esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
This umbrella review of current systematic reviews on the topic of air powder water jet technology in dentistry was written in accordance with the PRISMA statement for reporting systematic reviews of studies evaluating healthcare interventions and the methodology for summarizing systematic reviews [8]. This umbrella review was not registered prior to conducting the review process.  
The literature databases MEDLINE/Ovid, Embase, Cochrane library and Scopus were used for a systematic literature search for systematic reviews and meta-analyses published from 2010 to 2022 assessing the use of APWJT devices in periodontitis and peri-implantitis therapy. The literature search was conducted between the 22 nd of February and the 15 th of March 2022. The following search terms were applied: \"air-polishing\" OR \"air polishing\" OR \"air-flow\" OR \"air powder water jet technology\" AND \"glycine\" OR \"erythitol\" OR \"trehalose\" AND \"gingivitis\" OR \"periodontitis\" or \"periodontal disease\" OR \"peri-implant mucositis\" OR \"peri-implantitis\" OR \"peri-implantitis\" OR \"peri-implantitis\" AND \"randomized clinical trial\" OR \"prospective clinical trial\" OR \"clinical control study\". Additionally, a hand search on PubMed and Google Scholar was performed.  
According to the discovered literature, the focused questions for the review were formulated as follows:  
1. Does low-abrasive (glycine, erythroid or trehalose powder) APWJT have a beneficial effect on clinical parameters (PPD, BOP, gingival recessions, CAL) as compared to standard mechanical debridement in active or supportive periodontal and peri-implantitis therapy?Can low-abrasive APWJT powders be used in the active or supportive treatment phase alone or in combination with other treatment measures?  
Screening and Selection  
Two independent reviewers (CRH and JMT) initially screened the titles and abstracts of all the search results. All of the selected articles were agreed upon before being finally included or excluded with full text. Any discrepancies were discussed and resolved by consensus, or, if required, a third reviewer (CCL) was consulted.  
Systematic reviews and meta-analyses of clinical studies published from 2010 to 2022 with a full text available in English reporting clinical parameters such as pocket depth reduction, bleeding on probing, gingival recession and clinical attachment level gain were included.  
For periodontal treatment, peri-implantitis and peri-implant mucositis therapy, the reviews should include a comparison of APWJT use with conventional scaling and root planing with curettes or ultrasonic devices or its use as an adjunctive measure. If used as an adjunctive measure, the only difference should be between the control and test group.  
Systematic reviews and meta-analyses of ex vivo, in vitro and animal studies or publications that did not focus on the mentioned clinical parameters were excluded. Systematic reviews that did not conclude on the use of APWJT devices were also excluded.
## 3 Methodology  
This study is divided into four stages (Figure 3). First, historical landslide data and conditioning factors are collected. Next, VIF analysis, Random Forest, SHAP, and frequency ratio are used to build an initial LSP model. In the third stage, three strategies are applied to refine non-landslide sample selection. Finally, the AUC-ROC and landslide susceptibility index (LSI) metrics are used to evaluate the model's performance.
## 3 Results and Discussion
### Coherence  
The coherence between the sensors and the PZT was computed for all the configurations tested. Figure 4 shows the coherence for the sensors installed in the rotating frame and for the three different excitation frequency bands while the disk was standing in air and in water (no rotation). It is seen that coherence is very good for the accelerometer located in the disk (ADISK) and for the AE sensor located in the shaft (ESH) for all the frequency bands, and in air and in water. The points with minimum coherence correspond to antiresonances of the disk [36], so they can be neglected. However, the accelerometer located in the shaft (ASH) presents different results for the lower frequency band (1-2 kHz), where the response of the structure dominates and, therefore, it is not coherent in this zone. For the higher frequency band (20-21 kHz) this accelerometer is coherent both in air and in water.  
