esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Criteria for and Types of Probiotics and Its Use in Foods
\begin{table}
\begin{tabular}{l c} \hline \hline
**Bacteria** & **Reference** \\ \hline _Lactobacillus delbrucekii_ ssp. _bulgaricus_ & All yogurt \\ _Lactobacillus delbrucekii_ ssp. _lactis_ & [34] \\ _Lactobacillus acidophilus_ & [35] \\ _Lactobacillus amylocorus_ & [36] \\ _Lactobacillus crispatus_ & [37] \\ _Lactobacillus gasseri_ & [38] \\ _Lactobacillus helicticus_ & [39] \\ _Lactobacillus jensenti_ & [40] \\ _Lactobacillus johnsouli_ & [37] \\ _Lacticobacillus casei_ (formerly _Lactobacillus casei_ or _Lactobacillus paracasei_) & [41] \\ _Lacticaseibacillus rhamnosus_ (formerly _Lactobacillus rhamnosus_) & [42] \\ _Lactiplantithacillus plantarum_ (formerly _Lactobacillus plantarum_) & [43] \\ _Lactiplantithacillus parapplantarum_ (formerly _Lactobacillus paraplantarum_) & [44] \\ _Lactiplantithacillus pentosus_ (formerly _Lactobacillus pentosus_) & [45] \\ _Latinclobacillus sakeri_ (formerly _Lactobacillus sakeri_) & [46] \\ _Latinclobacillus curratus_ (formerly _Lactobacillus curratus_) & [47] \\ _Latinclobacillus parafarraginis_ (formerly _Lactobacillus parafarraginis_) & [48] \\ _Levilactobacillus brevis_ (formerly _Lactobacillus brevis_) & [50] \\ _Ligilactobacillus salivarius_ (formerly _Lactobacillus salivarius_) & [51] 1 \\ _Linosilactobacillus fermentum_ (formerly _Lactobacillus fermentum_) & [36] \\ _Linosilactobacillus mucosa_ (formerly _Lactobacillus mucosae_) & [30] \\ _Linosilactobacillus reuteri_ (formerly _Lactobacillus reuteri_) & [52] \\ _Loligolacobacillus coryiformis_ (formerly _Lactobacillus coryiformis_) & [38] \\ _Weisella (formerly _Lactobacillus_) viridescens_ & [34] \\ _Weisella diuria_ & [53] \\ _Weisella paramesenteroides_ & [45] \\ _Streptococcus thermophilus_ & All yogurt \\ _Streptococcus salivarius_ & [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bacteria, including posbiotics, and yeast with claimed probiotic properties, potential probiotic properties, or potential therapeutic application that have been incorporated into or isolated from yogurt or yogurt-related products.
## 6 Solid Model  
This section presents a brief description about the solid model made in ANSYS, which is used to compare results with the ones obtained by means of the present finite element model.  
The solid model is meshed using linear solid elements, i.e., SOLID185 elements. To emulate the mechanical property variations of the FGM, several layers were defined through thickness direction. An isotropic material was assigned to each layer, where its mechanical properties were evaluated according to its position along the thickness direction  
Figure 2: Flowchart for the overall finite element analysis process.  
1. Conical shell \(\bullet\) Mid-surface: \[\begin{array}{ll}&0\leq z\leq L\cos\alpha\\ &\mathbf{r}(\theta,z)=r(z)\cos\theta\,\hat{\mathbf{i}}+r(z)\sin\theta\,\hat{ \mathbf{j}}+z\,\hat{\mathbf{k}},&0\leq\alpha<\pi/2\\ &0\leq\theta\leq 2\pi\end{array}\] (25) where \(r(z)=r_{0}+\dfrac{r_{1}-r_{0}}{L\cos\alpha}z\). \(\bullet\) Thickness variation: \[\begin{array}{ll}h(z)=h_{0}+(h_{1}-h_{0})\dfrac{z}{L\cos\alpha},&0\leq z\leq L \cos\alpha\\ &0\leq\alpha<\pi/2\end{array}\] (26)
2. Cylindrical shell (\(\alpha=0\)) \(\bullet\) Mid-surface: \[\begin{array}{ll}\mathbf{r}(\theta,z)=r_{0}\cos\theta\,\hat{\mathbf{i}}+r_{ 0}\sin\theta\,\hat{\mathbf{j}}+z\,\hat{\mathbf{k}},&0\leq z\leq L\\ &0\leq\theta\leq 2\pi\end{array}\] (27)
* Thickness variation: \[\begin{array}{ll}h(z)=h_{0}\bigg{[}1+\dfrac{h_{1}-h_{0}}{h_{0}}\dfrac{z}{L }\bigg{]},&0\leq z\leq L\end{array}\] (28)
