esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Synthetic Data in Native Network Form  
#### 5.2.1 Girvan-Newman Graphs  
The Girvan-Newman graphs were generated as described in [2]. These graphs have 1024 nodes, with either 64, 32, 16 or 8 equal-sized clusters. The graphs have a regular degree structure, with each node's degree equal to half the cluster size. The strength of the community structure depends on the mixing coefficient, \(\mu\). Ten different graphs were randomly generated for different mixing coefficients from \(\frac{1}{32}\) to \(\frac{1}{8}\). However, for this type of graph, higher mixing coefficients are not effective as the resulting graphs are very well connected. Hence, many of the resilience measures do not return a useful attack set.  
Nevertheless, these graphs are interesting as they have a very large number of clusters with a small set of members per cluster. For example, each cluster of the 64-cluster graphs contains only 16 members. The large number of clusters implies that either a large critical attack set or a large number of iterations is required. The results for the Girvan-Newman graphs are shown in Table 2, where # represents the number of clusters in the graph, and \(\mu\) is the mixing factor. Each data point represents an average over 10 randomly generated graphs. As can be observed, NBR-Clust using any measure other than scattering number performs very well on these types of graphs. Even with the most difficult graphs, with the mixing coefficient \(\mu=\frac{1}{8}\), the lowest performance (scattering) is 85% accuracy while all other NBR measures attain at least 92%. Note that tenacity was able to cluster every graph in only one iteration. This is due to the large size of the generated attack sets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **VAT** & **Integrity** & **Toughness** & **Tenacity** & **Scattering** \\ \hline political books & **83** & **83** & **80** & **80** & **49** \\ karate club & **68** & **97** & **68** & **82** & **85** \\ college football & 89 & 90 & **53** & **53** & **61** \\ food web & **73** & **73** & **48** & **70** & **48** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percent accuracy for real data in native graph form. Bold text indicates one-step clustering.
## 5 Strengths and Limitations  
Our study holds some strengths. The study's guidelines can help combat sports athletes strategically manage their body composition for improved performance within specific weight categories. By discouraging extreme low body fat levels, the study prioritizes athlete health while ensuring sustainable practices for both performance and well-being. The study's clear classification of body fat percentages aids coaches and athletes in effective training, nutrition planning, and competition strategies.  
The limitations of our study are related to the evidence that the advanced cut-off points for the classification of body fat were completed along with the body composition assessment via the BIA method, which therefore is not in a position to be assigned for the results obtained from the second level of validity methods such as computed X-ray tomography, magnetic resonance, and DXA or the third level of validity method such as anthropometry [7; 40]. In addition, the readings of the BIA devices are generated on the basis of proprietary forecasting equations concealed from potential users [40], which may lead to biases in the interpretation of fat mass assessment results.  
Taking into consideration that our study focused on combat sports male athletes with weight categories who engaged in weight cycling before the competitions, the results might not apply universally to athletes from various sports and sex. These suggestions are maintained by the fact that, depending on bodily hormone variability, female athletes have higher body fat levels compared to male athletes [62; 63; 64].  
Therefore, further research is needed to calculate the percentiles for the classification of body fat percentage in the populations of female athletes playing weight-sensitive sports. On the other hand, the well-developed muscle-to-fat ratios of Lithuanian male athletes playing boxing, judo, and wrestling matched the body composition profiles of elite male athletes from other countries [52; 60; 65; 66]. Therefore, our proposal for fat classification could be generalized for well-trained elite male athletes representing combat sports in other countries.  
Another limitation of our study concerns the relatively small, but representative, sample size. An international multi-center sample size may be more beneficial for disclosing the empirical data more accurately. Taking into account that the proportion of professional male athletes from combat sports who were candidates for the Olympic games was relatively low in Lithuania, the external validity of the findings of our study should be assessed with caution.  
Finally, perspective cohort studies in design are needed to investigate the changes in body composition in response to training load in combat sports athletes.
## 3 Results and Discussion
### Influence of Embedment Depth  
The change in the embedment depth only involved the change in earth pressure on the sidewalls, as well as loading on the top slab of the utility tunnel. The effect of the embedment depth of the utility tunnel on the stiffness reduction coefficient and the bending moment adjustment coefficient of the sidewalls is shown in Figure 8, taking the depth as 1 m, 2 m, 3 m, 4 m, 5 m, and 6 m and considering the general embedment depths for prefabricated utility tunnels.  
