esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
The observation of pore pressure that jumps above the basement highs can be explained by the lateral transfer [97] because of the regional lateral distribution of the Middle Miocene breccias and conglomerates, which are proved hydrocarbon migration pathways in the Pannonian Basin [88]. The effective lateral transfer theory is supported by the presence of effective migration pathways within the Middle Miocene series. Moreover, the current 2D basin models and published 3D basin models also could simulate this mechanism [97]. According to the current study, the contribution of the tectonic stress in the abnormal pressure regime is not negligible, but results suggest that tectonic stress is not necessary to form the current subsurface pressure conditions. Experimental studies suggest that the aquathermal expansion is only able to generate relative overpressure of a few percent, when the rock matrix has lower permeability than \(-12\) log mD [98]. According to [PERSON] and [PERSON] [7], the mentioned low permeability conditions could be present near evaporites. The lowest measured permeability values from the Late Miocene marks, calcareous marks are not lower than \(-8\) log mD, and only thin evaporites have been deposited in local restricted subbasins during the Middle Miocene [99]. The log signatures have not supported the presence of a fluid expansion mechanism in the overpressure generation. The contributions of overpressure generation mechanisms have been studied in the Carnavon Basin [100], where the main abnormal pressure generation mechanism was the under-compaction. According to the study, the hydrocarbon generation, the chemical compaction, and the fluid expansion mechanisms has only a minor impact on the subsurface pressure conditions, so the under-compaction had one magnitude order higher impact. Effect of the lateral transfer has not been incorporated in the Carnavon Basin study. The two most important overpressure generation mechanisms are the disequilibrium compaction and the lateral transfer in case of the study area. Hydrocarbon generation and chemical compaction have been identified as minor phenomenon with local pore pressure anomalies mainly in the basin centres. According to the numerical simulations, the under-compaction makes one order of magnitude higher impact on the overpressure values than other identified mechanisms. The results of the basin simulation indicate that the absolute maximum contribution of the unique mechanisms: under-compaction 58 MPa, lateral transfer 4 MPa, chemical compaction 1.8 MPa, and hydrocarbon generation and cracking 0.5 MPa (Figure 15).
## 1 Introduction  
Aging is a significant social factor, and understanding the factors contributing to healthy cognitive aging has become a public health priority [1]. Neuroimaging studies have revealed that grey matter (GM) volume gradually decreases after an individual's 20s [2]. Thus, elucidating lifestyle factors that can help prevent age-related decreases in GM volume is especially important [3].  
Lifestyle-related diseases such as obesity, diabetes mellitus, and hypertension can lead to a decrease in brain volume [4; 5; 6; 7; 8; 9]. A large population-based study reported a relationship between reduced GM volume and cardiovascular risk scores calculated from diabetes diagnoses, a cholesterol-lowering medication, an anti-hypertensive medication, and waist-to-hip ratio [10]. However, most studies have focused on the diagnosis of diseases such as diabetes, hypertension, and hypercholesterolemiaaemia. The relationships between brain volume and continuous variables such as fasting blood glucose, systolic blood pressure, and blood cholesterol have not been well investigated. Furthermore, it remains unclear which of the multiple factors associated with lifestyle-related diseases exert a particularly negative effect on GM volume.  
To study GM volume decline, we used the grey matter brain healthcare quotient (GM-BHQ), which was approved as an international standard (H.861.1) by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T). GM-BHQ represents the average value of the standard deviations of the volume of each region in the whole brain [11]. The advantage of the BHQ is that it makes it relatively easy to understand the state of the brain in terms of GM volume. Previous studies showed that GM-BHQ was correlated with age [11], fatigue [12], and dietary balance [13]. Furthermore, GM-BHQ was more strongly correlated with cognitive function than its regional subscales of hippocampus or parahippocampus, indicating that GM-BHQ reflects cognitive function better than hippocampal or parahippocampal volume [14].  
In the present study, we investigated the relationship between GM-BHQ and blood pressure, obesity measures, and blood test data, including glucose tolerance, dyslipidaemia, liver function, and renal function. In addition, we aimed to elucidate lifestyle factors that could promote GM volume loss and brain aging.
