esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
An electronic search was conducted in PubMed, Scopus, and Google Scholar accessed directly through their respective online platforms ([[https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)]([https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)), [[https://www.scopus.com/](https://www.scopus.com/)]([https://www.scopus.com/](https://www.scopus.com/)), [[https://scholar.google.com/](https://scholar.google.com/)]([https://scholar.google.com/](https://scholar.google.com/)), accessed on 24 December 2024) using relevant terms derived from the Medical Subject Headings (MeSH) of the US National Library of Medicine, along with keywords associated with OSA and PD. The search included studies published in English, focused on adult populations (age > 18 years), and applied no restrictions on publication date or study design. Reference lists from included studies were also consulted. The electronic search was updated on 8 November 2024, prior to article submission, to identify any additional studies relevant to the review.  
A narrative review was conducted, synthesizing the key findings from primary studies and SRs (Figure 1). To enhance the critical evaluation of the available evidence and guide future research, the methodological quality of the included SRs was evaluated independently by two reviewers ([PERSON], [PERSON]) using the AMSTAR-2 checklist, a tool designed to assess the quality of SRs. Any disagreements between reviewers were resolved through discussion, and if consensus could not be reached, a third reviewer ([PERSON]) was consulted [38].  
Figure 1: Overview diagram illustrating the topics addressed in this narrative review.  
AMSTAR-2 includes 16 items addressing key aspects of methodological rigor in SRs. These items assess criteria such as the development of an a priori protocol, use of duplicate processes for study selection and data extraction, execution of a comprehensive literature search, application of specific inclusion criteria based on publication status, clear documentation of included and excluded studies, detailed reporting of study characteristics, assessment of the scientific quality of included studies, consideration of quality in formulating conclusions, appropriate synthesis of results (including meta-analysis methods), evaluation of publication bias, and declaration of potential conflicts of interest.  
Each checklist item is rated using a standardized response format: \"Yes\" (criterion explicitly met), \"No\" (criterion not met), \"Partial Yes\" (criterion partially addressed), or \"Not Applicable\" (item is not relevant to the review). This structured approach ensures a thorough and transparent evaluation of the methodological quality of SRs.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 6 Conclusions  
This paper emphasized FFT excision as an interference mitigation technique that can provide significant improvements in current receiver performance. This proposed approach can deal with different levels of \((J/No)\) and two types of narrowband interference, which are the continues-wave (CW) jammer and sweeping jammer. Previous researchers showed a significant impact of this approach in terms of software development while none of them implemented this algorithm due to the complexity and the significant hardware resources required for hardware implementation. The aim of this work was to show that the use of fixed-point implementation in a current FPGA can now be efficiently realized and utilized. This makes the proposed approach using 1024 points of FFT feasible and doable for obtaining the desired performance with the required hardware resources. The results show that this approach is powerful and can deal with different levels of interference while maintaining the desired \((\ (\ C/No)\cong 40\) dB). The algorithm was implemented using VHDL to be uploaded into FPGA for hardware implementation. The hardware implementation has two designs, which are fixed-design and float-point. Each design was validated and compared with the MATLAB-developed algorithm. The float-point design showed the best performance using a larger number of hardware resources compared to the fixed-point design. However, the fixed-point with 22-bits design provided a performance that is very close to that of the float-point design performance with fewer hardware resources. Therefore, the fixed-point with a 22-bits FFT excision algorithm was chosen as the optimal design and compromised solution.  
Data curation, T.Y.; Formal analysis, T.Y.; Funding acquisition, T.Y.; Investigation, T.Y.; Methodology, T.Y.; Project administration, P.B.; Software, T.Y.; Supervision, P.B.; Validation, T.Y.; Writing--original draft, T.Y.; Writing--review & editing, P.B. All authors have read and agreed to the published version of the manuscript.  
This research is funded by UKRI, EPSRC.
