esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Implementation
### Compound Faults Dataset  
Compound faults data was acquired from an electrical experiment system described by the diagram illustrated in Figure 5. The testbed was constructed by two rotational shafts: a drive-end shaft (DES) and a non-drive-end shaft (NDES) [8]. The DES and NDES were connected by a gearbox with a reduction ratio of 1.52:1. Rolling element bearings (FAG NJ206-E-TVP2) were installed at the ends of these shafts to alleviate the friction, which hampers movement. DES was activated by a three-phase induction motor at various shaft speeds (200, 250, 300, 350, 400, 450, and 500 RPM). As shown in Figure 6, bearings were seeded not only for a single-mode but also for compound faults of variable lengths of crack size: 3 mm, 6 mm, and 12 mm. The vibration signals were acquired by an acquisition system using a wideband vibration sensor sampling rate of 65,536 Hz mounted near the bearing at NDES, an amplifier (NEXUS condition amplifier type 2692-C), and a portable data acquisition device (PULSE type 3560-C). Specifications of the vibration accelerometer sensor and data acquisition system are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Training Sample Speed (RPM)**} & **Test Sample Speed (RPM)** \\ \hline Dataset 1 & 1796 & (1772, 1748, 1722) \\ \hline Dataset 2 & 1772 & (1796, 1748, 1722) \\ \hline Dataset 3 & 1748 & (1772, 1796, 1722) \\ \hline Dataset 4 & 1722 & (1772, 1748, 1796) \\ \hline \end{tabular}
\end{table}
Table 1: Dataset for single bearing faults.  
Figure 5: Testbed for collecting vibration signals from compound faults: (**a**) scheme of the experimental setup and (**b**) real experimental system.
## 3 Experimental Design and Implementation
### Experimental Procedure
#### 3.3.6 Result Visualization  
Visualization is an important means for understanding and presenting analysis results:  
1. Stop point density visualization: Extracting trajectory points with zero speed, using 2D histograms to analyze spatial distribution, and generating heat maps to display congestion distribution.
2. Traffic parameter visualization: Using box plots to display distributions of speed, travel time, and flow, generating time series plots to show temporal variation characteristics of parameters, and creating spatial distribution maps to show spatial differences in parameters.
3. Comprehensive analysis charts: OD matrix heat maps, road network flow distribution maps, and key indicator comparison charts.
## 2 FE Modeling and SSI Analysis Method
### Characteristics of the Input Seismic Motion and SSI Analysis Procedure  
In this study, the Gyeongju earthquake, which occurred on 12 September 2016, in South Korea, with a magnitude of 5.8, was utilized. This is the largest earthquake observed  
Figure 5: Embedded FE models: (**a**) High-rise structure; (**b**) low-rise structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**High-Rise Structure**} & \multicolumn{2}{c}{**Low-Rise Structure**} \\ \cline{3-5}
**Mode** & **Soil Type** & **1 st** & **2 nd** & **1 st** \\ \hline x-bending & FI & 0.20 & 0.88 & 1.97 \\  & AS & 0.20 & 0.89 & 2.81 \\  & WS & 0.21 & 0.89 & 3.27 \\  & WR & 0.22 & 0.90 & 3.66 \\ \hline y-bending & FI & 0.21 & 1.24 & 3.25 \\  & AS & 0.23 & 1.27 & 3.43 \\  & WS & 0.23 & 1.29 & 3.46 \\  & WR & 0.25 & 1.35 & 5.11 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Natural frequencies of the embedded models (unit: Hz).  
in South Korea since full-scale seismic instrumentation began in 1978. The seismic waves were recorded at the DAG2 (Daegu) station, the observation point closest to the epicenter, and Figure 6 shows the acceleration time history. The peak acceleration of this earthquake was approximately 4.61 m/s\({}^{2}\) in the east-west direction and 5.32 m/s\({}^{2}\) in the north-south direction [24].  
The shallow soil layers in Korea, with bedrock depths of 15 to 30 m, exhibit characteristics that amplify seismic responses in the high-frequency range. In particular, the Gyeongju earthquake used in this study is dominated by high-frequency components, as shown in Figure 7, resulting in a high seismic response in the 2.5 Hz to 10 Hz range [1].  
The Gyeongju earthquake wave was used to conduct free-field and SSI analyses following the procedure outlined below. First, the seismic waves presented in Figure 6 were assumed to be the response at the bedrock. For the free-field analysis, the seismic waves were applied at the base of the four soil types (FI, AS, WS, and WR) to calculate the free-field response at the center of the exposed foundation level of the structure. The calculated free-field response was applied to the fixed-end model (Figure 4) and the embedded model (Figure 5).