## 2 Materials and Methods
### Implant Insertion and Artificial Mucosa  
For each mandibular model, an artificial mucosa was made from vinyl-polysiloxane impression material (3M\({}^{\text{TM}}\) Express\({}^{\text{TM}}\) XT Light Body Quick, Seefeld, Germany) of uniform thickness (2 mm). To ensure uniform thickness, the molds into which the impression material was injected were designed virtually (Amira v4.1, Zuse Institute Berlin; Visage Imaging GmbH, Berlin, Germany) for the 2.0 mm thick mucosa with one, two, three or four perforations at sites where the MDI insertions were planned. The molds were 3D-printed. Impression material (a-silicone) was injected into each mold, and after setting, the artificial mucosa was transferred to the respective model of the mandible, depending on the number of holes.  
In each hole (2.3 mm wide), an MDI with a diameter of 2.4 mm and a length of 10 mm ([PERSON]\({}^{\text{\textregistered}}\) Mini Implant, Institute Straumann AG, Basel, Switzerland) was inserted. The gingival MDI height of 2.8 mm (i.e., the height of the polished neck of the implant) was chosen from the Straumann\({}^{\text{\textregistered}}\) Mini Implants. Mini-implants were inserted using the torque wrench BLX Torque Control Device (Institute Straumann AG, Basel, Switzerland). The insertion torque (measured using a torque wrench during insertion) varied for a very small amount among MDIs (from 36 to 43 Ncm). The torque values recommended by the manufacturer for immediate loading are \(\geq\)35 Ncm.
## 3 Results and Discussion
### Comparative Analysis of Performance with Other Model Methods  
This study provides a detailed performance comparison of three different model methods on japonica protein datasets. DeepGOCNN uses only the protein sequence data. Sequence and structural data were combined in DeepFRI using graph convolutional networks (GCN). In this study, DeepFRI and MMSNet used the same structural data. Each experiment was repeated five times, and Figure 6 shows the performance comparisons of MMSNet and the other two algorithms on the three sub-ontology datasets. In the MF subontology dataset, DeepFRI outperformed DeepGOCNN for all the metrics. This may be because the three-dimensional structural information of proteins utilized by DeepFRI plays a crucial role in MF sub-ontology, as molecular functions are often closely related to specific structural domains or protein conformations. In the BP and CC sub-ontologies, DeepFRI performed worse than DeepGOCNN, which may be because the structural features extracted by the GCN contain more noisy data, which significantly reduces the performance of the model in predicting biological processes and cellular component functions. Among the three sub-ontologies, MMSNet outperformed DeepFRI and DeepGOCNN in terms of the five evaluation metrics. Although both MMSNet and DeepFRI use structural information, they aim to fully utilize high-precision protein structures and multimodal integration techniques. Case studies of MMSNet have highlighted the importance of integrating multisource data to improve the accuracy and stability of predictions.
## 2 Data
### VIRS Enterprise Cloud Mask  
VIRS Enterprise Cloud Mask (CM) provides a 4-level cloud mask including cloudy, probably cloudy, clear, and probably clear [43]. However, this product has a high false detection rate (commission error), which means that smoke areas could be misclassified as clouds [44]. Therefore, instead of using the final 4 level cloud mask, we used the intermediate cloud mask tests and flags (including cloud shadow, oceanic glint, snow, and cirrus), which are used to compute the final cloud mask available from the CM product, as additional datasets to reduce noise in smoke detection [43]. The CM cloud shadow is detected using a geometry-based cloud shadow detection procedure based on the satellite viewing geometry, solar azimuth and zenith angles, cloud edge distribution, and cloud altitude. The ocean glint mask is defined based upon the glint zenith angle which is also a geometry-based algorithm using viewing and illuminating geometry information. Snow is determined mainly based on a Normalized Difference Snow Index (NDSI) calculated from the reflectance at 0.64 \(\upmu\)m and 1.6 \(\upmu\)m wavelengths. And thin cirrus cloud cover is detected using reflectance at 1.38 \(\upmu\)m wavelength, which resides in a strong water vapor absorption band that could mask the surface under most conditions.
