esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, ECM mixed with conductive fibers CF and SF was prepared, and its heating performance, microstructures and flexural strength properties were analyzed. The results were also compared and reviewed with PM. The conclusions drawn from this study may be summed up as follows:  
1. The heating performance of ECM specimens mixed with CF tended to increase as the fiber contents and applied voltages increased. It was evident that the ECM specimens mixed with CF secured higher heating performances than the PM specimen and the ECM specimens mixed with SF. Particularly, the ECM-CF125 specimen in which 1.25% of CF was mixed showed the highest heating performance, which increased by about 7.3 times compared to the initial temperature (20 \({}^{\circ}\)C);  
Figure 13: Results of weight analysis for samples by TGA.  
2. The infrared thermal images made it clear that the heat was distributed and the temperature was transferred depending on the parameters of each specimen. The infrared thermal images became sharper and clearer as the heating performance increased; the heating performance could be confirmed even with the naked eye;
3. The flexural strength of ECM was significantly improved as the contents of CF and SF increased. However, the flexural strength of the ECM-CF125 specimen was significantly reduced due to fiber balling. Considering that a certain level of flexural strength is required in order to use ECM mixed with CF, it is determined that the use of ECM mixed with up to 1.0% of CF is most appropriate;
4. From the result of images analysis using SEM, it was confirmed that the CF formed an electrically conductive network and, in some cases, fiber clumping and balling were observed following the heterogeneous dispersion of CF. Such fiber clumping and balling phenomena of CF cause a decrease in flexural strength;
5. Although there was a slight difference in the amount of thermal weight loss for the samples before and after the heating performance test, it was confirmed that the ECM samples were relatively unchanged even under repetitive electrical heating.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology, [PERSON]-[PERSON] and [PERSON]-[PERSON]; validation, [PERSON]-[PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON] and [PERSON]; visualization, [PERSON]-[PERSON], [PERSON] and [PERSON]-[PERSON]; supervision, G.-H.H.; project administration, G.-H.H.; funding acquisition, G.-H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) of the Ministry of Education, Republic of Korea (Grant no. NRF-2018R1A6A1A03025542).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
A bone classification scale was proposed by [PERSON] [36], depending on objective bone density measurements on CT. CBCT has replaced multi-slice CT (MST) due to the lower radiation, shorter acquisition time, higher resolution, and lower cost. When CT scans are used, the Hounsfield unit reflects the degree of X-ray attenuation assigned to each pixel and, in this way, the tissue density is expressed. When CBCT is used, it is the gray scale (voxel value) that indicates the degree of X-ray attenuation of each tissue. So, HU expresses the density of body tissues when compared to a calibrated gray-level scale. Now, it is accepted that it is better for mini-implants to be placed at the attached gingiva [37]. The width of the attached gingiva is measured from the sulcus of each tooth, as it extends to the surface of the gums, to the mucogingival junction [38]. The mucogingival junction is a well-determined line between the movable and immovable mucosa [39]. The methods that are used to spot the mucogingival junction are the visual method (VM: identifying the line by the difference in color between the gingiva and alveolar mucosa), the VM after histochemical staining (HM), and the functional method (FM), which consists of distinguishing the borderline between stable and immovable tissues with a light pressure, delivered by a periodontal probe) [40; 41]. So, the attached gingiva is easy to record, and it might be a guide when placing mini-implants, and seeking a satisfying primary stability. For the buccal segments of the upper jaw, the interradicular space between the second premolar and first molar is an indicated region for mini-implant placement as, in this region, an adequate mesiodistal buccal/palatal distance is recorded [28]. Of course, the roots and the supporting periodontal ligament are notable limitations. As to height, however, in order for the best primary stability to be achieved, the cortical and trabecular bone quantity and quality should be defined, and this would be easy if each patient were subjected to a CBCT. As indicated above, acquiring a CBCT for that reason only is not appropriate. However, if a correlation exists between the height in the attached gingva region, and the underlying bone thickness and density, this would favor the selection of the most suitable point for insertion. This study aims to investigate a possible correlation between the height in the attached gingiva, and the underlying bone thickness and density, between the second premolar and first molar in the maxilla, as this would help in the selection of the most favorable point for insertion in terms of primary stability. The null hypothesis is that there is no significant correlation between the different heights in the attached gingiva, and the corresponding bone thickness and density.