## 4 Discussion
### Dry Mass and Leaf Growth  
In this study, plants in LD13 + 4B and LD13 + NI - 4B had greater shoot and root dry masses compared to plants in the SD10 control (Figure 3B). These results indicate that a prolonged photoperiod contributed to the dry matter production. Moreover, B light supplementation increases  
Figure 5: The effects of the supplementary and night-interrupting 10 \(\upmu\)mol-m\({}^{-2}\).s\({}^{-1}\) PPFD B light on the relative gene expression levels of) \(D\). _grandiform_ ‘Gaya Yellow’ determined by real-time PCR of _PHYA_ (**A**), _PHYB_ (**B**), and _CRY1_ (**C**). (Details of the NI light qualities are presented in Figure 2). At each sampling date, the individual gene expression levels for the plants in the photoperiodic light treatments were divided by the mean gene expression level for plants in the SD10 control. The data are presented as the mean \(\pm\) S.E of the 3 biological replicates. Means accompanied by different letters indicate significant differences (\(p<0.05\)), according to [PERSON]’s studentized range test at a 5% significance level.  
the photosynthetic carbon assimilation and may also allow greenhouse crops to accumulate a greater biomass [38].  
Plants in all the photoperiodic light treatments had a higher number of leaves per plant compared to plants in the control, and plants in SD9 + 4B had the greatest number of leaves (Figure 3C). Plants in SD10 + NI-4B had a smaller mean leaf area than plants in the SD10 control did (Figure 3D), resulting from shorter leaf lengths and widths (data not shown). All other treatments with B light led to a greater leaf area than that of plants in the SD10 control. [PERSON] et al. [39] reported similar results, where light treatments with weak 50 \(\mu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPFD B light lead to increases in the leaf area. [PERSON] et al. [26] proposed that B light is required during the photoperiod for the normal growth of lettuce seedlings grown under R LEDs; if the B light level was low, long, narrow leaves developed. [PERSON] and [PERSON] [40] reported that total leaf area per plant in cherry rootstock 'Colt' was significantly greater in B LED-exposed plants than other treatments. However, these photomorphogenic responses are specific to the particular species. [PERSON] and [PERSON] [41] reported that increasing the B light proportion resulted in decreasing leaf area in soybean, while in lettuce, increasing the B light proportion resulted in increasing the leaf area. Eskins [42] observed that the _Arabidopsis thaliana_ leaf area was negatively correlated with the B light proportion, as a high-intensity B light irradiance corresponded to the development of small leaves.  
[PERSON] et al. [43] observed that the chlorophyll levels increased as plants transitioned from the vegetative to the reproductive growth, and decreased during maturation. Correspondingly, the lower chlorophyll content of plants in LD13 + 4B compared to that of plants in the other treatments in this study (Figure 3E) may be due to the continued maturation after the plants transition from the vegetative to reproductive growth.
## 5 Discussion
### Validation and Comparison with Physical Methodologies
These occurrences can be explained as the combined consequence of the algorithm's inherent nature and the explanatory variance retained by the variables that feed the model. ANN tends to interpret the results in a more effective way compared to the other models, especially when the features do not have a linear distribution. RF models are often good algorithms in the presence of missing data, but they have high variance, meaning that they tend to change predictions rapidly with even a slight change in the input data they were trained on. SVMR is often good a algorithm for nonlinear feature separation. Figure 10 compares the co-seismic slope instability susceptibility maps of the two best models in terms of the SR indicator (ann_cp and svmr) for the model constructed from principal components (Figure 10a) and that developed without principal components (Figure 10b), with the susceptibility map obtained using the physical-based method performed previously in [27] in the same area (Figure 10c). The physical-based model proposed in [27] utilizes the probabilistic tree approach to generate co-seismic landslide susceptibility maps related to low-to-moderate magnitude earthquakes (M\({}_{\rm w}<\) 5.5). This approach allows for the objective quantification of causative factors, identifying cohesion, friction angle, and Newmark displacement as relevant variables. The combination of parameters that yielded the best results in landslide prediction includes a failure surface depth of 2 m, a 10 th percentile of unit weight, a 10 th percentile of cohesion, a 90 th percentile of friction angle, and the Newmark displacement model by [30]. This approach achieved a score of 38.80% using the SR coefficient, which is significantly lower than the value of the ANN model constructed from principal components (63.60%) and closer to the value developed using the SVMR model (47.20%). Regarding the maps derived in terms of susceptibility, a substantial difference is observed: ANN using principal components provides more information with respect to its counterpart, obtaining a more pessimistic vision as it contains larger areas with high and very high susceptibility. The SVMR model shows a prediction similar to that provided by a physical-based map in relation to co-seismic landslides. In contrast, the SVMR model is more cautious, but it agrees in predicting areas with high susceptibility to co-seismic landslides.
