esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Statistical Analysis  
PRS was analyzed as a quantitative or nominal variable, dividing it into quartiles (1 st/2 nd-3 rd/4 th) or deciles (1 st/2 nd-9 th/10 th). Logistic regression was utilized to analyze the role of genetic factors in the risk of dementia. ROC analysis was performed to assess the discriminative ability for genetic and social factors. Linear regression was applied to investigate the associations of PRS and _APOE_ alleles with CSF biomarkers and MMSE scores. Statistical analysis and visualization were conducted using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria), with the \"pROC\", \"psych\", and \"ggplot2\" packages. We checked for correspondence to the Hardy-Weinberg equilibrium using the online service SNPStats ([[https://www.snpstats.net/](https://www.snpstats.net/)]([https://www.snpstats.net/](https://www.snpstats.net/)), accessed on 21 April 2023) [41] and calculated the adjusted \(p\)-values using the Benjamini and Hochberg (BH) method. A comparison of logistic regression models, incorporating social and genetic factors, was conducted using JASP software (Version 0.16.3). Statistical significance was determined when the \(p\)-value was less than 0.05.  
Statistical power was assessed using MedCalc\({}^{\circledR}\) Statistical Software version 22.013 (MedCalc Software Ltd., Ostend, Belgium). We hypothesized that the ROC curve for the studied PRS model will be different from the referenced 0.5, and the expected AUC will be 0.57, according to [PERSON] et al. [9]. Based on this, as well as on an alpha of 0.05 and a power of 0.8, the sample size was estimated to be at least 249 individuals in each group.
## 2 Method
### Warning of Dangerous Area of USV Based on GeoNetworking Mode  
In the marine environment, USVs encounter challenges such as incomplete nautical charts and unforeseen incidents, complicating the process of marking all the danger zones. Effective communication is crucial, but traditional Internet Protocols (IP) struggle in high humidity and fog conditions, impacting the transmission of critical warning information [27; 28; 29; 30].  
Additionally, when multiple USVs operate simultaneously, traditional networks struggle to identify warning needs, leading to redundant data transmission and network congestion [31; 32]. To address these issues, USV communication networks can adopt the GeoNetworking model. By sending obstacle avoidance warnings to USVs in target areas, the GeoNetworking model avoids large-scale broadcasting, reducing information redundancy and network congestion [33; 34; 35; 36]. Compared to the traditional IP model, the GeoNetworking model has significant advantages in the maritime field, effectively addressing the challenges of incomplete nautical charts and unexpected events, avoiding irrelevant communications, and improving information exchange efficiency and safety.  
Considering the potential maritime obstacles and dangers that USVs might encounter, a maritime obstacle-avoidance scenario is designed, as shown in Figure 6. In this scenario,  
Figure 5: Inflection point diagram. (**a**) Eliminate collinear nodes. (**b**) Eliminate redundant inflection points.  
the red area represents the stop zone and the yellow area represents the warning zone, explicitly indicating the geographic area where the GeoNetworking modal sends warning information. The USV needs to be aware of these areas in advance to avoid obstacles effectively. Therefore, the obstacle avoidance warning zone is configured as a circular area. This design is described using three pieces of information: the center coordinates, radius, and identifier of the zone. This simplifies the warning information design and improves information transmission efficiency.  
The GeoNetworking routing base station receives and broadcasts obstacle avoidance alert information in this scenario. The USV updates its location information in the cooperative awareness message format to the onboard GeoNetworking protocol stack, which packages the cooperative awareness message information into Beacon packets and periodically sends them to the GeoNetworking routing base station. Upon receiving the Beacon packets, the base station checks whether the USV is within the alert area. If the USV is in the alert area, the base station sends an alert message to the USV; if it is not, no alert is sent.  
In obstacle avoidance scenarios, GeoNetworking's broadcast function is used to transmit information to USVs within the obstacle warning range. This method improves information exchange efficiency, reduces communication pressure, and enhances the flexibility of obstacle avoidance information transmission. Applying this multimodal communication network effectively enhances the operational capabilities of USVs in complex maritime environments.