## 2 Algorithm
### Other Systematic Errors
#### 2.3.2 Time Tag Error  
In the estimation process of the satellite position at a given time, the time tag of the receiver is used to propagate estimated satellite positions. In common GNSS systems, the receiver time tag is calibrated by a satellite clock via a navigation message. However, there is ambiguity in the signal traveling between satellites and the rover. As a result, the receiver time tag entails continuous bias error. As such, an estimated satellite position \(\mathbf{X}^{s}=\left(x^{S},y^{S},z^{S}\right)\) is deteriorated by the receiver clock bias \(d\tau_{R}(t_{i})\), and has some error relative to the true positions (\(\Delta X^{S}_{time\ tag}\)), such as  
\[\Delta X^{S}_{time\ tag}(t_{i})=\left(V_{xR}^{S}(t_{i}),V_{yR}^{S}(t_{i}),V_{zR }^{S}(t_{i})\right)\times d\tau_{R}(t_{i}) \tag{40}\]  
where \(\left(V_{xR^{\prime}}^{S},V_{yR^{\prime}}^{S},V_{zR}^{S}\right)\) is a pre-estimated satellite relative velocity in a topocentric frame. Basically, satellite position error induced by a time tag error is eliminated from the estimation by the double difference method in a manner similar to the way in which satellite orbit determination error is removed.
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 3 Data Retrieval and Pre-Processing  
Our study utilized two primary datasets: the Retraction Watch database [23] and a curated collection from Web of Science. The Retraction Watch database, which has garnered significant attention in the scientific community for its insights into publication integrity [24], initially contained 12,195 retracted articles with comprehensive metadata including retraction reasons and citation metrics both pre-retraction and post-retraction. To establish a comparative baseline for non-retracted articles, we also collected 10,000 non-retracted articles from the Web of Science on 3 June 2024. Our sampling strategy employed a systematic approach focusing on five pairs of related research domains, with 1000 articles selected for each domain among the following pairs:  
1. Photosynthesis and Cellular Respiration;
2. Quantum Mechanics and Quantum Computing;
3. Ecology and Environmental Science;
4. Virology and Epidemiology;
5. Bacteria and Viruses. This paired-topic approach ensured a balanced representation across different scientific disciplines while maintaining thematic coherence within each pair. However, the fragmented nature of scholarly metadata across different platforms presented a significant challenge. While repositories such as Google Scholar provide citation metrics and h-indexes, they lack alternative metrics and impact factor data. For the data sources of this study, the Web of Science and Retraction Watch databases also presented limitations in metadatacompleteness, particularly regarding journal impact factors and alternative metrics. To address these gaps, we implemented a multi-source data enrichment process:  
* Altmetric Data Integration: We utilized the Altmetric API to retrieve attention scores for articles using their Digital Object Identifiers (DOIs). * Impact Factor Collection: Journal impact factors were obtained through the Journal Citation Reports (JCR) database, supplemented by direct extraction from journal websites when necessary. Following the initial data collection, we conducted a thorough pre-processing phase to address missing values, particularly in impact factor and Altmetric scores. After removing entries with incomplete metadata, our final dataset (Table 1) was reduced to 16,052 articles (11,649 from Retraction Watch and 4403 from Web of Science), providing a robust foundation for our subsequent analysis.
## 2 Current Status of GNSS Integrity Service
### Current Status of Integrity Services of GPS, Galileo, and GLONASS  
In addition to the BDS-3, other GNSS constellations, including the GPS, Galileo, and GLONASS, also provide fundamental integrity services to global users and provide SBAS integrity services to regional users. This section will briefly introduce the current status of the integrity services of the GPS, Galileo, and GLONASS.
