esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Microbiome, Dysbiosis and Osteoporosis
### Oral Microbiome/Microbiota and OP  
One of the first investigations about the correlation between oral bacteria and OP was provided by [PERSON] et al., who, in 2013, reported statistically significant reductions in the oral bacterial lining of the tongue of OP women compared to non-OP women [61].  
Five years later, in 2018, [PERSON] et al. [62] published the results of a 15-year prospective cohort study, called \"OsteoPerio Study\", to explore the risk factors for the development and progression of OP and PD in postmenopausal women and highlight the relationship between oral microbiota and oral and systemic health in postmenopausal women. The OsteoPerio Study results confirmed that: (1) OP women had almost double the risk of oral BMD loss compared to non-OP women; (2) systemic and oral BMD loss was greater in older women; (3) severity of PD was inversely associated with BMD. Furthermore, the same authors found, in PD subjects, a strong positive association between specific oral periodonontopathogen bacteria (_Porphyromonas gingivalis_, _Tannerella forystthia_, _Prevotella intermediia_ and _Campylobacter rectus_) and severe oral bone loss. Similarly, [PERSON] et al. [63] tested the association between the presence of oral bacterial infection and oral bone loss in 1256 postmenopausal women, thus reporting eight subgingival bacterial species were mainly associated with severe oral bone loss: _Streptococcus sanguis_, _Prevotella intermediia_, _Tannerella forystthensis_, _Capnocytophaga_ sp., _Eubacterium saburreum_, _Campylobacter rectus_, _Porphyromonas gingivalis_ and _Fusobacterium nucleatum_. On the contrary, [PERSON] et al. [27], who also conducted an observational cross-sectional study to profile the oral microbiota of postmenopausal women with and without OP, found that nearly 79% of the women with PD also suffered OP, but no statistically significant differences were found in the quantity and quality of periodonontopathogen bacteria investigated (_Fusobacterium nucleatum_, _Aggregatibacter actinomycetemcomitans_, _Porphyromonas gingivalis_, _Campylobacter rectus_, and _Tannerella forystthia_) (Figure 1).
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
## 2 Mode Selection Based on Semi-Analytical Finite Element (SAFE) Method
The dispersion curves of the above modes are shown in Figure 5. When the frequency dispersion occurs, the wave packet composed of different frequency components will be dispersed, causing the waveform of the received signal to be distorted relative to the transmitted signal. Therefore, it is necessary to select modes with good non-dispersion characteristics. At the frequency of 35 kHz, the dispersion curves of mode 7 are gradual than that of mode 20. This means that the non-dispersion characteristic of mode 7 is better than that of mode 20. Hence mode 7 is selected to detect the rail head defect. Similarly, mode 3 is selected to detect the rail web defect. Mode 1, mode 2, and mode 10 are suitable to detect rail base defects.
# Diffusion and Consolidation of Slag-Based Geopolymer for Concrete Payment Rehabilitation
## 2 Experimental  
### Preparation of Geopolymer Grouting Material  
For geopolymer grouting material, considering its easy availability and environmental friendliness, blast furnace slag was selected as the raw material for geopolymer. The density of the slag powder is 2.93 g/cm\({}^{3}\) and the specific surface area is 459 m\({}^{2}\)/kg. As shown in Table 1, the main components of slag included silicon oxide, calcium oxide, aluminum oxide, magnesium oxide, etc. Among all components of slag powder, silicon oxide, calcium oxide, and aluminum oxide serve as the primary sources of gels for polymerization reactions.  
The XRD patterns of blast furnace slag powder are shown in Figure 1. When the diffraction angle was at 30\({}^{\circ}\), a diffuse diffraction peak region appeared, indicating that the main structure of the material was amorphous, with a large amount of glass phase components.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Material** & **CaO** & **SiO2** & **Al2O3** & **MgO** & **SO3** & **TiO2** & **FexOy** & **Na2O** & **K2O** & **MnO** \\ \hline slag & 39.46 & 26.01 & 13.24 & 8.41 & 2.25 & 1.04 & 0.68 & 0.60 & 0.60 & 0.30 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of raw materials (wt.%).  
