esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical Applications of Nano-Hydroxapatite in Dentistry
## 3 Results
### Study Characteristics
#### 3.2.1 Tissue Remineralization  
[PERSON] et al. [15] evaluated the degree of enamel remineralization following exposure to dentirices containing different concentrations of nano-HAp (5% or 10% nano-HAp) versus 1100 ppm fluoride dentirifice and 10% nano-HAp delivered through an intra-oral appliance. The authors did not identify statistically significant differences between groups in terms of mineral loss and lesion depth. All dentirice formulations promoted significant mineral gain in the enamel subsurface lesions.  
[PERSON] et al. [16] compared the effects of four different dentirices on remineralization. The experimental dentirices contained a combination of zinc-carbonate and hydroxyapatite nanocrystals, different fluoride concentrations (1100 or 5000 ppm F), and a fluoride free control. The authors found that fluoride free and nano-HAp containing dentirice did not hamper demineralization, which was reduced on a dose-dependent response for fluoride containing dentirices.  
[PERSON] et al. [17] evaluated enamel remineralization on initial approximal caries following treatment with a nano-HAp gel, gaseous ozone therapy, or a combination of both. The authors found at one year follow-up a regression of the initial caries lesions in 36.5% of patients treated with nano-HAp gel, in 60% of patients treated with ozone therapy and in 69.3% of patients treated with both. At two years, caries reversal was observed in 18% of cases for the nano-HAp group, 38% of cases in the ozone therapy group, and in 45% of the combined treatment group. The authors concluded that the combination of nano-HAp gel and ozone therapy produced the best effect compared with nano-HAp or ozone therapy alone  
Results are summarized in Table 1.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 2 Motivation: The Need to Revolutionize the Water Supply System
### Related Studies and Contributions  
With the new industrial trend, the use of emerging digital technologies in the water sector is gradually becoming a reality. The initiative started a few years ago, when [PERSON] [22] conceptualized the idea. The German water partnership has championed Water 4.0 initiatives since 2016. The concept of Water 4.0 is relatively new in the research community.  
Thus, only a very few papers on this subject matter are available in the literature. As of late, more studies are being conducted to assess the potential of these technologies in the water sector. [PERSON] [22] was credited for the first insight into the subject matter. The author presented the problems with the water cycle and proposed the use of digital technologies to rescue degrading water facilities. [PERSON] [23] also discusses the potential of Water 4.0 in providing sustainable climate resilience. In [24], the relationships between 4.0s for industrial and other engineering processes that are focused on the organizational effectiveness were presented. A similar paper in [25] discussed a number of case studies reporting the application of digital technologies to minimize water losses. The author [26] discussed the problems with disinfection and corrosion control in the treatment plant, suggesting the need for Water 4.0. [PERSON] et al. [27] took a close look at the business side of Water 4.0 from the industry 4.0 perspective and developed an integrated business model for the water 4.0 revolution.  
Based on our examination of the existing literature studies, we realized that no work has presented a comprehensive review and in-depth overview of Water 4.0 in the context of water supply system sustainability. To address this shortcoming, this paper presents a detailed overview of the trends and application of digital technologies for improving water supply network operation as well as research progress related to the use of each technology in the water domain. The key contributions of this paper are as follows:  
1. This study presents an in-depth description of the key drivers of Water 4.0, and several tools and approaches used in each digital technologies of Water 4.0 as well as research trends;
2. Presentation of how the full potential of digital technologies could be harnessed for sustainable smart city development; thus, we cover different potential application domains of Water 4.0 in the water sustainability context;
3. A clear vision of the possible challenges of Water 4.0 during full scale implementation;
4. Introduction of some new research guidelines for future studies in this field.  
To the best of our knowledge, this is the first article with such a review of Water 4.0 technology. The paper aims to contribute to a broader and deeper understanding of Water 4.0 and also motivates research scholars to become personally involved in an effort to improve water supply system operations through cutting edge research in the use of digital technologies for smart city development.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 4 Results
### Pixel Statistical Analysis
The low susceptibility category accounts for 32.08%, while the very low and high susceptibility categories account for 17.77% and 6.34%, respectively. The CatBoost model's prediction results reveal a balanced proportion between the moderate and low susceptibility categories. susceptibility categories, at 39.00% and 34.57%, respectively. The very low and high susceptibility categories account for 19.71% and 6.73%, respectively. The bagging model's prediction results indicate that the moderate susceptibility category represents the highest proportion at 41.16%, followed by low susceptibility at 33.79%, very low susceptibility at 19.06%, and high susceptibility at 5.99%. In regard to the stacking model's prediction results, the low susceptibility category represents the highest proportion at 46.02%, followed by very low susceptibility at 22.43%. Moderate and high susceptibility categories account for 21.53% and 10.02%, respectively. The stacking model exhibits the highest proportions in regard to both low and high susceptibility categories among the models, demonstrating its potential in regard to identifying extreme risk areas. Figure 10: Susceptibility level of models based on training/testing datasets considering all factors: (**a**) RF model, (**b**) XGBoost model, (**c**) LightGBM model, (**d**) CatBoost model, (**e**) bagging model, and (**f**) stacking model. Figure 11: Susceptibility level of models based on training/testing datasets considering optimal factors: (**a**) RF model, (**b**) XGBoost model, (**c**) LightGBM model, (**d**) CatBoost model, (**e**) bagging model, and (**f**) stacking model.
