accurate disease diagnosis, and planning effective preventive treatments. By creating virtual models that mirror individual patient conditions and responses, healthcare providers can tailor treatments and interventions more precisely to each patient's unique needs.  
Additionally, the growing accessibility of health data from wearables and various medical data sources has the potential to expand precision medicine to a wider patient population. This can be achieved by offering tailored therapies that enhance efficacy and efficiency while also reducing costs for the healthcare system [56].  
Moreover, the insights gained from these simulations extend beyond individual care. They can offer a representative overview of how a drug might affect a broader population. As discussed in Section 4.1, this aspect of DT technology is particularly valuable in pharmaceutical research and development. It allows for the assessment of a drug's efficacy and safety across diverse patient demographics and conditions, leading to more effective and safer medications. This approach can significantly enhance the precision of clinical trials and the development of personalized medicine strategies.  
Researchers and industry experts, as highlighted in works by [39; 54; 55], underscore the potential of DT technology in providing a more holistic and accurate understanding of medical treatments and their impacts. This technological advancement is poised to bring about a significant shift in how healthcare professionals approach patient care, drug development, and the management of health outcomes on both individual and population levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Potential Use Cases** & & **Benefits Gained** \\ \hline Managing Risk & \(\bullet\) & Create a safe environment where you can test all possible changes and crises \\ \hline \multirow{4}{*}{The Rise of Medical Digital Twins} & \(\bullet\) & Enable physicians to leverage thousands of variables to intelligently model a patient’s best course of treatment \\  & \(\bullet\) & Enable the analysis of an individual’s unique reactions to therapies \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 3 Results and Discussion
### Test Results of the Fiber-Reinforced LPC
#### 3.2.1 Compressive Strength and Elastic Modulus of the LPC  
Table 11 shows that the 14-day compressive strength of each series of LPC was between 4.05 and 5.96 MPa. Among them, the LC2 mix had the lowest compressive strength (4.05 MPa) and the LC3 mix had the highest compressive strength (5.96 MPa). Overall, as the age increased, the compressive strength of each LPC also increased. The 28-day compressive strength of the LPC specimens prepared in this research was between 4.80 and 7.78 MPa, as shown in Table 11. In contrast, it can be seen from Table 2 that the 28-day compressive strength of the LPC prepared with lightweight aggregates by other researchers ranged from 1.80 to 5.99 MPa [46; 47]. This result shows that the 28-day compressive strength of the LPC was superior, irrespective of the coarse aggregate size or fine aggregate content used. It can also be observed from Figure 7a that the compressive strength of the LC8 mix was the lowest (4.80 MPa), while the compressive strength of the LC5 mix was the highest (7.78 MPa). On the other hand, as can be seen from Table 11, the elastic modulus of each mixture of LPC at 14 days of age was between 3858 and 15,026 MPa. It can be clearly seen from Figure 7b that the LC6 mix and the LC4 mix had the lowest and highest elastic moduli, respectively. Similarly, as the age increased, the elastic modulus of each LPC also increased. The elastic modulus at 28 days of age was between 5.771 and 16.721 GPa. The average value was about 11.3 GPa, which was about 32% of the elastic modulus of conventional concrete (i.e., 35 GPa). The elastic modulus of the LC6 mix was the lowest (5.771 GPa) and the elastic modulus of the LC4 mix was the highest (16.721 GPa), as shown in Figure 7b. The research results of [PERSON] and [PERSON] [69] showed that for cylindrical specimens of PC with a porosity of 15-31%, the elastic modulus ranged from 1 to 16 GPa. However, the research results of [PERSON] and [PERSON] [70] showed that the elastic modulus of PC with a porosity range of 17-31% was 16.2-22.3 GPa, which was significantly higher than the results of [PERSON] and [PERSON] [69].