# Rib Design for Improving the Local Stiffness of Gearbox Housing for Agricultural Electric Vehicles
## 2 Finite Element Model Update and Validation of the Gearbox Housing
### Finite Element Model Validation  
Numerically predicted dynamic characteristics of the FE model were validated using modal assurance criterion (MAC) values. A MAC value is a statistical indicator that compares the mode shape vectors of two different models. It is calculated by the following equation:  
\[\text{mac}_{\text{ij}}=\frac{\left|\left|\{\Phi_{EMA}\}_{i}^{T}\{\Phi_{FEA}^{* }\}_{i}\right|\right|^{2}}{(\{\Phi_{EMA}\}_{i}^{T}\{\Phi_{EMA}^{*}\}_{i})(\{ \Phi_{FEA}\}_{j}^{T}\{\Phi_{FEA}^{*}\}_{j})} \tag{1}\]  
where \(\{\Phi_{EMA}\}_{i}\): eigenvector of 'i'th mode for EMA; \(\{\Phi_{FEA}\}_{j}\): eigenvector of 'j'th mode for the FE analysis (FEA). If the value is close to 1 in the same mode, the two models match [11].  
Figure 6 shows that the MAC results between the updated FEA and the EMA were nearly 1 in the lower order modes (1 st to 4 th mode), which was the main range of interest, and the values were about 0.8 in the higher order modes (5 th and 6 th mode). These results indicate that the FE model used in this study was reliable. In Figure 7, the mode shapes of the FEA and EMA results were compared, and the dynamic behaviors of the two models were similar.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**EMA, Hz**} & \multicolumn{4}{c}{**FEA**} \\ \cline{3-6}  & & **Initial, Hz** & **Error, \%** & **Updated, Hz** & **Error, \%** \\ \hline
1 & 1352.7 & 1317.7 & \(-\)2.586 & 1374.1 & +1.582 \\ \hline
2 & 1720.5 & 1715.8 & \(-\)0.268 & 1743.8 & +1.355 \\ \hline
3 & 2344.8 & 2219.1 & \(-\)5.363 & 2368.8 & +1.022 \\ \hline
4 & 2539.0 & 2483.6 & \(-\)2.182 & 2559.7 & +0.815 \\ \hline
5 & 2688.8 & 2591.9 & \(-\)3.604 & 2708.4 & +0.727 \\ \hline
6 & 2939.0 & 2718.6 & \(-\)7.500 & 2855.5 & \(-\)2.841 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the FE model update.  
Figure 6: Modal assurance criterion (MAC) results between the experimental modal analysis (EMA) and finite element analysis (FEA).
## 2 Methodology  
### Kinematic Theorem of Limit Analysis  
It is shown that, after long-term development and improvement, the limit analysis theorem has been widely accepted by researchers and designers due to its rigorous theory and robustness. [PERSON] [44] initially summarized the existing contributions of limit analysis and provided a systematic elaboration of the theoretical background, formula derivation, and practical application. It is known that the principal part of limit analysis is made up of the kinematic theorem which addresses the geotechnical engineering problem from the perspective of kinematics and the stress equilibrium theorem which solves the equilibrium problem in the context of stress analysis. Because of the intrinsic theory feature of the limit analysis, the kinematic method is capable of addressing the problems analytically and is usually preferred by scholars while the lower bound method focuses on establishing a statically permissible stress field which is complicated to construct analytically and is usually solved by the finite element method.  