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 3 Results and Discussion
### Multivariate Analysis of Wheat-Acorn Muffins Quality
Their color was lighter and more uniform, with higher lightness (L* parameter) values and well-balanced red and yellow hues. Chemically, muffins in this cluster showed elevated levels of phenolic compounds and flavonoids, reduced tann content, and improved antioxidant capacity. Cluster 2 included samples with dry microwave-treated flour, mostly processed at moderate microwave power and shorter durations. These muffins showed intermediate characteristics between the other two clusters. Their sensory and technological properties were balanced, with moderate volume, acceptable porosity, and firmness. The color parameters and bioactive compound content were also within a moderate range, indicating that these treatments preserved quality without producing extreme changes. In contrast, Cluster 3 comprises muffins made with acorn flour subjected to more intense conditions--either higher power, longer microwave duration, or both. These samples generally scored lower in organoleptic evaluation, particularly in texture and overall acceptance. Technological traits such as crumb firmness were elevated, and both muffin volume and porosity were reduced, indicating a denser and less-desirable crumb structure. The color was notably darker, with significantly lower lightness values. Moreover, these muffins showed reduced levels of phenolic compounds and antioxidant capacity, while the tann content was comparatively higher, likely due to less effective degradation under the harsher processing conditions. In summary, the clustering results emphasize the influence of microwave treatment parameters on muffin quality. Milder treatments, especially under moist conditions, tend to  
Figure 3: Dendrogram for wheat-acorn muffins with dry and wet microwave-treated acorn flour. AF—acorn flour; 0—control samples; W—wet microwave treatment; 925, 1295, 1850—microwave power (in watts); 0.5...3.0—microwave processing time (in minutes). preserve or enhance desirable characteristics, whereas high-power or prolonged treatments may have detrimental effects on product quality.
# Viewport Rendering Algorithm with a Curved Surface for a Wide FOV in 360\({}^{\circ}\) Images
## 5 Simulation Results
### Quantitative Evaluation  
In this section, an objective evaluation is performed for the various conventional techniques and the proposed algorithm, where straightness and conformality are used as criteria, as demonstrated by [PERSON] et al. [27]. Figure 15 explains how to calculate the straightness and conformality as criteria in the objective evaluation. The evaluation equations for these are as follows:  
\[\text{Straightness}= \frac{\alpha_{2}}{\alpha_{1}+\alpha_{2}}. \tag{45}\] \[\text{Conformality}= \frac{\min(\beta_{1},\beta_{2},\beta_{3},\beta_{4})}{\max(\beta_ {1},\beta_{2},\beta_{3},\beta_{4})} \tag{46}\]  
The maximum values of straightness and conformality are 1. As these values get closer to 1, the degradation in the straightness and conformality decreases.  
Figure 16 displays the patterns that occur using the various algorithms when nested squares are used as a test pattern to evaluate the straightness and conformality. It can be observed that some lines are curved and some red circles have been deformed. The bent lines imply that the applied methods result in poor performance with respect to the straightness. In a case where the circle is badly deformed, we know that the conformality of the algorithm is not good.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{F_{h}}\)- & \begin{tabular}{c} **Perspective** \\ **Projection** \\ \end{tabular} & \begin{tabular}{c} **Stereographic** \\ **Projection** \\ \end{tabular} & \
\begin{tabular}{c} **Curved UV** \\ **Surface with the Optimized \(r\)** \\ \end{tabular} &
\begin{tabular}{c} **Curved UV** \\ **Surface with \(r\)=0.3** \\ \end{tabular} \\ \hline
75 & 0.039604 & 0.002697 & 0.000010 & 0.173255 \\
90 & 0.078799 & 0.005504 & 0.000051 & 0.335975 \\
120 & 0.229415 & 0.016779 & 0.000289 & 0.559911 \\
150 & 0.517675 & 0.039523 & 0.000701 & 0.597737 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Degradation evaluated with (44) for the various techniques.  
Figure 15: Straightness and conformality as the criteria in the quantitative evaluation.  
Based on (45) and (46), the evaluation results for the straightness and conformality are summarized in Table 2, where the best results and the second-best results are represented with red and blue numbers, respectively.  
As observed in Figure 16 and Table 2, the perspective method shows the best performance in the straightness, whereas it provides one of the worse results for the conformality. In contrast, the stereographic method shows the smallest value for the straightness and the largest value for the conformality. This implies that the stereographic algorithm outperforms the other techniques in maintaining the shape of the circles in the viewport, but it bends the lines more than the other schemes. Although the proposed algorithm does not provide the best performance for the straightness and conformality, the overall performance of the proposed algorithm is in the upper ranks. In particular, Figure 16 shows that the overall subjective deformation is much less than the other techniques.