The geopolymer was prepared using slag powder and alkaline activator, and, considering that the content of alkali activator affects setting rate of slag-based geopolymers, it was essential to determine an optimal mix proportion. The alkali activator was prepared with sodium silicate and sodium hydroxide with a ratio of 10.1:1. Considering characteristics of early strength and fast setting of geopolymer, in order to study the diffusion and consolidation of geopolymer slurry, it was found that geopolymer grouting material with a water-slag powder ratio of 0.4 was suitable. Due to the lack of corresponding geopolymer testing codes, the grouting tests were conducted with reference to \"Testing Methods of Cement and Concrete for Highway Engineering\" (JTG 3420-2020) [33].
## 6 Performance Evaluation
### Comparative Analysis
#### 6.3.1 Response Time  
The primary objective of our proposed framework is to reduce response time. A comparison was conducted between the response times of our proposed work and TTSA. Response time refers to the time that elapsed among task arrivals until complete execution of the tasks. The simulation result indicates that the response time of our proposed work is less than that of the TTSA method.  
The comparison of the response time between the proposed and existing methods is depicted in Figure 5. For a user with size 20 kB, our proposed work achieves a task response time of 15 s, whereas the TTSA method has response time of 20 s. For a 40 kb user, the response time of our proposed work is 10 s, whereas that of TTSA is 15 s. Therefore, the proposed work has a shorter response time than that of the existing method, which is realized via VM clustering. VM migration is reduced, and tasks are allocated and processed rapidly, thereby decreasing response time.
###### Abstract  
In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS speech recognition.  
speech recognition; code-switching; language model; domain adaptation; acoustic model; shallow fusion +
Footnote †: journal: Medical  
intra-sentential, where language transitions occur without the interruption of speech in the middle of sentences [1].  
Figure 1: An application of the proposed method: English–Korean automatic speech translator.  
Figure 2: Typical code-switching (CS) sentences: **(a)** lecture domain; **(b)** travel domain.  
In this paper, we focus on intra-sentential CS problems. In Section 2, we introduce the CS research results obtained in other studies, and Section 3 explains the difficulties in modeling phonetic variations in English spoken by Koreans. Section 4 handles how to extract sentences with similar meanings for the language model (LM) domain adaptation. Section 5 summarizes the experimental results for the proposed method and concludes the paper.
## 2 Problem Formulation and Lipschitz Constant Estimates
Therefore, in the case \(\alpha<L\), we obtain  
\[\|x_{2}-x_{1}\| =\|p_{K(t_{2})}(x_{2}-\lambda F(t_{2},x_{2}))-p_{K(t_{1})}(x_{1}- \lambda F(t_{1},x_{1}))\|\]5  
while in the case \(\alpha=L\), we obtain  
\[\|x_{2}-x_{1}\| =\|p_{K(t_{2})}(x_{2}-\lambda F(t_{2},x_{2}))-p_{K(t_{1})}(x_{1}- \lambda F(t_{1},x_{1}))\|\]6  
If we set \(z=\lambda L\) and \(\widetilde{\alpha}=\alpha/L\), then we can write \(s=\sqrt{z^{2}-2\widetilde{\alpha}z+1}\) and the estimate (8), which, in case \(\widetilde{\alpha}<1\), reads as  
\[\|x_{2}-x_{1}\| =\|p_{K(t_{2})}(x_{2}-\lambda F(t_{2},x_{2}))-p_{K(t_{1})}(x_{1}- \lambda F(t_{1},x_{1}))\|\]7  
In case \(\widetilde{\alpha}=1\), we have \(s=|z-1|\), and the estimate (9) reads as  
\[\|x_{2}-x_{1}\| =\|p_{K(t_{2})}(x_{2}-\lambda F(t_{2},x_{2}))-p_{K(t_{1})}(x_{1}- \lambda F(t_{1},x_{1}))\|\]8  
where\[f(z)=\frac{M}{1-|z-1|}+\frac{z(1+z)}{|z-1|(1-|z-1|)}= \begin{cases} \frac{M}{z}+\frac{1+z}{1-z}&\text{if }z\in(0,1),\\ \frac{M}{2-z}+\frac{z(1+z)}{(z-1)(2-z)}&\text{if }z\in(1,2).\end{cases} \]  
An analytical study of the function \(f\) (see Appendix A.1) provides  
\[\inf_{z\in(0,1)}f(z)=M+2\sqrt{2M}+1,\]  
and  
\[\inf_{z\in(1,2)}f(z)=\frac{M^{2}+7M+8+2\sqrt{2M+12}}{M+8-2\sqrt{2M+12}}.\]  
Moreover, it is easy to check that the following inequality  
\[\inf_{z\in(1,2)}f(z)\geq\inf_{z\in(0,1)}f(z)\]  
holds for any \(M\geq 0\). Therefore, we obtain  
\[\inf_{z\in(0,1)(1,2)}f(z)=\inf_{z\in(0,1)}f(z)=M+2\sqrt{2M}+1,\]  
