esa-sceva/satcom-chunk-collection huggingface dataset:
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 2 Materials and Methods
### Pretreatment Digital Setup and Planning of Bracket Positions  
The intraoral scanner was also adopted to establish intraoral digital models of participants. Specifically, pretreatment digital models were applied for a virtual setup using the digital modeling software Ortho Analyzer (3 Shape Dental Systems, Copenhagen, Denmark). A virtual setup, facilitated on software, can separate each tooth and move it to the planned position. According to the software program, unwanted and nonexistent tooth positions were excluded, after which the mesial and distal positions of each tooth were marked. Subsequently, the program semiautomatically marked the gingival margin of each tooth and allowed manual adjustment of unclear margins. After confirmation, the program defined proximal contact points according to the obtained gingival margin and mesiodistal direction. According to the six keys to normal occlusion proposed by [PERSON] et al. (1976; i.e., molar relationship, mesiodistal crown angulation, labiolingual crown inclination, no rotations, tight contacts, and a flat occlusal plane) [17], we defined the final occlusion status. To avoid mesiodistal deviation of brackets causing crown rotation during treatment, we overlapped the Y axis with the FACC; the incisor line was regarded as a reference line for calculating the height of each bracket. The vertical positions of each bracket were then aligned with the final occlusion plane, namely Andrews' plane [15], to ensure correct bracket position marking as planned. After bracket planning was completed using 3D digital setup software, we obtained the absolute coordinates of each bracket on the tooth crown. The teeth and brackets could then be bounded using the developed AR-assisted bracket navigation system.
# Comparative Assessment of Shear Demand for RC Beam-Column Joints under Earthquake Loading
## 4 Results and Discussion
### Bi-Axial Shear Demand Using NLTH  
Figure 16 shows the bi-axial shear demand curves obtained via NLTH analysis for the case of +x and +y excitation. This condition is privileged in this discussion because it is equal to the pseudo-dynamic experimental input [19]. Additionally, the elliptical domain prescribed by ACI352 [11] is defined at each beam-column joint. The ellipse radii represent the joint shear strength evaluated according to ASCE41-17 [14]. Moreover, the results are compared with the yielding threshold defined according to Equation (4). For the sake of synthesis, only the first and the second floor results are discussed. The latter was characterized by the highest shear demand peaks. The third floor has been omitted because shear demand results were moderate.  
As expected, the shear demand does not overcome the yielding threshold, this being inherently included in the definition backbone curves for the beams' plastic hinges. The elliptical envelope shape of the shear demand orbits is recognized for the majority of the cases. The principal axes of the envelope do not necessarily coincide with x and y directions. This result is consistent with what was proven, numerically, by [PERSON] [50] for bi-axial bending interaction at the columns.  
Cases where the shear demand exceeds the shear resistance deserve a particular explanation. In fact, the studies describing the experimental results [18; 20] of the SPEAR frame did not report beam-column joint failure and diagonal cracking has been described as occasional. In this regard, and by observing that the shear resistance is exceeded especially for the cases where bi-axial interaction becomes significant (i.e., corner joint), it follows that the elliptical strength domain is conservative. Nothing can be said about a possible over-estimation of shear demand which cannot be sustained conclusively due to the impossibility to validate the obtained results against experimental references. To the authors' knowledge, only [PERSON] [36] attempted to numerically evaluate the shear demand at the beam-column joint of the SPEAR frame. Results were reported as mean extreme values of NLTH analysis (the reader may find the definition of mean extreme value in the Section titled \"Probability Distribution for extreme values\" in [51]). Comparison with those is given in the following. Besides, [PERSON] [33] modeled the SPEAR frame including \"shear hinges\" at beam-column joints but shear demands were not presented explicitly.
## 4 Analysis of Electromagnetic Characteristics of DLSRG
### Analysis of Magnetic Saturation  
The magnetic density distribution clouds of the typical position of the A phase winding running at a typical position for one cycle with 10 A excitation current are shown in Figure 4. During the operation of the generator, the magnetic density of the stator yoke is always higher than that of the tooth, and the maximum magnetic density of the generator stator tooth alignment with the center line of the motor tooth is about 17475 GS [24], which is lower than the saturation magnetic density of the silicon steel sheet. The magnetic density of excitation stage and generation stage is lower than that of saturation magnetic density, which can be regarded as a linear model, so DLSRG meets the design requirements of magnetic saturation.  
