esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
They are thought to have a beneficial effect on atherosclerosis by protecting the gut barrier and lowering overall systemic inflammation [36]. Moreover, propionate may reduce lipogenesis, serum cholesterol levels, and carcinogenesis in other tissues [44]. The aforementioned prominent species may become unique targets with further investigations. The first of our limitations is the rather small sample size of the study; type II error may have rendered certain differences insignificant. Second, discordant twin examination was not possible as there were only 1 discordant MZ pair in addition to 6 concordant twin pairs (4 pairs with carotid atherosclerosis) and 8 patients as one member of a twin pair (7 with carotid atherosclerosis). Thus, we did not examine twinning, we compared the atherosclerotic sample with the non-atherosclerotic sample as if they were independent subjects; however, this may also partially influence our results due to the effect of genetics. Third, the quality of the incoming sample due to postal delivery and possibly partially different individual sampling techniques, despite that, participants got detailed sampling instructions. All samples received that were not of adequate quality were repeated separately. Forth, similar to most studies of this nature and topic, we used 165 rRNA sequencing, primarily due to limited resources, instead of the much more expensive shotgun sequencing, which would have enabled a more accurate and reliable taxonomic classification. Fifth, we lack data on diet and exercise which could have a significant impact on our findings. Sixth, since we used self-reported questionnaires, we did not become aware of any untrue or suppressed data and conditions. At the same time, the questionnaires did not include overly intimate or intrusive questions according to the general values; furthermore, for each question, we gave the respondent the opportunity to refuse the given answer, thus avoiding distortions resulting from false data as much as possible. Lastly, since none of the patients had CPAP, it is impossible to determine how the therapy affected the intestinal bacteria or whether it influenced atherosclerosis. In order to comprehend the impact of atherosclerosis on the gut microbiome in OSA patients, we trust that our preliminary findings will provide the basis for additional extensive research.
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 3 MAPE Framework Architecture
### Monitoring and Metrics Acquisition  
The monitoring service (MS) aims at the automated collection of different types of data for every instantiated VNF, orchestrated by the MANO framework, including CPU usage, memory allocation, active/passive network throughput, and packet loss. Additionally, it is compliant with the flexibility for adding new monitoring entities, as well as the support for alerting mechanisms tailored to VNF-specific and NS-specific alerting rules. Finally, MS supports near-real-time responsiveness, accessibility, and scalability, as well as easy integration with MANO functional blocks, to provide the monitoring information needed to fulfil the management and orchestration tasks.  
The MS collects several metrics to track not only the condition of resources in the integrated NVFI environments but also the performance of the network and the effects of the data traffic at the application layer. To share metrics in real time, a publish/subscribe type of message bus is realized by utilizing the open-source Apache Kafka platform [32]. Kafka is a software-defined implementation of a stream processing bus, aiming to provide a unified, high-throughput, low-latency platform for managing real-time data feeds. Kafka runs on a cluster of one or more servers called brokers. The collected metrics are stored for processing in an InfluxDB time series database, which empowers the handling of massive volumes of timestamped data produced by IoT-enabled devices [33]. It must be highlighted that the Kafka bus is accessible to all the internal and external services that may be interested in accessing, retrieving, and consuming either stored data or current  
Figure 1: Monitoring and analysis architecture.  
metrics from the monitored entities. Overall, the major types of Kafka clients using the Kafka broker are:  
1. [label=()]
2. Various data adaptation and transformation services wishing to push data from monitored environments, enrich the collected metrics, and prepare their proper consumption by other MAPE services.
3. An InfluxDB database that keeps records of collected metrics, either raw or processed, for future reference by external services.
4. ML-based processing and planning, with the goal of optimizing the performance of the running services based on QoS criteria.  
The detailed technical specifications of MS are beyond the scope of this study.