thus the thesis follows. **Corollary 1** (General operator and constant feasible region). : _Assume that \(F\) satisfies the same assumptions of Theorem 2 and the feasible region \(K\) does not depend on \(t\). Then, the solution \(x(t)\) of the VI\((F,K)\) is Lipschitz continuous on \([0,T]\) with estimated constant equal to \(\Lambda_{1}=L/\alpha\)._  
Proof. : Since \(K\) does not depend on \(t\), inequality (3) holds with \(M=0\). Hence, Theorem 2 guarantees that \(x(t)\) is Lipschitz continuous on \([0,T]\) with a constant equal to  
\[ \begin{cases}\inf_{z\in(0,2\widetilde{\alpha})}\frac{z(1+z)}{s(1-s)},&\text{ if }\widetilde{\alpha}<1,\\ 1,&\text{if }\widetilde{\alpha}=1,\end{cases} \]  
where \(\widetilde{\alpha}=\alpha/L\) and \(s=\sqrt{z^{2}-2\widetilde{\alpha}z+1}\).
## 3 Results
### Study Characteristics
#### 3.2.4 Rehabilitative Approaches
Two studies used the Trunk Control Robot 3 DBT-33 (TCR) [80; 110], which includes a sensor-based foot platform, screen, and robotic seat that can be used in static or wobbling mode. TCR can be used both while standing and while sitting and is well suited to sit-to-stand training, as the robotic chair can assist the subject during movement. These studies [80; 110] demonstrated that combining robotic training using TCR with conventional training resulted in substantial enhancements in balance and gait [80] and outperformed trunk extension training [110]. Other studies [55; 121] used a T-chair device, which consists of a robotic sensorized chair and a screen, to provide visual feedback. A chair represents a support that may be stable or unstable and allows active and passive movements. The seat can move up to \(\pm 50\) mm in the mediolateral direction and \(\pm 42\) mm in the anteroposterior direction [121]. When used for training stroke patients, this device led to greater improvements than a conventional training program, particularly in the areas of balance, gait, and trunk control [55]. Similarly, humova [131], which implemented both a movable chair and a movable base that integrated a computer screen for visual feedback and an inertial sensor to track trunk motion, demonstrated enhanced effectiveness in improving balance and trunk control in stroke subjects [16] and in improving balance in PD subjects [61]. In addition, [116] compared the effectiveness of robotic gait training with robotic gait training plus trunk rehabilitation using humova in stroke subjects. The authors of this study noted that while both groups showed improvements in balance, the addition of trunk rehabilitation exercises with humova resulted in a reduction in trunk displacement during dynamic exercises.
## 4 Practical Application of the E-Commerce Platform
### Analysis of Customers' Spatial Distribution  
The second application verifies the big e-commerce data platform and analyzes customers' spatial distribution through transactions. Finding distinct patterns in the spatial distribution of consumers is made easier by visualizing the distribution of the locations where orders are placed. Furthermore, comparing the number of transactions, the distributions, and their respective properties, such as the distance from warehouses, could reveal noteworthy differences and similarities between e-stores. Figure 6 shows the number of orders placed for a specific e-store during a month. From this visualization, decisions that are necessary for effective urban planning and determining an optimized location for warehouses can be made. Delivery options and priorities can also be set once demand locations are identified.  
Figure 4: Heatmap of transactions for a single e-store.  
Figure 5: Heatmap of transactions for around 10,000 e-stores.  
Further analysis can be conducted to investigate why certain cities may have no or fewer transactions, and corrective measures, such as location-specific commercial advertisements, can be implemented accordingly. Furthermore, the consequences of the actions can be witnessed through the feedback loop of the big e-commerce data platform.
## 4 Experimentation and Results
### DeepSORT Tracking Performance  
DeepSORT Parameters  
* Max Cosine Distance  
This parameter controls the similarity criterion for re-identifying objects across frames. By reducing identity shifts, lowering this threshold increased tracking precision at the expense of more frequent tracking failures. Raising the threshold resulted in more identity flips and increased flexibility in re-identifying items. After adjustment, a cosine distance of 0.3 was determined to reduce identity flips while preserving accurate re-identification.  