Figure 4: \(Cont\).  
Figure 3: When the winding current is 0 A, 2 A, 4 A, 6 A, 8 A, 10 A, winding inductance and mutual inductance are shown in Figure 3. (**a**) Phase A self-inductance; (**b**) Phase A-B Mutual Inductance; (**c**) Phase A-C Mutual Inductance.
## 4 Conclusions  
The partial least square regression analysis of spectra reflectance at the Vis/NIR region of the electromagnetic spectrum (400-1000 nm) that was captured at three distinct areas on the potato tubers, externally or internally, produced the most reliable models in assessing potato freshness, irrespectively of the cultivar, with a cross-validated regression coefficient (Rcv) equal to 0.981 and 0.947, as determined by external or internal measurements, respectively. Feature selection algorithms (variance inflation factor, variable importance scores, and genetic algorithms) identified specific regions of interest that mostly affected the accuracy of the model, in order to provide the most accurate multilinear model in terms of highest probability, strongest regression, and lowest collinearity and root mean cross validation error. Similarly, the best discrimination of the three potato cultivars, was achieved with the spectra reflectance measurements on the skin of the tubers (93% of the cross-validation sample sets), while R-G-NIR and R-B-NIR image components combined with selected chlorophyll fluorescence parameters (Fi, Fv, and Phi_Eo) performed better than color measurements in both potato freshness assessment or cultivar classification in all non-destructive trials, irrespective of the cultivar tested.  
Figure 6: Spectra reflectance data upon pre-processing (normalization, Savitzky–Golay first derivative and mean centering) that were externally captured from 3 commercial potato cultivars (Spunta, Banba, and Sylvana) during storage for 0, 2.5, or 5 months at 5 \({}^{\circ}\)C externally on the skin (**A**) or internally in the flesh (**B**) of the tubers. In each cultivar, the data were pooled from the samples stored for 0, 2.5, or 5 months at 5 \({}^{\circ}\)C.  
Figure 7: Classification of potato tubers into 3 commercial potato cultivars (Spunta, Banba, and Sylvana) that were stored for 0, 2.5, and 5 months at 5 \({}^{\circ}\)C based on the scores of LV1, LV2, and LV3 after the partial least squares discriminant analyses on the data from specific wavelength regions that were extracted with the genetic algorithm as a feature extraction technique of the spectra that were captured externally on the skin of the tubers.  
**Author Contributions:** Conceptualization, D.S.K., P.T. and A.S.S.; methodology, D.S.K., P.T., K.N. and A.G.; software, D.S.K., P.T. and K.N.; formal analysis, P.T. and A.S.S.; data curation, D.S.K. and P.T.; writing--original draft preparation, D.S.K., P.T. and K.N.; writing--review and editing, A.G., D.M. and A.S.S.; supervision, P.T. and A.S.S.; project administration, P.T. and A.S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### The MLE-PMM (2 rd Step)
#### 2.2.2 Estimation of the Covariance of the Measured FRFs  
The estimation of the uncertainty bounds on the identified modal parameters is only possible if the variance of the noise is taken into account in the cost function (6). In the formulation of the MLE-PMM proposed in this paper, it was considered the following assumptions for the estimation of this variance [7]:  
* the noise on the measured FRFs is circular complex normally distributed;
* the noise on the measured FRFs is zero-mean valued;
* the noise on the measured FRFs is uncorrelated over the frequencies; and
* the noise on the measured FRFs is uncorrelated over the outputs.  
