esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Microbial Community Composition and Diversity  
#### 3.3.1 Microbial Diversity Analysis in the Three _Daqu_ Samples  
The bacterial 165 rDNA fragment effective sequences of the three samples were in the range of 70,389-108,510, and the fungi ITS DNA fragment effective sequences were in the range of 74,457-123,470; these sequences in the samples were classified into OTUs with 97% similarity. The diversity of different samples was analyzed after normalization. Table 2 lists the \(\alpha\)-diversity indexes of the microbial communities, including the species richness (Chao1 and ACE), diversity (Shannon and Simpson), and coverage index. All samples had coverage of 99.9%, which indicated that the samples were reasonable. The species richness and diversity of the bacterial levels in the BQ and YQ samples exhibited similar trends, which were higher than in the WQ sample. At the fungi level, there was also no significant difference in species richness and diversity between the BQ and YQ samples. It suggested that compared with the WQ sample, BQ and YQ exhibited greater microbial species richness and diversity. Moreover, the bacteria in the samples had more species richness and variety than fungi.
## 4 Methodological Approach
### Detection of Change-Points in Time Series of Individual Components
We assume that the importance of change-point is increasing with its shortest distance from the connection of two adjacent change-points (Figure 6). The most significant changes in the trajectories were highlighted by red stars (Figures 10 and 11). Note that in the figure above (Figure 6b), the change-point is displayed as one single moment, but a closer view to the trajectory of the movement shows, that in case of such slow movement (daily displacement is smaller than random noise) there should be some temporal buffer around the change-point where the trajectory is changing. This enabled to decide if the change of the trajectory is the reaction on a given event (for example seismic). The time span of possible change-point location is in two- or three-dimensional space represented by error ellipse or ellipsoid, where its center is located in the change-point. The size of the major and minor axes of the ellipse/ellipsoid is derived from the magnitude of the standard deviations in the respective directions. All points of the trajectory located within the ellipse/ellipsoid are detected as possible change-points, and, therefore, each turn of the trajectory is represented by a set of relevant time stamps. As a consequence, one turn of the trajectory is now represented with various numbers of dates, which can be confronted with the time stamps of other events. Both approaches: change-point detection in time series of components and trajectory simplification leads to detecting time stamps, which were then confronted with time stamps of above normal events in the time series of potential landslide triggers. We assume that significant events in the movement are caused by significant events in the seismicity or precipitation. Statistics describe this relation as causality. We used Granger causality to quantify the relationship between sets of events with different extents, which allows determining if one set of values is caused by another on a given significance level [64]. There was a huge number of seismic events during the analyzed period--most of them with very low magnitude. To compare seismic events with significant events in the movement, it was needed to highlight above normal seismic events. For this reason, the average value and standard deviation of daily PGA maxima were computed. Then, only those seismic events were selected whose standard deviation of maximum PGA average was three times higher than the standard deviation. To highlight episodes with higher seismicity, five-day sums of PGA maxima were computed (Figure 7). Figure 6: (**a**) Importance of change-points was quantified using minimal distance di; (**b**) change-point defined using error ellipse.
# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
## 3 Acceptable Clearance for Lane Changing
### Research on Acceptable Clearance of Lane Changing
#### 3.2.1 Acceptable Lane Changing Clearance between Vehicle \(M\) and Vehicle \(TL\)
In the actual lane changing process, the running state of the leading vehicle in the target lane is very little affected by the lane changing vehicle and can be ignored, so the state of vehicle \(TL\) generally does not change during the lane changing process. The initial lane  
Figure 5: Lane changing process between vehicle \(M\) and vehicle \(TL\). changing speed and acceleration of vehicle \(M\) are set to be \(v_{M}\) and \(a_{M}\), respectively, and the initial speed and acceleration of vehicle \(TL\) are set to be \(v_{TL}\) and \(a_{TL}\), respectively. Based on the above assumptions, the safe critical time of vehicle \(M\) and vehicle \(TL\) under different moving states is plotted when \(v_{M}<v_{TL}\), as shown in Figure 6. By analyzing the diagram, it can be found that if vehicle \(M\) completes the lane change under the condition of meeting the safe critical time of lane changing, the distance difference between the two vehicles with the same speed will no longer change. Therefore, the shaded part of the figure can be used to represent the distance difference between the two cars, which is \(\Delta S=\frac{(v_{M}-v_{TL})T}{2}\), and the safe critical time for lane changing satisfies the formula \(T=\frac{v_{TL}-v_{M}}{a_{M}-a_{TL}}\), which is \(\Delta S=-\frac{(v_{TL}-v_{M})^{2}}{2(a_{M}-a_{TL})}\). Therefore, by synthesizing formulas (6) and (10), the expression of acceptable lane changing clearance between vehicle \(M\) and vehicle \(TL\) can be obtained as:  
\[\left\{ \begin{aligned} G_{a}^{M-TL}\geq-\frac{(v_{TL}-v_{M})^{2}}{2 (a_{M}-a_{TL})}+G_{\text{min}}+w\sin(\theta)\\ G_{\text{min}}=[t_{0}-c_{v}(v_{TL}-v_{M})-c_{a}(a_{TL}-a_{M})]v_{M} \end{aligned} \right. \tag{11}\]
## 1 Introduction  
In geotechnical engineering, various soil reinforcement techniques have been commonly used to enhance the engineering properties of soil. Soil reinforcement by fiber materials is considered an effective ground improvement method because of its cost-effectiveness, easy adaptability, and reproducibility [1]. Of particular concern is the strength of fiber-reinforced soils (FRS), which varies according to different fiber and soil properties. This indicates a need to understand the reinforcement mechanisms responsible for these differences by exploration with different perspectives and methods.  