## 3 Preliminary Plan for Human-Machine Collaboration in the Metaverse + Industrial Robots
### Concept of Implementing Industrial Metaverse Human-Machine Collaboration via WEB  
By integrating WEB, ROS, and WEBXR technologies, an industrial metaverse platform is constructed, enabling users to collaborate with industrial equipment in virtual reality, as shown in Figure 2.  
The real-time motion data of the robotic arm are transmitted to the ROS through a socket connection. The ROS (Robot Operating System), serving as the core backend, is responsible for processing and managing these data to achieve real-time interactive operation of the robotic arm [34].  
The ROS converts the processed data into model data and subscription messages, which are transmitted to the front-end Three.js application via WebSocket [35]. Three.js uses these data to render a 3D model of the robotic arm and display it on the webpage, allowing users to view the arm's actions and status in real time within a browser.  
Three.js integrates the WEBXR API to transmit the rendered 3D scene to VR devices. When users wear VR headsets, they can interact immersively with the 3D model in virtual reality. Operations performed by users in VR are transmitted back to Three.js through the WEBXR API, which then sends the operation data back to the ROS, achieving real-time virtual and physical interaction.  
Users operate within the virtual scene using VR devices, and these operation data are fed back to the ROS in real time. The ROS adjusts the robotic arm's actions based on the user's operations and updates the latest status in the virtual scene via Three.js, forming a closed-loop control system.  
This system has extensive application prospects in various fields, such as industrial manufacturing, remote training, and virtual experiments, providing robust technical support for the transition from Industry 4.0 to Industry 5.0.
## 5 Results and Discussion
### Average End-to-End Delay (E2E Delay)  
For the average E2E delay, the results of the proposed RALAR, KALAR, and LAR protocol are shown in Figure 6. It can be seen that the proposed RALAR algorithm significantly outperformed the other approaches. The performance of the proposed RALAR algorithm achieved an average E2E delay value equal to 0.6237 s. Meanwhile, the performance of the KALAR and LAR protocol reached an average E2E delay value of 1.568 and 3.051 s, respectively. The results proved that the proposed RALAR algorithm performs better than the KALAR and LAR protocol. This can be achieved because the proposed RALAR algorithm is based on a rectangular zone, which utilizes the GA optimization approach for selecting the most suitable timeout variable that helps reduce the E2E delay. Besides, KALAR and LAR performance are very much correlated; however, the KLAR performance is slightly better than the LAR protocol.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 3 Data Generation Process
### Time Series Contamination  
We apply a quite naive approach to introducing anomalies into our time series. We introduce the same fixed number of anomalies \(n^{anom}\) to each time series by applying a shock to some original values of the observed time series. Formally, the \(j\)-th added anomaly is characterized by its location \(t_{j}\) corresponding to the time stamp at which the anomaly has occurred, its shock \(\delta_{j}\), the amplitude of the shock is given by \(|\delta_{j}|\), and its sign by \(\mathrm{sgn}(\delta_{j})\). We use \(S^{\delta,i}\) to denote the time series resulting from the contamination of the \(i\)-th clean time series \(S^{i}\). For \(i\in\{1,\ldots,N\}\), let \(\mathcal{J}^{i}\) be the set of indices of the time stamps at which an anomaly occurs for the \(i\)-th time series. For \(j\in\mathcal{J}^{i}\), the abnormal values are  
\[S^{\delta,i}_{t_{j}}=S^{i}_{t_{j}}\bigl{(}1+\delta_{j}\bigr{)}. \tag{9}\]  
The location, sign, and amplitude of the shocks are generated randomly according to uniform distributions:  
\[\mathcal{J}\sim\mathcal{U}_{N,\mu^{anom}}(\{1,\ldots,p\}),\qquad\mathrm{sign }(\delta)\sim\mathcal{U}_{N,\mu^{anom}}(\{-1,1\}),\qquad|\delta|\sim\mathcal{ U}_{N,\mu^{anom}}([0,\rho]),\]  
where \(\rho\) is an upper bound on the shock amplitude.  