The estimation of this variance depends on the estimator used in the non-parametric estimation of the FRFs. If the \(H_{1}\) and \(H_{2}\) estimators are used to estimate the FRFs, the variance can be computed from the coherence function [22]. In case of Single Input Multiple Output (SIMO) systems, the variance of the \(H_{1}\) FRF estimator can be calculated by [23]\[\hat{\sigma}_{R_{oi}}^{2}=\frac{1}{N_{b}}\left(\frac{1-\gamma_{oi}^{2}}{\gamma_{oi} ^{2}}\right)\left|H_{oi}\right|^{2} \tag{22}\]  
where \(N_{b}\) is number of time data segments used to estimate the FRF and \(\gamma_{oi}^{2}\) is the coherence function. This function is commonly used in EMA to indicate how the output is correlated with the input. The closer to one this function is, the more the outputs are influenced by the inputs. On the other hand, the closer to zero it is, the more the outputs are contaminated by noise. Assuming Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) systems with noise uncorrelated over outputs, the covariance matrix of the \(o\)th row of the FRF matrix is given by [22]  
\[\text{Cov}\left(H_{o}(\omega_{f})\right)=\frac{1}{N_{b}}\left(1-m\gamma_{o}^{2 }(\omega_{f})\right)\hat{S}_{y_{a}y_{a}}\hat{S}_{uu}^{-1} \tag{23}\]  
where \(\gamma_{o}^{2}(\omega_{f})\), \(\hat{S}_{y_{a}y_{a}}\) and \(\hat{S}_{uu}\) are the multiple coherence function, the autopower spectrum of the output and the autopower spectrum of the input, respectively, with the subscript \(y_{o}\) denoting the DFT spectrum of a single measured output.
## 2 Experimental
### Experimental Laboratory Setup  
All the experiments were performed in the experimental setup presented in Figure 1[6]. A rotating disc 1 with a diameter of 20 cm and thickness of 4 mm, connected to an electric motor 2, with a variable adjustable speed, was encapsulated in an acrylic housing 3. On the rotating disc, textile wastewater treated previously with bentonite at a concentration of 2 g/L (this concentration was selected from the results onto different loaded wastewaters, studied in previous research reports [24]), was fed through a 3.5 mm nozzle at a location 10 mm from the disc center from a tank 4, using a centrifugal pump 5. The wastewater flowrate was maintained constant by adjusting tap 6 and measured with the flowmeter 7. The rotational disc speed was determined using a laser tachometer with an accuracy of \(\pm\)0.1 rpm. The outlet effluent used for the wastewater quality measurements was collected from tap 8.  
During the test runs, the temperature was verified using a calibrated thermometer and was found to be practically constant, with an initial value of approximately 20 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C.  
After collection from the effluent outlet, the wastewater samples were analyzed using a DR/2000 Direct Reading spectrophotometer in order to establish their quality indicators, and, finally, the SD's performance in textile effluent treatment.  
The efficiency of the SD technology applied in the case of the studied textile wastewaters was established based on the calculation of the removal efficiency (%), \(R\), using the following equation:  
\[R\left[\%\right]=\left[(C_{i}-C_{j})/C_{i})\right]\times 100, \tag{1}\]  
where \(C_{i}\) is the initial value of the studied quality indicator (mg/L, or FTU, or HU) and \(C_{j}\) is the final (or after specific \(t\) time) value of the studied quality indicator (mg/L, or FTU, or HU).
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Data** & **Encryption** & **Data** & **Integrity Detection** & **Integrity Detection** \\  & **Confidentiality** & **Method** & **Integrity** & **Method** & **Position** \\ \hline [PERSON] _et al._[23] & \(\times\) & – & \(\surd\) & MAC & Data aggregation \\ \hline \multirow{2}{*}{DAA [9]} & \(\surd\) & Hop-by-hop & \(\surd\) & MAC & Data aggregation, \\  & & symmetric & & & & forwarding and BS \\ \hline \multirow{2}{*}{CDA [12]} & \(\surd\) & End-to-end & \(\times\) & – & – \\  & & symmetric & & & & \\ \hline [PERSON] _et al._[13] & \(\surd\) & End-to-end & \(\surd\) & Homomorphic hash &
\begin{tabular}{c} Data forwarding \\ and BS \\ \end{tabular} \\ \hline \multirow{2}{*}{IPHCDA [14]} & \(\surd\) & End-to-end & \(\surd\) & MAC & BS \\  & & asymmetric & & & & \\ \hline \multirow{2}{*}{SDA-PH [15]} & \(\surd\) & End-to-end & \(\surd\) & MAC & BS \\  & & symmetric & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of secure data aggregation protocols with respect to WSN security requirements.
