esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cooperative Communication Algorithms
### Model Validation
#### 3.3.2 The Effect of \(f\) and \(k\) on End-to-End Reliability  
B is set from 1 to 10, \(f\) from 0.15 to 0.75, step size is 0.15, and \(\mu\) is fixed to 0. In order to keep up with the simulation scenario, the number of hops is set to 14. It is shown in the simulation results that the lower \(f\), the larger \(K\) and the higher end-to-end reliability. In order to increase the success rate of sending packets, Forward Error Correction (FEC) coding is used. The FEC-coded packets can recover the whole package as long as more than two-thirds of the packets are received. Therefore, under a given \(f\), the minimum \(K\) value can be found according to Figure 2(a). The pink region represents more than 2/3 reliability. If \(f=0.3\), then \(K\) will be greater than 3. If \(f=0.45\), then the minimum value of \(K\) will be increased to 5.
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
## 4 Numerical Simulation of CT Magnetohydrodynamics
### Influence of Finite-Resistivity Walls on CTs
The boundary conditions for a compact torus forming a force-free magnetic field configuration differ from those in the drift tube. As a result, when the compact torus moves into the drift tube, its original force-free magnetic configuration gradually decays, leading to the emergence of Lorentz forces. The higher the resistivity of the conducting wall, the faster the force-free magnetic configuration of the compact torus deteriorates, resulting in differences in its radial transport behavior. Figure 14 examines the time evolution of magnetic energy \(E_{m}=\int\frac{B^{2}}{2\mu_{0}}dV\) and internal energy \(E_{int}=\int\rho C_{v}TdV\) for two simulation cases, where \(V\) denotes the volume of the computational domain. The blue line represents magnetic energy, the green line represents internal energy, solid lines correspond to a resistivity of \(1\times 10^{-5}\)\(\Omega\cdot\)m, and dashed lines correspond to a resistivity of \(1\times 10^{-7}\)\(\Omega\cdot\)m. The change in internal energy shows little difference between the two resistivity cases; however, the internal energy is slightly higher when the resistivity is lower. This is because magnetic field dissipation at the boundary occurs more slowly, allowing more magnetic energy to be converted into internal energy via Ohmic dissipation. In contrast, there is a significant difference in magnetic energy dissipation. With a resistivity of \(1\times 10^{-5}\)\(\Omega\cdot\)m, the magnetic energy drops to one-third of its initial value after approximately 1.8 \(\upmu\)s, while with a resistivity of \(1\times 10^{-7}\)\(\Omega\cdot\)m, it takes about 3.6 \(\upmu\)s to reach the same level--a difference of nearly a factor of two in time. Figure 14: Time evolution of magnetic energy and internal energy under different electrical resistivities of finite-resistance walls. Figure 13: Simulation results under three configurations: perfectly conducting wall and finite-resistivity walls with resistivity values of \(1\times 10^{-7}\)\(\Omega\cdot\)m and \(1\times 10^{-5}\)\(\Omega\cdot\)m. (**a**) Radial distribution of magnetic flux density magnitude; (**b**) radial distribution of density.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Leveraging upon 6 LoWPAN to Realize the IoT
#### 4.3.3 Adoption of 6 LoWPAN in Real Life Use Cases  
As 6 LoWPAN is a key component in order to realize the IP-based integration of constrained devices, it is used in a multitude of projects, exploring a wide range of use cases such as smart infrastructures, smart buildings, smart environments, smart cities,... In all cases, constrained devices, forming 6 LoWPAN networks, are used to collect information from the real world and this information is used to generate intelligence and make the world around us smarter. Enumerating them all would be impossible, but in order to illustrate some application domains a limited number of specific examples is listed.  
The Smart Energy and Home Automation: Restful Architecture (Sahara) project aims at using web-services for smart energy and home automation. The project uses 6 LoWPAN on IEEE802.15.4, CoAP and other IETF standardized protocols [27]. Similarly, the European Union FP7 HOBNET project deploys an IPv6/6 LoWPAN infrastructure to be used for the automation and energy management of smart and green building [28]. Another example is the FP7 Outsmart project [29], where 6 LoWPAN networks are used for instance to optimize waste management [30]. CALIPSO is another FP7 project that aims at building IP-connected networks of smart object in the area of smart infrastructure, smart cities and smart toys by utilizing IETF standards (including 6 LoWPAN) and other start-ups [31]. Finally, in [32] 6 LoWPAN networks are being used for livestock monitoring applications and other similar use cases.