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 3 Results  
Here, we present the implementation of a 3D object-rendering AR application, focusing on methods to correct location errors caused by GPS inaccuracies and object occlusion by buildings, ensuring realistic and seamless integration of virtual objects within the real-world environment.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Service Provisioning  
#### 4.1.1 Financial Services  
With the aim of protecting users from financial loss and eradicating financial fraud, monetary transactions made by network members must be reliable and safe. In the KDN domain, in order to perform transactions securely, Deep Reinforcement Learning (DRL)-based secure transactions have been proposed to enable transaction communication with confidentiality and public divisibility [237]. Furthermore, as KDNs are often challenged due to the enormous amount of data required for generating knowledge using machine learning, through the assistance of the cryptographic technique employed in blockchains, an architecture defined as the Blockchain-Enabled Intelligent IoT Protocol (BEIIP) is offered for assuring high-level network availability and data integrity [238].
## 2 Preliminaries on the 3 GPP NR Physical Layer Specification
### Preemption-Based Dynamic Multiplexing of URLLC and eMBB Traffic  
When the URLLC traffic arrives sporadically in the AV, all resources may not be fully utilized in the given BWP. Therefore, the DM of eMBB and URLLC traffic in AVs is expected to enhance resource utilization [2]. Furthermore, the URLLC traffic should be scheduled immediately within the delay limit. For this, it is allowed to schedule URLLC traffic over resources that have already been scheduled for eMBB traffic. There are two ways to achieve DM in the 3 GPP specification: transmission of URLLC traffic by preemption (puncturing) of ongoing eMBB traffic and superposition of URLLC traffic over eMBB traffic. For the second option, an advanced receiver is required to detect superposed signals.  
In this study, we assumed preemption-based scheduling as the baseline DM scheme. Even though URLLC traffic would benefit from its excellent resource utilization and outage capacity, the performance of eMBB traffic can be severely degraded [12]. To alleviate this performance degradation, a CBG-based transmission and retransmission scheme is introduced in the 3 GPP specification, which is detailed in the following subsection. Furthermore, the control information of the preemption indicator is given in 14-bit bitmap, and 1-bit code block group flush out information is specified to indicate the preempted resource while flushing out the corrupted resources by URLLC preemption [13]. This is utilized to prevent the eMBB AV from decoding preempted resources.
## 8 Experimental Results
### Over-the-Air Demonstrations
#### 8.2.1 VTOL Test  
To demonstrate over-the-air ranging capabilities, we executed a simple VTOL manuever and plotted the range estimates against a tachymeter reference in Figure 21. The antennas and tachymeter are physically separated on the UAV, which induces a small range difference when plotted together. The system maintains accurate range estimates during all stages of the maneuver, even when the tachymeter reference fails at 90 s. During take-off and landing, performance is worsened by ground-bounce multipath. At the apex, the drone cannot maintain a perfect hover due to strong winds, so the range estimates appear artificially noisy, since the tachymeter reference is mounted to the center of the platform, and the antennas are mounted at the corners.  
The achieved ranging precision is summarized in Table 4 for the duration of the maneuver. Most channels maintain a standard deviation of around 10 cm except for UAV channel 1, which was obscured by the UAV frame. This precision may be increased by implementing the interpolation technique demonstrated in the previous result, which was developed after this experiment to improve performance. The ranging performance in this configuration is also limited by ground-bounce multi-path.
## 3 Results and Discussion
### Discussion of Issues  
To obtain this evaluation result, the construction cost was calculated based on the estimation formulas shown in Tables 4 and 5. However, this estimation formula was an estimation formula that was set considering land and personal costs in Japan and was not suitable for use when considering the introduction of equipment overseas. In addition, this evaluation was conducted assuming that Keiyo Coastal Industrial Zone will purchase the heat obtained during waste incineration unconditionally for 24 h. However, since the supply destination may have conditions such as pressure, temperature, and the time zone in which it was desired to be supplied, confirming whether the heat generated during waste incineration meets these conditions were necessary. Therefore, depending on the conditions of the supply destination, the value of income from heat sales may fluctuate greatly, and the values of Cases 2 and 4 may change. Conversely, it could be said that Cases 1 and 3 that used power generation were less affected by the conditions of the Keiyo Coastal Industrial Zone.  