## 2 Problem Description  
In a healthcare facility, elective and emergency patients flow through different departments. The general flow of patients moving through critical units of a hospital is illustrated in Figure 1. Figure 1 indicates that elective patients are added to the waiting list while emergency patients are sent to the planning process and assigned to the required resources. It further explains the flow of patients from one resource such as the operating room to the other resources such as the ICU and ward. For capacity planning during higher-level planning decisions, the forecasted patients are considered. It is considered that the patients can directly come for treatment in the operating room (OR), intensive care units (ICU), or wards.  
It can be seen from Figure 1 that the patients arriving in the OR have the possibility to move into the ICU or the wards or can be discharged after treatment in the OR; the patients arriving in the ICU can move into a ward or can be discharged after treatment in the ICU. Current research considered the multi-level planning of patients on critical resources. The process considered in the current research for the multi-level planning of critical resources is presented in Figure 2.  
Figure 1: Patient flow considered in this paper.  
Figure 2 illustrates that the higher-level planning involves the planning of patients on each critical resource of the hospital. At a higher level, the patients are allocated to the planning horizons based on their arrival, considering the infinite capacity of all critical resources. At the medium level, the patients from higher levels are allocated to their required critical resources, including the operating room, ICU, and ward. Then, according to the theory of constraints, the bottleneck resource is identified. The bottleneck resource is identified based on patients' workload on all resources and utilization of the resources. After identifying the bottleneck resources, at the lower level, the respective planning and scheduling model is performed, and a plan is released which is communicated to the other upstream departments and resources. All other resources follow the released plan and scheduling of the bottleneck resources. Figure 2 further explains the patients for each resource; for example, the patients in the ICU include the patients from the operating room and randomly arriving patients. The mathematical relations for the multi-level planning and scheduling of patients on each critical resource are presented in this section.
## 4 Discussion
### Drawing Conclusions for Designing LUMA
We therefore opted to heavily streamline LUMA's capabilities by offering users to search for a specific LOINC code and to carry out unit conversions based on the mappings created that way. Additionally, we made sure to provide users with a written-out human-readable description of each UCM unit provided and to filter flawed example units and unfitting recommendations based on the _Property_ of a specific LOINC code. Due to the fact that the set of valid UCM units is infinitely big, we only provide recommendations based on a previously aggregated finite set of UCM units. Although this does not mean it can not be added onto, we believe that the current amount of units should be sufficient for initial usage. Moreover, there is another idea that could increase the flexibility of the curated recommendations even more. Each unit symbol in USUM is associated with numerous additional information. Units can be grouped by classes such as \"SI-Units\" or \"US-Lengths\", for example, but they may also be grouped by property. This property (not to be confused with LOINC's _Property_ axis) allows one to group units by their physical makeup. Second, minute and hour obviously describe time whereas gram, ton and pound are masses. Another way of describing the relationship between LOINC's \"Mass Rate\" _Property_ and UCM might therefore be described as \"[UCUM unit that is of the property'mass'] / [UCUM unit that is of the property 'time']\". What this would enable us to do is to provide users with the ability to make controlled modifications to the curated UCM recommendations within LUMA. Each use case and laboratory setting is different, and while the ValueSet from which the recommendations are sourced from is quite extensive, it does not account for all possible related units, metric prefixes, specific scalars or numerous combinations of the three in the form of \"ug/(12.h)\" and \"mg/(30.min)\", for example. Our flexible UCM implementation allows us to analyze the specific operands of each of these terms in order to provide information on how they may be altered without breaking compatibility with their base unit representation.
## 4 Experiments
### Results and Discussion
#### 4.1.2 The Implications of \(Cr\) Classifier for Machine Learning Field  
The results of our experiments demonstrate that the \(CR\) classifier offers a promising approach for supervised classification tasks. By combining the strengths of FIL and out-ranking measures, the \(CR\) classifier is able to effectively handle noisy and imbalanced data, while also producing highly interpretable models. These findings have several significant implications for the field of machine learning.  
