esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bioactive Compounds Commonly Found in Green-Colored Fruits and Vegetables
### Potential Health Benefits
#### 2.4.3. Phenylethanolamines (Hydroxyrosol)  
General Characteristics, Structure, and Food Sources  
Hydroxytyrosol (HXT) is an amphipathic phenolic compound derived from olives, olive oil, and olive tree leaves [218]. It is also called 3,4-dihydroxyphenylethanol or 3,4-dihydroxyphenolethalanol, or 4-(2-hydroxyethyl)-1,2-benzenediol according to the IUPAC system [219]. In nature, HXT is generated by the hydrolysis of oleupopein by the enzyme \(\beta\)-glucosidase during olive ripening. In its chemical structure, HXT consists of a benzene ring with three adjacent -OH groups, and its molecular formula is C\({}_{8}\)H\({}_{10}\)O\({}_{3}\).  
Bioavailability  
Hydrophilic phenolic compounds are known to be absorbed in a dose-dependent manner and excreted in the urine mainly as glucuronide conjugates in animals and humans [220; 221]. Similar to other phenolic compounds, HXT undergoes an intense and rapid metabolism first inside enterocytes and then in the liver [222; 223]. Although the absorption process varies depending on the administration vehicle employed, HXT is mainly absorbed in the small intestine and colon by passive transport from 75% to 100% [221]. It reaches its maximum concentration in plasma approximately 7 min after intake and then becomes undetectable after 4 h [224]. HXT and its metabolites have excellent distribution capacities in tissues, such as the muscle, testis, liver, and brain, which may contribute to their health-beneficial properties [225]. It is noteworthy that phenolic bioavailability is influenced by different factors such as age, hormonal status, or gender (reviewed in [219]). In the case of HXT, the food matrix in which it is incorporated plays an important role in its metabolism. Extra virgin olive oil has been identified as the best matrix for HXT [226; 227; 228].  
Potential Health Benefits  
Mediterranean countries have lower rates of mortality from CVDs and cancer than Northern European or other Western countries [219; 229]. Olive oil, the major fat source of the Mediterranean diet, has been associated with the lower incidence of coronary heart disease in these countries. Especially HXT, one of the main compounds present in olive oil, inhibits radical-induced LDL oxidation and scavenges free radicals, and exerts other biological activities such as the inhibition of platelet aggregation and potentiation of immune response [218]. Thus, HXT features outstanding antioxidant, anti-inflammatory, and neuroprotective activities, which confer HXT with a critical role in the prevention of cardiovascular and metabolic syndromes of humans [230; 231; 232; 233]. Although the cardioprotectiveeffects of HXT remain unclear, its administration has been related to the decline in atheroma plaque in cadmium-intoxicated rats [234].
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Selected Figures of Merit for Performance Evaluation  
Besides the element-wise errors between the reference parameter vector \(\mathbf{\beta}_{ref}\) and the estimated parameter vector \(\mathbf{\hat{\beta}}\), we define the following figures of merit (FOM), to quantify the performance of the evaluated parameter identification algorithms:  
1. the average relative angle difference \(d_{q}\) defined as \[d_{q}=\frac{1}{N}\sum_{k=1}^{N}\left\|\mathbf{q}_{\mathbf{\beta}_{ref}}(t_{k})-\mathbf{q}_ {\mathbf{\hat{\beta}}}(t_{k})\right\|_{2}/\left\|\mathbf{q}_{\mathbf{\beta}_{ref}}(t_{k}) \right\|_{2}\] (69) where \(\mathbf{q}_{\mathbf{\beta}_{ref}}\) and \(\mathbf{q}_{\mathbf{\hat{\beta}}}\) are obtained respectively by direct measurements on a - real or simulated - robot and closed-loop noise-free simulation of the DDM using the estimated parameter vector \(\mathbf{\hat{\beta}}\),
2. the average relative torque difference \(d_{\tau}\) defined as \[d_{\tau}=\frac{1}{N}\sum_{k=1}^{N}\left\|\mathbf{\tau}_{\mathbf{\beta}_{ref}}(t_{k})- \mathbf{\tau}_{\mathbf{\hat{\beta}}}(t_{k})\right\|_{2}/\left\|\mathbf{\tau}_{\mathbf{\beta}_{ ref}}(t_{k})\right\|_{2}\] (70) where \(\mathbf{\tau}_{\mathbf{\beta}_{ref}}(t_{k})\) is the measured torque and \(\mathbf{\tau}_{\mathbf{\hat{\beta}}}(t_{k})=\mathbf{Y}_{\mathbf{\beta}}(\mathbf{\tilde{q}}_{d}(t_{ k}),\mathbf{\hat{q}}_{d}(t_{k}),\mathbf{q}_{d}(t_{k}))\mathbf{\hat{\beta}}\), \(\forall k=\{1,\cdots,N\}\),
3. the mean total time \(d_{t}\) that is required to compute one parameter estimate,
4. the mean number \(d_{N_{t}}\) of iterations until convergence,
5. the mean number \(d_{N_{sim}}\) of model simulations (for methods which require it).  
