esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Existing Brittleness Indices
### Log-Based Brittleness Indices (LBs)
Normalization procedures represented by Equations (5) and (9) lead to the interval of possible brittleness indices to be between zero and one. It will be shown below that this normalization is not a tool to make things simpler, but an important way to make different indices comparable. Secondly, even within this interval, brittleness indices obtained from different correlations show varying behavior. The results obtained for middle Bakken and Niobara formations reveal the same tendencies in different indices, i.e., zones of maximum values of different brittleness indices are in agreement. The same is not true for Marcellus and Chattanooga formations. It can be clearly seen from the figure that some brittleness indices show, in fact, opposite behavior: an example is depth interval between 2284 and 2290 m for Marcellus formation. In this interval, the zones of maximum LBI\({}_{2}\) coincide with zones of minimum MBIs, and vice versa. Moreover, this difference is not within the confidence interval: it is typical for the indices introduced in the mentioned papers to have an error of \(\pm 0.1\)[21], which is lower than the differences between brittleness indices plotted in \[LBI_{2}=\gamma DTC+\delta, \tag{3}\]0  
[PERSON] et al., 2019 [4] draw the following conclusion from the presented analysis: using a universal index to represent LBI or MBI results in misleading results related to selecting zones for perforation. Therefore, it is recommended that each rock type be assigned an index based on its lithology and mineralogy. Nevertheless, the question of choosing the proper brittleness index for site specific conditions remains an open question. The problem is still related to the lack of a method to check, whether the chosen brittleness index is suitable to describe failure in the particular rock. One possible answer is related to the mentioned regression problem: perhaps, the best brittleness index correlation is the one to result in the best match between brittleness evaluated from logs or mineralogy and laboratory tests. Nevertheless, there are two issues with this method of choosing a brittleness index determination process. Firstly, there still exists the scaling problem: there is no necessity for brittleness of heterogeneous rock to be the same at laboratory and well log scales. Secondly, determination of brittleness index from laboratory tests (TBI) is still a problem itself: similar to MBIs and LBIs, there are many ways to introduce TBIs from various laboratory tests on mechanical behavior of rock samples
# Artificial Intelligence (ChatGPT) and Bloom's Taxonomy in Theoretical Computer Science Education
## 4 Research and Methods  
Based on the literature overview and following our research question, we decided to compare the performance of ChatGPT with that of university students as part of this research. We chose to conduct this study as part of a theoretical (mathematical) informatics course. We selected first-year students on the Bachelor's degree program at the Department of Informatics and Computers at the University of Ostrava. ChatGPT is a subject of our research and therefore we do not use it as a tool for text creation or production for this article except for language stylistics.  
Phase 1 of the research focused on a smaller sample of first year students in their summer semester (these students had already completed the winter semester and we were able to perform categorization through their grades in course 7 ZAIN--Basics of Informatics), which provided a more diverse range of factors for data analysis (of course, this means that factor groups are small for generalization of results). However, even if these groups are small, the process suggests directions as to how to approach phase 2. In phase 2, we aim toto generalize the results and therefore we used bigger groups of winter semester students (with no previous categorization grades). The Study and Examination Regulations of the University of Ostrava do not require standard requirements for courses to be evaluated by an ethical committee (experimental tests were included as partial alternatives to evaluation).  
During phase 1, the students had to complete specific tasks that were also presented to ChatGPT for processing. Then, the results were processed using selected statistical methods. Detailed information about the assignment of individual tasks and their results is described within the following sections of this article.  
The tentative hypothesis is formulated as follows:  
There is no significant difference between AI and human subjects in their educational performance.  
The term \"educational performance\" in our concept means the ability to understand and process information and provide as accurate and correct an answer to a question as possible.  
Resulting data from experimental tests were processed using JASP statistical software version 0.19.0.0, and particularly by classical ANOVA methods (where standard parametric tests underwent assumption checks with negative results--test for equal variance was rejected, and we also used non-parametric tests). Classical ANOVA with subjects group as factor was used (along with descriptive data, box plots and raincloud plots) and, in some cases, non-parametric Kruskal-Wallis tests and Bonferroni and Bonferroni-Holm corrections were observed.