## 5 The Validation of Experiments
### The Sideband Vibration Comparative Test  
To confirm that the MRCFM approach can effectively suppress the sideband vibration response, vibration response tests are conducted on the experimental prototypes under various PWM modulation strategies. Figure 19 shows the measured sideband vibration response spectra of six classical generalized DPWMs before and after MRCFM optimization. It can be seen that the vibration response of DPWMs without optimization is mainly concentrated near the carrier frequency and corresponds to the distribution characteristics of current harmonics, further confirming that the sideband current harmonics are the main factors causing high-frequency vibration of PMSM. Combining the analysis results of the previous simulation prediction section, it can be seen that the multi-physical coupled numerical prediction model can accurately predict the characteristics of sideband vibration response of the prototype. The main-order components of various PWM strategies disappear, and the vibration peaks decrease significantly after optimization. Figure 20 displays the comparison curve of simulated and measured peak values of sideband vibration response of the prototype using different PWM strategies. It can be seen that MRCFM has a significant suppression effect on sideband vibration response caused by PWM strategies. The simulated values are slightly lower than the measured values, but the vibration error is not exceeding 0.04 m/s\({}^{2}\).  
Figure 19: Experimental comparison of sideband vibration response before and after MRCFM optimization: (**a**) Fixed carrier frequency modulation; (**b**) Markov chain random carrier frequency modulation.  
Figure 18: Comparison of simulated and measured harmonic peaks of sideband currents with different modulation strategies.
# Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion
## 5 Results  
### TCN Model vs. Bi-LSTM  
First, we compare the performance of the proposed TCN model with the conventional Bi-LSTM. The Bi-LSTM model follows the parameters aforementioned in Section 4. The TCN net was implemented with different channel numbers to evaluate the impact of the number of parameters. The target function weight \(\lambda\) was set to be 0.5 to optimize the model from 2 aspects evenly. Table 1 demonstrates the overall performance of the models in RMSE, PCC, and number of the parameters (Params) 3 aspects. All the 16-dim articulatory features are taken into account and the RMSE and PCC are computed per output dimension and then average.  
Bi-LSTM baseline model achieves 1.44 mm mean RMSE with 0.46 standard deviation and 0.77 Pearson Correlation Coefficient, which is consistent with [28]. When the channel number is set to be 512, the TCN model greatly outperforms the baseline with relatively comparable params. The mean RMSE decreases from 1.44 mm to 1.09 mm (24.3%) with 0.32 standard deviation and PCC improves to 0.873 (13.4%). However, this brings a lot more computation during training and evaluation stages, and it's fairly hard to train. 256-channel TCN model gets 1.18 mm RMSE (18.1% decrease) with 0.43 standard deviation and 0.845 PCC (9.7% increase). Note that the params is only \(\frac{1}{4}\) of Bi-LSTM. The performance of the 128-channel model slightly precedes the baseline with a nearly \(\frac{1}{20}\) model size. The experiment results depict that a deep temporal convolution network can outperform the RNN architecture with a smaller model size at the meantime when modeling the time sequence contextual information.  
Figure 5 demonstrates predicted trajectories of the tongue tip articulator for a single speech recording randomly chosen from the test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE (SD)** & **PCC** & **Params** \\ \hline Bi-LSTM & 1.44 (0.46) mm & 0.770 & 11.0 M \\ TCN-512 channel & 1.09 (0.32) mm & 0.873 & 9.9 M \\ TCN-256 channel & 1.18 (0.40) mm & 0.845 & 2.5 M \\ TCN-128 channel & 1.30 (0.46) mm & 0.801 & 0.6 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between proposed TCN-based AAI model with different channel number and Bi-LSTM baseline model. The TCN model outperformed the baseline with much fewer parameters.  
Figure 5: Trajectories of one specific articulator: tongue tip predicted by the proposed 256-channel TCN AAI system. The plot of the movements shows the target trajectory and the predicted one. (**a**) TT_X_; (**b**) TT_Y_.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Space Topology Modelling  
The problem of describing indoor space as a graph was analysed in a document published by the OGC (Open Geospatial Consortium) in 2010 [41] and, based on the findings, the open standard IndoorGML was proposed in 2014 [42]. It defines general principles for describing the interior of a building as a graph, in terms of modelling spatial data for navigation. The basis of the model is a graph describing geometrical and topological relationships between rooms and/or structural elements, providing more complex information, e.g., to move between rooms, one needs to use specific doors. Multilayer representation of the space can model different types of relationships, such as adjacency of objects or 'navigability', i.e., the physical connections that allow movement between rooms [43].  
As shown in studies [44, 45], room topology graphs can be directly used for routing in buildings, and the route can be represented as a list of rooms. In addition, it was shown [46] that such a graph can be combined with a transport network outside the building to determine routes between buildings.
