esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The protocol for this systematic review was written and registered on PROSPERO (registration number: CDR42023389750). The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines.  
The inclusion criteria are listed here following the PECO(S) statement:  
* Population (P): individuals with primary dentition or mixed dentition.
* Exposure (E) and Comparison (C) factors: dietary habits (such as frequency of consumption of fruit juice, soft drinks, and fruits).
* Outcome (O): prevalence of erosive tooth wear, measured by different erosion indices.
* Studies (S): observational studies, such as cohort, case control, and cross-sectional.  
The exclusion criteria included the following:  
* Studies on the cause of the erosion that is not due to diet.
* Studies on the pediatric age that has been exceeded.
* Studies on diagnosis and management of erosive tooth wear only.
* In vitro studies, laboratory studies, conference papers, and reviews.  
A systematic electronic search was carried out from November 2022 to December 2022 in four electronic databases (Pubmed [23], Embase [24], Web of Science [25], and Scopus [26]). Some of the authors had to be contacted by email in order to request additional information. MeSH terms and the subject index used for the search were as follows: pediatric dentistry, young adults, diet, water, milk, juice, carbonated, soft drinks, dental erosion and tooth erosion.  
The designed search strategy considered previous studies in the field and their most cited descriptors. The keywords were: (\"pediatric AND dentistry\" OR \"young adult\") AND (diet OR water OR milk OR juice OR carbonated OR \"soft drink\") AND \"dental erosion\"). A duplicates search was performed by identifying references of the search strategy and exporting them from each database to Mendeley's reference management software (Elsevier, Amsterdam, The Netherlands). After that, two reviewers (MD-CR and L-MM) independently assessed the titles and abstracts of all identified articles. In the case of discrepancy, a third author (E-GM) had to be consulted. Several articles needed to be read in the cases where the abstract did not provide enough information in order to conclude. Finally, those articles which met the requirements were included in the study.  
Different variables for the study characteristics, methodology and results were established. Author, location and year were used in order to identify the features of the different studies. The erosion index was registered as well as the evaluation factors. The significant results and the conclusions obtained from each study analyzed were included in the outcome variables.  
The quality of the studies was assessed by the same reviewers independently, using the NIH quality assessment tool for observational cohort and cross-sectional studies. In case of any discrepancies in quality assessment, consensus was reached between the reviewers and if this was not possible a third reviewer was consulted.
## 4 Classification Based on Taxonomy and Design Drivers
### Load Balancing
To estimate sensor density, the WSN is divided into \(n\)\(x\)\(n\) non-overlapping square regions with a side length \(p\). A sensor node closest to the center of a zone is referred to as a watch point and is called a sentinel node. Each sentinel node broadcasts a request to its neighbors to count them. The number of neighbors is used as an estimation of the local density in the region. One _proactive_ (Broadcast) and two _reactive_ protocols (Stripes and FatStripes) are used to  
Figure 7: Multiple Shortest Routes [19]. deliver the estimate computed by sentinels to other sensor nodes. After collecting estimates a sensor uses \(d_{{}^{\cdot},^{\cdot}}=\frac{w_{{}^{\cdot}}}{\sum_{j}w_{{}^{\cdot}}}\) and \(d_{{}^{\cdot},^{\cdot}}=\frac{m*d_{{}^{\cdot}}^{\cdot}+\sum_{{}^{\cdot},^{\cdot} \cdot,^{\cdot}}d_{{}^{\cdot},^{\cdot}}^{\cdot}}{2*m}\) to calculate the density in each region and the final approximation based on the first computation, respectively. Here, \(w_{\hat{y}}\) is the density estimated by the sentinel for the \(i,j\) region and \(m\) is the number of neighbors in this region. In two situations referred to as _false zeroes_ and _over reporting_, \(d_{{}^{\cdot},^{\cdot}}^{\cdot}\) might give a misleading density estimation value. A region with sparse nodes near the watch point or a concentration of nodes near the border may report a zero or very low density representing _false zeroes_. In another situation, a region with a high concentration of nodes near a watch point and sparse nodes near borders may report a higher density than normal representing _over reporting_. To overcome these situations the final approximation \(d_{i,j}\) is computed as a weighted mean of the approximation computed in the first step for region \(ij\). Table 4 shows a summary of load balancing DCS schemes based on their functionalities.
