esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper established the calculation model of the sound scattering field of targets in the deep-sea channel by using equivalent directional point source theory, the FEM of multi-physics coupling, and acoustic ray theory; analysed and compared the calculation results of the sound scattering field using the FEM and the equivalent directional point source method; and, verified the accuracy and reliability of the proposed method. The equivalent directional point source method was also compared with the FEM in terms of model freedom, calculation time, and memory utilisation, whilst the same target's sound scattering field was calculated under the same channel. When compared to the finite element calculation in the entire channel, the amount of mesh is greatly reduced, the calculation efficiency is significantly improved, and the calculation time can be reduced by more than one order of magnitude.  
The calculation error of the directional point source equivalent method can be attributed to two factors. One is the selection of equivalent positions in the process of point source equivalence. The comparison of the position of the decomposition surface of the spherical wave decomposition and the equivalent distance in the process of point source equivalence proves that the directional point source equivalent method should be performed in the far field, and the position of the decomposition surface of the spherical wave decomposition has little effect on the calculation of the sound scattering field. The boundary of the channel is the other. The distance between the boundary and target is long. Thus, the influence on the calculation of the acoustic scattering field in the equivalent directional point source method will be small. Under the sound field conditions in this study, the influence of the boundary can be ignored when the distance from the elastic structure to the ocean boundary is equal to the wavelength. Additionally, this method is not suitable when the target is very close to the interface.  
In the future, further analysis regarding the sound field of directional point sources in complex marine environments and the applicability of the equivalent method of directional point sources in complex marine environments will be the primary research content.  
Conceptualization, C.Z. and D.S.; methodology, C.Z. and J.L.; software, C.Z., Z.Z., Y.J. and J.L.; validation, J.L., C.Z., Y.J., Z.C. and Z.Z.; formal analysis, C.Z. and J.L.; investigation, J.L.; resources, D.S.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, C.Z.; visualization, J.L., Y.J., Z.C and Z.Z.; supervision, C.Z. and D.S.; project administration, D.S.; funding acquisition, C.Z. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 1197040430).  
The authors declare no conflict of interest.
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
## 3 Results and Discussion
### The Local Supply Approach  
The estimation of the costs for the local supply approach is relatively simple. The analysis takes into account the number of facilities required to support local water supply systems for each settlement in the municipalities and includes water treatment plants (WTPs), pumping pools, pumping stations, and water towers (for settlements with greater water consumption) or booster pumps (for smaller settlements). For the operating costs, accurate estimations of energy costs were not included, as they are impacted by the changing dynamics of daily peak water and electricity demands, a level of detail beyond the scope of this work.  
The cost of the individual WTPs, which is based on the technological schemes from Table 1 and the amounts of water required, is given in the final column of Table 4. Note that for the analysis of the overall costs below, both the local and centralised supply approaches include not just the WTPs but also the supporting infrastructure required--water towers, pumping pools, pumping stations, and booster pumps. The operational costs include long-term maintenance such as inspection and sanitation.  
For the entire municipality of Kanjiza, the total estimated costs of the local supply to all the villages together is shown in Table 5, and the costs per location are broken down in Figure 8. For the municipality of Senta, the total estimated costs of the local supply approach are given in Table 6 and Figure 9.  
Figure 8: The estimated costs for the local water supply approach in Kanjiza.  
Figure 9: The estimated costs for the local water supply approach in Senta.  
As shown in Table 3, Kanijza municipality has a population of 23,000, whilst Senta is slightly smaller, with a population of approximately 21,000. The total cost of investments for the local supply approach reflect this, with Kanijza requiring EUR 6.6 million in investment compared to Senta, which requires EUR 5.8 million. Per capita, Kanijza is approximately 3.6% more expensive than Senta, which is to be expected, as the population is distributed across twice as many settlements as in Senta, so the local supply approach requires more WTPs and more pumping infrastructure. Meanwhile, despite its smaller population and fewer settlements, the annual operating costs for Senta are actually higher: EUR 54.1/capita/year in Senta and EUR 45.8/capita/year in Kanijza. This is because of the considerably worse water quality in Senta (almost twice as much As) requiring more complex treatment schemes with higher maintenance costs.
