esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To measure the RF EMF exposure level in a large area, we modified a commercial car for E-field strength measurement. Prior to the E-field measurement using the car, the effect of the car body was evaluated in an anechoic chamber. The measurements between May 2021 and February 2022 were carried out within a radius of 100 km centering on Nihonbashi, Tokyo, including the whole areas of metropolitan Tokyo, Kanagawa, Saitama, and Chiba prefectures, and parts of Ibaraki, Tochigi, Gunma, Shizuoka, and Yamanashi prefectures with the measurement distance of about 13,800 km. The measurement results were averaged in a reference area mesh (1 km\({}^{2}\)). It was found that the E-field strengths of FM/TV are lower than that of mobile phone base stations. It was also found that the E-field strength of only 5G is approximately 20-30 dB lower than that of all mobile phone systems. However, note that it is possible to depend on the data traffic of 5G. The E-field strength of all bands is higher in Tokyo than in other prefectures.  
The effect of switching a three-axis isotropic probe on the measured E-field strength was investigated. The results suggested that the effect of switching a three-axis probe is small. The results showed that there are no large differences compared with the results shown in Section 2.2, even though the measurement is performed by switching the three axes. From the repeated measurements in the same areas, the standard deviations in the same routes were less than 3 dB, and a similar tendency of E-field strength by the car to the time-averaged results of the spot measurements at the same locations in the past was confirmed. In addition, the relationship of E-field strength with population density was investigated. It was shown that the E-field strength from mobile phone base stations had a positive relationship with population density. To the authors' knowledge, this is the first time that we have made a quantitative assessment of the relationship between E-field strength and population density over a large area. In the future, the measurement area will be expanded to all over Japan, and the measured frequency range will be higher, covering the 5G FR2, such as 28 GHz.  
Conceptualization, T.O.; measurement, K.T. and T.O.; data analysis, K.E., T.O., and M.L; writing--original draft preparation, T.O.; supervision, M.T. and S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Internal Affairs and Communications (JPMI10001), Japan.  
The authors would like to thank [PERSON] and [PERSON] at NICT for their kind help and fruitful discussions.  
The authors declare no conflict of interest.
## 4 Simulation Results and Discussion
Figure 14: Voltage reference tracking performance during the steady-state operation with non-linear load using: (**a**) conventional M\({}^{2}\)PC, (**b**) proposed ANN-M\({}^{2}\)PC. Figure 13: Comparison of total harmonic distortion at different sampling times. Figure 15: Current of non-linear load at steady-state operation: (**a**) conventional M\({}^{2}\)PC, (**b**) proposed ANN-M\({}^{2}\)PC. Figure 17 highlights the switching frequency characteristics, which shows a fixed-switching operation at 10 kHz. It can be noticed that the low order harmonics in the conventional M\({}^{2}\)PC cause a high THD while, in the same zone, the proposed ANN-M\({}^{2}\)PC has lower harmonics distortion and thus avoid resonance issues. Over and above, the controller is operating at a sampling time of 100 us, and the execution time with the conventional M\({}^{2}\)PC is calculated as 6.24 us, while with the proposed ANN-M\({}^{2}\)PC is 9.87 us. It is clear that the computation burden is slightly higher at the same ground truth of the converter parameters and the operating point. The algorithm is running on a personal computer employing an Intel(r) Core(tm) i5-8265U processor operating at 1.60 GHz and 16 GB of RAM. On the other hand, the robustness of the controller at different mismatch conditions is an essential aspect. Therefore, a couple of tests, as an example, have been conducted to assess the performance of the ANN-M\({}^{2}\)PC under the mismatch of filter inductance or capacitance. It can be clearly seen from Figures 18 and 19 that there is a significant enhancement in the controller robustness compared to the conventional M\({}^{2}\)PC. Figure 16: Optimal switching sector for the 2L-VSI: (**a**) conventional M\({}^{2}\)PC, (**b**) proposed ANN-M\({}^{2}\)PC. Figure 17: FFT spectrum for the phase voltage: (**a**) conventional M\({}^{2}\)PC, (**b**) proposed ANN-M\({}^{2}\)PC. Figure 11:0 \(L_{f}\) mismatch test for both control algorithms (i.e., after the instant 0.1 s, \(L_{f}\) is reduced by 10% of its initial value): (**a**) conventional M\({}^{2}\)PC, (**b**) proposed ANN-M\({}^{2}\)PC. Finally, Figure 20 validates that the proposed ANN-M\({}^{2}\)PC determines the duty-cycle optimally rather than with unnecessary magnitudes as obtained from the conventional M\({}^{2}\)PC. In addition, the optimal duty-cycle can be obtained by correctly training the ANN network based on accurate and diverse dataset. The benefit of the proposed system is that the FCS-MPC algorithm for UPS applications does not need to add additional terms into the cost function to improve the power quality and therefore increase the frequency of switching to higher levels. This means that the proposed idea in this paper can be applied as an explicit FCS-MPC without the need to add the voltage tracking and its derivative into the cost function to enhance the power quality.