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.5 PMVS Dense Reconstruction Results
As shown in Table 1, VSFM demonstrates the best computational speed in point cloud generation (Statue1: 65.732 s, Archi2: 605.858 s), although its reconstruction density is significantly lower (Statue1: 424,949 points, Archi2: 840,909 points). The COLMAP algorithm shows a substantial increase in point cloud quantity compared to VSFM (Statue1: +150%, Archi2: +465%), but this comes with a considerable increase in time cost (Statue1: 3085.794 s, Archi2: 3785.947 s). The results of the algorithm presented in this paper, as illustrated in Figure 25, clearly demonstrate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**VSFM**} & \multicolumn{2}{c}{**Colmap**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-7}  & \(N\uparrow\) & **Times/s** & \(N\uparrow\) & **Times/s** & \(N\uparrow\) & **Times/s** \\ \hline Statue1 & 424,949 & 65.732 & 1,059,895 & 3085.794 & **3,033,703** & 2163.256 \\ Statue2 & 911,398 & 252.537 & 1,571,687 & 2948.587 & **2,964,349** & 2489.657 \\ Archi1 & 631,484 & 439.327 & 2,476,828 & 2628.669 & **3,909,707** & 3252.891 \\ Archi2 & 840,909 & 605.858 & 4,749,228 & 3785.947 & **8,574,136** & 3459.859 \\ \hline \hline \end{tabular} The arrow indicates that a higher quantity of point clouds is preferable; the bolded data represents the optimal results for each section.  
\end{table} 
Table 9: The quantity of dense reconstructed point clouds across four datasets using various matching algorithms. Figure 25: Quantitative results of various dense reconstruction algorithms applied to four sets of self-collected data: (**a**) Comparison of Dense Reconstruction Point Cloud Quantities; (**b**) Comparison of Dense Reconstruction Time. The improved algorithm achieves the highest reconstruction density across all datasets, generating point clouds that are 7.14 times that of VSFM (Statue1: 3,033,703 points) and 1.80 times that of COLMAP (Archi2: 8,574,136 points). Although its computation time is only reduced by 8.2% (Arch1) to 29.8% (Statue1) compared to COLMAP when considering both the number of point clouds and runtime, the proposed algorithm demonstrates superior performance.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
###### Abstract  
Autonomous Underwater Vehicles (AUVs) are becoming increasingly popular for large number of civil and military applications, such as environmental monitoring, oceanography, archaeology, or mine warfare. Operational safety issue still prevents the exploitation of the full potential of AUVs. Operations of AUVs are limited by constraints including the need to guarantee no collision with manned surface vehicles. To avoid collisions, a solution is to relay surface vessel positions to the AUVs by an underwater communication link. The communication must be optimized to establish a robust and reliable link at various range and depth. Commercial underwater acoustic modems are often dedicated for specific distances and bit rates with performances strongly dependent on the environmental conditions. For the considered application, the modern must be adaptive at the time and frequency selectivity of the channel which is varying according to the operation context. In this work, we propose an adaptive underwater acoustic waveform optimized to exploit at the best the time and frequency diversity the channel. To show the potential gain obtained by using diversity, the performance of the designed modem is evaluated using theoretical and realistic underwater channels. In addition, we propose a method for adapting the waveform based only on the knowledge of the transmission geometry. Finally, the proposed modem is tested to relay Automatic Identification Systems (AIS) picture of an area to AUVs.  
