esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 1 Introduction
This algorithm solves the problem of capturing fine texture edges and inaccurate edge localization when using large Gaussian windows while also avoiding the drawback of retaining excessive texture edges when using small-scale windows. We conduct a thorough comparison between the proposed MSC-MMP algorithm and traditional edge detection techniques (Log, Prewitt, Sobel, Zero Crossing, and Canny) using a custom-built eye diagram dataset. The results demonstrate that the MSC-MMP outperforms these methods in terms of edge detection accuracy and robustness. 4. Performance evaluation in spoofing detection coverage: Through extensive simulation experiments, we evaluate the performance of the EDDM-MSC-MMP method in detecting spoofing attacks under various conditions, including power, code phase offset, and carrier offset. Our results show a significant improvement in the detection rate and coverage compared to the existing SQM metrics and CN0 method. 5. Real-data validation using the TEXBAT dataset: We validate our method using the TEXBAT dataset, which contains real-world spoofing signals collected by the University of Texas. The experimental results confirm that the EDDM-MSC-MMP exhibits better performance in detecting spoofing attacks, with a superior ROC curve and a higher detection rate compared to existing methods.
# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 3 Automated Rock Quality Classification Using Digital Tunnel Face Mapping
### Automated Rock Mass Classification System
The rock mass strength at the tunnel excavation face is quantified using various parameters: R1 represents the rock mass strength, R2 denotes the joint density score, R3 signifies the joint spacing score, R4-1 corresponds to the average length score of the joints, R4-2 indicates the average void size score of the joints, R4-3 reflects the average roughness score of the joint surface, R4-4 represents the filling material score of the joint surface, R4-5 corresponds to the weathering degree score of the wall rock adjacent to the joints, and R5 signifies the groundwater outflow score on the excavation surface. These values are summed, and the final RMR rating is computed by subtracting a correction value (R6), which is based on the orientation of the main joint set relative to the tunnel axis. Figure 13 illustrates the final RMR report, which is automatically generated by selecting the evaluated parameter values obtained through the tunnel digital mapping process from the corresponding RMR classification table. This automation significantly improves both the efficiency and accuracy of documenting rock mass assessments.
## 3 Results
### Trends in Standardized Death Rates of IHD from 1985 to 2019 According to Sex
#### 3.1.2 Standardized Death Rates of IHD among Males  
During 1985-2019 IHD mortality trends among males, expressed as AAPC, significantly decreased in France \(-3.43\%\), 95% CI [\(-3.60\), \(-3.30\)], Italy \(-2.92\%\), 95% CI [\(-3.05\), \(-2.80\)], Slovenia \(-2.85\%\), 95% CI [\(-3.20\), \(-2.42\)] and Spain \(-2.55\%\), 95% CI [\(-2.64\), \(-2.46\)]. Croatia also decreased mortality by \(-0.18\%\), 95% CI [\(-0.78\%\), 0.38\(\%\)], but without significance.  
Spain, Italy, and France experienced a decline in male mortality, while Slovenia increased in 1988-1992 and Croatia in 1990-1993. All countries significantly decreased in the latest time periods, from \(-2.12\%\) in France to \(-10.62\%\) in Croatia (Figure 2, Table 2).  
When SDR in 2019 vs. 1991 were compared, the percentage change of IHD mortality in Slovenia was \(-60.60\%\) and in Croatia \(-29.80\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**County** & **Period** & **APC** & **Period** & **APC** & **Period** & **APC** & **Period** & **APC** \\ \hline Spain & 1985–1998 & \(-0.61\) & 1998–2003 & \(-3.40\) & 2003–2019 & \(-4.88\) * & & \\ France & 1985–1994 & \(-3.86\) * & 1994–2003 & \(-2.27\) * & 2003–2011 & \(-6.37\) * & 2011–2017 & \(-3.61\) * \\ Croatia & 1985–1990 & \(-0.27\) & 1990–1993 & \(+46.08\) * & 1993–2019 & \(-1.91\) * & & \\ Italy & 1985–2004 & \(-1.58\) * & 2004–2019 & \(-4.24\) * & & & \\ Slovenia & 1985–1987 & \(-22.48\) * & 1987–1991 & \(+21.55\) * & 1991–2019 & \(-5.06\) * & & \\ \hline \end{tabular}
\end{table}
Table 1: Joinpoints and annual percentage changes of IHD mortality among females.  