In the context of Monte Carlo experiments, we will discuss the mean values and standard deviations of these different figures of merit across the \(M=1300\) runs.
## 4 Results Analysis and Discussion
### Model Parameter Settings  
The study utilized Decision Tree (DT), Gradient Boosting Regression (GBR), and Random Forest (RF) models to learn the behavior of vessels within the channel. To enhance the accuracy and stability of the models, hyperparameter tuning was performed before finalizing the models. For the Decision Tree, the hyperparameters max_depth, min_samples_split, and min_samples_leaf were optimized. Max_depth controls the maximum depth of the tree; a greater depth allows the model to capture more complex patterns but also increases the risk of overfitting. Min_samples_split determines the minimum number of samples required to split an internal node, and min_samples_leaf controls the minimum number of samples required to be at a leaf node. For Gradient Boosting Regression, additional parameters n_estimators and learning_rate were tuned. N_estimators is a key parameter  
Figure 8: Distribution of Training Data: (**a**) Length Distribution, (**b**) Width Distribution, (**c**) Draft Distribution, (**d**) Speed Distribution, (**e**) Length-to-Width Ratio, (**f**) Width-to-Draft Ratio.  
that determines the number of decision trees, with higher values leading to a more complex model. Learning_rate decides the contribution of each tree to the final model. Random Forest's hyperparameter tuning included n_estimators, in addition to those of the Decision Tree. Hyperparameter tuning was performed on the training dataset using grid search combined with cross-validation. The range of hyperparameters and the final selected values are listed in Table 1.
###### Abstract  
Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entropy 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer's accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation.  
cotton root rot; Sentinel-2; random forest; binary logistic regression; spectral and texture analysis; airborne multispectral imagery 2
## 3 Results
### Validation of the ABIS-E  
#### 3.2.1 Reliability  
The test-retest reliability of the ABIS-E questionnaire was evaluated by intraclass correlation (ICC). For this purpose, the questionnaire was returned to the participants 7-10 days after the first test and a total of 48 responses were received. The ABIS retest results were very similar to the initial evaluation (51.29 \(\pm\) 11.240 vs. 51.42 \(\pm\) 11.776) (moderate body image disturbance). The test-retest reliability was considered excellent (ICC = 0.847) 95%CI (0.777-0.904) and the internal consistency was adequate-good when analyzed by [PERSON]'s alpha, with a value of 0.753 for the test and 0.806 for the retest (\(p<0.05\)).  
Table 1 shows the total-item correlation for each of the items of the ABIS-E questionnaire. The correlation expressed by [PERSON]'s alpha is very similar for all the items (>0.7), demonstrating adequate internal consistency.