Nevertheless, the results of the sensors installed in the stationary parts of the test rig present different results (see Figure 5). The accelerometer in the bearing (ABEAR) shows similar results than the one located in the shaft, so it is thought that both are measuring the same kind of information but in  
Figure 4: Coherence between the sensors of the rotating frame with respect to the PZT. The blue line is the “in air” configuration, and the black line is the “in water” configuration. Non-rotating case.  
a different reference frame. The other sensors present better coherence when the disk is submerged in water than in air. This means that the vibration of the disk is mainly transmitted to the stationary parts through the water, and that the mechanical transmission from the disk to the stationary parts is poor. The AE sensor has a good coherence for all the frequency bands with the disk in the water, whereas the microphone better detects the disk vibration for the middle frequency band tested (10-11 kHz), also with the disk in water. The accelerometer located in the lower casing presents only good coherence for the higher frequency band (20-21 kHz) and again only with the disk submerged in water.  
Figures 4 and 5 show the coherence for different excitation bands and sensors, in air and in water in the non-rotating case. The same kind of analysis was performed with the disk rotating at 4.1 Hz and the results obtained were almost the same.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 3 Potential Applications
### Vehicular Control Systems  
Edge computing can address challenges in control systems of autonomous vehicles, which are: limited sensing areas, the possibility of deadlock, and the expensive cost of the vehicle equipped with rich sensors. In [8], the authors proposed a cloud-based vehicle control system that periodically aggregates sensor data from multiple vehicles in the vicinity and controls them from the cloud. And edge server is used as a computational node together with the cloud-based vehicle control system. Both the cloud and edge servers have vehicle controllers and are referred to as the cloud controller and the edge controller. To balance the computational load between the edge and cloud servers, an automatic switching method of vehicle controller between the edge and cloud servers is proposed. For performance evaluation, the authors implement an interesting prototype system using microcars and evaluate the stability of the driving trajectory, the control ratio, and the amount of traffic.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 8 IoT Security in 5G
### Security Issues of a Layered IoT Architecture  
The **Physical Layer** of an IoT system usually includes sensors, actuators and any other node that gathers information and transmits it into the Network layer. These building blocks of a system are the most vulnerable parts of the whole infrastructure, as they mostly operate in external and outdoor environments, which means that they can be accessed physically. These types of attacks are refereed to as tampering, which includes not only ruining the device physically, but modifying it, such as replacing its firmware [84]. Tampering also can be the first step of executing a Denial of Service (DoS) attack against other nodes in the network. Generally, DoS attacks are major threats in each layer of an IoT architecture, but in the physical layer, they can even target closed systems by jamming. However, there is no general solution to avoid DoS attacks [83], and although spread-spectrum techniques would be efficient defense mechanism, but they can not be widely applied due to the limited computation capability and power consumption [82].  
The heterogeneity of devices in the **Network Layer** implies that the types of security threats and attacks vary in a wide range. Although endpoints in the cloud and in the edge are more powerful devices where traditional security solutions can be adopted, the majority of the nodes still lack of these features such as public key cryptography techniques [85]. Therefore, these nodes often limit the defensing capability of the whole network, while they are its weakest links. For example, compromising a node may win the trust from other devices, so the trustworthiness of the system can be ruined this way. However, there are indirect attack types that may be even more dangerous than the direct ones, as it is harder to discover them. A man-in-the-middle--as the most prevalent indirect attack type--can be more harmful to any IoT system by violating the confidentiality, integrity, and privacy of restricted data. Such an attack can be executed in different ways like eavesdropping the messages or replaying them, which violates privacy, but it can also deteriorate QoS by changing not encrypted routing information [86].  
The **Application Layer** usually consists of different web applications, mainly interfaces, service management tools and middlewares [87], thus traditional software attacks mean the major security risk in this layer. For instance: insufficient validation of an input may enable injecting malicious input. As a consequence, attackers can perform various operations, depending on the software, such as stealing data, compromising database integrity, or bypassing authentication [88].