# Potential Applications of Yeast Biomass Derived from Small-Scale Breweries
## 7 Yeast Cell Walls and Their Derivates  
The protein extracts derived from yeast post-beer production are not the only components that can be repurposed. Examples of yeast cell wall fraction applications are listed in Table 6. As previously highlighted, yeast cell walls encapsulate the majority of polysaccharides within the cellular matrix. Among these polysaccharide fractions, beta-glucan emerges as a significant component, possessing thickening and emulsifying capabilities, along with a notable efficacy in water retention [58]. Furthermore, glucans harbor the potential to serve as substitutes for fats in specific applications. Owing to their inherent structural characteristics, glucans are characterized by a low caloric profile, rendering them an exceedingly coveted supplementation in both pharmaceutical formulations and functional foods. Glucans sourced from by-products of beer production have demonstrated heightened apparent viscosity, increased water retention capacity, and superior emulsion-stabilizing properties when compared to commercially assessed products. Nevertheless, their oil-binding capacity remains unaltered [59]. Beta-glucans sourced from residues of beer brewing have also found utility in the realm of bakery products, exemplified by bread formulations. Bread produced by incorporating flour enriched with yeast beta-glucan at a concentration of 2.02 g of extract/100 g of flour exhibits a more uniform structure, concomitant with a heightened loaf volume [60]. This phenomenon may be ascribed to the stabilizing influence of glucans on gas cells within the dough, similar to the impact observed with other cereal-derived glucans. An additional prospective application of the glucan fraction lies in its potential to serve as a substitute for fats and emulsifying agents. Extracts derived from yeast cell walls were employed to replace xanthan gum as an emulsifying and stabilizing agent in the production of mayonnaise. Sensory analyses revealed no deleterious effects on the sensory attributes of mayonnaise, even following prolonged storage [58]. This investigation underscored the potential of mannoproteins persisting post-beer brewing as synthetic emulsifying and stabilizing agents in the food industry. Furthermore, an endeavor was undertaken to utilize yeast glucans as a replacement for oil in reduced-fat mayonnaise formulations at levels of 25%, 50%, and 75% of the fat equivalent. Given the pivotal role of fat in numerous culinary preparations, particularly in high-fat products such as mayonnaise, a low-calorie substitute for oil would prove invaluable in the development of reduced-fat food products. In contradistinction to antecedent studies, the sensory characteristics of reduced-fat mayonnaise experienced marginal diminution. However, substitution levels of up to 50% of oil with glucan were deemed acceptable by the sensory panel [61].
# SIC-Free Based Indoor Two-User NOMA-VLCP System
###### Abstract  
In this letter, an integrated dual-user visible light communication and positioning (VLCP) system based on non-orthogonal multiple access (NOMA) is proposed. The system consists of a single light-emitting diode (LED) and five photodiodes (PD), and the adaptive feedback threshold (AFT) algorithm is used to reduce error propagation (EP) to improve noise immunity. The particle swarm optimization (PSO) algorithm is employed to construct a joint optimization function that optimizes the power allocation factor of the two users and the roll-off coefficient of the square-root-raised-cosine(SRRC) filter. The simulation results demonstrate that, in the given indoor environment, the bit error ratio (BER) of each user in the proposed system is lower than the front error correction (FEC) limit and the average positioning errors of the two users are 4.62 cm and 5.74 cm respectively.  