# Technology Behavior Model--Impact of Extended Reality on Patient Surgery
## 2 Literature Review
### Eye Tracking  
Eye-tracking is based on the light-sensitive movement of the eyes to achieve the transmission of human-machine command recognition [20]. It is the newest interaction method at present [21]. To be precise, it uses infrared light to track and detect pupil changes and the length of the response time of the cornea to changes in light for recognition [22]. The tracking data is transformed into an image through context and transmitted to the display. The pupil change is detected at the edge of the pupil, locating the pupil position, and determining the user's movement intention, based on the detection data [23]. Common abnormalities include iris, eyelashes, and some interference filters. Interference filtering is the unintentional use of the eye while gazing, i.e., the natural blinking of the human eye, which results in false commands. Physiological operation can be ruled out by calculating the eye movement threshold. It was found that, when the threshold is \(>\)0, the focal point for recognizing light perception is at the end of the eye [24]. When the threshold \(<\) 0, the photoreceptor focal point falls at the front of the eye [24]. To reduce the error and to eliminate the physiological variance of the pupil, the user should maintain the eye's gaze for more than 800 ms to improve the accuracy of the recognition of the glasses and to ensure the correct operation of the instructions [25]. Due to the differences in human behaviors, the recognition time can be self-regulated to speed up the access rate and reduce the waiting time for an operation.
# Experimental and CFD Simulation Studies on Bell-Type Air Nozzles of CFB Boilers
## 3 Numerical Simulation  
### Numerical Method  
The object of this study was to investigate the pressure drop characteristic of bell-type air nozzles. CFD simulations were conducted using the Fluent software (ANSYS Inc., Pittsburgh, PA, USA). Actual operating conditions and properties of CFB boilers were adopted to simulate the flow of the air nozzles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Description** & **Units** & **Nozzle A** & **Nozzle B** \\ \hline \multirow{4}{*}{Inner tube} & Internal diameter & mm & 60 & 60 \\  & External diameter & mm & 76 & 76 \\  & Number of orifices & - & 16 & 16 \\  & Diameter of orifice & mm & 15 & 13.5 \\ \hline \multirow{4}{*}{Cover} & Internal diameter & mm & 103 & 105 \\  & External diameter & mm & 159 & 159 \\ \cline{1-1}  & Number of outlets & - & 8 & 10 \\ \cline{1-1}  & Diameter of outlet & mm & 22.5 & 23 * \\ \cline{1-1}  & Angle of outlet & - & horizontal & 20\({}^{\circ}\) \\ \cline{1-1}  & Fixed method with inner tube & - & threaded & welding \\ \hline \hline \end{tabular}  
* equivalent diameter.  
\end{table}
Table 1: Structure of nozzle A and nozzle B.  
Figure 3: Three kinds of nozzle A used in the engineering project: (**a**) cover of nozzle A1; (**b**) cover of nozzle A2; (**c**) inner tube of nozzle A3.  
Because the standard \(k\)-\(\varepsilon\) turbulence model adopts the isotropic assumption, the simulation results greatly deviated from the experimental values. The calculation results of the Reynolds Stress Model were difficult to converge. The renormalization group (RNG) \(k\)-\(\varepsilon\) model has a greater calculation accuracy. Therefore, the RNG \(k\)-\(\varepsilon\) was used for calculation. The model was developed by [PERSON] and [PERSON] et al. [27; 28] using the RNG method based on the standard \(k\)-\(\varepsilon\) model, which is suitable for the simulation of high-speed gas flow [7; 14; 29; 30; 31].
## 1 Introduction  
Welding residual stresses can have a crucial influence on the crack resistance of a steel component under service load. It was found that phase transformations during the cooling of the weld seam can have a significant influence on the residual stresses around the weld seam. Therefore, it is advantageous to control such phase transformations during cooling to minimize residual stresses and consequently improve the crack resistance of the welds [1]. Compressive residual stresses can thereby have a positive influence on crack prevention. A unique possibility of generating compressive residual stresses already during the welding procedure is offered by so-called low transformation temperature (LTT) filler wires [2, 3, 4]. Compared to conventional filler wires, these materials show lower phase transformation temperatures, which can work against the cooling-specific contraction. In consequence, distinct compressive residual stresses can be observed within the weld and adjacent areas.  