## 2 Preliminaries and Problem Description
### USV Model  
Consider a class of networked multiagent systems consisting of \(n+1\) USV (labeled as 0 to \(n\)) moving in a special formation pattern as shown in Figure 2. The motion of the \(i\)-th USV can be described with kinematics  
\[\eta_{i}=J_{i}(\eta_{i})v_{i},\]and dynamics  
\[M_{i}\dot{v_{i}}=-C_{i}(v_{i})v_{i}-D_{i}(v_{i})v_{i}+w_{i}+\tau_{i}+\Delta_{i}, \tag{3}\]  
where \(\eta_{i}=\left[x_{i},y_{i},\psi_{i}\right]^{T}\) represents the position and yaw angle of the USV in the earth-fixed frame. \(J_{i}\left(\eta_{i}\right)\) is a nonsingular transformation matrix defined as \(J_{i}\left(\eta_{i}\right)=\left[\begin{array}{ccc}\cos\left(\psi_{i} \right)&-\sin\left(\psi_{i}\right)&0\\ \sin\left(\psi_{i}\right)&\cos\left(\psi_{i}\right)&0\\ 0&0&1\end{array}\right].v_{i}=\left[u_{i},v_{i},r_{i}\right]^{T}\) denotes the surge, sway and yaw velocities with respect to body fixed frame. \(M_{i}\in\mathbb{R}^{3\times 3}\) is a symmetric positive definite inertia matrix, \(C_{i}\left(v_{i}\right)\) is the matrix of Coriolis and centripetal terms, \(D_{i}\left(v_{i}\right)\) is the damping matrix, \(w_{i}\) is the external disturbances induced by wind, wave, and ocean currents, etc., and \(\Delta_{i}\) represents the unmodeled dynamics. The control inputs of the \(i\)-th USV is denoted by \(\tau_{i}\).  
The matrices \(M_{i}\), \(C_{i}(v_{i})\) and \(D_{i}(v_{i})\) are given below:  
\[M_{i}=\left[\begin{array}{ccc}m_{11i}&0&0\\ 0&m_{22i}&m_{23i}\\ 0&m_{32i}&m_{33i}\end{array}\right],D_{i}(v_{i})=\left[\begin{array}{ccc}d_ {11i}&0&0\\ 0&d_{22i}&d_{23i}\\ 0&d_{32i}&d_{33i}\end{array}\right],\]  
\[C_{i}(v_{i})=\left[\begin{array}{ccc}0&0&-m_{22i}v_{i}-m_{23i}r_{i}\\ 0&0&m_{11i}u_{i}\\ m_{22i}v_{i}+m_{23i}r_{i}&-m_{11i}u_{i}&0\end{array}\right],\]  
Figure 1: The structure of a neural networks (NN).  
Figure 2: Unmanned surface vehicle (USV) formation configuration.  
where  
\(m_{11i}=m_{i}-X_{ai},m_{22i}=m_{i}-Y_{qi},m_{23i}=m_{32i}=m_{i}x_{gi}-Y_{ti},m_{33i }=I_{zi}-N_{ti}\),  
\(d_{11i}\left(u_{i}\right)=-(X_{ui}+X_{|ui|u_{i}}\left|u_{i}\right|+X_{uiuiui}u_{ i}^{2}),d_{22i}(v_{i},r_{i})=-(Y_{qi}+Y_{|qi|v_{i}}\left|v_{i}\right|+Y_{|ri|v_{i}} \left|r_{i}\right|)\),  
\(d_{23i}(v_{i},r_{i})=-(Y_{ri}+Y_{|qi|r_{i}}\left|v_{i}\right|+Y_{|ri|r_{i}} \left|r_{i}\right|),d_{32i}(v_{i},r_{i})=-(N_{vi}+N_{|qi|v_{i}}\left|v_{i} \right|+N_{|ri|v_{i}}\left|r_{i}\right|)\),  
\(d_{33i}(v_{i},r_{i})=-(N_{ri}+N_{|qi|r_{i}}\left|v_{i}\right|+N_{|ri|r_{i}} \left|r_{i}\right|)\),  
the mass of the ship \(i\) is denoted by \(m_{i}\) and the \(i\)-th ship's inertia matrix in the body-fixed frame is denoted by \(I_{zi}\).