1. Advancement of interpretable machine learning: The \(CR\) classifier's ability to provide clear explanations for its predictions contributes to the growing demand for transparent and accountable AI systems [43]. This is particularly important in domains where understanding the decision-making process is crucial, such as healthcare, finance, and legal applications.
2. Improved performance on challenging datasets: The \(CR\) classifier's robustness to noise and imbalance makes it well suited for real-world datasets that often contain  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Ranking** & **Average Normalized Score** \\ \hline
**CR1 (Percentile: 0.9–0.1)** & **0.631729** \\ \hline
**CR2 (t = 1.1)** & **0.62406** \\ \hline
**RF** & **0.622963** \\ \hline NB & 0.608517 \\ \hline SVM & 0.553853 \\ \hline k-NN & 0.515612 \\ \hline MLP & 0.473611 \\ \hline NC & 0.363622 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm rankings based on average normalized AUC scores.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Group** & **Classifiers** & **Threshold** \\ \hline High Performance & _CR1_, _CR2_, RF & Scores \textgreater{} 0.5427 \\ \hline Medium Performance & NB, SVM, k-NN & Scores between 0.5427 and 0.4544 \\ \hline Low Performance & MLP, NC & Scores \textless{} 0.4544 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classifiers grouped by performance.  
imperfections. This could lead to more reliable and accurate predictions in a variety of domains.
3. Potential for new applications: The interpretability and effectiveness of the \(CR\) classifier could enable its application in areas where traditional machine learning methods have fallen short. For example, it may be useful for analyzing complex medical data or for developing personalized recommendation systems.  
Overall, using outranking techniques instead of majority voting provides several advantages, including robustness to noise, consideration of feature relationships, enhanced interpretability, handling of imbalanced datasets, and flexibility in adapting to different types of data. These advantages make outranking techniques a powerful and versatile tool for classification tasks, particularly in complex and challenging real-world scenarios.
## 3 Results
### Variability of NDVI, EVI, and SIF in Driving Factors
#### 3.2.1 Effects of Air Temperature, Precipitation, and Relative Humidity on NDVI, EVI, and SIF
At the same time, the spatial distribution of the NDVI and EVI correlation is not consistent with the spatial distribution increasing from the northwest to the southeast, indicating that the response of the SIF to air temperature is more sensitive than that of the NDVI and EVI. The differences in the spatial distributions of the NDVI, EVI, and SIF correlations with precipitation, as shown in Figure 7D-F, are in the negatively correlated areas. We hypothesize that the reason for these differences is that although the response of the SIF to photosynthesis is more direct, there is a certain spatial lag in the effect of precipitation on the SIF, resulting in a negative correlation between the NDVI and EVI and precipitation in more areas. It also indicates that the NDVI and EVI are more sensitive to precipitation. The negative correlations between the EVI and relative humidity, as shown in Figure 7G-I, are apparent in the northwest, and the positive correlations are evident in the southeast. Although the EVI and relative humidity displayed negative spatial correlations in more northwestern regions, their spatial correlation distribution and \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Air Temperature** & **Precipitation** & **Relative Humidity** \\ \hline Temporal variation of NDVI & 0.375 & 0.42 & \(-\)0.689 \\ Spatial distribution of NDVI & 0.1472 & 0.1049 & 0.2526 \\ Temporal variation of EVI & 0.344 & 0.372 & \(-\)0.69 \\ Spatial distribution of EVI & 0.2407 & 0.1049 & 0.0787 \\ Temporal variation of SIF & 0.239 & 0.509 & \(-\)0.834 \\ Spatial distribution of SIF & 0.0742 & 0.2076 & 0.4366 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation of interannual variation of the NDVI with air temperature, precipitation, and relative humidity. Figure 6: The spatial distribution of mean air temperature, precipitation, and relative humidity in China from 2000 to 2019 with NDVI, EVI, and SIF. (**A**): Spatial distribution of temperature. (**B**): Spatial distribution of precipitation. (**C**): Spatial distribution of relative humidity. spatial distribution (Figure 3B) both showed an increasing trend from northwestern to southeastern areas. In contrast, the spatial correlation distribution and spatial distribution of the SIF exhibited a significant difference, indicating that the NDVI and EVI were more sensitive to a relative humidity response than the SIF.