SIC-Free; NOMA; VLCP; AFT +
Footnote †: journal: _Photonics_
## 3 Results and Discussion
### Traffic Loads  
Pavement may bear different loads during usage, and different traffic loads can generate different stress in the pavement structure. The principal stress and shear stress of the crack tip under different loads are shown in Figure 7.  
The curve in Figure 7a satisfies Equation (16), where the value of R2 is 0.9999:  
\[y=-46,682.23x+273.52 \tag{16}\]  
The curve in Figure 7b satisfies Equation (17), where the value of R2 is 0.9999:  
\[y=228,567x-849.93 \tag{17}\]  
The two curves indicate that the stress in pavement structure increases sharply with the increment of traffic loads. It can be seen that in the process of increasing the axle loads to 2.8 times the standard axle load, the principal stress increased by 181.29% and the shear stress increased by 180.82%, indicating that for every doubling of the load, the pavement structure will bear one more than the original standard axle load, especially in crack tips. The higher the stress, the faster the crack propagates, which leads to serious damage. The foregoing analysis also shows that the accumulation of road damage will accelerate under overloading, and the development trend will increase, which can be described using J-integral and stress intensity factors. Figure 8 shows the numerical changes of these mechanical factors related to crack growth under different overloads when the step time was set as 25 s.  
Consistent with the stress response, the J-integral and the stress intensity factors continue to increase when the traffic load increases. The J-integral increases by 681.83%, K1 increases by 179.43%, and K2 increases by 177.11%. The above curve and increase indicate that when the pavement load gradually increases to 2.8 times of the standard axle load, the mechanical factors related to the growth rate of pavement reflective cracks are also greatly enlarged, and the reflective pavement cracks develop more rapidly, indicating that overloading affects the pavement structure. The impact of this is huge, and road traffic loads should be strictly restricted.  
Figure 7: (**a**) Principal stress change curve with load (**b**) Shear stress change curve with load.
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 4 Method
### Adaptive Density Peaks Selection
#### 4.1.3 Generalized Extreme Value Distribution  
Local density and dependency similarity approximately follow the Generalized Extreme Value (GEV) distribution. In statistics, the GEV distribution is a class of continuous probability distributions used to model the maximum or minimum values of samples. It plays a pivotal role in extreme value theory, particularly in identifying and analyzing extreme behaviors in datasets, such as the identification of cluster centers in this paper. The GEV distribution encompasses three types of extreme value distributions: Gumbel, Frechet, and Weibull, each corresponding to different tail behaviors. The GEV distribution can be expressed as follows:  
\[G(z;\mu,\sigma,\xi)=\exp\left(-\left[1+\xi\left(\frac{z-\mu}{\sigma}\right) \right]^{-\frac{1}{\xi}}\right) \tag{6}\]  
In the formula, \(z\) represents a standardized variable, while \(\mu\), \(\sigma\), and \(\xi\) are the location, scale, and shape parameters, respectively. The value of the shape parameter \(\xi\) determines the type of the GEV distribution. In this paper, we employ maximum likelihood estimation to automatically derive these parameters. As demonstrated in Figure 3(a),b, the fits for local density and dependency similarity are performed using the generalized extreme value cumulative distribution, which aligns perfectly with the empirical distributions.  
Figure 3: Local density and dependency similarity distribution.  
After obtaining the three parameters through maximum likelihood estimation, \(x_{p}\) can be obtained using the Equation (7), where \(p\) represents the probability of the quantile, and \(\hat{\sigma}\), \(\hat{\mu}\), and \(\hat{\xi}\) denote the estimated parameters of the generalized extreme value distribution:  
\[\hat{x}_{p}=\begin{cases}\hat{\mu}-\frac{\hat{\sigma}}{\xi}(1-y_{p}^{-\hat{\xi} }),&\text{if }\hat{\xi}\
eq 0;\\ \hat{\mu}-\hat{\sigma}\log y_{p},&\text{if }\hat{\xi}=0,\end{cases} \tag{7}\]  
\[y_{p}=-\log p \tag{8}\]  
where \(y_{p}=-\log p\) is the quantile function corresponding to the probability \(p\). Specifically, \(x_{pp}\) represents the threshold for local density, and \(x_{ps}\) represents the threshold for dependence similarity. These thresholds are computed using the parameters \(\hat{\sigma}\), \(\hat{\mu}\), and \(\hat{\xi}\). If the local density of node \(i\) exceeds \(x_{pp}\) and the dependence similarity is less than \(x_{ps}\), it is considered a density peak node. In the following section, we will introduce the specific design used to determine thresholds through the generalized extreme value distribution.