The use of neutrons allows for examining samples with thicknesses in the cm-range, e.g., for measuring residual stresses around weld seams by neutron diffraction [5, 6], or for visualizing grain orientations by tomographic neutron diffraction imaging [7, 8]. Another imaging technique based on diffraction contrast is neutron Bragg-edge imaging (NBEI). This technique allows the acquisition of monochromatic images at distinct wavelengths. Depending on the crystalline structure of the measured material, sudden transmission intensity changes, so-called Bragg edges, occur at wavelength positions \(\lambda\) equal to twice the lattice plane distances \(\mathrm{d_{hkl}}\) in polycrystalline samples, as stated by Bragg's law (Equation (1)), considering the transmission geometry with \(\theta=90^{\circ}\). Hence, taking radiographic images before and after a Bragg edge achieves a contrast, which relates to the crystallographic properties:  
\[n\lambda=2\mathrm{d_{hkl}}\sin\theta \tag{1}\]  
NBEI was already used by our team for visualizing the \(\gamma\)-austenite to \(\alpha^{\prime}\)-martensite phase transformation, where a super martensitic stainless steel sample was heated until complete austenitization and was subsequently cooled down to room temperature. The phase transformation process was visualized by acquiring the transmission images of a specific wavelength with a temporal resolution of 30 s and a spatial resolution of 100 \(\upmu\)m [9].  
In the present study, the aim was to film for the first time in situ the phase transformation of an LTT steel induced by bead-on-plate GTAW. Therefore, we used neutron imaging to visualize the sample remelting during the welding process using specific neutron wavelengths to two-dimensionally visualize the phase transformations in the samples. Additionally, some weldments were filmed using a polychromatic neutron beam.
## 4 Results and Discussion
### Limitations  
In this paper, for the first time--to the best of our knowledge--IPC and CPC taxonomies are applied as graph-based information resources for automated patent classifica  
Figure 5: Performance of language independent ensemble classification (F1) by combining the two top ranked IPC and CPC base learners with different fusion methods: four different stacking classifiers as well four fixed-combinations rules (FCR) are applied. The dotted line marks the top performance value of the base learners.  
tion. Despite the success, some limitations remain. Although we conducted an extensive hyperparameter tuning, the cost of 1 for each operation to calculate tree edit distance stayed untuned. Altering the costs in preliminary experiments modified the overall performance and should therefore be investigated in future work. Likewise, using solely the CPC/IPC's string representation to parse the corresponding tree will not extract all the available information. Thus, with a method to display the full subgroup relationship more information could be inserted to the CPC code tree.
## 3 Results
### Accuracy Evaluation of the Random Forest (RF) Classifier
However, it presented a high OE of 30.43%, confusing mainly with coverages of bare soil and low vegetation. Again, low UA was found for bare soil and low vegetation coverages, reaching a CE of 45.71% and 35.71% respectively, meaning that most of the assignments for these two coverages belonged to coverages associated with low vegetation in the case of bare soils, and oil palm and forest coverages in the case of low vegetation. The classification using the PC method resulted with an OA of 66.19% with a KC of 0.59, and OA of 8.10% below that obtained with the optical image. Compared with the GS method, the OA decreased by 5.95%. Oil palm and forest coverage showed a UA of 77.14% and 64.29% respectively, which are lower than those obtained by both the optical and the SAR images. For water body coverage, the highest UA was achieved, that is also the case for the optical image and in HFM method, reaching 78.57%. In this case, the forest coverage presented the lowest UA, corresponding to 64.29%, finding significant confusion with the low vegetation and grassland coverages and getting a high OE that reached 43.75%. Unlike the other fusion methods, high confusions were found in the classification of low vegetation and grassland coverages, with UA of 58.57% and 68.57%, respectively, and OE of 50.50% and 41.43%, respectively. Finally, the optical/SAR image stack classification gave the highest OA, reaching 82.14% with a KC of 0.79. This result corresponds to an increase of 11.67% in the SAR image, 7.86% in the optical image and 4.05% in the HFM method. Regarding the oil palm and forest classification, they presented high UA, very close to those from the SAR image and using the BR method, 92.86% and 82.86% respectively. Bare soil and low vegetation coverages showed significantly higher UA compared with the other methods. The low vegetation cover presented a high OE reaching 42%, confused mainly with forest cover and water bodies. Grassland cover reached a high UA of 90%, being similar to the BR, HFM, and GS methods. Figure 9 shows the OA and the KC obtained for each image analyzed. The highest value was achieved for the image resulting from the optical/SAR image stack, with a value of 82.14% for the OA and 0.79 for the KC, equivalent to an increase in OA of 11.67% for the SAR image and 7.86% for the optical image. The OA obtained in the Sentinel-2 optical image classification was similar to that found in the HFM and BR methods, slightly higher by 3.81% and 3.10 respectively. The OA found when using optical and SAR data sources was similar to those found by [6], which was close to 80%.