## 3 Results and Discussion
### Pca  
The PCA started with the development of a Pearson product moment correlation matrix, shown in Figure 3. It was observed that the indicators pH, TN, K, Mg, MBN, MBC, SMR and qCO2 were the only ones that presented significant interactions between them (\(r^{2}\geq\pm 0.6\)), thus forming the minimum data set for the _SQI_. A negative correlation between the indicators pH and MBN is remarkable and can be explained by the impact of pH on microbial metabolism, as acidic and alkaline pH can affect the availability of nutrients for microbial communities. The TN indicator showed significant positive correlations with the indicators SMR and qCO2, both related to the activity and maturity of microbial communities. This suggests that chemical fertilisation processes have a stimulating effect on the metabolism of microorganisms and possibly on soil nutrient cycling. Consequently, the MBC indicator also showed a significant positive correlation with the qCO2 indicator.  
A KMO adequacy test was conducted to assess the suitability of using the minimum database in PCA, and the following results were obtained: overall KMO = 0.74 (for each indicator, TN = 0.75, Mg = 0.76, SMR = 0.72, and qCO2 = 0.77).  
After performing the PCA, two PCs were identified that met the criterion of eigenvalue > 1, comprising 82.3% of the variability of the indicators analysed in the sampled soils (Figure 4). PC1 is composed of the indicators TN, SMR and qCO2, while PC2 exclusively includes the indicator Mg (Table 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **PCI** & **PC2** \\ \hline TN & 0.85 & — \\ Mg & — & 0.75 \\ SMR & 0.88 & — \\ qCO2 & 0.78 & — \\ \hline \hline \end{tabular}  
* PC, principal component; TN, total N; Mg, magnesium; SMR, soil microbial respiration; qCO2, metabolic coefficient. Non-significant correlations were omitted.  
\end{table}
Table 5: The weighting of indicators in the PCs of the sampled soils from the Bajio region of Guanajuato.  
The relationship between the resulting indicators and the PCs is shown in Figure 4.  
Figure 4: Bi-plot of correlation between PC1 and PC2. Mg, magnesium; SMR, soil microbial respiration; qCO\({}_{2}\), metabolic coefficient.  
Figure 3: Pearson product moment correlation matrix. pH, hydrogen potential; EC, electrical conductivity; WHC, water-holding capacity; TOC, total organic C; TN, total N; CEC, cation exchange capacity; K, potassium; Na, sodium; Ca, calcium; Mg, magnesium; MBC, microbial biomass C; MBN, microbial biomass N; FDA, fluorescein diacetate activity; SMR, soil microbial respiration; qCO\({}_{2}\), metabolic coefficient. Only significant interactions (\(p\leq 0.05\)) are shown, with those with a high correlation coefficient (\(r^{2}\geq\pm 0.6\)) being considered significant correlations.  
The incorporation of the aforementioned indicators in the respective PCs provided a representation of the relevance of microorganisms in agricultural development processes, especially under conditions of conventional agriculture using chemical fertilizers.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 4 xPON Activation Processes
### EPON Activation Steps  
The following subsection evaluates the EPON activation process according to [24]. The MPCP defines the autodiscovery mechanism used to detect the newly-connected ONUs, a circular delay and a MAC address [29]. This process is controlled by the OLT unit, which periodically creates an available discovery window, during which time it gives inactive units the ability to log in to the OLT. This periodicity is not specified by the standard and therefore depends on individual implementation. Autodiscovery uses the following four messages: GATE, REGISTER EQ, REGISTER and REGISTER ACK. These messages are transmitted in the MPCP frame. The autodiscovery process consists of four steps, which are illustrated in Figure 3.  
Figure 3: Activation process in EPON networks.  
Step 1: The Discoveryagent decides to initiate the discovery process and assigns a discovery window (the time interval when none of the initialized ONUs can send data). The Discovery Agentinitiates the discovery process using the Discovery GATE message, which includes the starting time and the length of the slot. During the forwarding of the GATE discovery message, the MPCP stores the OLT time.  
Step 2: Only previously uninitialized ONUs respond to the GATE message. After receiving such a message, the ONU sets the local time according to it. If the OLT clock reaches the starting time that is also included in the GATE discovery report, the ONU waits for a randomly selected time and then forwards the REGISTER REQ message. Accidental delays can lead to collisions when initiating multiple ONUs. The REGISTER REQ message contains the ONU source address and the time used to send the message from the ONU. When the OLT receives a REGISTER REQ message, it detects the MAC address and the circular delay.  
Step 3: After analyzing and verifying the REGISTER REQ message, the OLT sends the message REGISTER directly to the given ONU using the MAC address obtained during the previous step. The REGISTER message contains a unique logical link identifier (LLID) that is assigned to all ONUs. Next, the OLT sends the GATE message to the same ONU.  