## 5 Testbed and Simulation Results
### Performance Measurements  
#### 5.3.1 Performance Measurements for 5 Node Simulation Setup  
An AWK script was built to calculate the average of Throughput and End to End Delay on node 0 and node 2 before and after node 0 was affected by rain. Before rainfall we used the standard AOMDV protocol and after the rainfall we used our modified AOMDV protocol to compare its performance with the standard AOMDV. The highest priority data stream will be granted to cbr0 coming from node 0 because it is affected by rain and it has important data that should be sent to the destination GW. We re-routed the cbr2 coming from node 2 traffic through node 1 which has data with low priority. In this way we reduce forwarding load on node 0, that means the throughput and end to end delay will be better on cbr0 after node 0 is affected by rain because less traffic is now going through that node.  
The results listed in Table 4 clearly show improvement of Throughput and End to End Delay on cbr0 coming from node 0 which validate our assumptions and it showing that the event-driven approach does improve overall network efficiency.
## 3 Prototype Description
### Clustering Methods
#### 3.7.2 K-Medoids  
The K-medoids algorithm is a variation of the K-means clustering technique that aims to partition a dataset into \(K\) distinct, non-overlapping clusters [66]. Unlike K-means, which uses the mean (centroid) of a cluster to represent it, K-medoids employs the actual data points, known as medoids, as representatives. This makes K-medoids more robust to outliers and able to handle non-spherical clusters [67].  
The procedure of K-medoids is equivalent to the K-means clustering; the difference is the evaluation of medoids [68]. Given a dataset with \(N\) data points and medoids \(\{m_{1},m_{2},\ldots,m_{K}\}\), the assignment step involves selecting the medoid that minimizes the dissimilarity:  
\[\text{medoid}(x_{i})=\arg\min_{j}\text{dissimilarity}(x_{i},m_{j}).\]  
The dissimilarity function can be defined based on a suitable distance metric [69], such as the Euclidean distance. The update step aims to find the best medoid replacement for each cluster:  
\[m^{\prime}_{j}=\arg\min_{x_{i}\in\text{cluster}(j)}\sum_{x_{i}\in\text{ cluster}(j)}\text{dissimilarity}(x_{k},x_{i}).\]
## 1 Introduction
To fulfill this purpose, a methodology is developed to replicate: (1) the turbulence characteristics of a homogeneous wind tunnel flow and (2) the wake field arising from the introduction of porous disks representing the wind turbine. This procedure is employed to reproduce the inflow and wake characteristics measured in the experimental campaign carried out by [PERSON] and [PERSON] [17; 31], so the comparison with wind tunnel data serves as method of validation. The study of diverse features of the turbulence field both in the free decaying flow and in the wake is presented next to such comparison. The computational platform employed is OpenFOAM(r) v.2.1 [32; 33] (the OpenFOAM Foundation Ltd., London, UK) an open-source code amply used in flow simulations, chosen for its availability and access to apply ad-hoc modifications to existing solvers and utilities. While the synthetic turbulence is created with the Mann method, the flow is reproduced using LES computations. The methodology implemented follows--in part--a procedure developed on the CFD software EllipSys3D [34; 35; 36] (Department of Mechanical Engineering and RiseCampus, DTU, Lyngby, Denmark) to simulate wake flows with turbulent inflows [5]. It has been shown that this method provides good results to introduce ABL as well as homogeneous turbulence conditions [5; 6; 24; 25; 26; 27; 28]. By using this approach, we expect to assess: (a) how well the main turbulence characteristics can be reproduced (e.g., turbulence intensity and integral lengthscale) in the inflow and in the wake with LES, (b) how the main turbulence characteristics in the wind tunnel change due to the presence of the wind turbine model and (c) how the LES modelling change along the wake compared to the undisturbed flow (resolved/modelled velocity fluctuations). It should be noted that these questions are formulated in the context of a _limited mesh resolution_, which makes it more relevant for the wind energy field since it is often desired to minimize the computational requirements while successfully reproducing the requested flow features, which in this case consists mainly of the integral lengthscale. The work presented here is organized as follows: a brief theoretical background is provided about the homogenenous isotropic turbulence and its experimental approximation, the decayinghomogeneous turbulence, next to a description of the measurement campaign and data to be reproduced. Later, the methodology is described in detail, with special attention to the generation of turbulence inflow and the reproduction of the adequate characteristics in the absence of disk. This also comprises the description of the methodology employed to introduce the synthetic turbulence in the LES domain. Next, the results are presented and discussed for the different quantities computed in the turbulence field of the wakes. A final section presents a summary and the conclusions of the work.
