esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 2 Geological Structure of Strike-Slip Faults
### Fault-Controlled Reservoir  
The advancement of seismic datasets in the cratonic basin has led to an increasing number of scholars recognizing the significant influence of tectonic evolution, fault stage differences, and strike-slip fault systems on the modification of physical attributes of carbonate reservoirs, oil and gas migration pathways, and the control of oil and gas traps [34]. Under the extrusion effects of tectonic stress, deep strike-slip faults, particularly within brittle carbonate strata, give rise to a complex fault system that evolves into fault zones of considerable scale [35]. Subsequent dissolution processes further reshape these early fault zones, with karst water infiltrating along fault strikes, dissolving carbonate rocks. The presence of large strike-slip faults renders deep and surrounding strata susceptible to localized deep hydrothermal upwelling, resulting in further dissolution and the transformation of fault zones and adjacent strata, consequently forming paleokarst reservoirs exhibiting diverse spatial structures [36]. Notably, reservoirs in the Tarim Basin and Llanos Basin display oil- and gas-bearing characteristics, with localized enrichment occurring along the fault zones. The strike-slip faults serve as principal pathways for longitudinal oil and gas migration and accumulation, and the multistage activities of the faults manifest in the characteristics of multistage accumulation [16].
## 3 Results and Discussion
### Prediction of Nominal Stress and Comparison of Models' Performances
Besides, orientation is considered as an additional feature, similarly to the previously reported results for Tensile Modulus. The prediction accuracy of Gradient Boosting regressor model is 0.964 out of 1, and this means that we can already use this model for the prediction of Nominal Stress for a similar type of parts. However, if there is a need for prediction of Nominal stress value for a specific orientation group, the prediction can be done for the ZYX and Angle orientations at this moment. In order to improve models in other orientations, additional information on the material, as well as more data points, are required. The author will address these issues in the future. The prediction of Nominal Stress in Angle orientation is possible by using any of the described models in Table 6. However, when higher accuracy is required, the AdaBoost model would be the best choice. Otherwise, when a mathematical model is needed, then linear regression model can be used:  
\[y=-832.16*x_{1}-6.819*x_{2}+415.9*x_{3}+13.639*x_{4}+\\ +416.24*x_{5}--6.819*x_{6}-7.44e-04*x_{7}+\\ +0.823*x_{8}+0.197*x_{9} \tag{1}\]  
where \(y\) is the Nominal Stress in Angle orientation, \(x_{1}\) is the central coordinate \(x\), \(x_{2}\) is central coordinate \(y\), \(x_{3}\)--min coordinate \(x\), \(x_{4}\)--min coordinate \(y\), \(x_{5}\)--max coordinate \(x\), \(x_{6}\)--max coordinate \(y\), \(x_{7}\)--max corrdinate \(z\), \(x_{8}\)--volume, and \(x_{9}\)--surface.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 1 Introduction
### Content and Method of Study  
A novel method of calculating the speed limit using the traffic volume, road environment information, and accident data was developed to improve the method of calculating the speed limit based on the functional road class.  
The subject roads are one-way roads with two or more lanes in Seoul. The contents and process of this study are outlined in Figure 1.  
We selected variables that affect the driving speed and developed a speed limit calculation model equation using the correlation between different variables and the development of the adjustment factor of each variable. We also evaluated the effect of the calculated speed limit by applying the effect prediction method presented in the literature.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
### Path Similarity  
In this paper, path similarity means the degree of similarity between two paths. The path similarity is mainly used for graph matching such as skeleton graph matching as in Refs. [20; 21; 22].  
However, path similarity is often not used in routing algorithms. Similar to the path similarity, geographical data such as directionality are used for routing. For example, Leontiadis [23] and Chen [24] correspond to geographical routing. These geographical routing schemes are suitable for overcoming frequent topology changes in WSNs with high mobility.  
The reason why the path similarity is not used in the routing algorithm is that it cannot predict the entire movement path of each object in the system, such as WSN or VANET mentioned above.  
Since existing clustering algorithms often target fixed or infrequently moving nodes, it is difficult to apply them directly to V2X with active mobility If path similarity is available, it can increase the connectivity and maintainability of the cluster, but existing vehicles cannot know the full path information. So instead of path similarity, use directionality or the connection rate.  
However, this paper focuses on the autonomous vehicle environment. In this case, each node, that is, each vehicle can know all the routes to the destination. Therefore, it is possible to utilize the route information which has been used only in the graph algorithm, which can clearly know the entire route, in the routing. This means that we can utilize path information that is remarkably accurate compared to the directionality or connection rate that can only estimate the path. In other words, it can identify nodes that are moving in a similar path, which contributes to the improvement of the survival of clustering. It is therefore possible to identify the nodes that are moving in a similar path. Thus, by constructing clusters among the nodes going to the same route in the future, the algorithm contributes to improvement of the survival of clustering.  