We define the anomaly mask matrix \(\mathbb{R}^{N\times T}\) by setting \(i=1,\ldots,N\), and \(j=1,\ldots,T\),  
\[\mathcal{T}_{i,j}=\begin{cases}1+\delta_{j}&\text{if }j\in\mathcal{J}^{i}.\\ 1&\text{otherwise}.\end{cases} \tag{10}\]  
Under this framework, when we incorporate the anomalies into the clean time series driven by the geometric Brownian motion, we assign labels to the time series observations according to whether the values correspond to anomalies or normal observations. Wethus provide the labels \(Y^{i}_{l_{j}}\) associated with each value \(S^{a,i}_{t_{j}}\). Hence, for \(i=1,\dots,N\), and \(j=1,\dots,T\),  
\[Y^{i}_{t_{j}}=\begin{cases}1&\text{if }j\in\mathcal{J}^{i}.\\ 0&\text{otherwise}.\end{cases} \tag{11}\]  
Algorithm 6 recaps the time series contamination procedure, where AnomalyMask and GetLabels refer to the operators defined by (10) and (11).  
```
inputs:amplitude range \(\rho\), number of anomalies to add in time series \(n^{anom}\), data \(\mathbf{S}\) \(\text{sgn}(\delta)\leftarrow\mathcal{U}_{N,\mu^{anom}}(\{-1,1\})\) \(|\delta|\leftarrow\mathcal{U}_{N,\mu^{anom}}(\{0,\rho\})\) \(\mathcal{J}\leftarrow\mathcal{U}_{N,\mu^{anom}}(\{1,\dots,p\})\) \(\mathbb{R}^{N\times T}\
i\mathcal{T}\leftarrow\text{AnomalyMask}(\mathcal{J},\delta)\) \(S^{a}\leftarrow\mathbf{S}\circ\mathcal{T}\) \(Y\leftarrow\text{GetLabels}(\mathcal{J})\) outputs:time series with anomalies \(\mathbf{S}^{a}\), labels associated with each value of the time series \(\mathbf{Y}\).
```  
**Algorithm 6** Data Contamination
## 5 Discussion  
This case study shows that integrating global open and other sourced geo-information could serve as a practical approach to major disaster assessment, especially for those countries that lack national spatial data infrastructure. However, even as more sources of information become available, there are still major discrepancies between the information provided and requirements in terms of accuracy, timeliness, and readability. To address these gaps, communities must manage issues related to geo-information and damage assessment. First, datasets required for damage assessments are typically large and drawn from several different agencies, rendering it difficult to access these data within a short period of time. Second, although open data policies are promoted through several initiatives, most geo-information of less than a 10-meter resolution for affected element estimations is strictly limited for sharing or open use. High resolution remote sensing imagery is also very limited in terms of emergency use. Third, as geo-information typically takes several forms due to a lack of uniform standards and professional processing capacities in most developing countries, it typically takes a long time to prepare data and to conduct analyses, as datasets are not typically well organized. In addition, every methodology has advantages and limitations, and determining ways to use them in an integrated manner to support outcome synergy is a major challenge. Further, while new advances in the field of geo-information, such as crowdsourcing, are changing the ways in which information is collected and integrated from multiple sources and offered to the end user [50; 51; 52; 53; 54; 55], the validation and analysis of such information must be explored for better use.