Figure 2: Standardized death rates of ischemic heart disease—males. * \(p\) < 0.05.  
There were significant differences among mean SDR during the last ten years; Croatia had the highest values (183.38), followed by Slovenia (82.46), Italy (66.78), Spain (55.38), and France (46.97) (\(p<0.001\)). Croatia had significantly higher rates compared with the other four countries (\(p<0.001\)). France had significantly lower rates than Slovenia and Italy (\(p<0.001\)) and similar to Spain (\(p=0.55\)). Italy had rates similar to Spain's (\(p=0.25\)) and significantly lower than Slovenia's (\(p=0.05\)). Spain had significantly lower rates than Slovenia (\(p<0.001\)).
## 2 Heterogeneous Scenarios and APT Transmission Scheme
### Transmission and Operation Modes  
Figure 2 shows the medium access procedure for both SN classes over time. Observe that C1 SNs are given medium access priority over C2 SNs, by guaranteeing that C1 SNs complete their medium access procedure before C2 SNs do it. As the proposed methodology to determine the energy consumption takes into account the energy consumed during the _sync_ period, the _sync_ period for both SN classes is also shown in Figure 2.  
The operation of an SN almost repeats every cycle and can be summarized as progressing along the following steps. First, SNs determine a _sleep-wake up_ schedule during the _sync_ period. To this end, SNs exchange _SYNC_ packets among their neighbors. The next cycle initiation instant and the corresponding SN address are included in _SYNC_ packets. SNs use this information to synchronize their _wake up_ instants at the beginning of each _sync_ period. Observe from Figure 2 that both SN classes deploy the same _sync_ period.  
Second, during the _data_ period, the conventional CSMA/CA contention scheme is used to transmit PFs. Active nodes generate a random backoff time and perform common carrier sensing. If at backoff timer expiration, a SN senses an idle channel, it sends a PF using the \(RTS/CTS/DATA/ACK\) packet handshake. A PF is recognized as successfully transmitted when the SN receives an \(ACK\) from the _sink_.  
Also, the energy consumed during the _sleep_ period is taken into account. As mentioned above, during this period, nodes fall asleep to save energy or remain awake to listen to _SYNC_ packets transmitted by other SNs.  
Prioritization Scheme  
As C1 SNs have access priority over C2 SNs, the _data_ period is initiated and completed by active C1 SNs first, if any. Active C2 SNs _wake up_ shortly after C1 contention window (\(W_{1}\)) ends, and if they find an idle medium, C2 SNs initiate their medium access contention. However, if active C2 SNs sense a busy medium, they return to _sleep_ mode and _wake up_ again at the next cycle. We assume that the transmission time of the shortest C1 PF (one _DATA_ packet) exceeds \(W_{1}\).  
Please note that we assume that the energy consumed by C2 SNs to sense a busy medium takes a single backoff time slot and, therefore, it is negligible. Note also that when C1 SNs collide, C2 SNs will also detect a busy medium, and they will refrain from contending in this cycle.  
Figure 2: Transmission process in a transmission cycle for a heterogeneous WSN with two classes of nodes.