Accordingly, in this paper, we propose a clustering algorithm based on path similarity based on the assumption that each object knows all its paths.
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 4 Result Analysis and Discussion  
In this section, the performance of the multi-GNSS integrated POD with different LEO constellations and different station network schemes is analyzed. Several impact factors including LEO numbers, orbital types, and altitude, as well as the numbers and distribution of ground stations, are discussed in detail. The previously simulated orbits are regarded as the true values, and the differences between our estimated orbits and true orbits provide a method by which to assess the accuracy of the integrated POD results.
## 3 Results
### Intake Recommendations for Total and Specific Fatty Acids
#### 3.1.4 N-3 PUFA: Alpha-Linoleic Acid and EPA + DHA Intake Recommendations  
The FAO/WHO [17] concluded that the ALA minimum dietary requirement for adults (lower AMDR: >0.5%E) does prevent deficiency symptoms, while the highest value (upper AMDR, 2 or 3%E, depending on the age) can be part of a healthy diet. FAO/WHO set an AI for DHA up to the age of 2 years because of its critical role in retinal and brain development and, after the age of 2 years, an AI for EPA + DHA as part of a healthy diet in view of preventing the risk of chronic diseases (total CHD events, stroke). This AI is age adjusted up to the age of 10 years, after which the adult value applies. FAO/WHO also mentions that a specific minimum to prevent n-6 and n-3 PUFA deficiency is unclear.  
Similar to LA, EFSA [18; 19] found that the mean ALA intakes were below the AI (0.5%E) in some populations of young children in Europe. However, there is no clear relationship between intakes or biomarkers of n-3 PUFA status and clinical outcomes. Thus, EFSA concluded that they could not quantify the risk of inadequate intakes of ALA and DHA in young children living in Europe. Given that DHA is needed for the normal development of the nervous system and the retina, and accumulates in the brain and retina during early childhood, they considered DHA as a conditionally essential FA for infants. The AI of 100 mg DHA per day for the age range 6-24 months was based on \"intervention studies with DHA-enriched formula or complementary foods from age 1.5 to 12 months in formerly and continuing breast-fed infants and which was found to be effective for visual function\". After the age of 2 years, the same AI as for adults was proposed (250 mg EPA + DHA/day), an amount considered as enough for primary prevention of CHD in healthy subjects [18].
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 5 Experiments & Results
### Long-Short versus Equalized Short-Short  
Our experiments in the Short-Short scenario have revealed that utterances are better classified as long as the relative phonetic distance between enrollment and test decreases. Nevertheless, further information is available in other scenarios, as in the Long-Short scenario. While the short utterance has limited information in it, maybe missing some phonemes, the long utterance has complete information about all phonemes. Hence we must check whether this extra information is worthy or not.  
For this reason we compare the originally defined Long-Short experiment with the Short-Short experiment with lowest relative phoneme distance. This Short-Short experiment implies the equalization of the enrollment utterance to match the phonetic content in the test utterance, getting rid of any extra information. In this comparison both experiments share the same test utterances. The results for this experiment are shown in Table 4 and DET curves are shown in Figure 8.  
The results in both Table 4 and Figure 8 show that the extra information has a very small effect in the evaluation task. Nevertheless, despite both experiments have obtained very similar results, the Long-Short original experiment is slightly better. This issue can be partially justified by the range of the relative phoneme distances of the short-short experiment. The equalization imposes the relative distance to be equal to zero. In this range of values the non-target trials experiment an increase of the score, possibly causing the degradation. In order to confirm this explanation we analyze the score  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Utterence** & **EER(\%)** & **MinDCF** \\ \hline Long-Short & 5.67 & 0.27 \\ Short-Short Equalized & 5.98 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparion of results, EER(%) and minDCF, for scenarios Long-Short Random and Short-Short with equalized results. SRE10 \"coreext-coreext det5 female experiment\".  
Figure 8: DET curves for the scenarios Long-Short Random and Short-Short Equalized in SRE10 “coreext-coreext det5 female”.  
distribution for population of target and non-target trials in both experiments. These distributions are represented in Figure 9:  
Figure 9 confirms our hypothesis of harmful non-target trials. The scores for the target trials overlap in both scenarios, Long-Short and Short-Short Equalized. By contrast, the score distribution for the non-target trials in the Short-Short equalized experiment is slightly positively biased with respect to the Long-Short counterpart. This extra deviation is responsible for the experimented small degradation of performance.
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 6 Conclusions  
Today, the growth of space weather data is rapidly demanding data availability to accommodate the needs of researchers. This paper proposed a new system to increase data availability for supporting SWIFtS using a decentralized storage method. Our proposal leveraged the IPFS network to distribute and store data on the IPFS node. A directory watcher was added, forcing both the instrument's PC to synchronize data and nodes to upload data automatically. Moreover, the implementation of the  
Figure 12: Mean throughput of a node.  