## 3 Results and Discussion
### Color
The a* value was \(-\)4.6 \(\pm\) 0.7 at 40; however, the values were positive above 50 \({}^{\circ}\)C: 1.0 \(\pm\) 0.4 (50 \({}^{\circ}\)C), 3.8 \(\pm\) 0.2 (60 \({}^{\circ}\)C), 2.0 \(\pm\) 0.2 (70 \({}^{\circ}\)C), 2.2 \(\pm\) 0.3 (80 \({}^{\circ}\)C), 2.4 \(\pm\) 0.4 (90 \({}^{\circ}\)C), and 1.4 \(\pm\) 0.3 (100 \({}^{\circ}\)C). The images also showed browning of the leaves at higher drying temperatures, consistent with the color results (Figure 1C). These results can be attributed to the heat-induced formation of a brown substance in the Maillard reaction [27]. The results indicate that drying temperature has a greater effect on the color difference of Genova tea than did S time (Figure 3). Among the L*, a*, and b* values, the a* value was the most sensitive to heat and drying temperature conditions, showing higher values with increases in heat time and drying temperature. FD showed the best results in terms of color retention. FD using sublimation is an excellent method for preserving the tea's quality [27] but requires high capital investment and running costs. The a* value was closest to that of FD during MD 40 \({}^{\circ}\)C, and the color was bright, with high L* and b* values, and had an excellent appearance (Figure 1B), indicating that this drying method is effective in maintaining color at a low cost.
## 4 Experimental Results and Discussion
### Validity of the Fitting  
The accuracy of defect detection is directly tied to the precision of the selected fitting model. Figure 16 presents the fitting results for both the y- and x-directions. In the y-direction, 2000 contours were selected for fitting from a total of 3200, a fixed number determined by the factory settings of the laser scanner. To ensure a representative selection, we first identified all unique y-values, sorted them by frequency, and then selected the 2000 contours with the highest occurrences. The standard contour was subsequently derived using the method described earlier. This frequency-based approach was crucial, as defect regions often exhibited complex shapes and may suffer from data loss or poor scanning quality, leading to these points being excluded as outliers. As a result, the fitted model was primarily based on intact contours that were less likely to be influenced by scanning artifacts.  
In the x-direction, the ideal fitted contour was a horizontal straight line. However, due to practical scanning limitations, slight deviations often introduced a small slope, though the contour remained fundamentally linear. Compared to nonlinear methods, such as quadratic curve fitting, linear fitting introduces significantly fewer errors due to its simpler, more stable nature. When surface defects were small, all available contours were used for fitting. For larger or deeper defects, we applied a similar method to the y-direction: the 5000 contours with the highest frequency were selected, and their average was used to generate a standard x-direction contour. Once this standard contour was obtained, it was adjusted for vertical displacement by translating it based on the average height difference in the z-direction. This ensured the resulting fitting model accurately reflected the nominal structure, while still accounting for the influence of surface defects.  
Figure 16: Model results based on single-frame and multi-frame fitting. (**a**) Fitting results in the y- and x-directions. (**b**) Difference (residual) between the actual and fitted values in the z-direction. (**c**) Actual model fitted in the y- and x-directions.  
Figure 15: Normal estimation on the magnetic tile dataset.  
In both the y- and x-directions, the average \(R^{2}\) value of 0.998 indicated that the fitted models closely approximated the original point cloud. However, there were clear differences in the defect regions, where the fitting diverged from the actual data. This divergence was expected, as the fitted models represented the ideal, defect-free surface, providing a reliable baseline for detecting defects. Ultimately, the fitting process effectively captured the overall structural characteristics, while also highlighting discrepancies in the defect regions, which is essential for precise and reliable defect detection.
## 3 Development and Verification of the Inspection Device for Field Application
### Cross-Assessment of Measurements Data through MCNP Simulation
#### 3.2.2 Geometry Modeling  
Table 7 outlines the material characteristics simulated for radiation safety assessment. The actual tungsten collimator was modeled based on manufacturer-provided drawings and physical measurements, detailed in Figure 7.  