3. Parabolic shell \(\bullet\) Mid-surface: \[\begin{array}{ll}\mathbf{r}(\varphi,\theta)=r(\varphi)\cos\theta\,\hat{ \mathbf{i}}+r(\varphi)\sin\theta\,\hat{\mathbf{j}}+\dfrac{r(\varphi)^{2}}{4F} \,\hat{\mathbf{k}},&\varphi_{0}\leq\varphi\leq\varphi_{1}\\ &0\leq\theta\leq 2\pi\end{array}\] (29) where \(r(\varphi)=2F\tan\varphi\), with \(F\) being the focal distance [55]. \(\bullet\) Thickness variation: \[\begin{array}{ll}h(\varphi)=h_{0}\bigg{[}1+\beta\bigg{(}\dfrac{\varphi- \varphi_{0}}{\varphi_{1}-\varphi_{0}}\bigg{)}\bigg{]},&\varphi_{0}\leq\varphi \leq\varphi_{1}\\ \end{array}\] (30)
4. Hemispherical shell \(\bullet\) Mid-surface: \[\begin{array}{ll}\mathbf{r}(\varphi,\theta)=r_{0}\sin\varphi\cos\theta\, \hat{\mathbf{i}}+r_{0}\sin\varphi\sin\theta\,\hat{\mathbf{j}}+r_{0}\cos \varphi\,\hat{\mathbf{k}},&\varphi_{0}\leq\varphi\leq\varphi_{1}\\ &0\leq\theta\leq 2\pi\end{array}\] (31)
5. Thickness variation: \[\begin{array}{ll}h(\varphi)=h_{0}\bigg{[}1-\beta\bigg{(}\dfrac{\varphi- \varphi_{0}}{\varphi_{1}-\varphi_{0}}\bigg{)}\bigg{]},&\varphi_{0}\geq\varphi \geq\varphi_{1}\end{array}\] (32)  
An additional parameter, used in the following sections when reporting natural frequencies of shells with variable thickness, is the average thickness, \(h_{m}\), which is defined as  
\[h_{m}=\dfrac{h_{0}+h_{1}}{2}. \tag{33}\]
# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 1 Introduction  
Diffuse liver conditions, including fatty liver, are the world's leading cause of death [1], and Fatty Liver Disease (FLD) has a prevalence of 25 percent worldwide. Nonalcoholic Liver Fatty Disease (NALFD) and its subtype, nonalcoholic steatohepatitis, affect 30 percent and 5 percent of people in the USA, respectively [2]. Asia and Africa were found to have the most observed prevalence of FLD [3]. There are several categories of hepatic disorders; those caused by poisons, too much alcohol, or drugs. Other kinds of diseases are caused by viruses, such as hepatitis B and C.  
Liver diseases can be caused by many other factors, including high blood sugar, insulin resistance, and obesity. Fatty liver can be treated if it is possibly identified at an early stage, a changeable condition, which is categorized by the accumulation of liver fat, and this deposition can be due to nonalcoholic origin or excessive consumption of alcohol. FLD isassociated with several features such as insulin resistance, diabetes, hyperlipidemia, and viral hepatitis [4].
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 4 The Velocity Prediction of Two Robotic Fishes
### The Simulated Experiment Settings  
We set the same environmental parameters as Section 3 and two formations in Figure 8 configured by two robotic fishes. For the tandem shape in Figure 8a, fishes are swimming in a line with interval spacing \(dx\). In this configuration, the robot swimming in the front will generate flow in the wake, and thus the fish swimming downstream will be affected by the unsteady flow. For the phalanx configuration in Figure 8b, fishes are placed in a column with spacing \(dy\), and the effect of the lateral flow oscillating is considered. For all formation configurations, fishes are swimming in the same phase difference. In each task of formation, the shape of formation (tandem or phalanx) and the appropriate spacing \(\Delta d\) between robotic fishes are set in advance. The number of each fish in the formation is also given in Figure 8, the kinematic pressure of the No. 1 fish is recorded as \(p1_{i}\) and the kinematic pressure of the No. 2 fish is recorded as \(p2_{i}(0\leq i\leq 20)\). We study the kinematic pressure of the robotic fish surface with the spacing \(dx\), \(dy\) and the inlet flow velocity \(U\) varying in Table 4.  