Figure 13: _Iperf_ measurement results.  
IPFS-Cluster was useful to replicate the data between peers as well as to limit the distribution process only in cluster peer members.  
Furthermore, a combination technique for system monitoring was implemented and was seen to be useful in providing real-time data flow and node status. On the evaluation of the finished work, aiming to learn the proposed system performance compared with the existing system, the evaluation showed that the IPFS-based solution was able to reduce the time of file replication and support high-throughput.  
Future work is needed to improve the speed of data distribution among peers and include a more extensive exploration of the hardware effect on system performance. Moreover, traffic monitoring of bandwidth usage is also an important approach to better understand the network cost from this system.  
Conceptualization, Y.A., H.K. and H.J.; Formal analysis, Y.A.; Funding acquisition, H.J.; Investigation, Y.A.; Methodology, Y.A. and H.J.; Project administration, H.J.; Software, Y.A.; Supervision, H.J.; Validation, Y.A.; Writing--original draft, Y.A.; Writing--review & editing, H.K.  
The authors would like to thank Keimpung University and the Indonesian National Institute of Aeronautics and Space for providing facilities and other support for this research. The main author specifically thanks to the Ministry of Research and Technology under Secretariat of Project Management Office (PMO) Research and Innovation in Science and Technology Project (RISET-PRO) for supporting and facilitating activities during the Master study program. Also appreciated are the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00539, Development of Blockchain Transaction Monitoring and Analysis Technology). Finally, the authors express gratitude to the reviewers for providing helpful suggestions.  
The authors declare no conflict of interest.
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 2 Theory of Inductive Power Transmission
The skin effect depth for the conductors in the printed flat coils is of the order of 1 mm and is therefore negligible in this case but is included in the calculations for completeness. Figure 2: Design of circular-spiral flat coil. The external diameter of the coil is designed based on the application, and other parameters are optimized for the maximized unloaded \(Q\) factor. The coils are printed on interface-coated 65/35 polyester/cotton textile. The parallel combination of parasitic capacitance and inductance of the coil causes self-resonance of a coil at a particular frequency. The self-resonant frequency (SRF) is an essential parameter of an inductively coupled coil because the effective inductance is zero at the SRF which prevents an alternating electromagnetic field from being induced. A connection cannot be made without an alternating electromagnetic field. The parasitic capacitance is affected by the surrounding materials which are air, the insulator, and the substrate textile. This can be calculated as described in [12] as:  
\[C_{p}=(0.85\varepsilon_{rCo}+0.15\varepsilon_{rSu})\varepsilon_{0}l_{gap} \bigg{(}\frac{t_{c}}{S}\bigg{)} \tag{5}\]  
where \(\varepsilon_{rCo}\), \(\varepsilon_{rSu}\) are the relative permittivity of the coating insulator material and the substrate textile material, \(\varepsilon_{0}\) is the vacuum permittivity, and \(l_{gap}\) is the overall length of the gap between the tracks. The unloaded \(Q\) factor of inductors expresses the ratio of stored against lost energy which is used to evaluate the quality of coils, and is found as [17]:  
Figure 1:0  
To employ the printed coils in a WPT system, the coils are attached with resonant tank capacitors which are calculated as [18]:  
Figure 1:1  
where \(R_{eTX}\), \(R_{eRX}\), \(Q_{uTX}\), and \(Q_{uRX}\) are the ESRs and unloaded \(Q\) factors of the TX and RX coils, respectively, \(L\) is the inductance of the coil with the resonant tank capacitor attached. The coefficient is based on the unloaded \(Q\) factors of both coils and the coupling factor, \(k\), which has an approximate formula for flat spiral coils of [19]:  
Figure 1:2  
where \(D\) is the center separation distance between the TX and RX coils, \(d_{extTX}\) and \(d_{extRX}\) are the external diameters of the TX and RX coils, respectively. For coils with a known size, the coupling factor \(k\) is affected by the distance between two coils. In this work, the coupling factor was also measured for each pair of printed coils to determine the resonant tank capacitor required for each individual coil at the desired operating frequency.