# Sediment Dynamics of the Neretva Channel (Croatia Coast) Inferred by Chemical and Physical Proxies
## 4 Results  
### Sediment Features  
Sediment grain size analyses indicate that the NC deposits are mostly clayey silts with a silty dominant fraction and a low-fraction of sand. In particular, on surficial samples, the coarser materials were found in the southernmost zone (sand content of 31% in site 15 BC) and in the north-western sector of the channel (sand fractions of 51%, 39% and 31% at sites 07 BC, 06 BC and 10 BC, respectively; Figure 2a).  
Surficial TOC (Figure 2b) varies from 0.6% (06 BC) to 1.18% (17 BC) and \(\delta^{13}\)C from -22.3%\({}_{\infty}\) (06 BC) to -27.0%\({}_{\infty}\) (17 BC). Total nitrogen (TN) content ranges from 0.15% (17 BC) to 0.09% (06 BC), resulting in C/N ratios from 5.9 to 8.5.  
X-radiograph images, together with depth distributions of sediment porosity and sand fractions of selected BC samples, are shown in Figure 3. X-ray images of BCs do not display evident inner sedimentary structures; instead, bioturbations are observed in several sediment intervals; in particular, in cores from the area close to the river mouth (i.e. site 01 BC). Sample porosities of the surficial level along the entire channel range between 0.62 and 0.71. In addition, at each sampling site porosity slightly decreases with depth (toward the bottom of the BC) without abrupt changes through the sedimentary sequence (Figure 3).  
Figure 2: (a) Log-normal sediment mean diameter [38] areal distribution and (b) organic carbon surficial areal distribution in the Neretva Channel with (c) covariation of N/C versus \(\delta^{13}\)C.  
Vegetal remains are found mostly in sediments near the river mouth (sites 01 BC, 02 BC, 09 BC and 15 BC), whereas fragments of molluscs, bryozoans and ostracods are widespread in the surficial sediments of the NC, with the exception of the nearest site to the river mouth (01 BC), where only fragments of molluscs are found, and of the deepest site (07G) where the benthic macrofauna is almost absent.
## 3 Results
### Sensitivity Experiments  
In order to explore the relative contributions of winds and tides to the LSTM training network, we tested a set of inputs including different physical factors: (1) Exp. 1: radar and winds; (2) Exp. 2: radar and tides; (3) Exp. 3: radar, winds, and tides. Table 2 shows  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multicolumn{2}{c}{**PCCs**} & \multicolumn{2}{c}{**RMSEs (m/s)**} \\ \cline{2-5}  & **Radar** & **LSTM-Corrected** & **Radar** & **LSTM-Corrected** \\ \hline A & 0.3848 & 0.7614 & 0.2739 & 0.1381 \\ B & 0.4759 & 0.8843 & 0.1838 & 0.0906 \\ C & 0.2706 & 0.9167 & 0.2108 & 0.0749 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Domain-averaged PCCs and RMSEs in areas A, B, and C.  
Figure 6: EOF ellipses calculated from LSTM validation results (blue) and model (red) in area B and area C (marked in Figure 2b), which are separated by a solid black line. The whole area C was corrected by the LSTM network trained by point C and area B was corrected by the LSTM network trained by point D. Point C (113.15E, 21.05°N) and point D (113.35°E, 21.5°N) are marked with ***.  
the domain-averaged PCCs and RMSEs for the sensitivity experiments. The original radar data without correction were listed as reference. In the cases of Exp. 1 and 2, we see that including one physical factor (winds or tides) can significantly reduce the domain-averaged RMSEs, from 0.22 m/s to 0.12 and 0.13 m/s, respectively. This indicates that both winds and tides are almost equally important in respect of modulating the current variability in the whole region. In Exp. 3, both winds and tides were included in the LSTM network; the domain-averaged PCC increased to 0.88 and the RMSE decreased to 0.09 m/s. It is known that the HF radar current data were inverted from original electromagnetic echo signals, without any constraints from ocean physical processes in the traditional inversion algorithms. Our results showed that a LSTM correction algorithm combining ocean physical factors can effectively reduce radar data errors. This provides useful guidance for radar raw data quality control.