The tandem formation and phalanx formation have different phenomena, thus we study the flow fields and the kinematic pressures of the tandem formation and phalanx formation separately. Then, we train the predictive models of inlet flow velocity of the two formations separately.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spacing (\(L\))** & **U (\(L\)/s)** \\ \hline
0.2, 0.4, 0.6, 0.8, 1.0 & 0, 0.2, 0.4, 0.6, 0.8, 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The spacing and velocity setting.  
Figure 7: The fitting image of the 3 rd-order predictive model trained by \(p_{0}\) and \(p_{1}\), respectively.  
Figure 8: Graphical representation of the two-fish configurations: (**a**) tandem; and (**b**) phalanx. Lateral spacing between neighbors is given by \(dy\) and longitudinal spacing by \(dx\).  
Figure 9: The wake of two fishes in the tandem formation: (**a**) at the spacing of 0.2 \(L\) and \(U=0\)\(L/s\); (**b**) at the spacing of 0.2 \(L\) and \(U=1\)\(L/s\); (**c**) at the spacing of 1 \(L\) and \(U=0\)\(L/s\); and (**d**) at the spacing of 1 \(L\) and \(U=1\)\(L/s\).  
Figure 10: The relationship between \(U\) and \(p_{i}\) at different spacing. The dotted line is the average value of \(p_{i}\) at different spacing at a certain \(U\).
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 3 Results and Analysis  
To verify the effectiveness of the proposed algorithm in this paper, we conducted some experiments in this section to compare the performance between the traditional STAP method and the proposed STAP method based on a dual-platform radar system. Three heterogeneous environments with strong pollution, hot clutter, and nonuniform power were used in the experiment.  
The down sight angle was 55\({}^{\circ}\). For convenience, we assumed that the radar just operated in the zero-squint side-look imaging mode. The background was a scene with a size of about 800 \(\times\) 800 m\({}^{2}\). The number of array elements was 20, and the interval was 0.12 m. The number of pulses in a CPI was 32. The pulse repetition frequency was 654. The experimental parameters are shown in Table 2.  
The clutter background without a moving target simulated the forward track image, and the mixed echo was obtained by adding a moving target with a velocity of \(v_{y}=10\) m/s to the pure clutter background, which simulated the latter platform image.  
In this paper, the superiority of the proposed method was analyzed for three cases: clutter with strong pollution, junction between hot and cold clutter, and clutter with nonuniform power. Of course, the proposed method is applicable in most heterogeneous environments, not only in these three cases.
## 1 Introduction
The inter-system biases between different GNSSs affect the resolution of integer ambiguity when a user attempts to combine the measurements from several GNSS constellations. The study of different combinations of the GNSS differential inter-system biases is presented in [8]. In addition, because the special design of the BDS constellation includes GEO constellation, IGSO constellation and MEO constellation satellites, the BDS has inter-satellite-type biases between the different constellations which are described in [9]. Besides these inter-system biases and inter-satellite-type biases, it is of practical interest to investigate the SIS performance for GPS and BDS. The objective of the work presented here is to evaluate the differences between the two kinds of navigation data from GPS and BDS. GPS and BDS broadcast two kinds of satellite positioning information in the navigation data, almanac and ephemeris. In this research, both the ephemeris and almanac data will be verified for both systems. This research utilizes the GPS as a standard to compare the SIS data quality with the BDS. Five analysis methods \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & & **BDS** & \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\ \hline
**Orbit Radius** & 20,200 & 35,786 & 35,786 & 21,528 \\ \hline
**Coordinate** & WGS84 & & CGCS2000 & \\ \hline
**Time** & GPST & & BDT & \\ \hline
**Time Start** & January 6 th, 1980 & & January 1 st, 2006 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of satellite constellation. will be presented to verify the consistency of the ephemeris and almanac data for both systems. The five data quality analyses (DQAs) discussed are as follows:  
(1). Satellite position difference when ephemeris updates  
(2). Satellite clock correction difference when ephemeris updates  
(3). Ephemeris applicable period  
(4). Satellite position difference between almanac and ephemeris  
(5). Almanac applicable period  
The SIS signal used in this paper is from 10 July to 13 August 2013. Both the GPS and BDS signals are recorded at the same time in the same place. The receiver is the NovAtel FlexPak 6 (NovAtel Inc., Calgary, AB, Canada), and the antenna is the NovAtel 703 GGG. The user is located at the National Cheng Kung University of Taiwan, which is also marked in Figure 1 as a red square. Each test method will be described in detail in Sections 2-6. Finally we present the conclusions and suggestions for combining GPS and BDS data.