Factors that influence the shear strength of fiber-reinforced soil have been explored in a variety of laboratory experiments using, for example, triaxial tests and direct shear tests [2; 3; 4; 5; 6; 7; 8; 9; 10]. The existing body of research suggests that different soil conditions (i.e., the density, particle shape, and size grading) and fiber traits (i.e., type, fraction, dimension, and orientation) present significant effects on the reinforcement of fibers [11]. Several theories on the reinforcing mechanisms have also been proposed, such as force-equilibrium/mechanistic, energy dissipation, statistical, and superposition of the effects of soil and fibers [11; 12; 13; 14; 15; 16]. In addition, different authors have investigated different problems in fiber reinforced soil using numerical methods, including continuum models [17] and discrete element method (DEM) models [18; 19; 20]. In a comparison of the two numerical approaches, [PERSON] et al. [21] quantified and evaluated the effect of root reinforcement by simulating direct shear tests.  
The principal advantages of fiber-reinforced soil are generally considered to be the relative isotropy of the composite material, and many researchers have assumed isotropy for simplicity during analysis [22]. However, [PERSON] [23] and [PERSON] et al. [24] suggested that anisotropy would be a more realistic assumption. They argue that anisotropy is brought about by the limitations of the construction equipment and the method by which the fibers are incorporated into the soil and then compacted. The design shear strength of the fiber-soil composites should account for the preferential orientation of fibers. So far, however, there has been little discussion about the influence of fiber orientation on reinforcement [11; 23; 25; 26; 27; 28; 29]. In addition, these previous studies can only obtain macroscopic responses and far too little attention has been paid to the micromechanical mechanism that governs the reinforcement. These two aspects motivated this study. This article places emphasis on the analysis of the cause of the shear strength anisotropy of fiber reinforced sands (FRS) induced by a preferential orientation of fibers with DEM.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
###### Abstract  
Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the \(\pm\)5% and \(\pm\)10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.  
Intelligent Transportation System; missing values imputation; fuzzy C-means; genetic algorithm +
Footnote †: journal: Sensors
## 4 Discussion  
### Model Efficiency Evaluation  
With the help of the three-class metaheuristic technique, the predictive ability of the RF model is enhanced, and the ALO-RF, WVO-RF, and GOA-RF hybrid models are obtained. The RMSE was chosen to calculate the fitness values of each developed model. As illustrated in Figure 6, the fitness values of all developed models with different populations had been minimized and stabilized after reaching the maximum number of iterations.  
As can be seen in this picture, it is easy to find that the convergence time of GOA-RF hybrid models with different populations is slightly faster than ALO-RF and WVO -RF models. In conclusion, the variation of the fitness function confirms the effectiveness of the three types of optimization algorithms and visualizes the optimization efficiency of the different algorithms. The predictive performance of the three types of hybrid models for _GS_ is evaluated more fully in Section 4.2.
