esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Future Work  
This research highlights the distinct advantages of EIGRP over OSPF, particularly in terms of packet loss and link-failure convergence time when operating in an IPv6 environment. While both protocols exhibited minimal differences in network latency and jitter, the absence of authentication led to lower network latency for both. However, the introduction of authentication introduced variability in delay levels. EIGRP consistently demonstrated superior performance in link-failover scenarios, whether authentication was enabled or not, recovering faster than OSPF. Additionally, the lower packet loss rate observed with EIGRP suggests its better capability in handling network challenges, contributing to more reliable data transmission.  
The importance of selecting an appropriate routing protocol is underscored by the significant impact that even minor variations in performance metrics can have on overall network performance and reliability. This study provides a comprehensive approach, expanding the focus beyond traditional link-failure scenarios to include a more holistic strategy that addresses both connection failover and load sharing across various traffic types in an IPv6 context. This research fills a crucial gap in existing literature, offering valuable insights for network managers and engineers seeking to optimize their networks through intelligent protocol selection and parameter tuning.  
The future work of this study aims to further enhance network resilience by incorporating hardware failover techniques. By integrating hardware failover options, the research seeks to bolster the network's defenses against potential hardware failures, ensuring continuous connectivity with minimal disruption to data and internet traffic. This expanded module will conduct an in-depth analysis of how the combination of EIGRP, OSPF, and BGP, when paired with hardware failover mechanisms, impacts critical performance metrics such as network latency, jitter, packet loss, and link-failure convergence time within an IPv6 environment.  
By pursuing this avenue of research, the goal is to develop a more robust and resilient network infrastructure that can withstand both software and hardware challenges, ultimately leading to more reliable and efficient communication systems.  
Conceptualization, K.S. and S.N.A.; methodology, K.S.; software, S.N.A.; validation, K.S., S.N.A. and S.T.H.R.; formal analysis, K.S.; investigation, K.S.; resources, S.T.H.R.; data curation, S.N.A. and S.T.H.R.; writing--original draft preparation, K.S. and S.N.A.; writing--review and editing, K.S. and S.N.A.; visualization, K.S. and S.T.H.R.; supervision, K.S. and S.T.H.R.; project administration, K.S. and S.T.H.R; funding acquisition, S.T.H.R.. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets presented in this article are not readily available because the data are part of an ongoing study.  
The authors would like to acknowledge [PERSON] at Wateen Telecom, for his assistance in developing the network topology.  
The authors declare no conflicts of interest.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 4 Discussion  
Here, a comparison between the dc-exponent method and the MSE-based method is performed for the entire section from 1960 m to the bottom of the well. Gas drilling was performed in the 361 m to 1950 m section of the well M, and the overall low dc-exponent values were tried to correct. Of these, a normally compacted, clean mudstone section from 430 m to 960 m was used for the establishment of NCT for the dc-exponent. Simultaneously, large changes in bit diameter and drilling fluid density in subsequent sections usually cause significant changes in the dc-exponent, which are easily misinterpreted as significant changes in pore pressure. Therefore, an attempt was made to correct the bit diameter and drilling fluid density for the NCT-established section to re-establish the NCT for the new section.  
In well M, actual pore pressure measurements from two offset wells were used as a rough reference because the measured pore pressure data were not sufficient. In the section above 3850 m, the main lithologies are mudstone, sandstone and shale. As can be observed in Figure 5, both the dc-exponent and MSE-based methods provide reasonable estimates of pore pressure, and the MSE-based method has less scattering in its estimates. However, in the section above 2891 m, the MSE-based method appears to over-detect the pore pressure, and the dc-exponent provides more practical estimates. In the section below 3850 m, where the predominant lithologies are limestone, dolomite and gypsum, the pore pressure estimated by the MSE-based method matches better with the measured values in the current and offset wells. Meanwhile, it can be found that the dc-exponent approximately presents a tendency to a stable value at each hole section in the marine carbonate formation. The carbonate skeleton is stiffer, and its porosity does not necessarily tend to decrease with increasing burial depth, which may lead to the dc-exponent not being sensitive enough to changes in pore pressure.  
Figure 4: Comparison of detection results using torque from the wellhead, downhole and WOB for well M. (**a**) shows the torque from the wellhead, downhole and WOB; (**b**) plots the MSE computed from the different torque source; (**c**) presents the pore pressure detection values for the three cases.  