In this section, only the kinematic approach of the limit analysis is taken into consideration. Some basic assumptions are made as follows to accommodate for the applicability of the kinematic limit analysis. The soil materials in the limit state present an ideal elastic-plastic deformation and the plastic flow respects the normal flow rule. Assuming the principal rate of the plastic strain and principal stress is coaxial, the following relationship between stress and the rate of strain is derived from the associated flow rule and the normality of the yield function:  
\[\dot{\epsilon}_{ij}=\dot{\lambda}\frac{\partial f\big{(}{\sigma^{\prime}}_{ij} \big{)}}{\partial{\sigma^{\prime}}_{ij}},\ \dot{\lambda}\geq 0 \tag{1}\]  
where \(\dot{\epsilon}_{ij}\) is the rate of the plastic strain, \({\sigma^{\prime}}_{ij}\) is the effective stress, \(f\big{(}{\sigma^{\prime}}_{ij}\big{)}=0\) is the yield function, and \(\dot{\lambda}\) is a non-negative coefficient. The upper-bound theorem typically takes the following form:  
\[\int_{V}{\sigma_{ij}\dot{\epsilon}_{ij}\mathrm{d}V}\geq\int_{S}{T_{i}v_{i} \mathrm{d}S}+\int_{V}{F_{i}v_{i}\mathrm{d}V} \tag{2}\]  
where \(T_{i}\) and \(F_{i}\) denote the external loads that apply to the surface \(S\) and volume \(V\), respectively, \(v_{i}\) represents a kinematically admissible velocity field, \(\sigma_{ij}\) is effective stress, and \(\dot{\epsilon}_{ij}\) is the plastic strain rate along the plastic shear band. Solving this equation, a closed-form limit load that is no less than the true limit load can be derived.
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 3 Methodology
### Stabilization Method  
When one wheel encounters a pothole, the servos responsible for controlling the other wheels react intelligently by lowering their position and adjusting their angles closer to the ground, usually to less than 90 degrees. This adjustment compensates for surface irregularities, redistributes the vehicle's weight, and helps stabilize the vehicle to maintain balance. Dynamically responding to obstacles, the system allows the car to navigate challenging obstacles smoothly while keeping all wheels in optimal contact with the road surface, ensuring stability and passenger comfort.  
Similarly, if the car encounters two potholes simultaneously, the remaining two wheels adjust their angles downward to counterbalance the disturbance. If three wheels are impacted, the last wheel alters its angle to provide maximum stability. This dynamic adjustment mechanism ensures the vehicle maintains a steady and balanced ride, regardless of uneven surface roughness. Order of sequential dependency also check to remove any dependency based on sequential patterns of input that cater for GY-521 sensor.  
The system's innovative dual-part design enhances stability by connecting the vehicle's lower engine and wheel section to the upper passenger compartment with four servo motors. The control system monitors and adjusts motor movement in real-time based on input from the GY-521 sensor and YOLOv8. This lets the vehicle respond immediately to changing road conditions and maintain stability without manual intervention.  
To ensure precise and reliable adjustments, the system uses a proportional-integral-derivative (PID) control system, which continuously calculates the optimal position for each servo motor based on real-time sensor feedback. The PID controller minimizes oscillations and overshooting by controlling motor movement precisely, ensuring a smooth and controlled ride. The system dynamically stabilizes the upper passenger compartment to decrease the impact of road irregularities detected by the YOLOv8 and GY-521 sensors. This intelligent integration of detection and stabilization technologies enhances safety, reduces wear and tear on vehicle components, and significantly improves the overall driving experience.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 8 Conclusions  
In this paper, the performance in terms of BER and computational complexity for an OFDM system with VC, including diversity in the received signal, has been evaluated. The use of VC allows maintaining the orthogonality of subcarriers by increasing the frequency spacing between subcarriers of the OFDM symbol, which translates into a reduction in ICI. From the analysis of the results, we observed a gain of 5 to 7 dB for the proposed system compared to the conventional OFDM system with OSIC and MMSE detection, respectively. Due to the use of VC, it was possible to adapt the low-complexity MRC detection in the receiver, offering a detector complexity of \(\mathcal{O}(N)\).  
**Author Contributions:** Conceptualization, J.A.D.P.-F. and F.R.C.-S.; methodology, J.A.D.P.-F., F.R.C.-S., and J.V.-C.; software, J.A.D.P.-F.; validation, F.R.C.-S., J.V.-C. and R.R.P.C.; formal analysis, J.A.D.P.-F. and F.R.C.-S.; investigation, J.A.D.P.-F., F.R.C.-S., J.V.-C., and R.R.P.C.; resources, J.A.D.P.-F., F.R.C.-S., J.V.-C., and R.R.P.C.; writing, original draft preparation, J.A.D.P.-F.; writing, review and editing, F.R.C.-S., J.V.-C., and R.R.P.C.; supervision, J.V.-C. and R.R.P.C.; project administration, J.A.D.P.-F. and F.R.C.-S.; funding acquisition, J.A.D.P.-F. All authors read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to thank Universidad Panamericana, for their support through the program \"Fomento a la Investigacion UP 2022\", and project \"Diseno de sistema de comunicacion vehichulo a vehiculcio MIMO-OFDM basado en modulacion por indice\" UP-CI-2022-GDL-ING. And to the Universidad Panamericana Direccion Institucional de Investigacion.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results and Discussion--Joint Characteristics
### Effect of Joint Spacing  
As shown in Figure 11, all three joints in Ulsan Bridge were considered, and the intervals between the joints were set to 10 m, 15 m, and 20 m. Figure 12 shows the load-horizontal displacement curves at different joint spacings.  