In addition, from the sensitivity analysis results shown above, when the waste quality value was changed, as shown in Figures 8 and 9, the CO2 emissions and economic values changed significantly. Therefore, when comparing with existing treatment methods, the superiority may change in some cases unless the evaluation considered future fluctuations in waste quality.  
When evaluating the introduction of the MBT system in various regions, it is necessary to consider these because the waste quality and maintenance costs will change due to differences in countries and regional characteristics. In Ichihara City and Chiba Prefecture, facilities with high heat demand existed in the neighborhood, so this was added to the conditions. For example, in developing countries, there were likely no facilities with high heat demand, so it was considered less meaningful to evaluate cases centered on heat utilization.  
There were some challenges in realizing the cooperation with the existing infrastructure for the integration of the waste incineration plant. For example, for linking with existing infrastructure, it was necessary to obtain the consensus of the organization that operates the infrastructure. However, obtaining an agreement may be difficult due to a lack of understanding of the residents or a lack of legislation. Furthermore, to reduce social costs, facilities with high heat demand in the neighborhood and businesses that purchase fermentation residues as fertilizer were necessary. Currently, decarbonization is gaining attention worldwide, and we hope that this growing interest in environmental issues will solve the issues mentioned above.
### Research Objectives  
Research has been conducted to improve the efficiency and accuracy of visual similarity-based methods. However, certain limitations remain. The NoM methods achieve high accuracy by matching features extracted directly from each image to determine similarity. However, despite strategies such as scaling or constraints, the execution time increases exponentially with the number of images, thus limiting the efficiency [12]. By contrast, BoVW vector-based image retrieval methods demonstrate high efficiency and apply to large datasets; however, their performance varies depending on the selection and representation of visual words [32]. Clustering image features can lead to significant information loss, potentially resulting in the omission of detailed texture information and reduced accuracy, particularly in scenarios involving repetitive patterns.  
This study aimed to develop an enhanced visual similarity-based method for determining overlapping image sets and improving the efficiency of image localization. The approach synergistically combines the BoVW and NoM techniques to leverage their complementary strengths. Initially, a BoVW vector-based image search rapidly extracts candidate image sets, significantly reducing the computational load. Subsequently, NoM-based similarity is applied exclusively to these candidates to derive the final image set, achieving high accuracy without relying on additional information such as GNSS data. This hybrid methodology effectively balances precision and computational efficiency, overcoming the limitations inherent in utilizing either method independently. Consequently, the proposed approach demonstrates superior accuracy compared to BoVW alone while simultaneously achieving improved computational efficiency relative to the exclusive use of NoM.  
The remainder of this paper is organized as follows. Section 2 provides a comprehensive explanation of each step and the evaluation method used in the proposed methodology. In Section 3, the proposed methodology and conventional NoM- and BoVW-based approaches are applied to UAV image datasets for overlapping image-set determination, and the outcomes are evaluated using actual overlapping images. Subsequently, bundle adjustment was performed on the image sets derived from each method to assess the impact of the proposed methodology on the accuracy of estimating the image positional information. Section 4 presents the conclusions of this study.
## 4 Descriptive Analysis of Data
### SPSS Analysis
#### 4.3.3 AI in Maintenance and Operations  
The total score for this variable was M = 1.7916 and S.D. = 1.1405, which was interpreted as AI being very influential in maintenance and operations. Participants were asked  
\begin{table}
\begin{tabular}{c c c} \hline
**Weighted Average** & **Result** & **Result Interpretation** \\ \hline
1.00–1.79 & Strongly agree & Very influential \\
1.80–2.59 & Agree & Influential \\
2.60–3.39 & Neutral & Neutral or do not know \\
3.40–4.19 & Disagree & Uninfluential \\
4.20–5.00 & Strongly disagree & Very uninfluential \\ \hline \end{tabular}
\end{table}
Table 4: Weighted Average for 5-point Likert scales [51].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measured Variable** & **No. of Items** & **Mean** & **Standard Deviation** \\  & & **(S.D.)** & **Result Interpretation** \\ \hline AI in Safety & 6 & 1.7783 & 1.2035 & Very influential \\ \hline AI in Airport Design & 6 & 1.8283 & 1.2193 & influential \\ \hline AI in maintenance and operations & 6 & 1.7916 & 1.1405 & Very influential \\ \hline AI in planning & 5 & 1.748 & 1.119 & Very influential \\ \hline AI in project management & 6 & 2.3066 & 1.2503 & influential \\ \hline AI in quality control & 6 & 2.7666 & 1.308 & Neutral \\ \hline AI in Handover, commissioning, and warranty period & 6 & 3.2116 & 1.2746 & Neutral \\ \hline \end{tabular}
\end{table}
Table 5: Results for the measured variables.  