Notably, the entire section from 1960 m to the bottom of the well was drilled extensively with PDM in conjunction with PDC bits. Both sections from 3174 m to 3850 m and 6141 m to 6189 m were replaced with roller-cone bits. Additionally, for both dc-exponent and MSE, the pore pressure estimates near the freshly replaced bits show varying degrees of scattering. The type of bits and the degree of wear on the bits both have a significant impact on the drilling efficiency and MSE. Therefore, when the bit is changed frequently and the bit wear is high, the MSE may not accurately reflect the effective stress, and the MSE-based method should be used with caution. Especially when drilling through pressure transition zones, the above factors can mask overpressure conditions.
# Surface Ripple Formation by Bombardment with Clusters: Influence of Mass
## 1 Introduction  
The nanopatterning effect caused by ion irradiation consists of self-organized nanoscale ripples that appear on the surface of the sample during the bombardment [1]. In fact, ion beam sputtering (IBS) is a technique that uses a source of ions to bombard a target material and leads to the formation of nanostructures on the substrate. The first theoretical model [2] stated that the process was the result of two opposing regimes. On the one hand, it consists of surface erosion, and on the other, it involves thermally activated diffusion dependent on curvature. However, recent work on the subject has shown that atomic displacement during bombardment can be the cause of nanostructure formation [3]. These studies, along with the theory of moments, have led to the formulation of new theoretical models [4]. The most recent developments focus on the analysis of the surface equations of motion (EOM) by studying atomic currents [5]. These models are not exempt from a complicated mathematical formulation. In general, almost all the theoretical research and a good part of the applied research have been performed on monatomic projectiles. However, ion bombardment with clusters also produces surface patterns [6].  
From a physical point of view, the oblique incidence bombardment process involves two phases: erosive and diffusive [2]. The latter is masked by the former under conditions of high fluence and/or low surface temperature [7]. It is under these conditions that our results take on a special meaning. Recently, under low-energy bombardment conditions, molecular dynamics has shown that atomic redistribution can also be the cause of this effect on Si surfaces [8]. In fact, some of these authors attribute the formation of nanostructures in amorphous materials to redistribution processes and the same formation, in metals,to sputtering for grazing angles of incidence [9]. From the experimental point of view, measurement of atomic displacement allows us to predict the critical angle of incidence (above which the effect is possible) in cluster irradiation at 30 keV [10].  
In this work, the influence of the mass of the cluster atoms on the bombardment process of a metallic substrate is analyzed. Clusters are used as projectiles in order to accelerate surface effects. This study also attempts to shed light on the phenomena that determine the nanostructure formation process for any angle of incidence above the critical angle. Clusters of a few atoms, specifically six, formed by different chemical elements: He, Ne, Ar, Kr, and Xe are used. A Co(110) surface is bombarded under different angles of incidence with cluster ions with an energy of 250 eV/atom. In the first part, the resulting superficial atomic morphologies after bombardment are compared. Next, the angular dependence of the sputtering and atomic displacement curves is studied. Finally, the specific weight of sputtering and redistribution using primarily volume considerations is discussed.
# A Novel Approach Proposal for Estimation of Ultimate Being Capacity Based on Pile Loading Test Data
## 4 Conclusions  
In the study, the data obtained from eight different pile loading experiments was examined. Pile loading tests were analyzed for ultimate bearing capacity by using the theoretical methods (based on SPT data, indirectly based on SPT data), evaluation methods of pile loading tests, and the finite element method based on soil and laboratory tests performed on the ground where the test pile was located. The dependence of the proposed method as a distribution was examined, and it was determined that the strongest linear correlation with a coefficient of 0.984 was obtained by the Chin-Kondner method [21]. This situation only expresses the linear dependence, and the methods that gave the closest results together with the proposed method in terms of the averages of the failure load values (quantities) were the [PERSON] and [PERSON] method with a 9.0% difference in bored piles in fine-grained soils, the [PERSON] and Kurkur method with a 6% difference in bored piles that reached failure in fine-grained soils (Test Pile 7), and the [PERSON] method with a difference of 1.5% (Test Pile 8) in driven piles in fine-grained soil and [PERSON] method with a 6.2% difference in the bored pile in coarse-grained soil (Test Pile 6) [4,7,10,24]. Multiple comparison tests of the proposed method with other methods were performed, and it was determined that it remained within the 95% confidence interval according to the applied variance analysis. Based on this situation, it was concluded that the proposed method can be used in both coarse-grained and fine-grained soils.  
As a result of the comparisons made, although the proposed method gives results close to the Ozkan-Alku method, excessive iteration is prevented since the load-settlement values of the linear section after failure are not used in piles that reach failure. Accordingly, the proposed method offers a more reasonable value of failure load, which is close to the truth in the parabola in the failure region.  
Other important advantages of the proposed method are that it shortens the test time and is economical, as the results will be obtained earlier than with other pile-bearing test evaluation methods.  