AUV; MC-SS; underwater acoustic communications; AIS; Doppler filter bank; security; adaptive bit rate; 2D spreading +
Footnote †: journal: Medical Imaging
# Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems
## 2 The Structure and Mathematical Model of the Quadrotor
### E-SSPC for Orbital Tracking
\begin{bmatrix}\widetilde{\mathsf{x}}(t)\\ \widetilde{\mathsf{u}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{v}}_{0}(t)\\ \widetilde{\mathsf{z}}(t)\mathsf{dt}\\ \end{bmatrix} = \begin{bmatrix}\mathsf{x}(t)-\mathsf{x}_{\mathsf{r}}(t)\\ \mathsf{u}_{0}(t)-\mathsf{u}_{0_{\mathsf{r}}}(t)\\ \mathsf{f}\left(\mathsf{x}(t)-\mathsf{x}_{\mathsf{r}}(t)\right)\mathsf{dt}\\ \mathsf{y}(t)-\mathsf{y}_{\mathsf{r}}(t)\\ \mathsf{v}_{0}(t)-\mathsf{v}_{0_{\mathsf{r}}}(t)\\ \mathsf{f}\left(\mathsf{y}(t)-\mathsf{y}_{\mathsf{r}}(t)\right)\mathsf{dt}\\ \mathsf{z}(t)-\mathsf{z}_{\mathsf{r}}(t)\\ \mathsf{w}_{0}(t)-\mathsf{w}_{0_{\mathsf{r}}}(t)\\ \mathsf{f}\left(\mathsf{z}(t)-\mathsf{z}_{\mathsf{r}}(t)\right)\mathsf{dt}\\ \end{bmatrix}  \tag{19}\]  
Using [PERSON]'s method, we get a discrete linear model with respect to time:  
\[\mathsf{x}_{\xi_{x}}(k+1)=\overline{\mathsf{A}}\cdot\mathsf{x}_{\xi_{x}}(k)+ \overline{\mathsf{B}}(k)\cdot\widetilde{\mathsf{u}}_{\xi_{x}}(k) \tag{20}\]  
In the given context, the control input \(\mathsf{U}_{1}(t)\) is treated as a time-varying parameter that governs the motion set in relation to the x- and y-coordinates. Additionally, considering the hierarchical control structure, the roll, pitch, and yaw angles are also regarded as time-dependent parameters. The model error (20) can be divided into two subsystems: pitch error and motion error in terms of x and y. The matrices \(\overline{\mathsf{A}}\), \(\overline{\mathsf{B}}\) for each subsystem are shown as follows:  
\[\left\{ \begin{array}{c}\overline{\mathsf{A}}_{\mathrm{z}}=\left[\begin{array} []{rrrr}1&\Delta t&0\\ 0&1&0\\ \Delta t&0&1\end{array}\right]\\ \overline{\mathsf{B}}_{\mathrm{z}}=\left[\begin{array}{rrrr}0&\Delta t\\ \overline{\mathsf{m}}\end{array}\end{array} \end{array}\right. \tag{21}\]  
\[\left\{ \begin{array}{c}\overline{\mathsf{A}}_{\mathrm{xy}}=\left[\begin{array} []{rrrrrr}1&\Delta t&0&0&0&0\\ 0&1&0&0&0&0\\ \Delta t&0&1&0&0&0\\ 0&0&0&1&\Delta t&0\\ 0&0&0&0&1&0\\ 0&0&0&\Delta t&0&1\end{array}\right]\\ \overline{\mathsf{B}}_{\mathrm{xy}}=\left[\begin{array}{rrrr}0&0&0\\ \overline{\mathsf{m}}\end{array}\end{array} \right. \tag{22}\]  
where \(\Delta t\) is the sampling time chosen to be small enough to retain all the translational errors but also large enough for steady-state rotation. Based on these analyses, the trajectory tracking problem for Drones can be defined as follows: finding control inputs within a constrained range of values to manipulate the state variables in Equation (20) from an initial position \(\mathsf{x}_{\xi 0}\) to the origin. Consequently, by considering the altitude difference and lateral movement, control rules are devised to compel the system to track the desired trajectory.
## 3 Results
### Professional Characteristics and Resin Material Selection  
The selection criteria differed significantly between anterior and posterior restorations based on clinical experience. For anterior restorations, the most frequently reported selection criterion was the anatomical features to be achieved (54.0%), followed closely by transparency and opacity (56.3%) and the color of neighboring teeth (50.6%). Notably, less experienced dentists (70.5%) were significantly more likely to consider anatomical features compared to those with more than five years of experience (37.2%). In contrast, experienced clinicians preferred using special anterior resins (51.2%), whereas only 22.7% of less experienced dentists selected this criterion. In addition, for posterior restorations, a similar trend was observed, anatomical features remained the most important criterion, (55.2%). Less experienced dentists were more likely to prioritize anatomical considerations (65.9%) than their more experienced counterparts (44.2%). Additionally, a higher proportion of experienced dentists (32.6%) reported using whatever material was available in stock compared to their less experienced counterparts (22.7%) (Table 3). Figure 2: Selection criteria for resin materials in anterior and posterior restorations. The selection of resin restorative materials influenced by dentists' professional characteristics, including clinical experience, age, postgraduate education, and workload is presented in Table 4.  