Figure 7: Geometry and modeling conditions of the tungsten collimator. (**a**) Status of the manufactured tungsten collimator and three-dimensional (3D) modeling. (**b**) Three-dimensional modeling of Se-75 isotope and inspection device.  
The inspection device geometry and radiation shielding were modeled in an open environment to simulate real-world radiographic testing conditions. Measurements were conducted using Se-75 with a maximum intensity of 47 Ci, applicable for field inspection scenarios [16; 23; 24; 25; 26].
## 3 Results and Discussion
### Modelling Results  
#### 3.2.1 Batch Reactor Model Validation  
A formic acid decomposition reaction was used as a model reaction for hydrogen production to evaluate the catalytic performance of the material. This reaction occurs in two different pathways: dehydrogenation and dehydration. The stirring rate (1400 rpm) was optimised in previous studies in order to ensure the chemical kinetic regime [4]. The experimental results for the decomposition of formic acid were compared to the process-modelling results to assess their validity. Figure 4 depicts the conversion of formic acid at varying reaction temperatures of 30-60 \({}^{\circ}\)C. It can be observed that the conversion increases with increasing temperatures, as expected. Furthermore, there is good validation between the experimental and theoretical results. Usually, temperatures greater than 60 \({}^{\circ}\)C are not investigated due to the requirement of mild operating conditions in portable devices, which utilise formic acid fuel cells. According to the Arrhenius expression, \(k=Ae^{-\frac{Et}{Et}}\), an apparent activation energy of approximately 22 kJ/mol was obtained for this reaction with a 2%Pd6 Zn4 catalyst for a batch reactor configuration. The catalytic deactivation is stronger at higher temperatures; therefore, higher reaction temperatures promote the dehydration pathway and CO formation, causing the catalyst to deactivate faster.  
Kinetic profiles of the catalyst were obtained at different concentrations (0.2-0.5 M). Figure 5 demonstrates the effect of formic acid concentration on the conversion. The varying concentrations of formic acid used were 0.2-0.5 M at a reaction temperature of 30 \({}^{\circ}\)C and a substrate/metal molar ratio of 2000. The kinetic regime and these optimal reaction conditions were previously obtained by [PERSON] et al. [5]. The results show that higher concentrations lead to higher conversions. In addition, the process-modelling studies were performed at constant reaction conditions with the experiment, and it can be concluded that there is good agreement between the experimental and theoretical data for the batch-reactor system.  
Figure 3: XPS (**A**) survey, (**B**) Zn 2p, and (**C**) Pd 3d spectra.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 5 Results and Analysis
### Results  
The results are presented in tabular format, highlighting the model's accuracy based on criteria such as MSE, RMSE, MAE, MaxPE, and R.
## 1 Introduction
This sensing node is connected to the ThingPeak cloud platform in order to visualize the data in real time. The results show that the power consumption of the ESP32 monitoring system and the sensors is very satisfactory; however, the introduction of the Raspberry Pi and the associated 5G module drastically increases the power consumption of the setup. Therefore, dedicated ESP32 5G modules are necessary to limit power consumption to a value that would allow practical autonomous operation. The contributions of this paper are the following:  
* It leverages 5G technology to improve real-time data collection and in parallel monitoring within the IoT sensor systems. * It addresses the limitations of traditional sensor networking like bandwidth, latency, and networking congestion. * It emphasizes edge computing in order to optimize local data processing before transmission. * It ensures the reliability of cloud connectivity for scalable and efficient system performance. * It aims to advance the next-generation of IoT systems, contributing to the evolution of wireless communication technologies. Some related papers have been identified from the literature. Specifically, [29] investigates energy savings in hydraulic press systems, reducing electrical consumption by replacing traditional Direct-On-Line (DOL) starters with VSD-controlled motors. This is an IIoT system allowing efficient remote data collection and analysis and in parallel reducing audit time and cost. In addition, [30] explores parallel deep reinforcement learning (DRL) methodology for 5G network resource allocation in order to achieve spectrum efficiency and latency. With the use of multiple DRL agents, there is an improvement in convergence speed and scalability. Paper [31] proposes an enhancement of IoT devices security by prioritizing 18 critical security features, ending up with a security priorities list that helps the researchers to focus on the most impactful features for securing an IoT system. Finally, [32] focuses on an Over-the-Air Computation with multiple shared wireless channels of an industrial environment. There are a large number of sensors continuously sending data-related information in order to monitor, power control, and detect any faults, leading to the improvement of data aggregation in industrial Internet of Things (IIoT) environments. The paper is organized as follows: Section 2 presents the system architecture that was experimentally studied in this paper, Section 3 presents the methodology and results of each of the experiments carried out for this study, Section 4 discusses the findings and suggests future work, and finally Section 5 contains some concluding remarks.