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 1 Introduction
Thus, in order to come up with an effective IR system, one has to deal with the complex morphological features of the language which itself leads to complex grammatical structure. As a result, finding effective term (word) representation for documents and queries has been an issue of theoretical discussion in Amharic IR. In many languages, the use of surface forms of words to represent documents and queries is not a choice due to the proliferation of words that can be generated from a single root form. Thus, the word forms that are considered for document and query representation are stems and roots. Although some efforts have been made to develop Amharic IR systems using stems, the effectiveness of the systems with respect to the use of various forms has not been systematically analyzed [32]. This current research analyzes the use of stems and roots for content representation and investigates their effects on Amharic IR. It is noted that a reference collection, annotated corpora and scientifically built resources would assist to carry out more research and development works on Amharic IR and NLP. To advance research on the development of Amharic NLP tools and IR system, there should be scientifically built resources and test collections. The development of Amharic NLP tools is a non-trivial task because of its complex morphology. Thus, the creation of morphologically annotated Amharic corpus is a long-sought resource for the research community. Morphological annotation of a language is the process of assigning linguistic information such as part-of-speech (POS) and morphological features to recognize each word in a document [33]. Morphological analyzers can help to automatically annotate text corpus. With regard to Amharic, there have been few attempts to develop morphological analyzers [16; 21; 22]. These tools are at a prototype stage and they are also limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools. The objective of this research work is: (1) to promote publicly accessible Amharic resources including a standard test collection for IR, morphologically annotated corpora and compiled stopwords list; and (2) to design Amharic IR system by investigating the optimal representation of documents and queries considering both root-based and stem-based text representations. The rest of this paper is organized as follows. Section 2 describes Amharic language and its morphology. Section 3 discusses related work. In Section 4, we present the proposed Amharic IR system architecture along with various options to represent Amharic documents and queries. Section 5 presents the evaluation framework of the proposed Amharic _adhoc_ retrieval system including the presentation of the resources and corpora, experimental setup, and experimental results. In Section 6, we present our conclusion and put forward further research directions.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 3 Results and Discussions
### NDVI Maps  
Figure 10 shows the NDVI maps of Gelephu. The NDVI is primarily a measure of the greenness and health of vegetation and is the most commonly used vegetation index [45]. The NDVI value range is between \(-1\) to \(+1\). The positive index value indicates a vegetated area, and the negative value suggests a non-vegetated area. In Figure 10, the 2016 NDVI map shows an NDVI range between \(-0.35\) to \(0.86\) and \(-0.07\) to \(0.71\) in 2023. The shift in the NDVI paradigm from \(-0.35\) to \(-0.07\) suggests an increase in the non-vegetation area and thus a decline in healthy vegetation patterns with a change in index value from \(0.86\) to \(0.71\).  
The reclassified NDVI maps (Figure 11) consist of five categorical vegetation classes including water bodies identified with NDVI values less than zero that have no vegetation. Other classes include no vegetation, low vegetation, high vegetation, and very high vegetation, indicating denser and healthy vegetation, respectively. Areas with NDVI values between \(0\) and \(0.2\) were classified as bare. NDVI values between \(0.2\) and \(0.4\) were labelled as low vegetation, indicating areas with limited vegetation cover. High vegetation was defined for NDVI values between \(0.4\) and \(0.6\), representing areas with moderate vegetation density. Finally, NDVI values greater than \(0.6\) were categorized as very high vegetation, indicating areas of dense and lush vegetation cover. This reclassification process allowed clear differentiation of vegetation classes and enabled a comprehensive detection of the changes that occurred between the 2016 and 2023 NDVI maps. By comparing the distribution and extent of each vegetation class, patterns of vegetation change or shifts in vegetation density are identified and analysed.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System