It can be seen from Figure 8a that, when the embedment depth increased from 1 m to 6 m gradually, the stiffness reduction coefficient \(\eta\) decreased slightly from 0.62 to 0.60, indicating that the stiffness reduction coefficient did not change much with the increase in the embedment depth, in general. It can be seen from Figure 8b that the embedment depth had no effect on the bending moment adjustment coefficients (\(\xi_{t}\) and \(\xi_{b}\)). The bending moment adjustment coefficient for the lower end of the sidewalls \(\xi_{b}\) was 0.10, the bending moment adjustment coefficient for the upper end of the sidewalls \(\xi_{t}\) was 0.13, and \(\xi_{t}\) was correspondingly slightly larger than \(\xi_{b}\) for a given embedment depth. Obviously, increasing the embedment depth increased the internal force and deformation of the structure due  
Figure 8: Influence of embedment depth: (**a**) stiffness reduction coefficient and (**b**) bending moment adjustment coefficient.  
Figure 7: Influence of coefficient of subgrade reaction: (**a**) stiffness reduction coefficient and (**b**) bending moment adjustment coefficient.  
to the increase in loadings, but the stiffness reduction coefficient and bending moment adjustment coefficient mainly depended on the values of relative stiffnesses of the structural components, which did not change in this investigation on the influence of embedment depth. Therefore, there were no obvious changes observed for \(\eta\), \(\xi_{t}\), and \(\xi_{b}\) associated with changes in the embedment depth.
# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 3 Results
### Definition of Variables in the Multivariate Regression Model  
A multivariate regression model consists of a set of independent variables as input data \(X_{\rm i}\) to predict the dependent variable as output data \(Y_{\rm i}\), as shown in Table 1. This means that the 15 input variables include data for three hours earlier at Kangnung (PM (PM\({}_{10}\)-K, PM\({}_{2.5}\)-K), meteorological (TEMP\(\_\)K, WIND\(\_\)K, RH\(\_\)K) and gas (SO\({}_{2}\)_K, CO\(\_\)K, O\({}_{3}\)_K, NO\({}_{2}\)_K)) and for 2 days earlier at Beijing (PM (PM\({}_{10}\)_C, PM\({}_{2.5}\)_C) and gas (SO\({}_{2}\)_C, CO\(\_\)C, O\({}_{3}\)_C, NO\({}_{2}\)_C)) that were used to make real-time output forecasting variables (PM\({}_{10}\)_K(N), PM\({}_{2.5}\)_K(N), SO\({}_{2}\)_K(N), CO\(\_\)K(N), O\({}_{3}\)_K(N) and NO\({}_{2}\)_K(N)) at Kangnung. Regression Equations and Correlation Matrix among PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{2}\), CO, O\({}_{3}\) and NO\({}_{2}\) Parameters (Kangnung) Affected by Meteorological Variables Associated with PM and Gases (Beijing)  
A multiple regression equation consists of independent variables \(X_{\rm i}\) to predict the dependent variable \(Y_{\rm i}\), such as in Equations (1) and (2). \begin{table}
\begin{tabular}{l l l} \hline  & **Input Variable** & **Output Variable** \\ \hline \(X_{1}\) & PM\({}_{10}\)\_K: 3 h before & \\ \hline \(X_{2}\) & PM\({}_{2.5}\)\_K: 3 h before & \\ \hline \(X_{3}\) & TEMP\_K: 3 h before & \\ \hline \(X_{4}\) & WIND\_K: 3 h before & \\ \hline \(X_{5}\) & RH\_K: 3 h before & at Kangnung city, Korea \\ \hline \(X_{6}\) & SO\({}_{2}\)\_K: 3 h before & Y\({}_{1}\) PM\({}_{10}\)\_K(N): present (now) \\ \hline \(X_{7}\) & CO\_K: 3 h before & Y\({}_{2}\) PM\({}_{2.5}\)\_K(N): present (now) \\ \hline \(X_{8}\) & O\({}_{3}\)\_K: 3 h before & Y\({}_{4}\): CO\_K(N): present (now) \\ \hline \(X_{9}\) & NO\({}_{2}\)\_K: 3 h before & Y\({}_{5}\) O\({}_{3}\)\_K(N): present (now) \\ \hline \(X_{10}\) & PM\({}_{10}\)\_C: 2 days before & Y\({}_{6}\) NO\({}_{2+}\)\_K(N): present (now) \\ \hline \(X_{11}\) & PM\({}_{2.5}\)\_C: 2 days before & \\ \hline \(X_{12}\) & SO\({}_{2}\)\_C: 2 days before & \\ \hline \(X_{13}\) & CO\_C: 2 days before & \\ \hline \(X_{14}\) & O\({}_{3}\)\_C: 2 days before & \\ \hline \(X_{15}\) & NO\({}_{2}\)\_C: 2 days before & \\ \hline \end{tabular}
\end{table} 
Table 1: Input and output variables in the multivariate regression model for real-time forecasting. \[