## 4 Experimental Approach
Decision Table classifiers and the Filtered Classifier were examined, and the results were captured. Decision Table classifier rules are described as building and using a simple decision table majority classifier. The output shows a decision on different attributes for each instance. The number and specific types of attributes can vary to suit the needs of the task. The Filtered Classifier, on the other hand, filters out the irrelevant data in favor of the important stuff [26]. The time required for computations and the effectiveness of algorithms will be affected. Attribute selection is performed before more advanced methods such as classification, clustering, etc. Attributes are chosen in sequential order in two parts. As the first step, subset generation makes use of searching to evaluate both the determined and potential subsets. The top performers have more value. This process must be repeated until completion. Step two is ranking, which involves utilizing either statistics or information theory to establish the relative weight of qualities [27]. Machine learning algorithms typically learn from a dataset, where separate sets of data are used for training and testing. Dataset characteristics are learned and analyzed in the training set, while algorithms are taught to use characteristics from a given dataset to determine if a given sample is malicious or not. ML improves classification accuracy by analyzing normal and malicious traffic. Classification and clustering are ML methods, where classification algorithms use labelled data samples to predict the output by studying input parameters. These strategies construct input-output relationships. The training set is used to train the classification algorithm's learning model. New data are predicted and classified based on what was learned during the training phase. In our simulation, the data are divided into two samples, namely, a training sample and a testing sample. The training sample undergoes the pattern analysis and then label learning by the machine learning process, while in the testing phase, the labels are hidden from the model, and the classification engine classifies the unlabeled data based on the machine learning process that was performed in the training phase. The outcome of the classification process in the testing phase is the labels predicted by the learned model, as illustrated in Figure 5, which shows the experimental approach and methodology used in building the simulation. The Proposed IDS will be placed at the EVCSMS (as the central point) to monitor all the traffic between the distributed EVCSs and the EVCSMS.
## 4 Prevention Mechanisms of the Proposed Scheme
### Proposed Scheme System Architecture Model (PSAM)
It is an asymmetric cryptographic algorithm. Asymmetric means that there are two different keys. This is also called public key cryptography because one of the keys can be given to everyone. The other key must be kept private. Figure 5 represents the authentication process of data packets using the vehicular RSA encryption algorithm. The transmission of data packets from the transmitting vehicle or node to the receiving vehicle or node is represented by an arrow. Every vehicle in VANET comprises an individual private key generated by each node along with the public key. The public key is same for every node, whereas the private key is different. Therefore, whenever a node wants to transmit the data, a private key along with the public key must be generated and transmitted along with the packet. In the case where the key is matched, it means that the node is genuine and the transmitting node transmits the data, otherwise the node is considered an attacker node and the route is changed without forwarding data to the attacker node. The process of authentication of data packets in the proposed scheme models are also required to be extended for further investigation on storage of data in a model, based upon the ESA, which is determined based upon the DJSAM elliptical segment transmission range. This is due to the fact that there is high anticipation of HDSA that can be identified in the ESA that would require further investigation within a specified transmission range in VANET. In addition, Vehicular Fog Computing and Cloud-based (VFC) integration, which utilize ESA, are important in the network design. These are used to solve any limitations in storage and efficient computation in VANET. VFC should also be deployed in the elliptical segment area transmission range (ESATR) in order to also investigate trustworthiness, using the storage prevention mechanism in the proposed scheme network, which will be investigated subsequently.
## 3 Construction of Comprehensive Index System
### Investigation, Screening, and Classification of Initial Indicator Sets
#### 3.1.2 Establishing Indicator Classification Criteria  
After the initial indicators are obtained, they need to be classified according to the similarities between the indicators. This commonality is the core criterion of the comprehensive index system and provides classification criteria for subsequent cluster analysis. In this paper, we summarize the commonality from the development needs of the SIN, and then verify its rationality by cluster analysis.  
By analyzing the typical application scenarios of relay satellite systems (including space test verification scenarios) and the development trend of SIN, the following requirements are obtained.  
* Real-time transmission and rapid response-described by time-domain related indicators.
* High-efficiency and large-capacity-described by frequency-domain and data quality-related indicators.
* Reliability and stability-described by system security-related indicators.
* Airspace and service coverage-described by antenna coverage performance related indicators.
* The measurement requirements required for the demonstration test-described by accurate measurement and simulation-fit related indicators.  