Step 4: After the REGISTER and GATE ON messages are received, the REGISTER ACK confirms that an acceptance of the previous messages has been sent. The REGISTER ACK should be sent in the time interval granted by the GATE message.
## 3 Results and Discussion  
### Consolidated Body in Two Kinds of Soil  
Under the aforementioned experimental conditions, multiple repeated experiments were conducted on medium-dense (void ratio e = 0.73-0.75) ordinary sand. After grouting completion, several sets of typical experimental results were obtained through excavation, as shown in Figure 3. Figure 3a indicates that, in the grouting test of ordinary sand, the grout consolidation body consists of grout stone body, mortar consolidation body, and grout vein body. Similarly, the same results can be clearly observed in the grouting test of transparent sand, as shown in Figure 3b. During the initial stages of grouting pressure, the permeation of grout into the sand dominates. For sand layers with good injectability, the grout generally diffuses through the sand particle medium in a permeation form. The grout effectively fills the pores within the particle framework and ultimately forms a relatively homogeneous grouting reinforcement body with the sand layer. Around the grouting holes in the sand, the grout permeates and expands, forming cohesive spherical stone bodies. This observation aligns with the understanding in engineering practice; as the grouting pressure increases, the compaction effect of the grout in the sand gradually becomes dominant. The grout bubbles expand gradually, exerting pressure on the surrounding soil. With the continuous increase in grouting pressure, when the grouting pressure reaches the strength of the soil at a certain contact point within the sand, cracking diffusion occurs immediately, and the grout rapidly fills the cracked fissures. Since the grouting pressure along the cracked fissures gradually decreases, permeation compaction occurs around the cracked fissures in the initial stage of cracking. As the grouting pressure continues to increase, the grouting pressure within the fissures gradually increases, causing the fissures to expand. Simultaneously, the increase in grouting pressure leads to the gradual expansion of grout bubbles and the generation of new cracks. It is worth noting that as the grouting pressure increases, when the pressure reaches a certain value, the phenomena of grout penetration, compaction, and cracking diffusion coexist. This finding is consistent with the conclusions of [PERSON] et al. [21].
# Fuzzy System for the Quality Assessment of Educational Multimedia Edition Design
###### Abstract  
The quality of educational multimedia edition design is determined by a set of characteristics that affect perception, readability and communication efficiency. Quality assessment of multimedia edition design is based on a comprehensive analysis of characteristics that affect the quality of output data, edition processing and content representation. Within the framework of this study, the goal is to develop a methodology for assessing the quality of educational multimedia edition design using fuzzy logic. An approach to determining an integral quality indicator based on fuzzy logic is proposed, which ensures that the influence of various factors which are difficult to characterize exclusively by numerical parameters, is taken into account. A multilevel model of fuzzy logical inference is constructed, representing the dependency between quality factors. Membership functions for linguistic variables are formed and their weight coefficients are determined using pairwise comparison matrices. The developed approach contributes to making informed management decisions in the process of creating multimedia products. The use of fuzzy logic methods allows one to assess the design quality even under conditions where the parameters are subjective or do not have clear numerical characteristics. Thus, quality prediction provides the opportunity to identify the design weaknesses at the stage of its development, optimize the process of creating multimedia editions, and increase the efficiency of their use in educational and professional environments. Further research aimed at integrating artificial intelligence for automated updating of the knowledge base and expanding the system by introducing additional assessment criteria is considered to be promising.  