## 3 Results
### How Do Students Respond to Receiving an ASR?
#### 3.2.3 Seeking out Information  
Few students (22.7%; n = 25) sought out information about their ASRs. Among those students, they most often went to the instructor or teaching assistant (68.0%; n = 17), then academic advising (56.0%; n = 14). Students typically looked for how to respond to the ASR (n = 12, 48.0%) (presented in Table 11). Students tended to agree that they were able to find the information that they were searching for (\(M\) = 4.8, \(SD\) = 1.3). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**“I Have Received**} \\  & **“I have Received** & \multicolumn{2}{c}{**Both a Negative**} & \multicolumn{2}{c}{**“I Have Received**} \\  & **a Negative ASR”** & \multicolumn{2}{c}{**and a Positive**} & \multicolumn{2}{c}{**a Positive ASR”**} \\  & **(n = 37)** & \multicolumn{2}{c}{**ASR”**} & \multicolumn{2}{c}{**(n = 39)**} \\  & **n** & **\%** & **n** & **\%** & **n** & **\%** \\ \hline Your parents/caregivers learning that you had & 21 & 56.8 & 14 & 42.4 & 8 & 20.5 \\ received an Academic Status Report & 21 & 56.8 & 14 & 42.4 & 8 & 20.5 \\ Your classmates learning that you had received an & 6 & 16.2 & 3 & 9.1 & 1 & 2.6 \\ Academic Status Report & 20 & 60.6 & 10 & 25.6 \\ Your instructor’s perception of you as a student & 16 & 43.2 & 20 & 60.6 & 10 & 25.6 \\ Maintaining your GPA & 30 & 81.1 & 28 & 84.8 & 17 & 43.6 \\ Failing the class & 29 & 78.4 & 24 & 72.7 & 12 & 30.8 \\ Your permanent academic record & 23 & 62.2 & 16 & 48.5 & 9 & 23.1 \\ Your ability to remain in your major & 10 & 27.0 & 12 & 36.4 & 2 & 5.1 \\ Your ability to finish your degree & 13 & 35.1 & 13 & 39.4 & 1 & 2.6 \\ Other & - & - & - & - & 6 & 15.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: When you found out that you had received an Academic Status Report (ASR), what, if anything, were you concerned about? [Check all that apply] (n = 109). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Feeling** & **Example** & **n** & **\%** \\ \hline I felt noticed & \multicolumn{2}{c}{“I liked getting feedback from the instructor, them willing to} \\  & help me meant they believed in me and helped with what I & 4 & 3.6 \\  & could improve on”. & \\ I felt the ASR was unexpected & “Supprised” & 3 & 2.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._ \begin{table}
\begin{tabular}{l l l l} \hline
**Information** & **Example** & **n** & **\%** \\ \hline What should I do next & “On what my next steps should be” & 12 & 48.0 \\ Information about ASRs & “what an ASR was and if it was bad” & 7 & 28.0 \\ Why I got it & “Why was I getting dropped from a class, why is the & 5 & 20.0 \\  & maximum absences the number it is”. & 5 & 20.0 \\ Other & “I was just concerned about the class specifically, so I sought & 2 & 8.0 \\  & help from those I thought fit best”. & 1 & 4.0 \\ N/A/None & “none” & 1 & 4.0 \\ \hline \end{tabular}
\end{table} 
Table 11: What kind of information were you looking for? (n = 25).