## 5 Need for Lab-Scale Experimental and Geochemical Studies  
Lab-scale experimental studies are essential for unlocking UHS in Lithuania, ensuring feasibility, safety, and efficiency. Limited studies are available in the literature dealing with UHS aspects on a lab-scale finding [103]; however, multiple studies are found based on simulation at the global level [104; 105; 106; 107]. Lab-scale studies allow for the characterization of materials, assessment of H\({}_{2}\)-rock interactions, testing of leakage and containment measures, and examination of microbial activity in a controlled environment. Moreover, microbial activity in subsurface environments also plays a crucial role in UHS, especially regarding the production of gasses in the subsurface such as methane or hydrogen sulfide, which could affect storage safety and capacity. Lab-scale studies can isolate specific microorganisms and evaluate their impact on hydrogen storage over time. However, simulation studies provide detailed geological modeling, risk assessments, and optimization of storage operations, predicting hydrogen behavior under various conditions. Integrating data from both studies can help create comprehensive models, validated by real-world data from pilot projects. These studies reduce costs, identify potential issues, optimize solutions, and ensure the safety and reliability of storage operations. Additionally, they inform policymakers with scientific data to develop effective regulations and standards, addressing safety and environmental concerns. Conducting these studies is crucial for understanding the complex interactions in UHS, leading to safer, more efficient, and economically viable storage solutions in Lithuania.  
Geochemical studies are essential for understanding the feasibility and safety of UHS [27; 108]. They assess interactions between hydrogen, reservoir rocks, and fluids, evaluating potential mineral reactions that could affect storage integrity. These studies will help determine the extent to which hydrogen dissolves in the formation of water and identify possible chemical reactions, such as the formation of methane or hydrogen sulfide, that could influence the storage capacity and stability of the reservoir. By analyzing these factors, geochemical studies ensure the long-term viability and safety of UHS.
## 2 Study Area and Materials
### Radar Altimeter L1A and L2 Products  
The algorithm primarily relies on operational Non-Time Critical (NTC) Sentinel-6 (S6) products, obtained from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Data Access Client (EUMDAC). These products include the following:  
* Level-1A (L1A) products, which comprise the received satellite echoes that have already been calibrated.
* Level-2 (L2) products, containing ranges obtained from both the ocean and Offset Center of Gravity (OCOG) retracker, along with corresponding geophysical corrections.  
For this study, only products processed with baseline version F08 are considered. The dataset spans a period of 2 years from 1 September 2021, to 1 September 2023. Sentinel-6 has an overflight periodicity of approximately 10 days [9].
# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Cross-Layer Optimization for Energy Conservation in MANETs
#### 3.8.2 Cross-Layer Designs for MANETs
\begin{table}
\begin{tabular}{p{56.9 pt} p{34.1 pt} p{34.1 pt}} \hline \hline
**Year** & **Ref.** & **Contribution** \\ \hline
2008 & [195] & A CLD approach for power conservation based on transmission power control. This approach includes three CLD proposals aiming to solve special problems. \\ \hline
2011 & [196] & The DELAR framework achieves energy conservation from multiple facets, including power-aware routing, transmission scheduling, and power control. DELAR focuses on heterogeneous MANETs. \\ \hline
2011 & [197] & A cross-layer energy-efficient real-time data multicasting architecture (MC-TRACE) for MANETs. MC-TRACE addresses group communications and provides superior energy efficiency while producing competitive QoS performance and bandwidth efficiency. \\ \hline
2012 & [207] & A cross-layer scheme jointly considers flow control, multipath routing, and random access control. The scheme is based on network utility maximization. \\ \hline
2013 & [208] & A CLD for random-access-based fixed wireless multi-hop networks. This CLD is based on a physical interference model. \\ \hline
2014 & [209] & A cross-layer distributed approach for maximizing the network throughput by jointly selecting stable routes and assigning channels based on mobility prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Important contributions of cross-layer designs in MANETs.