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 4 Data Processing  
### Data Processing of Ground 3D Laser Scanning  
The 3D laser scanning equipment could not be completely erected in the complex field environment of our study area, and the narrow space of the shaft roadway limited the field view angle of the scanner. These caused the problems of noise, data loss on a small scale, and data redundancy for the obtained three-dimensional point cloud of the shaft roadway. Therefore, the original collected point cloud data needed preprocessing of point cloud denoising and registration.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
### Notation Note  
All bold uppercase letters appearing in this manuscript represent matrices, and bold lowercase letters represent vectors. For a square matrix \(A\), \(A^{H}\), \(A^{\mathsf{T}}\), \(\operatorname{Tr}(A)\), \(\|A\|_{\mathsf{x}}\), and \(\operatorname{Rank}(A)\) denote its conjugate transpose, transpose, trace, trace paradigm, and rank, respectively. In addition, \(A\succeq 0\) denotes that the square matrix is a semipositive definite matrix. The diagonalization operation of the matrix is denoted by \(\operatorname{diag}(\cdot)\). \(\|\mathsf{x}\|\), \(|\mathsf{x}|\), \(\
abla_{\mathsf{x}}f(\mathsf{x})\), and \(\mathbb{E}[\mathsf{x}]\) denotes the Euclidean paradigm, the absolute value, the gradient vector, and the expectation of \(\mathsf{x}\). A circularly symmetric complex Gaussian (CSCG) random vector is denoted as \(\sim\mathcal{CM}(\mu,\mathbb{C})\), where \(\mathbb{C}\) represents the covariance matrix and \(\mu\) represents the mean value. \(\mathbb{C}^{M\times N}\) indicates \(M\times N\) dimensional complex matrices. \(\mathcal{O}\) stands for the capital \(\mathbb{O}\) symbol. The full names of all the abbreviations appearing in this paper are shown in Table 1.
## 4 Discussions
### Application to Biomedical Implants  
There are several applications for cellular materials including heat exchangers, filters, load bearing components, and biomedical implants. Lattice structures can improve the implants' performance significantly, from both mechanical and biological points of view. Obtaining the accurate characteristics and mechanical properties of the lattice structures is necessary to facilitate their use in orthopaedic implants. The mechanical properties of lattice structures depend mainly on the following three parameters: the material they are made of, the cell topology, and the relative density. Obtaining accurate analytical relationships for different cell topologies is a crucial factor in developing computational tools for designing patient-specific implants with non-uniform mechanical property distribution. In this paper, we presented a new and convenient method to transform the analytical relationships of a lattice structure from Euler-Bernoulli theory into Timoshenko theory. Furthermore, using the transformation relationships presented in this paper (Table 1), developing new analytical relationships based on Timoshenko beam theory becomes easier than how it has been before.  
For implant design, it is preferable to have a high yield strength and yet access to a wide range of stiffness. Moreover, having similar mechanical properties in three main directions is another factor that should be considered when choosing a unit cell for an implant. This will avoid unwanted deformations generated in the implant when the implant is placed inside the complex geometrical void it is designed for. The results of this study (Figures 6 and 8) show that among the six unit cells considered, truncated cube followed by diamond and rhombicuboctahedron can best satisfy the above-mentioned characteristics.