For anterior restorations, a significant negative correlation was found between clinical experience and the importance assigned to anatomical features (r = \(-\)0.334, \(p\) < 0.05), suggesting that more experienced dentists rely less on anatomical considerations when selecting materials. Similarly, performing over 20 restorations per week was negatively correlated with prioritizing anatomical features (r = \(-\)0.315, \(p\) < 0.05). Notably, the use of special anterior resins was positively correlated with both postgraduate studies (r = 0.337, \(p\) < 0.05) and clinical experience (r = 0.295, \(p\) < 0.05). Additionally, for posterior restorations, clinical experience was negatively correlated with prioritizing anatomical features (r = \(-\)0.218, \(p\) < 0.05). No significant relationships were found between purchasing frequency and material selection, indicating that procurement habits do not necessarily dictate material selection criteria. The variety of professional characteristics and familiarity with material specifications, importance of compliance, prioritized features, and preferences for green practices in resin restorative material selection are demonstrated in Table 5. A key finding in Table 5 is that clinical experience (r = 0.231, \(p\) < 0.05) and frequent purchasing of resin materials (r = 0.254, \(p\) < 0.05) were positively correlated with familiarity with material specifications. Conversely, those not responsible for supply were significantly less familiar with specifications (r = \(-\)0.341, \(p\) < 0.05), highlighting the role of procurement responsibility in material literacy.
# Defect Prediction for Capacitive Equipment in Power System
## 4 Experiments
### Performance Metrics  
In this study, four evaluation metrics are used to measure the prediction performance of the models, including accuracy, precision, recall, and F1-Score, calculated as follows:  
\[Accuracy\ =\ \frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[Precision\ =\ \frac{TP}{TP+FP} \tag{3}\]  
\[Recall\ =\ \frac{TP}{TP+FN} \tag{4}\]  
\[F1-Score\ =\ \frac{2\times Precision\times Recall}{Precision+Recall} \tag{5}\]  
Figure 5: OOB error curve of RF algorithm in defect occurrence prediction under different parameters.  
Figure 6: OOB error curve of RF algorithm in defect level prediction under different parameters.  
where _TP_ refers to the number of positive examples that are correctly classified as positive examples, _FP_ represents the number of negative examples that are incorrectly classified as positive examples, _TN_ is the number of negative examples that are correctly classified as negative examples, and _FN_ denotes the number of positive examples that are misclassified as negative examples.
###### Abstract  
This review was performed to analyze evidence from the scientific literature of the adjunctive effect of probiotics in the treatment of peri-implant mucositis (PiM). Only randomized clinical trials evaluating the effect of probiotics as an adjunct to mechanical debridement in PiM were included. A search was performed in PubMed/MEDLINE, ILLACS, ScienceDirect, and Web of Science to identify articles published in English up to January 2023. The quality of the studies was evaluated using the JADAD scale, and the risk of bias was assessed with the Cochrane Collaboration assessment tool. Of the 159 potentially relevant studies, only 9 studies were included. The most commonly used strain was _Lactobacillus reuteri_, and the meta-analysis was conducted on studies with a follow-up period of 3 to 6 months, observing an overall effect on the reduction of bleeding on probing (BOP) at 3 and 6 months (WMD = \(-1.13\), 95% CI = \(-1.95\) to \(-0.30\), \(p\) = \(0.007\); and WMD = \(-1.32\), 95% CI = \(-2.15\) to \(-0.48\), \(p\) = \(0.002\)), plaque index (PI) at 3 months (WMD = \(-1.22\), 95% CI = \(-2.25\) to \(-0.19\), \(p\) = \(0.02\)), and probing pocket depth (PPD) at 3 and 6 months, which was statistically significant in favor of the robotic group (WMD = \(-1.34\), 95% CI = \(-2.42\) to \(-0.25\), \(p\) = \(0.02\) and WMD = \(-1.36\), 95% CI = \(-2.61\) to \(-0.11\), \(p\) = \(0.03\)). On the other hand, there were no significant changes in the subgingival microflora around the implants with the use of probiotics. Probiotic therapy, as an adjunct to mechanical debridement, promotes a greater reduction in BOP, PPD, and PI, in relation to the control group without probiotics.  
Keywords:peri-implant mucositis; probiotics; systematic review; meta-analysis +
Footnote †: journal: _Sci._