## 4 Discussion
Therefore, the modified APPS of the ASG-EUPOS is capable of reaching results similar to those of global APPS with shorter satellite observation sessions. The value of the MRSE for all the analysed points indicates that the proposed modification with the UAM and five baseline vectors yields an accuracy of the POZGEO ASG-EUPOS of 0.016 m. The discrepancy of the geometrical height coordinate U of the analysed points regarding its reference value ranged from 0.01 to 0.014 m for the UAM and five baseline vectors with the linear discrepancy extreme values from 0.048 to 0.072 m. Therefore, if the APPS of the ASG-EUPOS is modified, it offers better accuracy than that of the APPS JPL, AUSPOS, and Trimble RTX. The following issue should be considered in light of the analyses. The reduction in the number of vectors will, beyond any doubt, lead to the rejection of vectors to the furthest stations, which will decrease the mean baseline vector length. Nevertheless, future research should investigate whether it is better to stick to the five baseline vector principle if one station fails and use the next nearest station or use only four baseline vectors instead. A recent case study for a point in the southern ASG-EUPOS network [15] indicated that the EAM with five or four baseline vectors yields an MRSE of up to 0.03 m. It has been further demonstrated that the APPS POZGEO algorithm is immune to failures of stations nearby because solutions based on five baseline vectors (with one station down) and the EAM give results similar to the standard solution (the EAM and six baseline vectors) [16]. Currently used global APPS, such as the SCOUT, OPUS, and AUSPOS, use GPS carrier phase observations [38]. Positioning accuracy is also affected by the number of GNSS constellations involved in the measurement and post-processing. According to [PERSON] et al. [39], the positioning accuracy increases for all three components and the session duration can be shortened for multi-GNSS observations. Therefore, the potential benefits of expanding the APPS of the ASG-EUPOS with multi-GNSS observations should be investigated further.
## 5 Empirical Results
### Central Composite Design (CCD)
As can be seen, the versus order did not show any pattern, hence, there was no time correlation in the residuals. Figure 13 shown the response optimizer of the CCD experiment to find the feasible response variables with CSL at 90% and 95%. From the result, the CSL set to target that was 90% with the possible minimum total cost 76,330 of the possible settings were T = 3, Q = 3428, and SS = 1336. Meanwhile, when the CSL was increased up to 95%, the possible minimum total cost was 77,090 of the optimal settings that were T = 3, Q = 3520, and SS = 1354. Besides, the surface and contour plots for CSL are displayed in Figure 14. According to the contour plot, the CSL located in an area of dark green color was more satisfying. The trade-off existed in the  
Figure 12: CCD—residual plots for TC and CSL. Figure 13: CCD—response optimizer for TC and CSL. objective function of the model. The more the customer service level is satisfied, the more of the total cost will be paid by the distributor.
## 2 Background and Related Work
### Designing an IIoT Solution for Gas Monitoring in Underground Mines  
In the harsh environment of underground mining, continuous and precise monitoring of gases is paramount for ensuring the safety of the mining personnel and optimizing operational efficiency. Integrating Industrial Internet of Things (IIoT) solutions offers a sophisticated approach to achieving enhanced monitoring capabilities, facilitating real-time data acquisition, analysis, and proactive response mechanisms. The IIoT system's comprehensive design aims to adapt to the diverse geological conditions and operational scenarios encountered in underground mines, thus providing a robust and reliable solution for gas monitoring. In this context, designing an IIoT solution for gas monitoring involves conceptualizing a multidimensional architecture that harmonizes sensor technologies, communication networks, data processing units, and user interfaces to formulate a coherent and efficient monitoring mechanism, as shown in Figure 1.  
Figure 1: IIoT proposed solution for gas monitoring in underground coal mines.  
The proposed IIoT solution comprises the following components:  
* Sensor nodes. Nodes equipped with gas sensors can detect and measure concentrations of gases such as methane, carbon monoxide, carbon dioxide, and sulfur dioxide. Two sensor nodes must be deployed in the mine: static and mobile. The static nodes will be deployed according to the normativity in work faces, roadways, and abandoned sites. Miners can carry the mobile nodes in their helmets. The nodes must be capable of performing initial data analysis, communicating data, and operating energy efficiently.
* Communication network. A robust and reliable communication network is crucial. The network should be capable of maintaining connectivity in the harsh and dynamic underground mine environment to transmit data from the sensor nodes to the processing units in real time. The communications network can be wireless, wired, or a combination of technologies. The wireless nodes are linked using a WSN.