## 2 Materials and Methods
### Identification of a Suitable Assistance Delay Time

Table 1: Multiple linear regression analysis results to identify optimal assistance delay time. Figure 4: Ankle angle with assumed ideal assistance timing and explanatory variables. Data from the other five stroke survivors with insufficient swing-phase dorsiflexion were used for verification and validation. The predicted delay time values were calculated using Equation (2), and the real values were determined as the duration from toe-off to the time stamp where the second derivative of the ankle angle curve equaled zero. The results are shown in Table 3. The maximum error between the real and predicted values is approximately 3 ms. From the results, we conclude that the identification equation can accurately determine the ideal assistance timing of swing-phase dorsiflexion. The identification procedure is expected to be simple even in real medical facilities. Before using our high-dorsiflexion assistive system, the gait data from the patient are measured with Rehag gait. From Rehag gait's analysis, the explanatory variables \(x_{1}\), \(x_{2}\), and \(x_{3}\) can be easily accessed. Lastly, using Equation (2), the ideal assistance delay time for swing-phase dorsiflexion can be determined. The parameter can then be set by our system through the self-made graphic interface.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 2 System Model and Signal Processing Methods  
This article presents a method for localizing GNSS spoofing interference sources using a moving array antenna, which is commonly employed in the scenario depicted in Figure 1. In this scenario, the unmanned aerial vehicle (UAV) for spoofing interference monitoring is equipped with an array antenna and a receiver dedicated to monitoring spoofing interference. It employs mobile monitoring to collect authentic satellite signals as well as navigation deceptive spoofing signals, which are then processed using the methodology proposed in this paper to effectively monitor and locate instances of GNSS deceptive interference behavior. The proposed method is also applicable to the monitoring and localization of GNSS spoofing interference sources in vehicular scenarios.  
Figure 1: Typical application scenarios for the method.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 3 Properties and Geomechanics of Bedded Evaporites
### Main Geomechanical Considerations in Bedded Evaporite Caverns
#### 3.3.1 Cavern Design
The minimum allowable internal pressure of hypothetical caverns in bedded salts in the U.S. was studied in [207]. The simulation utilized in this study considered the effect of various parameters on minimum allowable pressure, such as cavern roof span, depth, roof salt thickness, shale thickness, and shale stiffness. The internal pressure under high-frequency injection and production cycles of hydrogen was determined in [208]. A numerical simulation in [209] revealed a direct relation between the roof span and internal pressure and an inverse relation between the roof span and the depth of the salt cavern. Finally, ref. [210] used a geomechanical model to calculate the maximum allowable pressure for two caverns in China.