# A Sustainable Composite Cementitious Material Manufactured by Phosphogypsum Waste
## 4 Conclusions  
In this study, the effect of mechanical characteristics and hydration mechanisms on composite binder mortar are investigated. The primary experimental findings are as follows:  
1. As the amount of phosphogypsum is increased, the reaction degree and non-evaporable water are decreased. There is a linear relationship between the two parameters, and their maximum values at 28 days with 10% phosphogypsum are 22.34% and 21.13%, respectively.
2. The compressive and flexural strengths of mortar containing 10% phosphogypsum can be up to 49.8 MPa and 9.02 MPa greater, respectively, than those of pure cement mortar.
3. In terms of mechanical properties, mortar with 30% phosphogypsum was essentially comparable to cement mortar, which suggested that the dosage of phosphogypsum can be increased to 30%.
4. Phosphogypsum can participate in the secondary hydration reaction to create ettringite and consume portlandite, as revealed by XRD studies. In addition, it can contribute to the microaggregate effect, making the microstructure denser.
5. According to the MIP and SEM investigations, the maximum amount of phosphogypsum that can lower the overall porosity and refine pore size distributions is 30%. Over 30% phosphogypsum should result in harmful effects.
6. After 30% phosphogypsum replaces cement, the cost of mortar is reduced, and the amount of cement in mortar is reduced, which is beneficial to the realization of peak carbon dioxide emissions and carbon neutrality.  
Conceptualization, Y.G. and F.W.; methodology, F.W.; software, S.D.; validation, Y.G. and F.W.; formal analysis, S.D.; investigation, F.W.; resources, F.W.; data curation, Y.G.; writing--original draft preparation, Y.G. and F.W.; writing--review and editing, F.W.; visualization, L.L.; supervision, F.W.; project administration, F.W.; funding acquisition, F.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Guizhou Science and Technology support Plan Project (Qianke Joint Support [2021] General 486), Guizhou Provincial Department of Education General undergraduate University Scientific Research Project (Youth Project Qian Jiaoji [2022] 128), Guizhou Province Science and Technology Plan Project (Qianke He Foundation-ZK [2021] Key 021), Guizhou Normal University's 2017 annual academic seedling cultivation and innovation exploration special project (Qianke He Platform Talents [2017] 5726-03) and Rolling Supported Provincial University Scientific Research Platform Project of Guizhou Provincial Department of Education (Qian Jiaoji [2022] Key 012).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. Correspondence: [EMAIL_ADDRESS] (F.W.)  
Thanks Guizhou Qianyan Construction Engineering Technical Service Co., Ltd for providing financial support.  
The authors declare no conflict of interest.
## 2 Problem Description and Mathematical Models
### Problem Description  
Problems arise with robots which automatically move for cleaning floors. Under dynamic environment conditions, cleaning robots are required to execute the complete coverage path plan within the designated area while avoiding obstacles automatically. As there are some dynamic obstacles, it is inevitable for the robot to produce some uncleaned cells, as illustrated in Figure 1. The robot starts cleaning on the rectangular map. It will automatically avoid obstacles and record corresponding cells which have yet to be cleaned when it encounters pedestrians or other dynamic obstacles. At the end of each travel across the map, a total of 14 cells are left uncleaned. Then, the robot has to track back and clean the omitting cells. How to plan the shortest path so that the robot can traverse all omitting cells is significant to backtracking.  
The omitting cells are either occupied or free. When cells are occupied, it suggests that there are dynamic obstacles inside the cells. If they are free, the robot is allowed to clean it. Robot sensors monitor the status of omitting cells on a real-time basis, while the number of free omitting cells keeps changing with the environment. To unify the optimization of backtracking combinations for cleaning robots, in this paper, the re-entry path planning with a view to plan the shortest path across all omitting cells during real-time planning under changing environment is proposed.  
Travelling salesman problem (TSP) is primarily aimed at determining the shortest path across all cities. In conventional TSP, the size of cities and distance between cities are fixed. In a variety of different practical combinations, the size of cities dynamically changes over time. Thus, DTSP is obtained. The re-entry path planning for cleaning robots is specific to DTSP, where the size of cities varies over time.