## 4 Conclusions  
To the best of our knowledge, this is the first time that cryosections of the mussel digestive gland have been used to detect MP particles in samples captured from native locations. The advantage of the methodology used in our study is that the diverse shapes of MPs observed on the cryosections of mussel digestive glands did not result from the breakdown of larger plastics, as could be the case in studies that used a method of MP extraction from organic tissue. Thus, the observed sizes and shapes of the MPs found within this study were probably the same as ingested and stored in the mussel digestive gland. It is imperative to establish a standardized, efficient, and cost-effective approach that can be universally adopted by researchers in this field. Moreover, the contamination during the processing of samples was considered to be negligible. Standardization will enhance the accuracy and comparability of data across studies, contributing to a comprehensive understanding of MP pollution. We propose the detection of MPs inside an organism by using SEM-EDS as a unique and quite fast method for the detection of MPs, thus making it suitable for a future large-scale monitoring program. In our research, with the combination of two unique techniques, polarized light microscopy and the valued addition of SEM, we were able to not only see the sizes and shapes of MPs present within the wild mussel digestive gland, but also to detect at the same time the elements present in the MPs noted under the microscope, thus confirming the identifications of MP materials.  
A higher number of MP items was recorded in the mussels sampled from the harbors located near the urban areas. Nevertheless, it is of great concern that a very similar and statistically not different number of MPs was noted in the cultivated mussels and the ones from pristine areas. This research highlights that MP pollution is pervasive in both wild and farmed mussels found in the coastal waters of the northern Adriatic Sea. The mussel digestive gland could be used as the monitoring biomarker to detect sites polluted with MPs. To date, no data have been reported on the MPs of native mussels from the Slovenian and Croatian coastline of the northern Adriatic Sea; however, seawater monitoring will require additional research.  
Conceptualization, I.K. and P.B.; methodology, I.K., K.S. and V.S.; formal analysis, E.P. and M.B.; writing--original draft preparation, P.B. and. I.K.; writing--review and editing, M.B., E.P. and V.S.; project administration, I.K. and P.B.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Education, Republic of Croatia (grant for [PERSON]), the Juraj Dobrila University of Pula by project, Microplastics in marine organisms from Northern Adriatic (grant for [PERSON]).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
We highly appreciate [PERSON] who helped in the fieldwork, as well as [PERSON] who gave laboratory support at the Central for Marine Research Rovinj, Ruder Boskovic Institute.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 5 Discussion
The top and middle plots in (**a**) represent the accuracy of RF and SVM respectively, while the lowest plot is the accuracy of RF minus that of SVM. The top and middle plots in (**b**) represent the kappa coefficient of RF and SVM respectively, while the lowest plot is the kappa coefficient of RF minus that of SVM. the ice growth from November to December, the successful classification rate increases relatively obviously, which is consistent with the sea ice extent trend shown in Figure 16.
## 1 Introduction  
Ultrasound has various applications in food processing due to its ability to induce physical, chemical and mechanical changes in food materials. Ultrasound utilizes distinctive physical and chemical phenomena that stand in contrast to those employed in traditional extraction, processing or preservation methods. Ultrasound presents a clear edge in aspects of efficiency, output and specificity, showcasing improved processing durations, heightened quality and decreased chemical and physical risks, all while remaining ecologically conscious [1].  
The basic method used in traditional food preservation is heat treatment, which destroys dangerous pathogens or limits the growth of microorganisms [2]. Thermal processing, despite its high effectiveness inactivating microorganisms, also brings with it undesirable changes that adversely affect the final quality of the processed food. Many food ingredients are thermolable and decompose when exposed to heat. In addition, heat treatment can also cause adverse changes in the taste, colour and even texture of the processed food. This gave rise to the need to search for new alternative methods of food preservation, which undoubtedly include power ultrasound.  
Power ultrasound refers to the use of high-intensity sound waves with a range of 10 to 1000 W/cm\({}^{2}\) and frequency ranging from 20 to 100 kHz. They induce various physical and chemical effects in liquids, solids and gases. Power ultrasound is characterized by the application of intense acoustic energy, often leading to the phenomenon of cavitation, which involves the creation and collapse of tiny bubbles in the material being treated. Thesecollapses generate locally high temperatures and pressures, leading to mechanical and chemical effects that can be harnessed for different applications.  
Ultrasound technology has found applications in juice preservation, offering several benefits in terms of extending shelf life, maintaining nutritional quality and minimizing the need for chemical preservatives. However, it is important to note that ultrasound still has some flows resulting in degradation of primary and secondary metabolites [3]. Therefore, its application in juice preservation should be optimized to achieve the desired effects without negatively impacting sensory attributes or causing undesirable changes.  