educational multimedia edition; multimedia edition design; quality of output data; quality of edition processing; quality of content representation; fuzzy quality assessment system Article
## 3 Results
### Model Simulation for Spatial Patterns of _Re_ at the Regional Scale  
Based on the parametrized _RE_ model dependent on prescribed PFTs (alpine and temperate grasslands in Figure 1) and the spatial driver data of MODIS _EVI_, _LST_, and _LSWI_, the spatial distribution of the mean annual REs from 2001 to 2010 were obtained (Figure 7a). The predicted annual _RE_ of China's northern grasslands is 258.18 \(\pm\) 12.07 g C m\({}^{-2}\) yr\({}^{-1}\) on average. For the alpine grasslands on the Tibetan Plateau, the alpine shrub meadow has the largest _RE_ value (434.69 \(\pm\) 8.83 g C m\({}^{-2}\) yr\({}^{-1}\)), followed by the alpine swamp meadow (413.82 \(\pm\) 8.21 g C m\({}^{-2}\) yr\({}^{-1}\)), the alpine _Kobresia_ meadow (368.64 \(\pm\) 7.52 g C m\({}^{-2}\) yr\({}^{-1}\)), and the alpine steppe meadow (146.57 \(\pm\) 3.21 g C m\({}^{-2}\) yr\({}^{-1}\)), exhibiting a clear decreasing gradient from the southeast to the northwest with increasing elevation across the Tibetan Plateau. For the Inner Mongolian Plateau, _RE_ also shows a clear decreasing gradient from northeast to southwest. Specifically, the highest value of _RE_ occurs in the meadow steppe (470.27 \(\pm\) 39.24 g C m\({}^{-2}\) yr\({}^{-1}\)) in the northeast, and the lowest value of _RE_ occurs in the desert  
Figure 6: Time-series plots for observed (black square dot) and modeled (red round dot) ecosystem respiration (_RE_) at all sites. The text in the abscissa represents the abbreviation of the site name, and the following number represents the year.  
steppe in the southwest (144.91 \(\pm\) 21.75 g C m\({}^{-2}\) yr\({}^{-1}\)). The mean annual \(RE\) in the typical steppe is 193.6 \(\pm\) 30.84 g C m\({}^{-2}\) yr\({}^{-1}\).  
_Reref_ appears to be a key component in this model in terms of quantifying the spatial variability of \(RE\) across sites. Nevertheless, the spatial patterns of _Reref_ have rarely been investigated due to the lack of spatially explicit algorithms to allow the upscaling of _Reref_. Here, we took abiotic and biotic factors into consideration to parameterize _Reref_ spatially and revealed that the mean annual _Reref_ has a clear spatial heterogeneity among different vegetation types in northern China's grasslands (Figure 7b), ranging from 0.22 g C m\({}^{-2}\) day\({}^{-1}\) in the desert steppe to 2.25 g C m\({}^{-2}\) day\({}^{-1}\) in the meadow steppe. In addition, the spatial distribution of _Reref_, to a large extent, determines the spatial pattern of the mean annual \(RE\) (Figure 7a,b).
## 3 Results and Discussion  
### Chemical Composition  
Table 2 presents the basic composition, salt content, and cholesterol content in the study of fermented sausages. The moisture content of the sausages ranged from 40.10 to 45.20%, but the O2 treatment significantly differed from the others (45.20%; \(p\) < 0.05). The control sausage C contained significantly the highest fat content (30.10%, \(p\) < 0.05). The sausages did not differ significantly in protein content (22.85-25.85%). The carbohydrate content was below the detection level (<0.50%). The salt content was 2.64-3.17%, with the control sausage P containing significantly the most salt. The salt not only enhances the flavor of the product but also contributes to the aroma of the meat ingredients, affects the color, and influences the microbiological processes that occur in the sausage [31]. Samples containing nitrite demonstrated a higher salt content, which may be attributed to the accumulation of a different profile of organic acids in the other samples. This, in turn, impacted the properties of the proteins and the percentage content of various components. All sausages were characterized by high cholesterol content (78.33-84.97 mg/100 of product). Sausages with the addition of acetic acid bacteria had significantly lower water activity (0.87-0.88) than the control sausages (0.90-0.92). According to [PERSON] et al. [32], lower water activity  
The changes in pH value during storage are presented in Table 3. The pH value after production ranged from 5.01 to 5.26, and the sausages differed significantly from each other (\(p<0.05\)). Interestingly, the sausages with the addition of acetic acid bacteria (O1 and O2) were characterized by a higher pH value (5.18 and 5.26, respectively) than the C and P controls (5.15 and 5.01, respectively). However, referring to the results of the microbiological analysis, it cannot be stated that the addition of AAB caused changes in the pH of the sausages. At time 0, the number of AAB and LAB is at a similar level, only after 6 months were significant differences in the number of bacteria found. After storage, a significant increase in the pH value was noted in all the sausage samples tested (5.55-5.92; \(p<0.05\)). Changes in the pH value are a consequence of both physicochemical and microbiological changes [38]. In this case, the increase in pH is probably caused by proteolytic changes, which lead to the formation of peptides, amino acids, or non-protein nitrogen compounds [39].