* Data processing and analysis. Data collected from the sensor nodes are processed and analyzed, utilizing advanced algorithms and analytical tools to detect abnormal patterns and potential hazards. The system employs edge and cloud computing to ensure efficient data processing and storage.
* Dashboard for monitoring. Information is presented to end-users using friendly interfaces, enabling informed decision-making. The interface visualizes the measure of gases and alerts and sends real-time notifications.
* Deployment methodology. Considering the unique mine structures and conditions, the sensor deployment should be strategic to ensure optimal coverage and connectivity. The deployment can incorporate a deterministic and random strategy to adapt to scenario conditions, optimizing sensor locations based on environmental risk factors and mining activities.
## 2 Materials and Methods
### Monitoring Scheme  
The WSN were utilized for real-time monitoring of temperature and humidity during eggs' storage in two scenarios. The WSN architecture was composed of two sensor nodes, a network coordinator and the wireless network (Figure 1).  
The sensor node is an integration of a microcontroller, an antenna with a 433 RF module, storage and clock chips, temperature and humidity sensors and power supply (Figure 2a). The microcontroller (STC12 LE5A60S2, STCTM, Shanghai, China) were used to interconnect the slave modules and realize the functionality of sensor nodes by improving processing speed and the capacity of interference resistance. The antenna with a 433 RF module was designed to communicate with the coordinator, which performed low power consumption and high mobility performance. The clock chip can offer the time when data was collected, and the storage chip was used to save the sensor and time information. Six lithium batteries (LR6-4B/1.5 V, Nanfu, Nanping, China) were used to supply voltage for the sensors.  
Temperature and humidity data were monitored by the digital sensors AM2302 (AOSONG, Guangzhou, China), whose data accuracy for temperature and humidity was 0.1 \({}^{\circ}\)C and 1% respectively. The working parameters of AM2302 are as follows: temperature ranges from \(-\)55 to +125 \({}^{\circ}\)C, relative humidity from 0% to 99%, operating voltage between 3.0 and 6.0 V.  
The network coordinator was constructed of a wireless microcontroller, the General Packet Radio Service (GPRS) transmission module (WG 8010-485, Conway, Lewes, MD, USA), the crystal oscillator, and the antenna with an amplifier and power supply (Figure 2b). The environmental data were transferred to the network coordinator for grouping and then sent to the client side by the GPRS module. The CC2530 module (TI, Dallas, TX, USA) was chosen as the wireless microcontroller to integrate the slave modules and optimize their performance.  
Figure 1: Architecture diagram of the Wireless sensor network (WSN). GPRS, General Packet Radio Service. PC, personal computer.
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 5 Vulnerability Analysis of CRN Under Emergency
### CRN Node Failures  
This section investigates the vulnerability of the CRN under node failures caused by different sudden events. These events are categorized into random failures and deliberate attacks based on their characteristics. To enhance the credibility and generalizability of the simulation results and conclusions, based on the average coupling degree of each city in the actual network and existing literature, we set the topological coupling coefficient  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Ranking Based on Node Degree**} & \multicolumn{3}{c}{**Ranking Based on Node Strength**} & \multicolumn{3}{c}{**Ranking Based on Node Betweenness**} \\ \hline
**Rank** & **City** & **k** & **Rank** & **City** & **s** & **Rank** & **City** & **B** \\ \hline
1 & Xi’an & 9 & 1 & Xi’an & 25,341 & 1 & Xi’an & 0.091788 \\
2 & Wuhan & 6 & 2 & Chongqing & 14,852 & 2 & Taiyuan & 0.055275 \\
3 & Ulanqb & 6 & 3 & Chengdu & 11,091 & 3 & Hefei & 0.047341 \\
4 & Hefei & 6 & 4 & Deyang & 10,178 & 4 & Ulanqb & 0.047017 \\
5 & Beijing & 5 & 5 & Lanzhou & 8892 & 5 & Wuhan & 0.044409 \\
6 & Nanjing & 5 & 6 & Wuhan & 7375 & 6 & Wuwei & 0.041031 \\
7 & Qiqihar & 5 & 7 & Yiwu & 5645 & 7 & Jiujiang & 0.040001 \\