# A Serious Mixed-Reality Game for Training Police Officers in Tagging Crime Scenes
## 1 Introduction
Among the benefits of serious games, there is an increased level of interactivity of the application, adaptation to the user's level of competence, repeatability, and continuous feedback. Moreover, serious games exploit the power of competition and create a sense of achievement to motivate participants to improve their own abilities. Serious games can be supported by different technologies. The main ones are those included in the umbrella term \"extended reality\" (XR): (i) virtual reality (VR) immerses users in a fully artificial digital world; (ii) augmented reality (AR) enhances the physical world with digital elements, typically by using the camera on a smartphone; (iii) mixed reality (MR) combines aspects of VR and AR by enabling interactions with virtual objects in the real world. Our proposal is based on MR, since this allows interaction with digital items, unlike in AR, and, at the same time, it keeps in touch with the physical world, unlike in VR. In addition, in the literature, several proposals have shown the benefits of using mixed reality [9; 10; 11]. In detail, the proposed serious mixed-reality game, which is called TraceGame, allows police officers to visualize and manipulate digital objects representing common tags used to report traces at crime scenes. The purpose of the game is to identify the greatest number of useful traces present at a crime scene that has been physically reconstructed at the training site as quickly as possible. At the end of the game, the police officers' performance is calculated and represented by a score. TraceGame allows the storage of the scores in all the game sessions in order to monitor police officers' improvements. The proposed serious mixed-reality game was tested in a physically reconstructed crime scene in which evidence related to a simulated criminal event was placed. The evaluation of the proposed system was carried out by using methods to assess the usability, the engagement of the users, and the ability to enhance novices' forensic capabilities. According to the experimental results, TraceGame is a promising and innovative tool to be used to train novice police officers. The remainder of this manuscript is structured as follows. Section 2 describes the state of the art related to serious mixed-reality games applied in the forensic domain. Section 3 gives a detailed description of the materials and methods used to design the proposed system, as well as the architecture and functionalities of TraceGame. Section 4 presentsthe configuration of the experimental session, whereas Section 5 reports the results of the evaluation of TraceGame. Finally, in Section 6, conclusions and future work are discussed.
## 1 Introduction
Nevertheless, the correlation levels obtained between the satellite-derived backscattering coefficients and the predicted values are often mixed (with values between 0.05 and 0.85, depending on the modeling approach and satellite configuration), as are the errors made in the estimation of backscattering coefficients (with values close to 1 dB in rare cases). Statistical approaches present a tangible alternative to those used in previous studies, offering precise performance even when processes are complex and multifactorial. Often used with SAR data for classification purposes [34; 35; 36; 37], statistical algorithms can also constitute one of the links in the chain of methods to provide estimates of surface moisture [38; 39; 40; 41; 42; 43], roughness [38; 44; 45] or texture [46]. In most of these approaches, the statistical algorithm is considered an inversion tool, trained on a synthetic database derived from the models discussed above [38; 39; 40; 41; 42]. The limits of the representativeness of the models are then reflected in the estimation of the surface parameters, as evidenced by the performance levels. Only a few studies have avoided the use of backscattering models by training and validating the statistical approach directly on an in situ dataset [47; 48; 49; 50; 51]. In this context, the originality of our study is the estimation of backscattering coefficients using two statistical approaches, multiple linear regression (MLR) and random forest (RF), to simulate the backscatter coefficients acquired in the X- and C-bands in HH, VV and VH polarization states in bare ground agricultural areas. This paper is structured as follows: First, the study site, ground measurements and satellite data are described (Section 2). The methodology description presents the two statistical algorithms and steps required to estimate microwave signals and to analyze the importance of the input variables (Section 3). The results are analyzed considering the overall performance and the importance of the explanatory descriptors. The main findings are discussed and compared with previous studies concerning the modeling of backscattering coefficients or signal analysis (Section 4).