Conceptualization, I.V. and H.K.; methodology, H.K.; validation, I.V., H.K. and S.P.; formal analysis, H.K.; investigation, H.K.; resources, S.P. and H.K.; data curation, H.K.; writing--original draft preparation, H.K. and S.P.; writing--review and editing, S.P.; visualization, H.K. and S.P.; supervision, I.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article. The data presented in this study can be requested from the authors.  
Figure 15: Evaluation according to the Chin-Kondner method.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 3 Results and Analysis
### Combined Models of GFO-LRI and FPG  
Since GFO carries both LRI and KBR, the combinations between GFO and FPG will be carried out in GFO-LRI & FPG and GFO-KBR & FPG modes respectively. In the GFO-LRI & FPG mode, the information from GFO-LRI will dominate the combined model because it has much larger weight in Equations (9) and (11) than the KBR of FPG, based on Table 3. Therefore, we can expect only minor differences among different GFO-LRI & FPG combined models. Even so, comparisons can still be carried out by calculating the error reduction rate (ERR) of different GFO-LRI & FPG models with respect to the GFO-LRI standalone model. Here, we use the ERR in terms of DGHE, and therefore set \(q_{i}=DGHE_{GFO-LRI}\) & FPG and \(q_{j}=DGHE_{GFO-LRI}\) in Equation (16). Figure 4 plots the degree-wise \(ERR_{GFO-LRI}^{GFO-LRI}\) & FPG\((DGHE)\) for all four GFO-LRI & FPG models, which can be regarded as contributions of adding different FPGs' information to the GFO-LRI data.  
As expected, the contributions are not significant. Among four combined models, GFO-LRI & FPG-93 has the largest ERR for the majority of SHC, and GFO-LRI & FPG-91 is next after that. Moreover, even with the same polar gap, retrograde FPGs (FPG-91 and FPG-93) outperform the prograde ones (FPG-89 and FPG-87). This can be attributed to the more isotropic integrated system of GFO & retrograde-FPG than GFO & prograde-FPG, as learnt from Bender-type SFF design [37]. More specifically, the orbit inclination differences between the GFO (in prograde orbit) and retrograde FPGs are larger than that in GFO & prograde-FPGs, which enables the GFO & FPG integrated system to observe the gravity signals in a more isotropic sense. However, degradations occur at some very low degrees of the combined models, which is likely due to insufficiently perfect data weighting between GFO-LRI and FPG [61].  
Figure 3: Absolute spherical harmonic coefficients (SHC) differences of different FPG standalone models in January 2006: (**a**) FPG-87; (**b**) FPG-89; (**c**) FPG-93; (**d**) FPG-91.
## 2 Material and Methods
### Literature Review
#### 2.1.1 Previous Tsunami Modeling  
Tsunami modeling is incredibly beneficial for visualizing tsunami events that have already happened and will happen in the future [28]. Current modeling is in the forms of numerical simulations [29; 30; 31], 2D tsunami simulation modeling [24; 32; 33], and 3D tsunami simulation modeling [12; 34; 35].  
A deeper review of 3D tsunami simulation modeling identified three types of 3D tsunami simulation modeling that are widely used today. They are high-resolution Modeling based on Computational Fluid Dynamics (CFD), modeling based on photorealistic particle visualization methods, and photogrammetry/unmanned aerial vehicles (UAV) methods [14]. Of these three models, the photogrammetry/unmanned aerial vehicle (UAV) types the most visually realistic, has a high level of accuracy, and is most easily understood by ordinary people [12; 34; 35; 36; 37].  
Several types of software are commonly used to create virtual cities for tsunami modeling, such as Lumion and City Engine. However, the results obtained with virtual cities do not represent the original environment, because they are made based on the properties provided in the software. Therefore, the photogrammetry/UAV technique is better, because it can produce a virtual city that follows the original environment [12; 35; 36]. However, this technique also has weaknesses because it takes a long time to carry out the production process and requires a computer with very high specifications. Moreover, the production costs are expensive compared with those required to create a virtual city with the software [14].
## 4 Enological Yeasts and Health-Related Compounds
### Resveratrol-Increasing Content  
Resveratrol (3,5,4\({}^{\prime}\)-trihydroxy-trans-stilbene) belongs to the polyphenols' stilbenoids group, possessing two phenol rings linked by an ethylene bridge. This natural polyphenol has been detected in more than 70 plant species, especially in grapes' skin and seeds, and was found in discrete amounts in red wines and various foods [166]. Resveratrol has been associated with a wide range of pharmacological properties such as anti-inflammatory, anti-oxidative, anti-aging, anti-diabetic, cardioprotective, and neuroprotective attributed to its anti-oxidative, anti-inflammatory, and immuno-modulating effects [167]. Furthermore, the antimicrobial activity of this compound has been widely demonstrated [168]. However, due to the alcohol content, these beneficial effects cannot be achieved exclusively by consuming of wine [169]. In this context, finding biotechnological solutions for increasing the availability of resveratrol in wines could be necessary to achieve a healthy effect. The average concentration of total resveratrol in red wine is 7 mg/L, 2 mg/L in rose wines, and 0.5 mg/L in white wine, but it ranges widely according to grape variety, geographical indication, and winemaking processes [170]. Despite the agronomic practices that can be adopted to increase resveratrol availability, the use of selected yeasts during winemaking could be a suitable alternative, especially those producing beta-glucosidase enzymes [171].  