# Microbial Community and Functional Analysis of Regionally Produced Traditional Korean Grain Vinegar
## 3 Results
### Organic Acids  
Organic acids, which contribute to the taste and functional characteristics of vinegar, vary in composition and concentration depending on the raw materials and fermentation conditions used during production [33]. The results for this stage of the study are shown  
Figure 3: Richness and \(\alpha\)-diversity (Shannon and Simpson) in grain vinears produced via different fermentation processes across four regions and over three fermentation stages. in Table 2. Acetic acid, the primary organic acid in vinegar, was found to be present in the highest concentrations in all four types of grain vinegar at the end of fermentation. Among the vinegars, the five-grain vinegar (YO3), which had the highest acidity (14.67%), also contained the highest level of acetic acid, at 58.07 mg/mL. This was followed by rice vinegar (JR3), in which the level of acetic acid increased sharply from 2.53 mg/mL (JR1) to 42.99 mg/mL, representing approximately a 17-fold rise in acetic acid production. The barley vinegar (SB3) contained 36.99 mg/mL of acetic acid, while the brown rice vinegar (HB3), which had the lowest final acidity (4.87%), also had the lowest acetic acid concentration, at 13.59 mg/mL. The levels of lactic acid, detected in all the samples, generally decreased as acetic acid fermentation progressed. This is likely due to the fermentation broth used in the process. Lactic acid levels should be minimized to ensure product standardization and safety in vinegar production [34]. A slight increase in lactic acid levels was observed in SB2 (20.00 mg/mL) and JR2 (12.75 mg/mL) during fermentation, possibly due to an insufficient nutrient supply for acetic acid production. In such conditions, lactic acid bacteria may convert sugars into lactic acid instead of acetic acid. Therefore, adjustments in the manufacturing process, such as increasing the inoculation volume of the starter culture to accelerate pH reduction or enhancing the oxygen supply to inhibit lactic acid bacteria, may be necessary. Oxygen exposure can produce reactive oxygen species that suppress the activity of these anaerobic bacteria [35]. The levels of malic acid, which was present in trace amounts before fermentation, completely depleted in some samples as fermentation progressed. In particular, malic acid was no longer detected in the SB and JR samples during the acetic acid fermentation stage. This appears to be closely related to the increase in lactic acid bacteria numbers in these samples, as _Lactobacillus_ species became more dominant over time. This strongly suggests that malic acid was converted into lactic acid via malolactic fermentation [36]. These results are consistent with previous studies reporting that the metabolic activity of lactic acid bacteria can significantly influence the organic acid composition during fermentation [37]. Additionally, propionic acid was found to be present at levels of 2.07 mg/mL in SB3 and 2.38 mg/mL in JR3. This acid occurs naturally during vinegar fermentation and is known to contribute to vinegar's flavor.
# A Novel Swarm Optimisation Algorithm Based on a Mixed-Distribution Model
## 3 Population Distribution Model Based on a Mixed Distribution
#### 4.4.1 Statistical Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Experimental Functions** & **Algorithms** & **Optimal Solutions** & **Average Results** & **SD** \\ \hline \multirow{4}{*}{\(F_{7}\)} & BOA-6 & \(-9.091\times 10^{3}\) & \(-7.94\times 10^{3}\) & \(6.41\times 10^{2}\) \\  & NBOA-3 & \(-8.99\times 10^{3}\) & \(-7.28\times 10^{3}\) & \(7.04\times 10^{2}\) \\  & NBOA-6 & \(-1.11\times 10^{4}\) & \(-7.80\times 10^{3}\) & \(7.55\times 10^{2}\) \\  & PSO & \(-7.69\times 10^{3}\) & \(-6.09\times 10^{3}\) & \(6.39\times 10^{2}\) \\ \hline \multirow{4}{*}{\(F_{8}\)} & BOA-6 & \(1.61\times 10^{-8}\) & \(1.62\times 10^{-3}\) & \(3.07\times 10^{-3}\) \\  & NBOA-3 & \(2.11\times 10^{-7}\) & \(8.04\times 10^{-4}\) & \(1.97\times 