## 3 RUL Prediction Methodology
### Envelope Analysis for Bearing Fault Detection
#### 3.1.3 Bearing FCFs for Fault Identification  
Based on the envelope analysis, the modulating components that carry the bearing defect information can be extracted from the acceleration signal. Also, the fault type can be efficiently recognized based on the identification of FCF within the averaged envelope spectrum. Specifically, the FCFs of different bearing components, respectively, the ball-pass frequency of inner-race fault (BPFI), the ball-pass frequency of outer-race fault (BPFO), the ball (or roller) spin frequency (BSF) and the fundamental train frequency (FTF, or the cage frequency) can be computed based on the bearing dimensions and the operating speed, as in [27]. As a reference, for the test bearings utilized in the XJTU-SY data [7], the bearing dimensions and their FCFs under different operating conditions are listed in Table 2.  
The spectral patterns of the envelope spectrum can differ due to the varying formations of amplitude modulation in different bearing faults. Based on the presence of the FCFs in the envelope spectrum, different types of bearing faults can be identified. A schematic illustration of the envelope spectrum is shown in Figure 2. For example, the impact stroke by the inner-race fault will be modulated by the shaft speed due to the fault entering and exiting the load zone within one shaft revolution. Thus, the BPFI and its higher-order harmonics are accompanied by a series of side bands at the integer multiples of the shaft speed. For roller-element faults, the even harmonics of BSF dominates the envelope spectrum as the fault strikes both inner and outer races in one ball spin [26]; meanwhile, it is modulated by the cage speed (FTF). On the above basis, spectral inspection is conducted on the AES for robust detection and identification of early bearing faults.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions  
The current topology of sensors in transmission and distribution systems face certain unique challenges during their deployment and functioning in transmission and distribution systems. Knowledge about these challenges could help open new avenues of research in the future. The key challenges discussed here can be addressed by the future generation of researchers to accommodate the matured sensor technology by improving it to fit into the upcoming era of smart grids. Some of the key areas and issues are described below.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 2 Related Work
The link quality of the body communication channel is of great significance when determining the network performance. Different researchers have proposed numerical solutions to handle the problem of the link disconnection in numerous methods. The authors of Reference [8] employed Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) communications, while Reference [19] deployed the store and forward routing technique to analyze the link quality. Moreover, energy efficient modulation techniques are advantageous in reducing the hardware structure and decreasing the noise interference when considering the limited UE battery capacities. References [20; 21] pointed out that M-ary phase shift keying (M-PSK) modulation schemes are applicable to energy saving and maintaining channel reliability. As demonstrated in Reference [21], given the high QoS requirements of healthcare applications, the authors selected a bit error rate (BER) of \(10^{-3}\) to ensure that the communication performance was acceptable. This paper proposes a hospital/home-based medical monitoring framework that can be employed in potential diversified scenarios by providing a small-sized C-MEC server and task offloading strategy. We first focus on the data transmission scheme between the relays and the C-MEC rather than the intra-body networking design, as reported in Reference [19]. As proved in References [8; 22], the energy consumption of the implanted sensors is heavily influenced by the transmission distance. Relay-basedtransmission is proposed as a promising technique to minimize the communication distance and thus improve the network lifetime. Moreover, sufficient link quality should be ensured, as stated in References [23; 24], because data transmission from the on-body relay to the C-MEC suffers from high energy attenuation. Moreover, similar to one of the key research challenges in IoT offloading strategies, the issue of where to execute the computation tasks should be further investigated when designing the healthcare monitoring systems [25; 26].
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 4 Simulation and Experiment
### Path Planning Simulation
#### 4.1.4 Bug Trap Environment  
The bug trap environment is created by adding concave traps to a narrow environment to assess the algorithm's ability to handle such obstacles. Figure 12 shows the planning process of the four algorithms. Notably, both the RRT and Fast-RRT algorithms performed extremely poorly in the Bug Trap environment, as evidenced by a significant accumulation of tree vertices inside the trap. The RRV algorithm can only recognize convex obstacles and mistakenly identified the concave traps as entryways, resulting in multiple attempts to expand within the trap area, which greatly reduced the algorithm's performance, with the number of tree vertices still high. The CERRT algorithm effectively utilizes each vertex to explore the space and can quickly discover and pass through the real channel.  
Table 4 illustrates the performance of the algorithms in the Bug Trap environment. The planning success rates of RRT and Fast-RRT were only 0.90 and 0.92, respectively, while the other two algorithms achieved rates of 1.00. The average running time of RRT was 101 times that of CERRT, the average running time of Fast-RRT was 20 times that of CERRT, and the average running time of RRV was 52 times that of CERRT, with the new algorithm having the shortest planning time. The average number of tree vertices of RRT was 29 times that of CERRT, the average number of tree vertices of Fast-RRT was 21 times that of CERRT, and the average number of tree vertices of RRV was six times that of CERRT, indicating that the new algorithm had the highest vertex utilization rate. Moreover, RRT's time standard deviation was 526 times higher than CERRT, Fast-RRT's time standard deviation was 371 times higher than CERRT, and RRV's time standard deviation was 329 times higher than CERRT, underscoring the superior stability of the CERRT algorithm.  