8 & Shijiazhuang & 5 & 8 & Zhengzhou & 5627 & 8 & Nanchang & 0.037326 \\
9 & Xuzhou & 5 & 9 & Nanjing & 5398 & 9 & Urumqi & 0.035892 \\
10 & Zhengzhou & 5 & 10 & Jinan & 5181 & 10 & Jinan & 0.032794 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top 10 important nodes in the CRN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Ranking Based on Edge Betweenness**} & \multicolumn{3}{c}{**Ranking Based on Edge Weight**} \\ \hline
**Rank** & **Edge** & **B** & **Rank** & **Edge** & **w** \\ \hline
1 & Wuwei–Urumqi & 0.173639 & 1 & Xi’an–Chongqing & 14,050 \\
2 & Nanchang–Jiujiang & 0.167315 & 2 & Chengdu–Deyang & 9762 \\
3 & Taiyuan–Xi’an & 0.157569 & 3 & Lanzhou–Xi’an & 8092 \\
4 & Xi’an–Hefei & 0.151045 & 4 & Wuhan–Chansha & 4588 \\
5 & Wuwei–Taiyuan & 0.135271 & 5 & Zhengzhou–Luoyang & 3931 \\
6 & Qiqihar–Ulanqab & 0.126617 & 6 & Shenyang–Tieling & 2895 \\
7 & Taiyuan–Ulanqab & 0.124167 & 7 & Hengyang–Guangzhou & 2858 \\
8 & Wuhan–Xi’an & 0.105902 & 8 & Jinan–Dezhou & 2750 \\
9 & Jinan–Xuzhou & 0.103893 & 9 & Qingdao–Zibo & 2291 \\
10 & Urumqi–Shihezi & 0.103665 & 10 & Nanjing–Yiwu & 2230 \\
11 & Nanchang–Ganzhou & 0.102263 & 11 & Nanjing–Suzhou & 2118 \\
12 & Wuhan–Changsha & 0.100581 & 12 & Jinan–Xuzhou & 1524 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top 12 important edges in the CRN.  
\(\varepsilon_{1}=0.3\) and the cargo flow coupling coefficient \(\varepsilon_{2}=0.3\), as well as the cargo flow distribution coefficient \(\gamma=0.5\).
# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 5 Conclusions  
In this paper, the correlation effects of antenna elements on a Massive MIMO system in 3-D space are investigated. The measurement is conducted in an outdoor scenario, and a ULA and UCA antenna topology are employed with bandwidth of 100 MHz at 1.4725 GHz. We focus on the in-field data analysis and modeling method of Massive MIMO non-stationary channel. In the method, KPowerMeans algorithm is used to achieve efficient clustering of sub-channels. The ECD distance measure algorithm is used to program the stationary interval of clusters in an antenna array. Note that the KPowerMeans algorithm is a primary clustering of multipath components in the antenna channel, and the ECD distance measure algorithm is a secondary clustering based on the distance matrix, which increases the constraint requirements of continuous distribution in some or all antenna intervals.  
All in all, it is of great significance to accurately recognize the channel propagation characteristics and to properly establish the channel propagation model as to the framework design of 5G and the future large-scale multiantenna system. As we know, propagation characteristics of typical scenarios can be obtained by channel measurements, which helps in the establishment of the channel model in the given scenario. On the contrary, once we have a good grasp of the channel model, this can guide the construction of wireless communication systems, such as antenna layout design, transmit power allocation and network optimization. Therefore, it is necessary to measure and study the Massive MIMO communication system to establish the channel model in typical scenarios. It is important to research and develop communication systems to meet the requirements of 5G communication.  
Conceptualization, methodology and funding acquisition, L.L.; validation, formal analysis and writing Y.L.; investigation and supervision, L.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation (NO. L172030) and NSFC Project under Grants No. 61931001.  
The data used to support the findings of this study have been deposited in the figshare repository ([[https://doi.org/10.6084/m9.figshare.11436522.v1](https://doi.org/10.6084/m9.figshare.11436522.v1)]([https://doi.org/10.6084/m9.figshare.11436522.v1](https://doi.org/10.6084/m9.figshare.11436522.v1)) (accessed 14 April 2021)).  
The authors would like to thank [PERSON] and [PERSON] from Beijing Jiaotong university for their help on channel measurements and useful discussion of Massive MIMO antenna topology and configuration. The data related to the results shown in the paper may be obtained by contacting the corresponding author.  
The authors declare no conflict of interest.