Different reviews have explored the utilization of ultrasound technology in the preservation and processing of food products [4; 5; 6; 7]. However, a comprehensive review addressing the specific application of power ultrasound in juice preservation has not been previously presented. This article provides a concise overview of the impact of ultrasound on the physical and chemical properties of juices, their microbiological stability and further prospects for using this type of treatment in juice preservation.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 6 Simulations and Result Discussions
### Regular eNodeB Scenarios, Results, and Discussions  
#### 6.2.1 Simulating a Normal Cycle Scenario  
The ordinary eNodeB with the five previously described groups and various variable average arrival rates is the only one we take into consideration at first.  
The uniform arrival rate during normal cases is considered as follows:  
\[\lambda_{\text{(0)}}=\frac{5+10+15+20+25}{5}=15\]  
Figure 8 illustrates how \(\lambda_{\text{(h)}}\) = 25 is used to represent a constant average arrival rate for H2H traffic.  
We observe the following in the results displayed in Figure 8:  
* Because there are only 6 resources available to handle 15 instantaneous requests, a uniform average arrival rate with \(\lambda_{\text{(0)}}=15\) and a 40% completion rate (scr(m) = 40%) and ru(m) = 100% is expected under normal operation.  
Figure 8: In a normal cycle scenario, the regular eNodeB is represented by scr, the service completion rate, and \(\lambda_{\text{(m)}}\), the average arrival rate for M2M traffic.  
* There is a noticeable decline in the service completion rate, from \(\lambda_{(1)}=5\) with a 100% completion rate to \(\lambda_{(5)}=25\) with a 24% completion rate, only when a single storm is received from a synchronized group (Group(\({}_{(1)}\) to Group(\({}_{(5)}\)). These findings are clear given that the network only has a set number of resources (rb(\({}_{(m)}=6\)) set aside for M2M traffic, despite the fact that demand for M2M services is rising (scr(\({}_{(m)}=100\%\); 60%; 40%; 30%; 24%)).  
Additionally, the system utilization peaks to its cut-off point with a ru(\({}_{(m)}=100\%\) due to the high load of arrivals compared to the available resources in the system except for Group (1).  
Since the network allocates the majority of its resources to H2H traffic (rb(\({}_{(h)}=94\)), there are no restrictions on H2H traffic, knowing that the average number of requests per time interval is 25 (\(\lambda_{(h)}=25\)) with scr(\({}_{(h)}=100\%\) and ru(\({}_{(h)}=26.5\%\).
## 8 Conclusions  
In the future, wireless networks beyond 5G are expected to support a wide range of new TI applications with varying QoS requirements. Due to the need to communicate haptic information in addition to the traditional audio and visual traffics over wireless media, several non-conventional challenges have arisen and have to be addressed. Ultralow latency, ultra-high reliability, very high data rate, energy efficiency, spectral efficiency, and network throughput are the main technical requirements in this regard. To this end, this paper has presented a comprehensive survey on various aspects of wireless TI, beginning with TI's vision and recent advances as well as a generalized framework for wireless TI, which includes the main technical requirements, key application areas, a TI architecture, and the main enabling technologies. Furthermore, key enablers for supporting TI applications/users in B5G systems, as well as the main sub techniques, have been identified and discussed. Finally, as it impacts the steps that are required by the manufacturers side, it is necessary to learn about 5G, its benefits and drawbacks, as well as how it will interact with other technologies that are already used. Next, the manufacturing industry should contribute to the development of 5G standards and regulations so that the technology meets their needs, and last but not least to collaborate with telecoms operators to shape how they commercialize 5G in the market and ensure that they are an integral part of this developing ecosystem.  
Moving on, as it regards future work, future 5G networks will need to have significantly more system capacity than current networks in order to avoid a spectrum shortage. In order to successfully integrate a 5G network in modern manufacturing environments, many infrastructure improvements will be required. The requirements will range from upgrading the backbone infrastructure of the organization to developing a suitable strategy for managing the use of the following: (1) spectrum adoption, (2) fiber rollout internally (10 Gb minimally), (3) high-speed switches and routers, (4) on-site, and (5) deploying edge-connecting devices. Future research will focus on the wireless AR/VR investigation and the suitable B5G network architecture that can deliver AR/VR capability and to identify interfaces to connect AR/VR devices with wireless network. Additionally, one more issue to be investigated is the advanced signal processing techniques in order to use low-complexity machine learning techniques to extract the meaningful information from raw data acquired by tactile sensors computing.  
All authors have participated in the modelling of the research project. More specifically: supervisor, D.M.; research, conceptualization, and original draft preparation, J.A.; research, conceptualization, and original draft preparation, N.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.