## 3 Materials and Methods
### Construction Stage  
Water ingress into mountain tunnels can be challenging to predict based solely on surface geological conditions [44]. To better understand the hydrological conditions of the Lowari Tunnel during its excavation, extensive mapping was carried out. This assessment considered the source and quantity of water ingress, water characteristics (such as color and odor), and the potential impact of water on the surrounding rock formation. The excavation of the tunnel encountered significant amounts of water from both the north and south portals, as shown in Figure 3A. In April 2007, heavy water ingress was observed at chainage 1 + 923 in the compacted and jointed granite and gabbro, as seen in Figure 3B. To manage the water seepage at the dripping and flowing stations, grouting was applied to seal the cracks, and plastic pipes were installed to divert the flowing water into the main drainage system of the tunnel. The tunnel was divided into dry, damp, dripping, and flowing stations, and their relationship with the geological conditions was studied. Natural water outflows were observed over a 3875 m stretch along the north drive, from chainage 4 + 634 to 8 + 509, which flowed towards the north portal. In contrast, along the south drive, water encountered during excavation, covering a 4634 m stretch from chainage 0 + 000 to 4 + 634, was diverted to a pond at chainage 0 + 590, due to the tunnel face acting as a barrier. To ensure proper drainage, the accumulated water was then pumped towards the south portal using appropriate pumping systems, as depicted in Figure 3B. To gather  
Figure 2: Methodology flow chart, which provides a visual representation of the research methodology and the procedures that were used to collect and analyze data.  
data on water flow rates, Elster water flow meters were installed in the vicinity of the pond from 10 April 2007 to 14 January 2009.  
The provision of potable water for workers in the Lowari Tunnel was facilitated by positioning a plastic water tank at station 1 + 662 on the south drive. A water quality assessment was subsequently conducted on 2 February 2007 to determine its suitability for drinking purposes. For the chemical analysis, water samples were collected in polythene bottles, pre-washed three times with the sampled water (APHA 2005), labeled, and stored in a refrigerator at 4 \({}^{\circ}\)C. Various water quality parameters were analyzed during the study period using specialized equipment. The pH, electrical conductivity, total dissolved solids, and total suspended solids were measured using the CONSORT C931, Belgium, while chlorine, nitrates, sulfates, and total hardness levels were determined using the DR 2800 spectrophotometer, USA. The concentration levels of calcium, magnesium, sodium, and potassium were analyzed using an Analyst 700 atomic absorption spectrometer from Perkin Elmer, Waltham, MA, USA, following acidification of the water sample. Finally, the total alkalinity was determined through titration methods adapted from the APHA 2005.
# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 1 Introduction  
The field of graph mining has witnessed a surge in popularity in recent years, primarily fueled by the exponential growth of data that can be effectively represented as graphs and its wide range of application areas. This has led to the rise of graph databases such as Neo4J, AllegroGraph, and OrientDB [1], which offer robust platforms for modeling and storing data as interconnected nodes and relationships. Leveraging these databases, researchers and practitioners can apply advanced graph mining algorithms to accomplish various tasks, including community analysis, centrality identification, pathfinding, and exploring structural patterns. In various application domains, such as Business Information Systems [2], Financial Crime Detection Systems [3], Transport Information Systems [4], and Recommendation Systems [5], graph databases have gained widespread acceptance. However, relying solely on graph databases is insufficient. A comprehensive methodology is necessary to establish the task and stages for effectively conducting information analysis and extracting valuable insights.  
This paper reviews existing work of graph mining and presents the frameworks and methodologies most used for knowledge discovery, such as KDD [6], CRISP-DM [7], and SEMMA [8]. We describe the required tasks for deriving value from large graphs absent in methodologies and frameworks used in the literature. Finally, based on KDD, CRISP-DM, and the required tasks for graph mining, we propose a new methodology for knowledge discovery in graphs.  
The contributions of the paper are the following:  
* A new and specific methodology named KDG (knowledge discovery in graphs) to guide users to find insights from data represented as graphs.  
* Three use cases applying the proposed methodology.  