## 6 Discussion
An important aspect not explicitly addressed in this study is the uncertainty associated with the machine learning model predictions. While metrics such as MAE, MSE, and RMSE provide an indication of the average prediction error, they do not quantify the uncertainty in individual predictions. This uncertainty can arise from various sources, including the inherent randomness of the data, the limited size of the training dataset, and the approximations made by the machine learning models. Future research should explore methods for quantifying model uncertainty, such as using bootstrapping techniques to estimate confidence intervals for the predictions or employing Bayesian machine learning approaches that explicitly model uncertainty. Furthermore, hypothesis testing could be incorporated to formally assess the statistical significance of the observed differences in performance between the different machine learning models. For example, a paired \(t\)-test could be used to compare the predictions of two models on the same test dataset to determine if the difference in their performance is statistically significant. Addressing model uncertainty and incorporating hypothesis testing would provide a more rigorous and comprehensive evaluation of the machine learning models and enhance the reliability of the findings.
## 2 Timeline of the Proposed DR Scheme  
The proposed DR scheme is implemented in hour-ahead scheduling in a power system. The timeline for the hour-ahead scheduling and the proposed DR scheme is shown in Figure 1. In practice, for example, in California ISO (CAISO), the hour-ahead scheduling is provided approximately 75 min before the actual beginning of an operating hour while the load and RES forecasts are provided 2 h beforehand [44]. If the fluctuation of the RES results in an obstacle for conventional generators to adjust, the DR can be implemented to help with regulatation. In the process of DR, the LSE needs to broadcast the total rebate and the CDL to customers through social networks, and each customer claim for their ideal rebate ratio after a limited time, e.g., 5 min. LSE just needs to gather every DR customers' claiming rebate ratio after this limited time to evaluate the total ideal rebate ratio of customers. As for customers, the claiming of the ideal rebate ratio corresponds to the regulation task. During this time interval, customers are able to exchange information with others to choose an optimal strategy and ensure their own welfare maximization. This process is modeled as a non-cooperative game and the Gossip algorithm is used to model the information exchanging process between customers, which will be described in Section 4. For the financial settlement, this paper assumes that each customer is trustworthy; thus, the settlement procedure will be completed by the LSE based on each customer's claiming rebate, and customers will get the rebate in their social network account.
###### Abstract  
The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F\({}_{1}\)-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature.  
protected health information; natural language processing (NLP); named entity recognition (NER); de-identification; conditional random field (CRF)
## 1 Introduction  
A slope refers to the rock and soil mass with a particular inclination angle formed by natural factors or human interference. When a weak part of it deforms relative to the main body of the slope due to gravity and other external factors, a landslide forms [1, 2, 3]. In addition, factors such as rainfall, earthquakes, and engineering disturbances can easily disrupt a slope's equilibrium state, leading to landslide hazards. Landslides are sudden geological disasters that are often accompanied by secondary disasters such as mudslides and floods [4; 5; 6]. They have strong destructive power and cause a large number of casualties and property losses worldwide every year (Figure 1). Long-term and large-scale landslide movements can even cause changes in terrain and landforms.  
Many countries have realized the severe harm of landslide hazards in recent years. Taking China, which experiences frequent landslides, as a case study, the application of technologies such as 3S (Remote Sensing, Geography Information Systems, and Global Positioning Systems) in landslide monitoring has become increasingly widespread [7; 8]. Moreover, with the in-depth integration of various optical and electrical technologies and artificial intelligence algorithms, a large number of new technologies, such as DFOS (Distributed Fiber Optical Sensing) and InSAR (Interferometric Synthetic Aperture Radar) technology, have been widely applied in the field of landslide monitoring [9; 10], which have achieved automated, high-precision, and real-time monitoring of landslides. Early warning and prevention of landslide hazard has significantly improved, and the overall number of disasters has shown a downward trend [11] (Figure 2). However, the success rate of early warning is still unstable. Despite numerous studies on landslide susceptibility and critical thresholds for triggering landslides, it is still difficult to achieve comprehensive and precise control of landslides on a global scale [12]. Therefore, it is of great significance to achieve real-time monitoring of landslides in all dimensions by researching advanced landslide monitoring technologies.  
In this paper, bibliometric analysis is used to visually analyze essential indicators (keywords, authors, years, and advanced monitoring technologies, etc.) related to global  
Figure 1: Typical landslide hazards in various countries around the world.  