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 4 Performance Evaluation
### Effectiveness of Prediction Result Sharing
Therefore, if the target vehicle drives aggressively, the ego vehicle will have a desired distance of 30% more than the basic safety distance. In the simulation, the target vehicle performs 50 s of defensive behavior, 60 s of normal behavior, and 120 s of aggressive behavior. To clearly show the effect of the target vehicle's aggressive behavior to the ego vehicle, the target vehicle was set to perform periodic acceleration and deceleration. Figure 13(a) represents the movement of the target vehicle in the simulation. The target vehicle decelerates at a maximum of 0.2658 m/s\({}^{2}\) from 0 to 50 s in defensive behavior, and accelerates at a constant rate of 0.2 m/s\({}^{2}\) in normal behavior from 50 to 110 s as shown in Figure 13(b). Aggressive behavior is expressed an acceleration of 1.3 m/s\({}^{2}\) to \(-\)1.3 m/s\({}^{2}\) through a \(sin\) function at a 15-second cycle from 110 to 230 s. As shown in Figure 13(c), the driving behavior of the target vehicle through the 4 layer CNN-2 stacked LSTM showed high-accuracy classification results. Figure 13(d) shows the driving velocity of the target vehicle and the ego vehicle. The black line represents the velocity of the target vehicle, and the blue and red lines represent the velocity of the ego vehicle without V2X-based behavior sharing system and with V2X-based behavior sharing system, respectively. When the target vehicle has defensive and normal behavior, the velocity of the ego vehicle is similar regardless of the application of the proposed system. However, the desired distance of the ego vehicle increases with the target vehicle's behavior, as shown in Figure 13(e). At about 110 s, the target vehicle accelerates, but the ego vehicle slows down as the desired distance is increased for safety, as shown in the green area in Figure 13(e). The risk of collision with the target vehicle is reduced while inducing an increase in Time to collision (TTC) of approximately 3 s or more compared to the case where V2X based behavior sharing system was not applied as shown in the Figure 13(f). As a result of the simulation experiment, we confirmed that the sharing of driving behavior of a vehicle can have a positive effect on traffic safety.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 3 Integrity Risk Probability Distribution  
In this section, a fault tree model for the BDS satellite SIS is constructed, and the main integrity risk bottom events from the space segment and the ground segment are sorted out and decomposed. By analyzing them one by one, the design values of \(\mathrm{P_{sat}}\) and \(\mathrm{P_{const}}\) of the system are deduced and determined. At present, these technical indicators have been written into the latest draft of ICAO SARPs.
## 3 Results
### Seasonal-Scale Contrast of GPP Estimations
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **2009 (\(n=19\))** & **2010 (\(n=18\))** & **2011 (\(n=19\))** & **2012 (\(n=19\))** & **All (\(n=75\))** \\ \hline GPP\_EC & 46.91 (\(\pm\)24.45) Da & 45.60 (\(\pm\)19.41) Ca & 37.93 (\(\pm\)20.52) Ca & 40.89 (\(\pm\)20.50) Ca & 40.74 (\(\pm\)22.39) C \\ GPP\_MOD & 13.62 (\(\pm\)8.91) Aa & 13.90 (\(\pm\)9.91) Aa & 13.75 (\(\pm\)8.49) Aa & 14.17 (\(\pm\)9.67) Aa & 13.86 (\(\pm\)9.06) A \\ GPP\_MODR1 & 10.88 (\(\pm\)4.84) Aa & 10.18 (\(\pm\)4.79) Aa & 11.47 (\(\pm\)5.09) Aa & 11.66 (\(\pm\)4.65) Aa & 10.06 (\(\pm\)4.78) A \\ GPP\_MODR2 & 15.94 (\(\pm\)6.55) ABa & 15.40 (\(\pm\)5.83) ABa & 16.18 (\(\pm\)6.65) Aa & 16.49 (\(\pm\)6.12) ABa & 16.01 (\(\pm\)6.19) A \\ GPP\_MODR3 & 39.84 (\(\pm\)16.39) CDa & 40.77 (\(\pm\)15.43) Ca & 31.65 (\(\pm\)13.00) BCA & 38.56 (\(\pm\)14.31) Ca & 37.67 (\(\pm\)14.96) C \\ GPP\_MODR4 & 27.21 (\(\pm\)12.11) BCA & 26.95 (\(\pm\)12.69) Ba & 22.43 (\(\pm\)9.96) ABa & 27.26 (\(\pm\)10.86) BCA & 25.99 (\(\pm\)11.46) B \\ \hline \end{tabular}  
* GPP estimation methods are similar to Table 1. Data is the mean (\(\pm\)1 standard deviation) of 8 days composited GPP estimations.