With reference to the naming rules of the ADC model of the United States Weapons System Effectiveness Industry Advisory Committee (WSEIAC), the five categories of index criteria mentioned  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Type** & **Representative Indicator** & **Remark** \\ \hline \multirow{4}{*}{terrestrial network} & Classic IP Network & Round-trip Delay, delay jitter, Packet Loss, network connectivity... & IETF:RFC2681 RFC2678 RFC3393 RFC7679 RFC7680 \\ \cline{2-4}  & Mobile communication network & Spectrum efficiency, peak rate, traffic density, mobility... & ITU,JMT-2020’5g’ promotion group \\ \hline \multirow{4}{*}{Space network} & Measurement & coverage, Maximum service time... & \\ \cline{2-4}  & Remote sensing & imaging quality, revisiting period... & \\ \cline{2-4}  & & & international civil aviation organization Standard and Recommended Practices \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \multirow{4}{*}{Other system} & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } &
\begin{tabular}{} \end{tabular} & Metrics for Cyber defenses (NASA) \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indicator investigation of various related fields.  
above are named coverage, timeliness, capability, accuracy, and dependability. They comprise the first level in the comprehensive index system.
## 4 Results and Discussion
### Influence of the Operational Parameters  
To observe the effects of HTF inlet temperature and flow rate on total rejected energy, numerical investigations are performed for T\({}_{\text{inlet}}\) = 5, 7.5, 10, and 12.5 \({}^{\circ}\)C with V = 2, 4, 6, and 8 L/min. Before performing a parametric study to quantify the influence of both parameters on the melting process,  
Figure 5: Comparison in terms of total rejected energy between the present study and the experimental work of [PERSON] et al. [7].  
Figure 6: Effect of different grids on the total rejected energy (kJ).  
the case for T\({}_{\text{inlet}}\) = 5 \({}^{\circ}\)C and V = 4 L/min is considered first on Figure 7, which shows the contours of liquid fraction after 250 and 500 min for the shell and coil tube ice storage tank.  
At \(t\) = 0 min, a very thin layer of liquid forms around the hot inner coil tube. This layer expands progressively with time. Ice is clearly more melted in the top region of the ice storage tank. Natural convection plays a crucial role in this zone where warm liquid rises to the top and cooler liquid goes down by the conservation of mass. At the bottom region of the shell, which is full of ice, heat is transferred between the hot surface of the inner coil tube and the cold ice by conduction. Accordingly, the results show an unstable structure near the top region of the tank that leads to the formation of a wavy solid-liquid interface. As time passes, the liquid water layer reaches the outer surface. Also, results indicate that the melting process starts from the inner wall surface (Figure 7). A thin layer of liquid is formed around the hot inner coil tube. The heat transfer process is only governed by the conduction. After several minutes, this layer is spread and convection becomes the dominant mechanism.
## 2 Deterministic and Probabilistic Approach in Service Life Evaluation  
Durability design can be classified into a deterministic method and a probabilistic method. When clear design variable definition and design formulas are ready, a deterministic method is reasonable, but the probabilistic method can be adopted for considering engineering uncertainties [22; 23]. The deterministic method requires a clear critical condition, and it is determined by the physical governing equation. Regarding chloride attack, the durability design is usually followed by the procedure that the chloride concentration penetrated from the outside during the intended service life should be lower than the critical chloride concentration, which causes corrosion initiation at the outer steel [3; 5; 12]. In the case of carbonation, the increasing carbonation depth with period should be lower than the design cover depth during the intended service life [3; 24]. Service life can be secured by enhancing cover depth, protective coating, and improving concrete mix conditions. In addition, a margin for safe design is considered through an environmental load coefficient of 1.1 to 1.3 for the deterioration value over time and an environmental reduction coefficient of 0.7 to 0.8 for the threshold value to be resisted [3; 4].  