Based on the above analysis, it can be concluded that the new CERRT algorithm outperformed the RRT and RRV algorithms in the Bug Trap environment. The reason is that the vertex death mechanism can deactivate the vertices inside the traps, preventing the algorithm from becoming stuck in the concave traps. Combined with environmental awareness, the CERRT algorithm was quickly able to break through the bug trap environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Avg. Time (ms)** & **Min Time (ms)** & **Max Time (ms)** & **Std** & **Avg. Nodes** & **Success Rate** \\ \hline RRT & 2725.83 & 504.12 & 8782.20 & 1856.74 & 13902 & 0.90 \\ RRV & 1418.43 & 31.42 & 6234.62 & 1160.86 & 2679 & 1.00 \\ Fast-RRT & 1674.35 & 267.01 & 9574.50 & 1313.36 & 9798 & 0.92 \\ CERRT & 26.97 & 13.36 & 35.28 & 3.53 & 474 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for planning in a Bug Trap environment.  
Figure 12: Performance comparison of four algorithms in Bug Trap environment. (**a**) RRT; (**b**) RRV; (**c**) Fast-RRT; (**d**) CERRT.
# Multi-Modal Entity Alignment Method Based on Feature Enhancement
Finally, the distribution of the entity's modal information, including whether the entity has some modal information and its corresponding amount of data, is used as an auxiliary attribute of the entity to enhance the model's understanding and modeling ability of various modal information. In addition, we introduce the intra-modal contrastive loss and multi-modal alignment loss used in the MCLEA to better align entities in different knowledge graphs. We design and conduct experiments to verify the effectiveness of the model and validate it on DBP15K [8], which includes three bilingual datasets, and on two cross kg datasets FB15K-DB15K/YAGO15K [5]. The proposed model achieves state-of-the-art performance, demonstrating the positive effect of multi-modal feature enhancement on entity alignment. The contributions of this paper are as follows: (1) To address the issue of insufficient utilization of visual information in traditional multi-modal entity alignment models, we propose using pre-trained multi-modal models and OCR models to enhance the visual representation ability, enabling models to extract more entity-related knowledge from visual images. (2) To address the issue of the weak adaptability of vanilla graph attention networks in entity alignment tasks, which affects alignment accuracy, we propose using the dynamic GATv2 instead to improve the ability to extract structural features. (3) We propose the use of the modal distribution information of entities as a supplementary part of entity features in order to improve the model's understanding and modeling ability for multi-modal information. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Proposed** & \multirow{2}{*}{**Innovation**} & \multirow{2}{*}{**Adversarial**} \\  & **Year** & & \\ \hline PoE [5] & 2019 & \begin{tabular}{c} Combine multi-modal Features and matching \\ the underlying semantics \\ \end{tabular} & No \\ \hline MMEA [7] & 2020 & \begin{tabular}{c} Obtain the joint representations \\ \end{tabular} & No \\ \hline EVA [8] & 2021 & \begin{tabular}{c} Achieve in both supervised and unsupervised \\ Manner \\ \end{tabular} & No \\ \hline HMEA [9] & 2021 & \begin{tabular}{c} Model representations in the hyperbolic space \\ \end{tabular} & No \\ \hline MSNEA [10] & 2022 & \
\begin{tabular}{c} Adaptively allocates attention weights to \\ capture valuable attributes \\ \end{tabular} & No \\ \hline MCLEA [11] & 2022 &
\begin{tabular}{c} introduce contrastive learning \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of multi-modal entity alignment models. Adversarial column refers to whether the model introduces adversarial learning.
## 2 Modeling and Optimization Method
### BSCN Model and Optimization
#### 2.2.4 Basic Structure Parameters  
Combined with the structural parameters of the conventional straight cone nozzle, the structural parameters of the BSCN were determined. As shown in Table 1, the BSCM mainly increased the three structural parameters of groove depth, groove width, and slot pitch compared with the conventional straight cone nozzle.  
For the inlet section L1 and outlet section L3 of the BSCN, according to the example Formula (19) of the BCT, the inlet velocity of the inlet section L1 of the bionic straight cone nozzle was 1 m/s, the inlet velocity of the outlet section L3 of the BSCN was 14 m/s, and the liquid density was 1 kg/m\({}^{3}\). The inlet section L1 and outlet section L3 of the BSCN were regarded as two BCTs, and the minimum pressure drop of the BCT was taken as the optimization objective. In the range of 0.1 mm-14.5 mm in groove depth, the genetic algorithm was used to analyze and solve the pressure drop example in Formula (19), and the groove width and slot pitch of the structural parameters of the BSCN were obtained.