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Sum of Squares**} & \multirow{2}{*}{**Degrees of Freedom**} & \multirow{2}{*}{**Mean Squares**} & \multirow{2}{*}{**Z**} & \multirow{2}{*}{**\(p\)-Value**} & \multicolumn{2}{c}{**Significance (\(p\)-Value < 0.05)**} \\ \hline Corrected Model & 137025039.194 * & 9 & 15225004.355 & 192.521 & \textless{}0.001 & yes \\ Cement & 123643280.250 & 1 & 123643280.250 & 1563.476 & \textless{}0.001 & yes \\ GY & 3742290.250 & 1 & 3742290.250 & 47.321 & \textless{}0.001 & yes \\ \(\gamma\)d & 8969138.667 & 2 & 4484569.333 & 56.708 & \textless{}0.001 & yes \\ GY * Cement & 397950.694 & 1 & 397950.694 & 5.032 & 0.034 & yes \\ Cement * \(\gamma\)d & 194216.667 & 2 & 97108.333 & 1.228 & 0.309 & no \\ GY * \(\gamma\)d & 78162.667 & 2 & 39081.333 & 0.494 & 0.616 & no \\ Error & 2056139.556 & 26 & 79082.291 & & & \\ Total & 1410895085.000 & 36 & & & & \\ \hline \multicolumn{6}{c}{*Corrected \(R^{2}\) = 0.984.} \\ \end{tabular}
\end{table} 
Table 8: ANOVA Table for the Gō Results: Cement—GG Blends. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Sum of Squares**} & \multirow{2}{*}{**Degrees of Freedom**} & \multirow{2}{*}{**Mean Squares**} & \multirow{2}{*}{**Z**} & \multirow{2}{*}{**\(p\)-Value**} & \multicolumn{2}{c}{**Significance (\(p\)-Value < 0.05)**} \\ \hline Corrected Model & 107912179.250 * & 9 & 11990242.139 & 252.140 & \textless{}0.001 & yes \\ Cement & 80153224.694 & 1 & 80153224.694 & 1685.523 & \textless{}0.001 & yes \\ GG & 1296941.361 & 1 & 1296941.361 & 27.273 & \textless{}0.001 & yes \\ \(\gamma\)d & 24403275.389 & 2 & 12201637.694 & 256.585 & \textless{}0.001 & yes \\ GG * Cement & 1352181.361 & 1 & 1352181.361 & 28.435 & \textless{}0.001 & yes \\ \hline \hline \multicolumn{6}{c}{*Corrected \(R^{2}\) = 0.984.} \\ \end{tabular}
\end{table} 
Table 6: ANOVA Table for the Gō Results: Cement—CLW Blends.
## 3 Hyperledger Fabric
_Endorsement policies:_ Endorsement policies specify the number of peers on a channel that are necessary to execute the chaincode of a transaction and endorse the results of this execution for the transaction to be credited as valid. As part of the transaction validation process performed by the peers, each validating peer verifies that the transaction has the correct number of endorsements. _Membership Service Provider (MSP):_ MSP is a component of HF that abstracts membership activities. An MSP detaches all cryptographic processes and protocols underlying certificate issuance, certificate validation, and user authentication and allows peers to validate incoming transaction requests from clients and sign transaction outcomes. An MSP can establish its own concept of identity, as well as the rules by which identities are managed and authenticated. As described, HF's unique qualities make it a highly scalable system for permissioned blockchains that supports changeable trust assumptions, enabling the platform to accommodate a vast array of industrial use cases (e.g., banking, supply chain and more) from which one of them is on the scope of this research work: healthcare. The lightweight nature of HF, as well as the features that were presented in this section, is what makes this platform a suitable choice for our HF-based security architecture for IoMT-based health monitoring systems, keeping into consideration both the design needs as well as the resource constraint nature of IoMT nodes. The support for private transactions is one of the key reasons why HF is ideal for IoT networks. HF enables private transactions between specific parties, preserving the confidentiality of sensitive data while offering modular architecture, which allows it to be tailored to the exact requirements of an IoT network. Scalability is another significant property of HF that makes it suited for IoT networks. The volume of data created by an IoT network grows in direct proportion to the number of devices in the network. HF is built to manage massive amounts of data and can be horizontally scaled to accommodate more devices and transactions. Finally, the consensus method of HF is well-suited for IoMT networks as it ensures that all network participants agree on the state of the ledger, which is critical in an IoMT network because numerous devices may perform transactions at the same time. In conclusion, Hyperledger Fabric's support for private transactions, modular architecture, scalability, and consensus mechanisms make it an appropriate blockchain platform for IoT networks [12; 19; 33].