## 2 Research on 3D Temperature Field Reconstruction Algorithms
### Basic Principles of Acoustic Measurement in 2D Temperature Fields
Subsequently, this information can be used to calculate the temperature distribution T within the medium as follows [13]:  
\[T=\left(\frac{1}{x}\times\frac{1}{z}\right)^{2} \tag{5}\]  
The essence of acoustic temperature measurement lies in discretizing the 2D temperature field into a set of coarse grids and establishing an algebraic relationship between the grid temperatures and the TOFs of the acoustic waves. This transformation converts the temperature reconstruction problem into the task of solving a system of algebraic equations. By solving this system of equations, the average temperature of each grid is determined. It is worth noting that the accuracy of the solution depends on the predetermined number of acoustic paths and coarse grids, with higher accuracy achieved when each grid is intersected by acoustic paths. Moreover, preliminary information on the temperature field is obtained by solving the coarse grid temperatures. Subsequently, mathematical methods, such as interpolation, are used to reconstruct the temperature field. Using an adequate number of measurement points, paths, and discrete grids results in a reconstructed temperature distribution that provides suffi-cient information about the temperature field. Thus, there is no need for further grid temperature interpolation.
## 2 Numerical Simulation
### Mesh Generation and Independence Verification  
The Fluent Meshing 2024 software module is selected to generate the meshes for the entire fluid domain of the centrifugal pump. As depicted in Figure 3, the meshing process adopts the Poly-hexcore meshing method based on the \"Mosaic\" technology. Specifically, hexahedral elements with eight nodes are used to fill most of the internal flow field region. Meanwhile, high-quality stratified multi-prismatic elements are maintained in the boundary layer, and then general polyhedral elements are automatically employed to connect these two types of elements. This meshing approach has prominent advantages, such as strong adaptability in terms of mesh connection, high solution efficiency and accuracy, and good meshing flexibility.  
At the same time, local refinement is carried out for regions with complex shapes and structures, such as the solute tongue. Additionally, multiple layers of boundary layer elements are applied to resolve the near-wall flow, with the first-layer grid height calibrated to maintain an average y+ \(<\) 20 across all surfaces. In regions prone to strong flow separation (e.g., the impeller domain), additional grid refinement is implemented to achieve y+ \(<\) 10, ensuring accurate resolution of adverse pressure gradients and vortex shedding dynamics. For example, Figure 3a shows the overall grid of the fluid domain, and Figure 3c shows the mesh refinement and the boundary layer meshes in the root region of the blade, which is indicated by the dashed box in Figure 3b.  
To evaluate the influence of the grid size on the calculation accuracy, the changes in the two key external characteristic parameters of the centrifugal pump, namely, the head and the efficiency, under the rated flow condition are observed as the total number of meshes varies, as shown in Figure 4. It is easy to find from the figure that when the number of grids is less than \(5.4\times 10^{6}\), the fluctuations in the predicted results are quite significant, and the fluctuation range of the head is as high as \(1.1\) m. However, when the number of grids exceeds \(7.1\times 10^{6}\), both the head and the efficiency remain stable, and the fluctuation ranges of these two parameters remain within \(0.1\)m and \(0.4\)%, respectively.  
Figure 3: Mesh of the hydraulic model: (**a**) Whole model, (**b**) Impeller region, (**c**) Blade root area.  
Therefore, to balance both the solution accuracy and efficiency, a meshing scheme with a total number of elements of approximately 8.5 \(\times\) 10\({}^{6}\) is finally selected. The average mesh quality of this scheme is 0.91, and the numbers of elements and nodes in each fluid domain are listed in Table 2.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Wireless Sensor Networks (WSN) Technology
#### 2.1.4 Challenges Facing WSN  
Although WSN technology has had performance improvements over the years [47], with many research questions getting resolved, other challenges are still present. Some of these challenges arise from node structure, while others are posed by the environment in which they operate in. Reference [48] highlighted some of these design challenges encountered by WSN. These include the _operational environments_, where WSN are deployed in areas that affect its operation, such as the industries, underwater, underground, etc.; the _power_ consumed by nodes to process its data, transmit it, or receive any data; the _scalability_ of these nodes, since they are deployed in large numbers for a specific application;  
Figure 4: A WSN network architecture showing data sources, aggregation, storage, and control points.  
provision of good _quality of service; fault tolerance_ in the network; limitation in _computational and storage_ capabilities; _security_ of data in terms of confidentiality, integrity, and availability.