# Numerical Simulation of Tunnel Face Support Pressure in Layered Soft Ground
## 2 Numerical Modeling of the TBM Face Support Pressure  
### Simulation Program and Model Building  
The finite element analysis software MIDAS GTS NX 2018 was used in this numerical simulation experiment. This is a finite element analysis software designed by Midas for the geotechnical field. It can perform a variety of coupling analyses while supporting a variety of analysis types such as construction phase analysis. It is very practical for modeling and analysis of subways, slopes, tunnels, pile foundations, and other practical projects. The software has been used by many researchers (e.g., [PERSON] [29], [PERSON] [24], etc.) for various geotechnical problems related to tunnels and underground infrastructure.  
The model constructed in this study is shown in Figure 1. Due to the symmetry of the geometry, only half of the tunnel is used to simulate the excavation face behavior in case of failure. The boundary of the model adopts a displacement boundary condition and assumes that the groundwater level is much lower than the invert of the tunnel, so the influence of the groundwater level is not included in the analysis. An eight-node (3D), first-order, solid-element model is used for both tunnel segments and strata. The excavation method is the simplified single-step excavation method. It is assumed that the tunnel is excavated simultaneously in one-dimensional length and the pipe segments are laid simultaneously. Two construction phases are used to simulate excavation in GTS NX, with the first step being to determine the anisotropic geostress conditions under the initial conditions. The second step is to remove the soil mass of the inner diameter of the tunnel, lay pipe segments at the same time, and apply uniform support pressure on the excavation face vertically to simulate the excavation of the tunnel. In addition, the layered ground is divided into five groups. The stratification scheme is shown in Figure 2.
## 3 NSAS-Based Underground Structure  
### Mechanical Model  
This study proposes the idea that the NSAS is employed for the seismic-response control of underground structures, simultaneously utilizing the negative-stiffness behavior of the device and the energy-dissipation mechanism. Considering the dynamic interaction behavior between soil and underground structures, Figure 4 presents the soil-underground structure interaction system, wherein the rectangular underground structure consists of the upper slab, the bottom slab, the sidewall, and the central column. The NSAS is also applicable for other types of underground structures with more complex typology, including those of numerous stories and spans [7, 26]. Dealing with the control problem of the vulnerable component, i.e., the central column, the proposed NSAS was set in parallel with a linear isolator, and then inserted between the top of the central column and the bottom of the upper slab (see Figure 4). The combination of the NSAS and isolator is ideally represented as the mechanical network in Figure 4 and in Figure 2 (\(c=0\)). Conceptually, the combination of NSAS and isolator yielded an improved isolation effect, that more significantly reduced the shear force of the central column in comparison with the conventional isolator. At the same time, the NSAS also  
Figure 3: Illustration of energy-dissipation-enhancement effect of NSAS.  
Figure 2: Model of a single-degree-of-freedom (SDOF) structure with proposed NSAS.  
contributed to an enhanced mitigation effect for the displacement of the central column, which resulted from the enhanced damping effect.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.3 Effect of the Number of Moving Objects  
In this simulation, we increased the number of moving objects from 10,000 to 100,000 to study how the number of moving objects affects the performances of SR, MQM, QRT, BQRT, and GQRT.  
As shown in Figures 12 and 13, as the number of moving objects increases, the overhead of all the methods increases in terms of the CPU-time and the total number of messages. However, in all cases, GQRT outperforms SR, MQM, QRT, and BQRT due to the fact that only GQRT has the ability to fully utilize the capabilities of moving objects. Note that BQRT cannot fully utilize the capabilities of moving objects as mentioned in Section 4.  
Figure 11: Total number of messages _vs_. size of spatial query ranges. (**a**) _Uniform_; (**b**) _Skewed_.  
Figure 12: CPU-time _vs_. number of moving objects. (**a**) _Uniform_; (**b**) _Skewed_.