* Re-Identification Confidence Threshold  
This setting establishes the minimum confidence needed to match an object in one frame with its corresponding object in another. As the threshold was raised, the algorithm became less tolerant of occlusions or changes in appearance, though tracking failures decreased. A value of 0.65 was optimal for our retail application.  
The performance of the YOLOv5 + DeepSORT combined with conventional object recognition and tracking method is summarized in Table 15. Detection accuracy, tracking accuracy, real-time handling ability, and comprehensive aspect grade are all contained in the comparison. With a detection accuracy of 91.67%, compared to the standard method's 70.2%, the YOLOv5 + DeepSORT technique demonstrates a significantly improved performance. Additionally, tracking accuracy is higher--89.47% versus 60.5%. The efficiency of the YOLOv5 + DeepSORT methodology is demonstrated by its real-time handling capacity of 25 frames per second (fps), which is higher than the 15 fps of conventional methods. Moreover, the YOLOv5 + DeepSORT approach provides a higher level of detail and under  
Figure 16: YOLOv5 object detection performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Precision & 91.67\% \\ Recall & 84.23\% \\ F1-Score & 87.79\% \\ Average Detection Time per Frame & 0.050 s \\ \hline \hline \end{tabular}
\end{table}
Table 14: YOLOv5 object detection performance.  
standing in contrast to the lower level of detail offered by standard approaches, as shown in Table 15.  
Figure 17 illustrates the DeepSORT algorithm's tracking performance metrics, highlighting its accuracy and efficiency in maintaining object identity and minimizing tracking failures.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Algorithm Performance Analysis
#### 5.2.2 MASA in Real-World Environment
The fast convergence of \(\mathrm{LOSS}_{\mathrm{Actor}}\) in MASA proves its stronger learning ability in dynamic environments. Figure 9 presents the change of entropy \(\mathrm{H}(\pi_{\mathsf{G}}(\cdot|s_{\mathsf{t}}))\) in Equation (18), describing the task environment faced by the scheduler. As training progresses, the MASA network effectively resolves the job DAGs, leading to a gradual reduction of uncertainty in the environment. Lower values of \(\mathrm{H}(\pi_{\mathsf{G}}(\cdot|s_{\mathsf{t}}))\) are indicative of the fact that MASA effectively encodes the complex environment so that its complex state space can be mapped into a relatively stable encoded vector space.
# Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System
## 3 Results and Discussion
### Error Sensitivity Analysis of Practical Setup  
The theoretical study of error sensitivity analysis is extended to the practical setup as illustrated in Figure 1. Dissimilar to the theoretical setup, the true value of \(X\) is unknown, which needs to be estimated with the help of hand-eye calibration algorithms. The norms of errors associated with estimation of matrix \(X\) with the help of practically estimated \(A\) and \(B\) are given by:  
\[U_{R} =\frac{\sum_{i=1}^{n}\sum_{k=1}^{o}||R_{X}-\hat{R}_{X_{i,k}}||_{F} }{n\times o} \tag{29}\] \[U_{T} =\frac{\sum_{i=1}^{n}\sum_{k=1}^{o}||T_{X_{i,k}}||_{E}}{n\times o }, \tag{30}\]  
where \(n\) (=56) represents different combinations of flange positions, \(o\) (=1000) represents simulation runs for normally distributed noise, \(||\cdot||_{F}\) is Frobenius norm and \(||\cdot||_{E}\) is Euclidean norm obtained by solving system equations \(AX=XB\) and \(\hat{A}X=X\hat{B}\). \(R_{X}\) and \(T_{X}\) are the rotation matrix and translation vector of \(X\) when no systematic error is introduced in the system equation i.e., when both \(\delta_{A}=0\) and \(\delta_{B}=0\), while \(\hat{R}_{X}\) and \(\hat{T}_{X}\) are the rotation matrix and translation vector obtained after solving uncertain system equation \(\hat{A}X=X\hat{B}\).  