Figure 2: Statistics on landslide hazard in China from 2012 to 2022.  
landslide monitoring from 2010 to 2023. The development trends of various landslide monitoring technologies and the correlation among the leading monitoring technologies are discussed. A 'Space-Sky-Ground-Underground' monitoring system for landslides is further proposed. Finally, taking a reservoir landslide in the Three Gorges Region of China as an example, on-site monitoring research on landslides is carried out. The research results provide an essential reference for the rapid development of landslide monitoring technology.
# 5G/B5G Service Classification Using Supervised Learning
## 3 5G/B5G Service System Classifier Proposed  
Figure 1 shows a block-level diagram of the proposed system for the classification of services in 5G/B5G networks. The planned scheme first operates offline until the predictive model has been validated, and can learn to classify services effectively with few mistakes. In the next phase, the system is implemented online by the network operators, and the predictive model then classifies new services requested by the UEs. The output of the system corresponds to the requested service classification, and is feedback to the ML algorithm, making the predictive model more efficient. Each of these phases, and each block, is explained later in this article.  
The proposed system can classify services in next-generation networks. However, it is essential to clarify that this forms only one part of a system that a network operator can use to offer services. Our system needs to interact to connect with the rest of the operator's system, and we must therefore consider two options:  
1. Reprogramming the proposed system in the language of the operator's system. This has the disadvantage of requiring reprogramming of the systems used by each operator, including the cloud, which is not very feasible (due to future maintenance or update issues).
2. Incorporating into the proposed system an appropriate Application Programming Interface (API) to enable communication with the operator's system (accessible from a public or private server). The necessary security must be provided to ensure this is only employed in an authorized way.  
The second option is preferable, since 5G systems provide appropriate APIs to allow a trusted third party to create, modify, delete and monitor instances of the network segments used by the third party, and to manage a set of devices or capabilities including QoS functions [13].  
Figure 1: Schematic diagram of the proposed 5G/B5G service classifier.
# Skin Barrier Function: The Interplay of Physical, Chemical, and Immunologic Properties
## 6 Clinical Applications
### Atopic Dermatitis  
Studies have shown that the pH in eczematous skin and uninvolved skin of children with atopic dermatitis is higher in comparison to the skin of healthy children [133; 134; 133]. Free amino acids and urocanic acid, which are involved in creating the acid mantle, are reduced in atopic skin. When combined with a filaggrin mutation, this provides a danger signal to the keratinocyte nucleus [13; 21]. Sweat secretions that are rich in lactic acid, which are also thought to contribute to the acid mantle, are reduced in atopic skin [13]. The impaired secretion of lamellar bodies seen in atopic dermatitis may increase pH, as this process is a source of protons for SC acidification [13].  
This alkalization of the skin and changes to the skin barrier can increase the chance of pathogenic colonization. Growth of _S. aureus_ colonization is optimal at a neutral pH and markedly inhibited at pH values around 5 [13; 135]. Studies have shown that the skin of patients with atopic dermatitis have a decreased ability to produce AMPs including cathelicidin and \(\beta\)-defensins-2 and -3 [126]. Barrier defects permit an increased _S. aureus_ entry and subsequent expression of Th2 cytokines and IL-17 and a decreased expression of cathelicidin. This is consistent with studies showing that Th2 cytokines downregulate the induction of cathelicidin in the skin [126; 136]. Cathelicidin LL37 has been found to exhibit effective antimicrobial activity against _S. aureus_[137].  
Skin barrier changes can also induce symptoms, which may be alleviated by corrections in pH. Serine proteases, which have increased function at an elevated pH, induce pruritus via PAR-2 receptor activation in keratinocytes and nerves in atopic skin [138]. Moreover, studies on hapten-induced atopic dermatitis in at-risk mice show that lowering the pH reduces the Th2 inflammatory response, prevents epidermal hyperplasia, reduces tissue eosinophil, and normalizes the epidermal structure [13].