The remainder of the paper is structured as follows. Section 2 provides an overview of the frameworks and methodologies employed in data mining, including the processes, tools, visualization techniques, and graph analysis algorithms that have emerged in the past two decades. Section 3 introduces the related concepts that are employed throughout the paper. Section 4 presents our methodology for knowledge discovery in graphs, outlining its essential components and task. Then, in Section 5, we showcase the application of this methodology through three different use cases. Finally, in Section 6, we discuss our findings and avenues for future research.
## 2 The Proposed Multi-Objective Majority-Minority Cellular Automata Algorithm (MOMmCAA)
### Computational Complexity of the Proposed Algorithm  
The computational time and space complexity of the MOMmCAA depends on the number of smart-cells \(N\), number of objective functions to be evaluated \(m\), management of the repository (where \(cap\) non-dominated solutions are stored), and total number of iterations \(n_{it}\).  
* **Smart-cell evaluation**: Each iteration evaluates all smart-cells, with a cost of \(O(Nm)\).
* **Repository management**: The repository stores the non-dominated solutions. The cost of the Pareto dominance ordering mechanism can be high because the ordering of the non-dominated solutions has a complexity of \(O(cap^{2})\). Methods with similar  
Figure 3: PS repository management using \(PF\) hypercubes.  
strategies attempt to reduce this cost by limiting the repository size; however, it is still costly, especially as the number of non-dominated solutions grows.
* **Iterations of the algorithm**: The total number of iterations impacts the complexity, as evaluations and repository management are performed at each step.  
The overall complexity of the MOMmCAA can be approximated as \(O(n_{it}(Nm+cap^{2}))\). In summary, the time complexity of the MOMmCAA is quadratic in terms of the repository size of non-dominated solutions. The space complexity is linear with respect to the population of smart-cells, the number of objectives, and the number of iterations. One of the advantages of using adaptive hypercubes is that the computational cost is better than when using a niche strategy such as the one used by NSGA-II [53]. The only case where both strategies have the same quadratic complexity is when the hypercubes are updated at each generation [54]. Thus, the complexity of the MOMmCAA is similar to that of MOPSO.
## 4 Discussion
### Model Limitations  
The model here presented offers a new tool for unit load designers and researchers. It is of utmost importance to summarize the limitations of the research. In the development of the finite element model of the unit load segment, a board was used as a pallet analog. The study here presented does not consider the effect of different pallet designs, such as using block or stringer pallets, or the effects of specific pallet components. The approach selected for the study was to limit the results exclusively to the effects of the changes in the payload. In order to properly apply the developed model, researchers and users must conduct a subsequent step of evaluating the response on the specific pallet being utilized by considering the model results as one more input in the pallet design process.  
Time-dependent material properties, such as creep of the pallet analog for extended periods under load, were not considered. Although pallets in warehouse storage will  
Figure 15: Effect of the friction coefficient of the horizontal surface of the packages on board bending ratio by pallet friction, payload height (mm), and number of columns, with a fixed coefficient of friction of the vertical surface of 0.45.  
experience creep effects, it is not considered that it will change the factor significance and, overall, the main conclusions from the study.  
The simplified load bridging model developed using the Gaussian process regression is limited to the ranges of values where each of the variables was validated, be them significant or non-significant factors in the payload characteristics. Although unit loads outside of these ranges commonly exist, they are not considered for the prediction properties of the model. Users must carefully evaluate these limitations before considering the results in the unit load design process.
# Microscopic Experiments to Assess the Macroscopic Sweep Characteristics of Carbon Dioxide Flooding
## 3 Experimental Results  
### Microscopic Sweep Characteristics of Carbon Dioxide Flooding  
Through the visual material simulation of a glass etching model, we obtained microscopic simulations of water flooding and CO2 flooding. Figure 4 presents the results of the glass etching model for water flooding and CO2 flooding, including the distribution of residual oil. From these results, we can see that the following: [20].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme Label** & **Injection Position** & **Production Position** \\ \hline \multirow{2}{*}{\(1\)} & T12, T13 co-injection, with compartmentalized interlayer at & \\  & injection end (co-injection and co-extraction) & \\ \hline \multirow{2}{*}{\(2\)} & T12, T13 split injection, with the compartmentalized sandwich at & \\  & the injection end (split injection and combined mining) & \\ \hline \multirow{2}{*}{\(3\)} & T12, T13 split injection, isolated interlayer at extraction end & \\  & (combined injection and extraction) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different injection–production model schemes.  