## 3 Local Ice Load  
### Direct Method  
The direct method calculates the time series of ice loads through dynamic collision analysis using FE models of both ice and structures. The magnitude of ice load is significantly impacted by the failure mode of the ice; therefore, it is essential to accurately simulate the ice breaking and crack propagation that occur during the collision. In order to achieve this, [PERSON] and [PERSON] [8] proposed a damage-based erosion model that models the interaction between ice and a structure and estimates the ice load by considering the erosion of ice elements. The direct method calculates ice load by using the ABAQUS commercial FE analysis program with a damage-based erosion model. As shown in Figure 2, the direct method can simulate the fracture patterns of level ice. As the structure encounters the edge of level ice, the initial occurrence is the formation of radial cracks. As the structure progresses, circumferential cracks begin to emerge, leading to the generation of broken ice flakes.  
In the case of an inclined structure that experiences ice collision, the resulting ice load can be divided into two parts: the breaking force and the rubble force. The breaking force is caused by bending and is represented as an impact load with a relatively constant  
Figure 1: Flow charts for the fatigue assessment.  
Figure 2: Fracture patterns of level ice simulated by [PERSON] and [PERSON] [8].  
period, which is dependent on the physical properties of the ice and the angle of inclination. However, the rubble force, which arises from the accumulation of broken ice fragments, takes a considerable amount of time to build up and results in a continuous load on the structure. Although the average load may increase due to the massive weight of the rubble, it is expected to reduce the amplitude of the impact generated by the breaking force. To achieve accurate fatigue calculations, the implementation of rubble accumulation is crucial. However, in this study, the 'Direct method' focused on ice breaking, making it challenging to accurately represent rubble accumulation. As it is the amplitude of the load that is used for fatigue evaluation, a conservative fatigue evaluation was performed by ignoring the effect of rubble accumulation in this study, as shown in Figure 3. Further study is needed to incorporate rubble accumulation into the calculation of ice impact force.
# Development of Zirconium-Based Alloys with Low Elastic Modulus for Dental Implant Materials
###### Abstract  
The stress-shielding effect is a phenomenon in which the mutual coupling between bones and bio-materials of the human body is loosened due to the difference in elastic modulus, and bone absorption occurs due to the difference in density, which causes a shortening of the life of the material. The purpose of this study is to develop a zirconium-based alloy with low modulus and to prevent the stress-shielding effect. Zr-7 Cu-xSn (x = 1, 5, 10, 15 mass%) alloys were prepared by an arc-melting process of pure zirconium, oxygen-free copper, and tin, respectively. The Zr-7 Cu-xSn alloy has two phase \(\alpha\)-Zr and Zr\({}_{2}\)Cu intermetallic compounds. Microstructure characterization was analyzed by microscopy and X-ray diffraction. Corrosion tests of zirconium-based alloys were conducted through polarization tests, and zirconium-based alloys had better corrosion characteristics than other metal bio-materials. In general, the elastic modulus value (14-25 GPa) of the zirconium-based alloy is very similar to the elastic modulus value (15-30 GPa) of the human bone. Consequently, the zirconium-based alloy is likely to be used as a bio-material that negates the effect of stress shielding on human bones.  
biomaterials; zirconium; dental implant; elastic modulus; alloy design Article
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 6 Measurements and Results
### Latency and Throughput  
The latency was evaluated by measuring the time needed by the 16-bit payload to travel from the input of transmitter FIFO (see Figure 5) to the output of the receiver FIFO (see Figure 7). This time includes several steps of transmission-reception path: TX digital processing (packetization, serialization, chirp modulation), the pipelines of the DA converter, the delay of the analog amplifier, the time-of-flight, the delay of the photodetector and analog amplification, the pipeline of the AD converter, and finally the receiver digital processing (compressor filter, thresholding, packet reconstruction). The total delay is clearly dominated by the time required for the digital TX/RX processing, whereas the delays of the analog circuits and the time-of-flight can be neglected.  