# Code Edit Recommendation Using a Recurrent Neural Network
## 6 Evaluation Results
### RQ1: Recommendation Accuracies of CERNN and MI-EA
In Table 4, the second last row shows simple averages, where CERNN yielded 0.80 precision, 0.60 recall, and 0.69 F1-score with five projects. The last row shows weighted averages by the numbers of recommendations, where CERNN yielded 0.79 precision, 0.54 recall, and 0.64 F1-score with 4987 recommendations. Reviewing the precision results first, as shown in the second last row of Table 4, the averaged precision of CERNN is 0.80, while that of MI-EA is 0.71. The precision of CERNN is 9% higher than that of MI-EA. However, as shown in the last row of Table 4, the weighted average precision of CERNN is 0.79, and that of MI-EA is the same. Therefore, we conclude that the precisions of the two models are not much different. Concerning the recall results, the average recall of CERNN is 0.60, while that of MI-EA is 0.51. The recall of CERNN is 9% higher than that of MI-EA. The weighted average recall of CERNN is 0.54, while that of MI-EA is 0.50. The weighted recall of CERNN is 4% higher than that of MI-EA. In both cases, the recall values of CERNN are higher than those of MI-EA. Due to the recall values, the average F1 score of CERNN is 0.69, while that of MI-EA is 0.55. Additionally, the average F1 score of CERNN is 0.64, while that of MI-EA is 0.59. Thus, the F1-score of CERNN is at least 5% higher than that of MI-EA.
## 4 Discussion
With the data extracted from this study, it becomes possible to fit the material properties of meniscal tissue of different origins in the area surrounding the suture hole, thus contributing to the development of computational models with more realistic behavior. On the other hand, the study highlights the need for further research to analyze the mechanical behavior of the sutured meniscal horn in situations where the viscoelastic nature of the meniscal tissue has a significant influence, such as in the case of dynamic impact loads or quasi-stationary loads maintained over a long period of time. Additionally, the authors maintain it will be interesting to conduct a thorough study of the alteration that the suture produces in the mechanical behavior of the meniscal root in the immediate postoperative period. Furthermore, in vivo studies could allow for an assessment of the progress of the mechanical properties of the sutured root as healing progresses, evaluating whether its natural meniscal properties can be fully restored. This study is subjected to inherent limitations associated with in vitro testing, notably the absence of the healing effect observed over time. The use of an isolated meniscal setup implies a deliberate exclusion of the influences from surrounding soft tissues, ligaments, cartilage, and bones. The rationale for this decision has been discussed above. A single simple suture was employed, as also previously justified. Therefore, the cut-out and ultimate forces reported in the results section do not quantitatively reflect the maximum traction forces that the clinically repaired meniscus, using other more-common suturing techniques, might withstand. The load-to-failure test was conducted at 0.1 mm/s, representing a quasi-static displacement velocity lower than expected for knee movement in daily activities [54]. However, given the viscoelastic behavior of the meniscus reported in compression tests [55; 56], further research is warranted to comprehensively understand the dynamic response of the sutured meniscal tissue. This minimizes the potential influence of viscous effects on the test, a characteristic not addressed in this study. All the animals came from the same local slaughterhouse, which may raise questions about whether porcine models from other origins would give different results [57].
## 4 Discussion
The statistically significant decrease in the skeletal class angle (ANB) and Wits as a linear measure for class contributes to the reduction in the discrepancy between the upper and lower jaws, bringing their relationship closer to Class I. The results suggest that the increase in the SNB angle, accompanied by the increase in the effective mandibular length, is the main cause of the reduction in the skeletal class angle. The mean value of the maxillary prognathism angle (SNA) did not change significantly after Twin Block therapy, indicating that there was no inhibition of maxillary growth, also seen in other studies [27; 28; 29; 31; 32]. Similar results can be seen in the study by [PERSON] et al. (2024), which also reported statistically significant changes, including an increase in the SNB angle and a decrease in the ANB angle [35]. However, after therapy, the average value of the SNA decreased, albeit without statistical significance, similarly to our data, and without significant change in the control groups of the cited studies [16; 33; 34; 35]. In a study by [PERSON] and [PERSON] (2023), in addition to the decrease in the ANB angle and the increase in the SNB angle, there was also a statistically significant decrease in the SNA, which may indicate the minimal, but statistically significant, inhibition of maxillary growth [28]. The statistically significant decrease in the Wits value correlateswith the results from other studies [34,35]. A statistically significant decrease can also be observed in the angle of skeletal convexity (ANPG), which supports the reduction in the convexity of the bony facial profile. A review of the literature revealed contradictions in the results, with some studies indicating a significant decrease in the angle of skeletal convexity, while others report an increase [6,34]. The results did show statistically significant changes in the inclination and protrusion of lower incisors relative to the mandibular base and protrusion of upper incisors to the apical base of the maxilla. This suggests that the Twin Block appliance also affects the dentoalveolar level, leading to protrusion of lower incisors and retrusion of upper incisors. Most studies in this literature review confirm statistically significant changes in the dental segment, similarly to our data [16,17,34,35]. Limitations of this study include the absence of a control group, which prevents our drawing conclusions about the extent to which the changes in cephalometric and soft tissue parameters are solely due to the effects of the functional appliance, rather than the interaction between growth, development, and the appliance. Also, it would be interesting to see in future research whether the increase in the lower facial third would contribute to more harmonious relations, as compared to lower facial heights before therapy.