Probability random variables for design parameters and their patterns should be defined in the probabilistic method, and the probability of durability failure during the intended service period should be maintained below the target durability failure probability, around 0.08\(\sim\)0.10, which depends on the significance of the target structures and maintenance difficulties [4; 22]. Currently, several models with degradation evaluation with spatial variability (time and space domain) are actively studied and applied to durability design [23]. Table 1 summarizes the engineering uncertainties considering sulfate attack, and a schematic diagram of deterministic and probabilistic models for the deterioration depth equation is shown in Figure 1. The deterioration mechanism from sulfate ions is shown in Figure 2[11].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Uncertainties** & **Engineering Limitations** & **Definition** \\ \hline Physical & - Concrete cover depth & Inherent random nature of a basic variables \\  & - Concentration of sulfate concentration & - Diffusion coefficient of sulfate and cement compositions \\  & - Local condition (roughness) & \\ \hline Statistical & Limited sample size & Assumption for probability density function. \\ \hline Model & - Simplified equation of deterioration depth in steady state & Governing mechanism for linear deterioration depth with period \\  & - Assumption of material properties (Al\({}_{2}\)O\({}_{3}\) and fracture energy of cracking) & \\  & - Assumption of non-correlated variables & \\ \hline Decision & - The period that deterioration depth exceeds the cover depth & Definition of durability failure criteria \\ \hline \end{tabular}
\end{table}
Table 1: Engineering uncertainties for sulfate erosion.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 7 Conclusions  
In this paper, the concept of Augmented MIMO was proposed, and its effectiveness was empirically demonstrated. The experimental results demonstrate the throughput improvement with the proposed scheme, even if the overall antenna gain decreases with the external body-mounted antennas. The experimental results prove the existence of scenarios wherein the Augmented MIMO increases the channel capacity; however, its effectiveness depends on several factors, as discussed above.  
The advantages of the proposed scheme include the following: (1) a significant improvement in MIMO performance due to high diversity gain; (2) a high degree of freedom in antenna placement; and (3) room for increasing the number of antennas toward massive MIMO. However, the proposed scheme features various issues that do not arise in conventional MIMO systems, such as the following: (1) the stability of proximity coupling between wearable devices and clothing; (2) the comfort of users wearing the antenna-distributed clothing; and (3) the durability of the clothing. Further detailed investigations that include theoretical aspects and practical system integration methods are needed to overcome these issues.  
The proposed scheme can also be applied to multiband/wideband MIMO systems, although the experiment was designed for single-band operation for simplicity. This work contributes to the future development of yet another scheme to improve the communication performance of small wearable devices by using the human body as a spacious antenna fixture.  
This research was supported in part by the JSPS Grant-in-Aid for Scientific Research (21H05301) and the Murata Science and Education Foundation.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.
## 3 Results and Discussion
### Thermal Stability Analysis  
To demonstrate the efficiency of the chemical methods combined with ultrasound to produce cellulose nanofibers, thermal stability analyses (see Figure 3) were performed. Figure 3a,b show the mass loss and thermogravimetric derivatives, respectively, of PL-N, PL-DB, PL-H, and PL-HUS nanofibers.  
A first weak peak was found between 50 \({}^{\circ}\)C and 150 \({}^{\circ}\)C for all samples in Figure 3. This peak was related to the water evaporation. Another pronounced peak was found between 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, and maximum degradation points were observed in the range of 200 \({}^{\circ}\)C-400 \({}^{\circ}\)C. For PL-H and PL-HUS, a single maximum degradation point was found at around 306 \({}^{\circ}\)C, compared to PL-N, indicating higher thermal stability due to the removal of less thermally stable substances such as hemicellulose and pectin. These results were attributed to the chemical treatments combined with ultrasound [31]. However, it was also evident in Figure 3 that the degradation temperature of PL-HUS was lower with reference to the PL-DB sample (315 \({}^{\circ}\)C). As reported by [PERSON] et al. [32] this lower degradation temperature of PL-HUS could be attributed to the fact that the acid treatment resulted in shorter cellulose chains and, consequently, in a lower degree of polymerization and in an increase in the surface charge density, which led to a lower degradation temperature.  
Additionally, it was observed that the maximum degradation temperature of CC was higher (338 \({}^{\circ}\)C), compared to the produce nanofibers (LP-HUS, 306 \({}^{\circ}\)C), which could be attributed to the number of free terminal chains, which decompose at lower temperatures [33; 34]. Hydrolysis with H\({}_{2}\)SO\({}_{4}\) caused a significant decrease in the degradation temperature and an increase in the maximum degradation temperature range for PL-HUS between 181 \({}^{\circ}\)C and 360 \({}^{\circ}\)C, caused by the inclusion of sulfate groups in the cellulose chains [33; 35].  
The drying method is another factor that affects the thermal stability of cellulose nanofibers due to fiber rearrangements caused by water removal. By conventional sample drying, nanofibers are free to form a homogeneous phase with defined particle spacing, whereas freeze-drying requires initial freezing. In this state, the nanofibrils cannot be rearranged during water sublimation, resulting in dispersed nanofibril clusters and a large specific surface area, which affects thermal stability [36]. It was important to consider the aforementioned in this work, as the samples were dried in a circulating air oven after the hydrolysis process was completed and by freeze-drying after sonication.