## 2 Related Works
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Related** & & **Gap** \\
**Work** & & **Principal Topic** & **Gap** \\ \hline Article [8] & Throughput variations; regression models; PCA; performance analysis. & Further investigation of optimal hyperparameter settings. \\ Article [9] & Weighted PCA (WPCA); service recommendation; variable weighting. & Exploration of the impact of different weighting strategies in WPCA. \\ Article [10] & Text classification; PCA as a feature extraction method. & Examination of the trade-off between the number of principal components and classifier performance. \\ Article [11] & Service-level agreements (SLAs) in 5G networks; healthcare systems. & Investigation of SLA adaptation mechanisms in dynamic network environments. \\ Article [12] & Medical image segmentation; PCA for dimensionality reduction. & Evaluation of the proposed method on a larger and more diverse medical image dataset. \\ Article [13] & PCA as a dimensionality reduction method. & Exploration of applications of PCA in other fields within 5G networks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of previous research works and GAP. These studies support the idea that PCA is a powerful tool with which to improve the performance and efficiency of 5G networks by providing a more compact representation of data and enabling more accurate decision-making. These investigations establish the groundwork for forthcoming advancements and enhancements in the domain of 5G networks, fostering the examination of PCA-based methodologies in additional domains and applications inside this framework. In conclusion, PCA is a promising technique in 5G networks, offering opportunities to optimize and improve various critical aspects of these networks. Knowledge and understanding of PCA applications in this context are essential to continued advancement in the search for more efficient and effective solutions in designing and deploying next-generation networks. Our proposal takes advantage of some of the areas of opportunity shown in Table 1 (Articles [8], [11], and [13]). We use PCA's advantages to reduce dimensions in a 5G dataset, apply different SL algorithms that learn to classify services, and finish with the search for hyperparameters to obtain better results. We claim that a better service classification leads to a better QoS for the operator and, in turn, compliance with the SLAs.
## 3 System Model and Problem Formulation
### Problem Formulation  
Based on the assumptions of the above models, the following constraints can be established:  
* Link capacity constraint: At any given moment, the amount of traffic carried on each link must not exceed the link capacity: \[0\leq X_{t}^{d}(i,j)\leq C_{t}(i,j),\forall e_{i,j}\in E(t)\] (14) where \(X_{t}^{d}(i,j)\) represents the amount of traffic transmitted on link \((i,j)\) at time \(t\), and \(C_{t}(i,j)\) represents the capacity of link \((i,j)\) at time \(t\).
* Node capacity constraint: The amount of data stored on node \(u\) cannot exceed the node's cache capacity limit, i.e., \[0\leq\sum\int_{t}^{t+\Delta t}f_{v,u}(t)\,dt-\sum\int_{t}^{t+\Delta t}f_{u,u} (t)\,dt\leq St(u)\] (15) where \(St(u)\) is the cache size of the node \(u\), and \(f_{v,u}(t)\) and \(f_{u,w}(t)\) denote the flows into and out of node \(u\) at time \(t\), respectively.
* Service function constraint: The traffic with source node \(Src\), destination node \(Dst\), and routing path \(Path_{Src,Dst}\) needs to satisfy the service function constraints, i.e., \[Path_{Src,Dst}=\{V_{Src}\rightarrow\cdots\to VF_{i} \cdots\to VF_{FN}\cdots\to V_{Dst}\}\] (16) where \(VF_{i}\) denotes the ith functional node through which the end-to-end service transmission passes, and \(FN\) denotes the total number of functional nodes of the SFC in the path.  
In this paper, in order to achieve the joint optimization of network load and path delay for routing under SFC constraints in LEO-MSN, we formulate it as a weighted multi  
Figure 1: End-to-end traffic transmission model. Dashed arrows indicate wireless links and realized arrows indicate wired links.  
the use of a decision network to generate the action probability distributions based on the valid states and sampling from them to determine the next hop node.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Managing Scalability
#### 5.3.1 Bench Marking Delays  
The shortest path lengths and the diameter in baseline transaction graphs represent best-case timings. Growth trends in these metrics during peak loads or attacks quantify the extent of performance impacts [81]. Figure 15, ensuring a connected graph focusing on the largest connected component necessary to compute these metrics and benchmark delays effectively. Nodes represent entities in the blockchain network. Edges represent transactions between these entities, with weights indicating transaction delays.  
**Assumption 14**.:
* _The blockchain network is modeled as a transaction graph_ \(G(V,E)\) _with average edge weight_ \(w_{avg}\) _representing delays._
* _Shortest path lengths characterize best-case timings._  
**Application Statement 14**.:
* _Tracking growth trends in shortest path lengths and diameter of the baseline transaction graph during network attacks or congestion quantifies lower bounds on performance degradation._  
Proof.:
* Measure average shortest path \(l_{avg}\), diameter \(D\) and average degree \(d_{avg}\) on \(G\).
* Under attacks/congestion, adjust \(G\) by increasing edge weights to \(w^{\prime}avg(>wavg)\).
* Re-compute shortest paths and diameter \(D^{\prime}\) on updated \(G\).
* Benchmark slowdown as \(Ratio~{}=~{}\frac{D^{\prime}}{D}\) (Also compare \(l^{\prime}avg\) and \(d^{\prime}avg\)).  
This quantifies the extent of performance impacts under adverse events. Analyzing structural graph metrics on baseline vs stressed blockchain networks provides lower bound estimates on transaction delays.  
Figure 15: Benchmarking Delays.