10^{-3}\) \\  & NBOA-6 & \(1.36\times 10^{-9}\) & \(5.27\times 10^{-4}\) & \(1.41\times 10^{-3}\) \\  & PSO & \(9.63\) & \(2.79\times 10\) & \(8.74\) \\ \hline \multirow{4}{*}{\(F_{9}\)} & BOA-6 & \(7.71\times 10^{-6}\) & \(2.58\times 10^{-3}\) & \(4.37\times 10^{-3}\) \\  & NBOA-3 & \(1.15\times 10^{-13}\) & \(5.32\times 10^{-6}\) & \(3.76\times 10^{-5}\) \\  & NBOA-6 & \(2.58\times 10^{-14}\) & \(5.60\times 10^{-9}\) & \(2.21\times 10^{-8}\) \\  & PSO & \(2.02\) & \(3.89\) & \(8.18\times 10^{-1}\) \\ \hline \multirow{4}{*}{\(F_{10}\)} & BOA-6 & \(7.01\times 10^{-8}\) & \(3.90\times 10^{-4}\) & \(8.64\times 10^{-4}\) \\  & NBOA-3 & \(1.52\times 10^{-8}\) & \(1.94\times 10^{-4}\) & \(4.57\times 10^{-4}\) \\ \cline{1-1}  & NBOA-6 & \(1.59\times 10^{-9}\) & \(1.18\times 10^{-4}\) & \(2.83\times 10^{-4}\) \\ \cline{1-1}  & PSO & \(4.31\times 10^{-5}\) & \(1.65\times 10^{-4}\) & \(8.35\times 10^{-5}\) \\ \hline \end{tabular}
\end{table} 
Table 8: Experimental results of multimodal function optimization. * Average Results Average results are the average of 50 algorithm runs. In the experiments, NBOA-3 obtained the best average results for five functions: F1, F2, F3, F4, and F5. NBOA-6 obtained the best average results for four functions: F6, F8, F9, and F10. BOA-6 obtained the best average results for functions F5 and F7.
## 3 Results and Discussion
### Storage Stability
Figure 4B) showed higher ascorbic acid retention (89.7-97.2%, \(p<0.05\)) than group C1 after 56 days, regardless of the storage conditions. Group C3 (25% _w/v_ GA) stored in transparent packaging (TN and TV) showed a significant (\(p<0.05\)) and unexpected positive fluctuation in the ascorbic acid content at 21 days of storage. Similar behavior for ascorbic acid has been reported by [PERSON] et al. [73] when evaluating the storage stability of spinach. Although the mechanism that explains this behavior is not clear, authors have reported that light-induced reactions might have an influence on these results. Despite these during storage, at the end of the storage period (56 days), the samples displayed 94% and 90% ascorbic acid retention for C3-TN and C3-TV, respectively (Figure 4B). These results indicate that not only the packaging and storage conditions but also the protective effect resulting from higher GA concentrations during spray-drying microencapsulation influenced the shelf-life stability of ascorbic acid [74; 75; 76; 77]. A notable TPC decrease was observed after 7 days of storage (\(p<0.05\)) for both the C1 and C3 groups and regardless of storage conditions (Figure 4C,D), but after that, TPC remained virtually unchanged up to 56 days of storage. TPC fluctuations (degradation after 7 days of storage and a slight increase in TPC for group C3 after 56 days of storage) might be attributed to the oxidation of phenolic compounds or modifications of the phenolic profile under storage, which have also been reported for spray-dried cranberry juice [62] and spray-dried cantaloupe juice [78]. When analyzed altogether, spray-dried cashew particles retained between 55% and 76% of the initial TPC, which is similar to spray-dried horseradish particles produced with GA, MD, starch, and soy protein isolate that showed 56.4% to 79.9% TPC retention after 4 months of storage at room temperature, as reported by [PERSON] et al. [79]. Figure 4: Ascorbic acid (**A**,**B**) and total phenolic (**C**,**D**) retention (%) of spray-dried cashew particles under storage conditions. (**A**,**C**) C1: spray-dried cashew particles with 15% _w/v_ GA, IT 140 \({}^{\circ}\)C; (**B**,**D**) C3: spray-dried cashew particles with 25% _w/v_ GA, IT 140 \({}^{\circ}\)C. Retention (%) values after \(n\) days of storage (_n_ = 0, 7, 21, 35 or 56) compared to initial sample conditions (0 days of storage). GA: gum arabic; IT: inlet temperature. TV: packaged in transparent plastic and sealed under vacuum; TN: packaged in transparent plastic and sealed without vacuum; OV: packaged in opaque plastic and sealed under vacuum; ON: packaged in transparent plastic and sealed without vacuum.