Figure 5 shows the uncertainties in rotation and translation of transformation \(X\) obtained by solving the system equation using the same three hand-eye calibration algorithms. The change in the colour from navy-blue to yellow represents increasing deviation of \(R_{X}\) and \(T_{X}\) from \(\hat{R}_{X}\) and \(\hat{T}_{X}\) respectively. A quick glance of Figure 5 suggests good similarities in the overall behaviour with respect to that of the theoretical setup (refer Figure 4). A respective comparison of Figures 4a-d and 5a-d for \(\delta_{B}=0\) reveals that, for the practical setup, the hand-eye calibration methods are relatively sensitive to uncertainty and resulted into high magnitudes of errors.  
A similar trend can be observed for Andreff et al. method from Figures 4e,f and 5e,f, with much higher magnitudes of errors in the practical setup. One of the reasons behind such sensitive behaviour is pre-existence of errors in the image pattern estimation, which has impacted the hand-eye calibration process. It can be asserted that [PERSON] method performs way better than other methods in over-riding the robotic and camera errors in the calibration process.  
Figure 5: _Cont_.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 2 Existing Surveys
### Surveys Focusing on a Single IoT Extension
#### 2.1.2 Extensions Deployed on the Ground
They noted that the underlying information exchange platform of the IoV needs to be immutable, transparent, and secure in order to support the intended objectives of an Intelligent Transportation System (ITS). There are also other surveys briefly discussing different technological challenges of the IoT [33]. A survey on the Internet of Autonomous (Driverless) Vehicles has been presented in [34]. This survey compares traditional client-server communication models with centralized models. The authors of this survey demonstrate how this IoT extension will move from network-centric communication models to user-centric ones in the future. They also discuss the role of Value-Added Services (VASs) in the Internet of Autonomous Vehicles. The authors of [35] stated that frequent topology changes due to user mobility and posture alteration increase the complexity of routing and resource allocation in the Internet of Medical Things. They reviewed individual and group health monitoring architectures based on the Internet of Medical Things that allow users to freely move around. They discussed the enabling technologies for each of the studied architectures. Moreover, they investigated existing solutions for route breakage in this kind of IoT extension. Internet of Wearable Things (IoWT)  
Wearable things include smart clothes, smart jewelry, smartwatches, and similar personal mobile devices. The IoWT lies in the intersection of the IoT and the technologies related to these devices. The battery-powered nature of these devices raises energy efficiency as a critical requirement in the IoWT. A comparative, systematic literature review, along with a taxonomy of energy-efficient solutions proposed for IoWT-based scenarios, has been presented in [36]. This survey discusses related performance parameters as well as existing solutions for improving energy efficiency in IoWT. The literature comes with surveys on different aspects of the Internet of Nano Things. For example, a survey presented in [37] studies this IoT extension with a focus on applications in healthcare environments. Another relevant research presents a survey on routing protocols in the Internet of Nano Things [38].
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 5 Results Simulation and Analysis
### Evaluation Index and Parameter Setting of Simulation
#### 5.3.2 Results and Analysis of Optimal Parameter Combination of Model  
Hyperparameters have a significant influence on the deep neural network, which directly affects the quality of the model. In this experiment, it is verified that the selected hyperparameters are Learning_rate, the number of neurons, and the epoch. The specific selection is as follows:  
1. Learning rate: A large choice for learning rate will lead to the lowest loss, while a small choice will lead to the local optimum of the result. Therefore, an appropriate learning rate is crucial. As shown in Figure 7, with the iterative evolution of the PSO optimization algorithm, the learning rate stabilized at 0.073 in the 12 th iteration of the optimization algorithm. Therefore, the learning rate selected by the model was 0.073.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Value** \\ \hline CPU & Core i7-9700K \\ GPU & NVIDIA GeForce RTX 2080 TI \\ Memory capacity & 11 G \\ RAM & 64 G \\ Disk capacity & 2 TB \\ \hline \end{tabular}
\end{table}
Table 1: Basic information on hardware.  
2. Several neurons: The number of neurons will affect the learning ability and network complexity of the model. Too many nodes will prolong the network training time, while too few nodes will lead to poor network performance. As shown in the Figure 8, after the ninth iteration of the optimization algorithm, the number of neurons was stable at 35.
3. Epoch: Epoch means training the network model once with all the data in the training set. Through the continuous iteration of the neural network, the loss value can be minimized. As shown in the Figure 9, after the 11 th iteration of the optimization algorithm, the number of iterations of the network model finally stabilized at 500.  
Figure 8: Data number of neurons.  
Figure 7: Learning rate of neural network.  
Figure 9: The epoch of neural network.