Table 4 and Figure 13 details the delay of each of the digital blocks involved in the processing (see Figures 5 and 7). Let us start the description with the transmitter, which is clocked at \(1/T_{TC}=12.5\) MHz. At time t = 0, the 16-bit payload is uploaded in TX FIFO. The data exit the FIFO in 2 clock cycles (T\({}_{1}\) in Figure 13), and the 24-bit packet is formatted in 4 clock cycles (T\({}_{2}\) in Figure 13). The packet serialization takes \(24\cdot T_{B}=15.36\) us, from T\({}_{2}\) to T\({}_{5}\) in Figure 13. This relative long time is due to the modulator that accepts in input new bits at the transmission rate, i.e., \(1/T_{B}\). The first bit is available at the modulator input as soon as the packet is ready (T\({}_{2}\)), and the corresponding chirp starts to be transmitted after 4 clock cycles (T\({}_{3}\)). The modulator continues to transmit the data, until the last chirp corresponding to the last bit is out (T\({}_{6}\)). The transmission in air of the packet lasts \(23\times T_{B}+T\), where \(T\) is the chirp length. In summary (see Table 4), the packet is in air 0.8 us after the payload is fed in the FIFO, and the transmission ends in \(15.52+\) T us, where T is the length of the chirp employed in the modulation.  
Neglecting the small delays due to the analog circuits and the time-of-flight, the modulated data packet is present at the receiver input in the time range T\({}_{3}\)-T\({}_{6}\). The receiver works with a 125 MHz clock. The first compressed pulse is present in output when the first chirp is completely inside the compressor, plus 16 clock cycles of internal latency (see T\({}_{4}\)); the last compressed pulse is on output when the last chirp is inside, i.e., \(24\cdot T_{B}=12.36\) us later. The thresholding, the packet recognition, and the FIFO add two clock cycles each. In summary (see Table 4), in case no transmission errors occur, the 16-bit payload is available at the FIFO RX output \(16.336\) us + T after it was fed to the FIFO TX, which corresponds to less than 21, 24, 37 us for the 3 chirps tested.  
Figure 12: Theoretical (light blue curve) and measured (markers) distance gain towards chirp stretching. N represents the ratio between chirps’ temporal durations.
## 4 Results
### Verification of Results
#### 4.3.2 Validation Analysis Based on Online Reviews  
This study incorporated publicly available tourist review data to conduct a multi-source validation of the distribution of rest facilities in the scenic area. An analysis of the review texts revealed significant spatial variations in visitor satisfaction regarding rest facilities, which aligned with the findings from on-site surveys.  
In lakeside scenic areas (e.g., FangQiao and LingQiao regions), negative reviews primarily focused on insufficient rest facilities. Typical comments included \"Too few benches by the lake\", \"No available seats during peak hours\", and \"Some visitors occupy seats for hours, making it difficult for others to use them\". These observations were consistent with this study's conclusion that these areas exhibit low aging-friendly service facility adequacy, confirming that lakeside zones indeed suffer from inadequate rest facility allocation.  
Figure 10: Results of four experiments in the FangQiao region. (**a**) Full seating situation ratio. (**b**) Age distribution of tourists when seats are fully occupied.  
Conversely, in non-lakeside scenic areas (e.g., Ginkgo Boulevard), visitor reviews showed opposite trends. Multiple comments highlighted \"densely arranged seating\" and \"rest areas available every few steps\". This aligned with the study's findings of high aging-friendly service facility adequacy and a high facility density (10.5 per 100 m) in Ginkgo Boulevard, indicating sufficient rest facility provision in this region.  
Online review data effectively captured tourists' genuine feedback on rest facility usage, offering advantages such as large sample sizes, real-time insights, and spontaneous responses. Meanwhile, the on-site survey data quantitatively assessed service facility adequacy. The mutual validation between online reviews and field survey data further strengthened the credibility of this study's conclusions and provided more comprehensive decision-making support for scenic area service facility planning.