* All raw data of this table passed the normality (Shapiro-Wilk test) and homogeneity of variance test (Barlett test) (\(p>0.05\)). Statistical analysis is based on the 95% family-wise confidence level (\(\alpha=0.05\)). Rows with the same lowercase indicate no significant difference in different years (\(p>0.1\)), and columns with the different uppercase indicate that a significant difference exist among diverse GPP estimation methods (\(p<0.01\)).  
\end{table} 
Table 2: GPP estimation differences among diverse estimation methods and inter-annual differences during growing season in different years *. Figure 3: Seasonal variation of gross primary production (GPP) estimation from different methods from 2009 to 2013 (**a–e**). GPP_EC is from NEE partitioned of EC measurement data, while GPP estimations based on the MOD17A2 algorithm are showed in Table 1. Data are 8 days composited GPP estimation showed in the first day. of GPP_EC (Figure 4). However, the GPP_MODR2, GPP_MODR3, and GPP_MODR4 improved the accuracy of the GPP estimations by 3.0%, 53.0%, and 25.0%, respectively. Therefore, GPP_MODR3 presented a superior performance in daily GPP estimation compared to other MODIS-based models (Table 2, Figures 3 and 4).
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 3 Results
### Validation of CCC Estimation from MERIS Satellite Data  
The MERIS satellite data obtained from the US-Ne2 site in 2004 were used to validate the reliability and accuracy of the built models. The measurements from the XTS site were not used due to the lack of sufficient time-series data. Figure 10 shows Landsat NDVI time-series data of different resolutions at the US-Ne2 site in 2004. The 30 m Landsat NDVIs are consistent with the mean NDVIs derived from the 300 m-pixel MERIS data, which indicates the uniform growth of the soybeans at the site. Therefore, we used the ground-measured CCC to assess the usefulness of the MERIS spectral data for retrieving CCC. Table 8 shows the CCC estimation results for the three approaches. The VI-combination methods were found to be more accurate than using MTCI alone (R\({}^{2}\) = 0.24; RMSE = 136.54 \(\mu\)g cm\({}^{-2}\)). However, there was a limited improvement in accuracy when using the ULR and BLR approaches. These approaches showed reduced performance in alleviating the negative influence of the VIs on CCC estimation. However, the RFR approach resulted in good prediction accuracy. The relationship between the predicted and measured CCC for the RFR approach is shown in Figure 11. The validation results for the MTCI and LAI-VIs were better than those for the MTCI alone, and the MTCI and MTVI2 showed the best performance, achieving an accuracy of R\({}^{2}\) = 0.78, and RMSE = 47.96 \(\mu\)g cm\({}^{-2}\). It should be noted that the above analysis and conclusions were based on ground and satellite-based measurements.
# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 3 Materials and Methods
### Taguchi Method
The selected neural network architecture is a dual hidden-layer backpropagation neural network (BPNN) with 10 neurons in each layer (10-10), chosen after careful consideration of both theoretical and practical factors. The system involves six input parameters--such as temperature and pressure--and two outputs, namely weight and thickness, representing a moderately complex but not excessively nonlinear problem. Empirical testing indicated that adding more than two hidden layers offered diminishing returns; deeper networks suffered from vanishing gradient issues, while adding a third hidden layer increased overfitting risks, as evidenced by a rise in cross-validation MAE by 0.12. Taguchi optimization results further supported this choice, showing that increasing neuron count beyond 15 yielded minimal accuracy gains (<2%) but significantly longer training times (35% increase). Wider layers with 15-20 neurons caused overfitting, and complex variants like LSTM and CNN provided no accuracy benefits for static parameter optimization. Practical constraints also favored a simpler, more interpretable architecture that runs efficiently on standard industrial PCs, facilitating troubleshooting and deployment in production environments. Ultimately, this architecture strikes an optimal balance--achieving a prediction RMSE of 0.82, training in 143 epochs, and main  
Figure 2: BPPN architecture diagram. taining implementation practicality--making it the \"sweet spot\" for effective, sustainable process optimization. Data are collected from a plastic molding production line comprising 200 samples. A 70:30 split is used for training and testing datasets, aligning with standard machine-learning practices to balance model generalization and evaluation. RMSE is selected as the primary performance indicator to minimize prediction error and optimize model performance.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Dvor  
DVOR [66] aims to solve two major problems of opportunistic routing in underwater sensor networks, which are connectivity voids, and longer alternate path selection for routing. The problems are addressed by introducing distance vector based opportunistic routing. The distance vectors, initially estimated using short query messages, find the minimum number of hops from a source sensor node to a sink node. Then opportunistic routing is applied based on the estimated distance vectors, to route packets from source node to sink in a hop-by-hop fashion. This technique provides two advantages: Firstly, the distance vector can identify void area, thus these areas are avoided while making forwarding decisions. Secondly, due to shortest path routing, the problem of relatively longer detour is also eliminated. DVOR improves performance, as it does not require information about neighboring nodes while making forwarding decisions, thus reducing communication overhead. Moreover, it does not require carrying a list of candidate forwarders in the packet header. Different from the existing OR protocols, DVOR does not need any information about its adjacent nodes, and does not need to carry a relay candidate list in the packet header, thus reducing signaling overhead. Avoidance of long detour improves end-to-end delay, whereas void avoidance improves packet delivery ratio. However, DVOR ignores link quality and residual energy, which result in a lack of reliability and unbalanced load distribution, respectively.
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections
### Dendrimers  
Dendrimers with a low polydispersity index (PDI) and precise surface characters involve three main structural parts: central, branches, and active surface groups [90; 91]. Different families of dendrimers like PAMAM, poly(propylene imine) (PPI), carbosilanes, poly-l-lysines, and phosphorous dendrimers have been extensively explored for their usage in biomedical sciences [92; 93].  
Dendrimers with core-shell nanostructures, along with defined designs and less PDI, are manufactured in a layer-by-layer fashion around a central unit, resulting in the regulation of oversizing, surface functionality, and branching points [94]. The PAMAM dendrimers are the most focused nanoplatform, with a well-established structure, shape, low PDI (nearly 1), compatibility, and good solubility. Additionally, their globular construction offers a solid assembly (1-10 nm) for movement via biological layers as a novel carrier for the increased solubility of water-insoluble drugs. Besides, PAMAM dendrimers are elegant additives in different drug administration modes, like IV, oral transdermal, and ocular delivery [95].  
The surface engineering of dendrimers affects cell absorption. Adsorption endocytosis occurs within the cells by binding with negatively charged proteoglycans present inside the cell membrane with cationic dendrimers. Clotrimazole (CLO), an interesting antifungal agent belonging to the azole type, is widely used as first-line therapy for _Candidiasis_, along with econazole and miconazole. [PERSON] and coworkers successfully enhanced the solubility of the antifungal drug CLO by encapsulating it in dendrimers and observed its enhanced solubility in the PAMAM-NH\({}_{2}\) dendrimer in contrast to PAMAM-OH dendrimers [96]. In an experiment by [PERSON] et al., the enhanced solubility of AmB was obtained by forming complexes with PPI dendrimers [97].  
It is very interesting to mention that dendrimer-based formulations like dendrimeric lipopeptides and Trp-rich dendrimeric peptides alone showed some antifungal activity [98; 99]. [PERSON] et al. showed that the charges present on the surface of PAMAM dendrimers sturdily affected the enhancement of antifungal activity and solubility of antifungal drugs [100]. [PERSON] et al. examined the potential of different generations of PAMAM and PPI dendrimers to improve the antifungal activity of Terbinafine and Nystatin. The data presented that both the dendrimers enhanced the antifungal properties against _A. niger_, _S. cerevisiae_, and _C. albicans_[101].