if AI accurately predicts maintenance needs before issues arise, and (69.3%) agreed that it would. Participants believed that using AI in maintenance scheduling optimizes the efficiency of airport operations (76.2%) and minimizes downtime by optimizing maintenance processes (75.2%). This is in line with the studies that have been applied by using AI in maintenance which reduces the cost impact, time impact and assures the quality of the airport operations [7]. Furthermore, 86.1% of participants agreed that AI aids in the real-time monitoring of airport facilities, 76.3% agreed that AI helps automate routine maintenance tasks effectively, and 72.2% agreed that it improves the accuracy of maintenance logs and records substantially. This corresponds with findings from other studies previously mentioned in the literature, that revealed the importance of AI in this area.
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 6 Proposed Future Research Activities  
The investigation carried out in this paper shows that the assessment of the suitability of the algorithms presented is primarily based on a quantitative analysis in the form of a visual representation of the results. This is mainly the consequence of the original objective for the respective application. Another criterion that is often the subject of evaluation is the computing capacity required, as large volumes of data are usually analyzed. This applies in particular to ray tracing methods.  
However, we note that there is no general scheme for the evaluation of the developed algorithms according to a defined standard. We therefore propose, as future research work, to develop and establish a standardized test procedure in order to be able to generate reliable statements regarding the performance of the procedures to be examined with regard to the fulfillment of the task described in Definition 1 and to create a basis for the comparison of the individual procedures. In the field of numerics, the following categories are of particular interest, which we propose as evaluation criteria for isocontouring methods:  
* condition;  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Authors** & **Year** & **Structured** & **Unstructured** & **Quality** & **2D** & **3D** \\ \hline [PERSON] \& [PERSON] & [58] & 1987 & \(\bullet\) & & Time over grid subdivision & \(\bullet\) \\ [PERSON] et al. & [59] & 1998 & \(\bullet\) & & Visual inspection & \(\bullet\) \\ [PERSON] \& [PERSON] & [61] & 1998 & \(\bullet\) & & Visual inspection & \(\bullet\) \\ [PERSON] et al. & [63] & 2005 & \(\bullet\) & & Computational cost; visual inspection & \(\bullet\) \\ [PERSON] et al. & [73] & 2006 & \(\bullet\) & & Computational cost; visual inspection & \(\bullet\) \\ [PERSON] \& [PERSON] & [75] & 2006 & \(\bullet\) & & Root-mean-square error & \(\bullet\) \\ [PERSON] et al. & [65] & 2007 & \(\bullet\) & \(\bullet\) & Computational cost; visual inspection & \(\bullet\) \\ [PERSON] \& [PERSON] & [76] & 2008 & \(\bullet\) & \(\bullet\) & Computational cost; visual inspection & \(\bullet\) \\ [PERSON] & [74] & 2009 & \(\bullet\) & & Qualitative comparison & \(\bullet\) \\ [PERSON] \& [PERSON] & [77] & 2014 & \(\bullet\) & \(\bullet\) & Computational cost; visual inspection & \(\bullet\) \\ [PERSON] et al. & [64] & 2019 & \(\bullet\) & \(\bullet\) & Performance; skipping empty regions & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of research work related to ray tracing techniques.  
stability;
* consistency; and
* convergence.