Figure 4: Microscopic simulation and remaining oil distribution of water flooding in glass etching model.  
Figure 3: Different injection–production design schemes.  
Water flooding:  
In the process of water flooding, water predominantly occupies the large pore throats and the pores connected to them. Water has difficulty displacing oil in small pore throats, large pores controlled by small throats, and the blind ends of pores. When single-phase seepage reaches a steady state, the fluid flow rate at the center of the flow channel is higher than that at the entrance. The pressure drop is slower in the larger pore throats. Residual oil is mainly stored in small pore throats and also adsorbed on the walls.  
CO\({}_{2}\) flooding:  
CO\({}_{2}\) flooding is characterized by a mixed-phase flooding with crude oil, involving strong component exchange. CO\({}_{2}\) can displace oil throughout the pore space, even in the blind ends of pores, achieving nearly 100% microscopic sweep efficiency. As CO\({}_{2}\) is injected, the pore volume multiplier increases, allowing the crude oil in the blind ends of pores to be displaced. Supercritical CO\({}_{2}\) enters the blind ends of pores through component exchange with the residual oil, triggering mass transfer and the desorbing oil films from the pore walls.  
These observations highlight the superior sweep efficiency of CO\({}_{2}\) flooding compared to water flooding, particularly in terms of accessing and displacing oil from smaller pore spaces and blind ends.
### Optimization  
Design optimization is an effective method that finds a design which minimizes (or maximizes) a performance criteria (objective), while fulfilling predefined design constraints, see [31; 32; 33]. The design is defined by design variables, which can include geometrical properties (size, shape and topology), material, concept and operational parameters. The synthesis of flexible mechanical systems can be aided by numerical optimization, in the search for improved designs.  
Design optimization of multibody dynamics presents challenges in regard to, i.a., sensitivity analysis, high computational effort and transient behavior. Design optimization of rigid multibody systems is handled by [34; 35; 36; 37], while the optimization of flexible multibody dynamics is currently an active research field and reviewed in [38].  
Topology optimization answers the question of where material should be optimally placed, and a method to design compliant mechanisms was introduced by [39; 40]. Topology optimization of compliant mechanisms is reviewed in [41]. Active research includes consideration of nonlinear finite-element analysis, stress constraints, e.g., [42; 43; 44], material choice, e.g., [45], and extension to large-scale problems, e.g., [46].
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 3 Results
### Independent Model Evaluation  
The GPR model estimates when using the most explanatory band combinations demonstrated a high agreement to observations when validated using an independent dataset--the mean R\({}^{2}\) was 0.77 and the NRMSE was 12% (Figure 5 and Table 3). The GPR model performance was very similar when deriving the LAI and CCC estimates, with the R\({}^{2}\) being 0.85 for both variables and the NRMSE being 9 and 10% for LAI and CCC, respectively. In comparison to the other two variables, the model performance was weaker when estimating LCC, with R\({}^{2}\) and NRMSE being 0.60 and 18%, respectively.  
When compared to the GPR model estimates, a multivariate linear regression model using the same dataset for calibration and validation showed a generally weaker performance in the estimation of all three variables, with the R\({}^{2}\) ranging from 0.67 to 0.73 (Figure 6). The LCC estimates by the linear regression model, however, had a higher agreement to the observations, with the R\({}^{2}\) and NRMSE being 0.67 and 13%, respectively.  
Figure 4: Comparison of the normalised spectral responses and regression analysis between the most sensitive MAIA/Sentinel-2 bands and non-destructive ground measurements of (**a**) bias corrected LAI and (**b**) LCC.