## 4 Discussion
### Effect of Microbial Colonization on Structures
These sulfate attack processes resulted in reduced strength, altered composition, and increased surface area of concrete. The presence of weddellite in S-9 is associated with the metabolic activity of microorganisms and could cause biophysical damage to the structures. Calcium oxalate is the most insoluble oxalate and often found in living organisms and nature in the form of whewellite (CaC\({}_{2}\)O\({}_{4}\)\(\cdot\)H\({}_{2}\)O) and weddellite (CaC\({}_{2}\)O\({}_{4}\)\(\cdot\)2H\({}_{2}\)O) [59]. The biosynthesis of calcium oxalate can be presented by this equation: Ca\({}^{2+}\) + C\({}_{2}\)O\({}_{4}\)\({}^{2-}\) + xH\({}_{2}\)O\(\rightarrow\)Ca(C\({}_{2}\)O\({}_{4}\))\(\cdot\)xH\({}_{2}\)O(s)\(\downarrow\). However, some studies suggested that the precipitation of calcium oxalate is negligible or even beneficial because insoluble oxalate crystals can block pores and act as diffusion barriers, thus limiting the further leaching of calcium [60]. However, the precipitation of significant amounts of calcium oxalate occurring in mineral-based building materials is inclined to cause physicochemical damage. The complexation of oxalic acid with calcium ions causes the dissolution of matrices, and the expansion of secondary oxalate biominerals causes physical damage [61]. The presence of whewellite and weddellite is frequently found in the black and patina crusts of limestone and concrete building surfaces [62]. Fungal-induced whewellite and weddellite precipitation can form biomineralized crusts with dead hyphae [63]. Although the amount of weddellite found in S-9 is comparatively low, high levels of weddellite can cause crusting and discoloration in buildings because of the constant input of oxalic acid from surface microbial communities. Corrosion by biogenic acids and the crystallization of secondary minerals decrease the strength of building material, and together with rough surfaces (as discussed in Section 4.1), facilitate the colonization of filamentous microorganisms. Importantly, in addition to the distribution of microorganisms on matrix surfaces, microorganisms can thrive in cracks and air voids (Figure 2D-FJ). The internal expansion of microorganisms and penetration of concrete by hyphae may induce cracks and enlarge current cracks and pores and thereby increase the porosity and connectivity of material, even leading to the detachment of some fragments. These effects were indicated by the material debris attached to the biofilm (Figure 2G,H). This phenomenon causes biophysical damage to concrete and eventually leads to the dissolution and disintegration of concrete.
## 3 Results and Discussion
### Factors Investigation
#### 3.2.4 Liquid Viscosity  
Liquid viscosity is another factor affecting liquid distribution. It affects fluid turbulence directly and thus affects the flow form of the liquid inside TBRs. Investigation results of the variation of liquid viscosity are given in Table 7, and the liquid distribution inside the packing is shown in Figure 7.  
It can be clearly seen from Figure 7 that the quantity of liquid inside the packing increased with the rise of liquid viscosity. This is because the higher viscosity lead to a stronger liquid-solid shear, making it difficult for turbulence to develop, and resulted in a more uniform liquid flow on the particle surface. In high liquid viscosity cases the liquid-solid shear plays a greater role than the gas-liquid interactions [1], making the liquid harder to leave the particle surface and the whole packing. Film flow was easier to be found under high viscosity while rivulet flow can be observed under low viscosity [16], which is responsible for the increase in wetting efficiency. Differences between the first and the second was greater than that between the second and the third. For the wetting efficiency it increased by about 3% when the viscosity increased from \(4.4435\times 10^{-4}\) Pa-s to \(8.8871\times 10^{-4}\) Pa-s. However, a further increase of viscosity caused a slight drop in wetting efficiency, which did not vary distinctly. It shows the same tendency as liquid holdup, which means that the increase of liquid viscosity is more effective when the viscosity is low.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Liquid Viscosity (Pa-s)** & **Liquid Holdup** & **Wetting Efficiency** \\ \hline \(4.4435\times 10^{-4}\) & 0.2392 & 0.6237 \\ \(8.8871\times 10^{-4}\) & 0.2633 & 0.6566 \\ \(13.3305\times 10^{-4}\) & 0.2654 & 0.6518 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Liquid holdup and wetting efficiency of three liquid viscosities.  
Figure 6: Liquid distributions with different contact angles (\(\upsilon_{G}=0.245\) m/s, \(\upsilon_{L}=0.008\) m/s).