## 2 Materials and Methods
### Dataset
#### 2.2.4 WRF Simulation for the Yellow Sea Region in October 2021  
In this study, WRF simulations were carried out for the Yellow Sea region in October 2021 with a focus on lower temporal and spatial resolution requirements than the previous sensitivity experiments. A two-layer nesting strategy was employed, and the simulation center was centered at 34\({}^{\circ}\)N, 123\({}^{\circ}\)E, as depicted in Figure 2. The horizontal grid resolutions were configured as 300 \(\times\) 300 (9 km) and 286 \(\times\) 286 (3 km). The second-level nested area encompassed a significant portion of the Yellow Sea, certain regions along the coasts of Shandong and Jiangsu in China, and parts of the Korean Peninsula. The simulation period extended from 1 October 2021 at 00:00 (UTC), to 31 October 2021 at 18:00 (UTC), with outputs generated at 60 min intervals. The initial field was derived from GDAS data, which was updated every six hours, and the forecast had a lead time of six hours. The spatial resolution was set at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The Lin microphysics scheme, rrtmg longwave radiation scheme, Noah land surface model, K-F cumulus parameterization scheme (with the cumulus parameterization scheme disabled for the second-level nested area), MRF boundary layer parameterization scheme, and MMS near-surface parameterization scheme were utilized in the WRF model. For the purpose of maintaining the accuracy of the simulation results and mitigating the impact of accumulated errors, ten simulations were performed, with each simulation lasting for 96 h. The data from the final 24 to 96 h of each simulation was extracted for subsequent analysis.  
As an integral part of the climate system, the ocean has its sea surface temperature (SST) updated to maintain the model's consistency with real-world conditions. During the simulation process, SST undergoes temporal variations, exerting an influence on atmospheric dynamics and thermodynamics, thereby enhancing the accuracy and reliability of the simulation results. Consequently, dynamic SST was utilized in this simulation, with SST data provided by the GDAS reanalysis serving as one of the inputs for the initial conditions in the WRF model.
# Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS
###### Abstract  
After the Gyeongiu earthquake in 2016 and the subsequent one in Pohang the following year, there is an imminent necessity to evaluate the risk of earthquakes accurately as well as respond to the risks on the Korean peninsula. For this purpose, the existence and movement of a fault should be investigated in the area. In this study, we calculated the displacement of the crust around the mass production fault using GNSS (Global Navigation Satellite System) data and analyzed the deformation characteristics by applying the method of stress calculation. The Yangsan Fault Zone has been analyzed with a total of 24 GNSS stations between 2018 and 2021. Data processing was conducted with Bernese GNSS Software, which requires high-precision orbit, satellite clock, ionosphere information for high-precision position estimation. By accumulating daily solutions over the three years to produce the final solution with the velocity of the stations, the Yangsan Fault Zone moved about 32 mm per year southeast on average. Based on the results, the movements of the stations on either side of the Faults are almost the same. Stress analysis of the Yangsan Fault Zone showed a large east-west expansion during 2018-2019 but decreased in stress afterwards, thus it is evaluated to be relatively stable compared to the past. However, due to the nature of crustal variation continuous monitoring research with long-term data processing should be followed, which will be discussed in further research.  
earthquake; fault zone; GNSS; stress Article Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS +
Footnote †: journal: Applied
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.8 Multimetric Unicast Data-Dissemination Scheme (MUDDS)  
Two important problems were identified in VANETs due to the frequent changes of topology, broadcasting storm (due to high density) and network disconnection (due to velocity). MUDDS was proposed in [38], a new protocol that uses link availability (LA), based on distance, which guarantees fewer hops; and packet reception rate (PRR), which guarantees reliability.  
PRR is calculated as shown in Equation (9), where \(N_{s}\) is the quantity of successfully received packets, \(N_{t}\) is the total number of sent packets, and \(N_{l}\) is the number of losses. These lost packets are many due to collisions that can take place in the network. When the network is close to its saturation, the number of collisions increases.  
\[PRR=\frac{N_{s}}{N_{t}}=\frac{N_{t}-N_{l}}{N_{t}}. \tag{9}\]  
The LA was evaluated as shown in Equation (10), where \(distance_{i}\) is the space between actual node and selected forwarder, \(t_{i}\) is the duration of link availability, \(T\) total time, and \(R\) the maximum achievable transmission range. This metric considers \(link\ availability\ rate((l,t)=\frac{t_{i}}{T}\) as an indicator of link state and link length (distance between sender and possible forwarder). This metric goal is to minimize network-disconnection problems.  
\[\begin{array}{ll}LA_{i}&=distance_{i}\times availability\ rate_{i}=\frac{ distance_{i}\times t_{i}}{T}\\ &0\leq distance_{i}\leq R\\ &0\leq t_{i}\leq R\end{array} \tag{10}\]