It can be seen that the ultimate load increases as the joint spacing increases. This is because the number of joints affecting the anchorage became smaller, and the weight of the rock between the joints increased as the interval between the joints increased.  
Figure 11: The 3D view of an FE mesh based on joint spacing: (**a**) 10 m, (**b**) 15 m, and (**c**) 20 m.  
Figure 12: Load–horizontal displacement curve with joint spacing.
# Multi-Objective Optimization Study of Annular Fluid Flow Structure in Cordless Core Drilling Tools
## 2 Structural Design and Principles of Hydraulic Lifting Cordless Coring Drilling Tool  
The hydraulic lifting cordless coring tool includes an outer assembly and an inner assembly, of which the outer assembly includes the coring bit, the support ring, the outer pipe, and the adapter. The inner assembly includes the annular fluid flow structure, the single-acting mechanism, the valve gland, and the core pipe. The structure of the hydraulic lifting cordless coring tool is shown in Figure 1. The annular fluid flow structure is a crucial channel for achieving core recovery and debris removal, and it includes components such as the fishing spear, spool, valve body, and spring.  
Before coring, the drilling fluid enters the spearhead and spool from the inner and outer pipe inlets and then enters the gap between the spool and the valve body from the outlet. As the drilling fluid is pumped in, it fills the gap. As a result, the pressure at the bottom of the spool increases. When the pressure increases to the spring pressure, it starts to push the spool inward. When the spool's outlet moves to a certain position, the spring can no longer be compressed, and the spool stops moving inward. At this time, the spool pushes the steel ball to block the outlet of the valve body, and the drilling fluid continues to flow into the gap between the inner and outer tubing rings to the vicinity of the drill bit, forming a circulating channel between the outer tubing and the wall of the well. As a result, slag drainage and wall protection circulation could be achieved. After the sampling is completed, positive circulation is stopped, and the spring returns to its normal state. Following this, the circulation channel between the inner and outer assemblies is closed, and the reverse circulation is opened. During the reverse circulation, the mud is injected through the outer pipe, and the mud returns to the well wall to push the inner assembly and lift it to the wellhead.  
Figure 1: Hydraulic lift cordless core drilling tool structure.  
Numerical Simulation and Calculation of Fluid Flow in Spearhead and Spool Annulus of Cordless Core Drilling Tools
## 2 Materials and Methods
### Datasets
#### 2.1.1 NDVI Data  
The NDVI data employed in this study originate from the MOD13A3 data product released by the National Aeronautics and Space Administration (NASA), updated on a monthly basis with a spatial resolution of 1 km [18, 55]. This dataset has undergone comprehensive preprocessing steps, including geometric correction, radiometric calibration, and atmospheric correction, aimed at minimizing the impact of cloud cover and other atmospheric effects on the data [56]. To align with research requirements, necessary projection transformations and image mosaicking were performed using the MODIS Reprojection Tool (MRT), followed by clipping according to the geographical extent of China, resulting in an NDVI time series dataset spanning from 2001 to 2023. MOD13A3 is a globally standardized vegetation index product provided by NASA, which holds high authority and scientific recognition. It is widely used in vegetation dynamics monitoring, ecological research, and climate change analysis. The monthly update frequency allows it to capture changes in vegetation growth cycles, making it suitable for dynamic analysis over long time series (2001-2023). Additionally, its spatial resolution of 1 km provides sufficient detail at the regional scale, which is why this dataset was chosen for the analysis in this study.