The use of genetically modified _S. cerevisiae_ strains to increase resveratrol levels has already been amply demonstrated in previous years [172; 173; 174; 175; 176]. For instance, the industrial strain _S. cerevisiae_ EC1118 could in vitro produce 8.249 and 3.317 mg/L of _trans_-resveratrol with or without antibiotic addition, respectively, by incorporating the coenzyme A ligase _4 cl_ gene, lacking in _S. cerevisiae_ and coming from _Arabidopsis thaliana_, and the resveratrol synthase gene from _Vitis vinifera_[177].  
Alternatively, the use of selected native yeast strains is also a viable alternative; [PERSON] et al. (1978), isolated beta-glucosidase-producing strains of \(H\). _uvarum_ capable of increasing resveratrol content up to 102% during fermentation of Bordo must (_V. labrusca_) through hydrolysis of piceides. Lately, a study analyzed that the _trans_-resveratrol concentration in red Tempranillo wines inoculated with _T. delbrueckii_ was three times higher than in wine obtained by using _S. cerevisiae_ and 4.5 times higher when _Z. bailii_ was inoculated (179; 180). Nevertheless, more and more studies increasingly focus on the reuse of wine wastes for bioactive compound recovery, such as resveratrol; in this perspective, [PERSON] et al. (180) showed that the use of an engineered strain of _S. cerevisiae_, which is also capable of hydrolyzing xylose, on wine waste, such as musts, pruning residues, and wine lees, can lead to modest resveratrol production, representing a viable sustainable alternative in a circular economy context.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 4 Results and Discussion
### The Field Test Analysis  
Figure 4 shows the vehicle trajectories outputted by PUA-LightGBM (in yellow) and Novatel pwrpak7-e1 (in red) on the six road segments of the field test. The trajectory output from PUA-LightGBM is the model-predicted trajectory, and the Novatel pwrpak7-e1 integrated navigation system provides the trajectory as the vehicle reference trajectory. As can be seen from the trajectory figure, the PUA-LightGBM model predicted the position changes of vehicles on straight roads (Figure 4B,C,E,F) better than those of vehicles on curved road (Figure 4A,D). This is in line with the analysis by [PERSON] and [PERSON], which found that the results predicted by the position prediction model during U-turns or sharp turns (where there are significant dynamic changes in the vehicle's movement) produce significant position errors [13]. The vehicle motion is more stable when travelling on a straight road, and the PUA-LightGBM model is better at predicting position changes. However, the two turning segments chosen for the experiments were both sharp turning segments, where the vehicle experienced significant dynamic changes in motion and vibration of the vehicle body. The INS data at this point did not accurately reflect the vehicle's turning state, and the model's training set did not contain enough vehicle turning situations, causing the PUA-LightGBM model to be less effective in predicting the position change of the vehicle when driving around a turn.  
Figure 4: The vehicle trajectories outputted by PUA-LightGBM (in yellow) and Novatel pwrpak7-e1 (in red) on six road segments of the field test. (**A**) Turn and downslope road, (**B**) straight and level road, (**C**) straight and upslope road, (**D**) turn and upslope road, (**E**) straight and level road, (**F**) straight and downslope road.  
Table 3 shows the average absolute error between the predicted and true positions of the PUA-LightGBM and PUA-RandomForest models for the GNSS outage scenario. PUA-LightGBM outperformed PUA-RandomForest on road segments a, b, and f in terms of position prediction. On road segments c, d, and e, the position prediction accuracy of PUA-LightGBM was slightly lower than that of PUA-RandomForest. Overall, the prediction error of PUA-LightGBM was less different from PUA-RandomForest. The PUA-LightGBM model, on the other hand, had a much shorter training time than the PUA-RandomForest model (Table 2). This suggests that PUA-LightGBM is more efficient in training, costs less, and has the potential for dynamic updating. The model does not need to be trained in advance and can be dynamically trained or updated while the vehicle is moving. This provides some flexibility to the PUA-LightGBM model and allows it to predict vehicle position changes on a variety of complex road segments via dynamic updating of the model.