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
###### Abstract
In general, particles with a higher degree of oxidation have a greater tendency to present PL in the blue region when excited in the UV region [1]. On the other hand, the presence of nitrogen, sulfur, and boron can give rise to new energy states, allowing the expansion of the GQDs' excitation window, greater control of luminescent tuning in the visible and near-infrared range, and the reduction of phonon losses [1; 29]. As already mentioned, the precursors used for the synthesis of carbon-based quantum dots play a crucial role in determining the structure of the CQDs and GQDs and, consequently, their optical properties. In this work, we performed the synthesis of colloidal systems of GQDs from sucrose. Sucrose was chosen because it is a non-toxic and highly available carbohydrate. Although sucrose-based CQDs have already been reported in the literature [6; 33; 34], most of the works have reported the synthesis of CQDs with PL in the blue-green region and obtained the CQDs through hydrothermal syntheses [35; 36] or after acidic carbonization [37]. However, the use of sucrose to obtain GQDs with emission extending to the red and near-infrared regions has not been explored. On the other hand, CQDs emitting in the red region have already been performed using much less available, toxic, or more expensive precursors, such as isomers of phenylenediamine [38], 2,5-diaminotoluene sulfate [39], and reduced glutathione [40]. Quantum dots with intense emission in the red/near-infrared regions are key for applications in white light-emitting diodes (WLEDs) and biological fields since blue and green emissions cannot penetrate tissue deeply and are more likely to cause the autofluorescence of biosamples [1]. In this work, we propose a new approach with the use of sucrose, obtained from commercial refined sugar, as a precursor for red-emitting tunable photoluminescenceGQDs. We propose a simple synthesis method that is based on the carbonization of sucrose followed by the synthesis of the GQDs in a dimethylsulfoxide (DMSO) treatment instead of using water as a solvent. DMSO is a polar aprotic solvent; it has been used in other work to obtain GQDs with redshifted emissions [1], and its use for the chemical reduction of graphene oxide has been reported elsewhere [41]. Results regarding the doping of GQDs obtained from sucrose with nitrogen are also presented for the first time, to the best of the authors' knowledge.
## 3 Analysis of SCSRR-Based DGS MIMO Antennas with Different Feed Networks  
In this section, the simulation results of MIMO antennas with different feeding networks are discussed. All of the proposed MIMO antennas were composed of two symmetrical and identical radiating elements working at 5.8 GHz. The total size of each MIMO antenna was 58 mm \(\times\) 36 mm. The thickness \(h\) of the Rogers RO4003C substrate was chosen to be 1.52 mm. The center-to-center spacing between the two antennas was \(d_{1}=26\) mm (0.5\(\lambda_{0}\)), where \(\lambda_{0}\) is the free-space wavelength at 5.8 GHz. The proposed MIMO antenna designs were studied while considering full a ground plane, as well as SCSRR-based, DGS. The SCSRR design and dimensions described in Section 2 were considered for all cases. Accordingly, the antenna performance characteristics such as return loss, isolation, gain, BW, RE, and surface current distribution were investigated. Furthermore, the diversity  
Figure 8: S-parameters of the SCSRR.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values [mm]** \\ \hline L\({}_{out}\) & 6 \\ \hline g\({}_{out}\) & 0.3 \\ \hline t\({}_{out}\) & 0.6 \\ \hline d\({}_{in}\) & 1.6 \\ \hline S\({}_{L}\) & 2.4 \\ \hline S\({}_{W}\) & 0.6 \\ \hline L\({}_{in}\) & 4.2 \\ \hline t\({}_{in}\) & 0.5 \\ \hline d\({}_{out}\) & 0.85 \\ \hline \end{tabular}
\end{table}
Table 3: Dimensions of the SCSRR.  
Figure 7: Dispersion diagram of CSRR and SCSRR (Freq = Frequency).  
parameters, including ECC, DG, MEG, and CCL have been verified to justify the MIMO antenna performance.
## 3 Observing System Simulation Experimental Framework
### Nature Run and WRF Model  
The National Center for Atmospheric Research (NCAR), the NCEP, and other American scientific research institutions developed a unified meteorological model called the WRF model. The WRF model is a fully compressible, nonhydrostatic model (with a hydrostatic option). WRF V3.8.1 released on 12 August 2016 was used in this study. The parameterization scheme of the WRF model is shown in Table 1.  
The Nature Run used here is a two-week-long WRF forecast initialized with the ERA-5 reanalysis data. The WRF outputs saved every half hour were used to simulate all synthetic GEO-MW observations described below. For Typhoon _Bualoi_, the ERA-5 reanalysis data at 0600 UTC on 22 October 2019 were used as the initial field to run the WRF model forecast to 0600 UTC on 5 November 2019. For Typhoon _Hagibis_, the Nature Run data covers 0600 UTC on 8 October 2019 to 0600 UTC on 22 October 2019.