esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix  
### Non-linear methods for dimensionality estimation:  
Two different groups of methods can be distinguished: local and global estimators. Estimators based on local properties are based on the observation that the number of data points covered by a hypersphere around a data point with radius \(r\) grows proportional to rd, where d is the intrinsic dimensionality of the data manifold around that data point as defined above. As a result, the intrinsic dimensionality d can be estimated by measuring the number of data points covered by a hypersphere with a growing radius \(r\). Three local estimators for intrinsic dimensionality are applied here: the correlation dimension estimator (_CorrDim_), the nearest neighbor dimension estimator (_NNDim_), and the maximum likelihood estimator (_MaxLike_).  
Global estimators consider the data as a whole when estimating the intrinsic dimensionality. While _PCA_ as a linear but global method has already been described, two further methods are considered here: the packing number estimator (_PackNum_), and the geodesic minimum spanning tree estimator (_GMST_). _PackNum_ is based on the intuition that the r-covering number _N(r)_ is proportional to \(\mathrm{r}^{-\mathrm{d}}\). The _r_-covering number _N(r)_ is the number of hyperspheres with radius \(r\) that are necessary to cover all data points in the dataset. Because _N(r)_ is proportional to \(\mathrm{r}^{-\mathrm{d}}\), the intrinsic dimensionality of a dataset can be derived by:  
\[d=-\lim_{r\to 0}\left(\frac{\log N(r)}{\log r}\right) \tag{1}\]  
_GMST_ is based on the observation that the length function of a geodesic minimum spanning tree is strongly dependent on the intrinsic dimensionality \(d\). The _GMST_ is the minimum spanning tree of the neighborhood graph defined on the dataset. The length function of the _GMST_ is the sum of the Euclidean distances corresponding to all edges in the geodesic minimum spanning tree.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 6 Numerical Illustrations
### Example 3: Papillion River Basin Slope
#### 6.3.3 Simulation of CCRF for Soil Parameters  
In this section, a transversely anisotropic CCRF of the studied slope is constructed. It can be concluded from the previous section that the optimal margins of \(c\) and \(\phi\) adopt the LN distribution. The optimal model representing the dependent structure of \(c\) and \(\phi\) is the Gaussian copula. According to Algorithm 1 applied to the Gaussian-based CCRF proposed  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Margins**} & \multicolumn{2}{c}{\(\mathbf{c}\)} & \multicolumn{2}{c}{\(\mathbf{\phi}\)} \\ \cline{2-7}  & \(\mathbf{D_{n}}\) & **AIC** & **BIC** & \(\mathbf{D_{n}}\) & **AIC** & **BIC** \\ \hline TN & 0.2682 & 112.58 & 114.00 & 0.1548 & 101.06 & 102.48 \\ LN & 0.1367 & 105.11 & 106.52 & 0.1237 & 101.71 & 103.12 \\ TG & 0.2164 & 108.03 & 109.45 & 0.1320 & 103.48 & 104.90 \\ WB & 0.1817 & 108.73 & 110.14 & 0.1688 & 101.44 & 102.85 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Goodness-of-fit test results of different margins.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Copulas** & **Gaussian** & **Plackett** & **Frank** & **No. 16** \\ \hline AIC & \(-6.2223\) & \(-2.9015\) & \(-3.3795\) & \(3.2895\) \\ BIC & \(-5.5143\) & \(-2.1934\) & \(-2.6714\) & \(3.9976\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: AIC and BIC scores of different copulas.  
Figure 25: Observations of soil parameters in Papillion River Basin slope.  
in Section 4, a transversely anisotropic CCRF can be generated. A typical realization of this is shown in Figure 26. As expected, the spatial variability and cross-correlation of soil parameters can be successfully reproduced. The RF variables are distributed similarly to the measured data and can cover them well, demonstrating the efficiency of the proposed method.  
Table 12 gives the statistics of CCRF variables associated with candidate margins and copulas. The average comprehensive errors of the seven cases in Table 1 are 5.42%, 6.93%, 6.69%, 13.24%, 11.17%, 7.72% and 5.53%, respectively. The statistical characteristics of soil parameters are basically consistent. The proposed copula-based CCRF approach can better reflect the true distribution of cross-correlated soil profiles with a relatively high accuracy. Obviously, Gaussian copula-based CCRF in conjunction with LN margins can reflect the spatial distribution of soil parameters in the Papillion River Basin slope.
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 3 Methodology and Workflow  
Seismic sedimentology in essence is the combined study of seismic lithology and seismic geomorphology, aimed at high-resolution integration between geological and geophysical information. Converting the seismic waveform to the \(-90^{\circ}\) phase for relative impedance (inversion), frequency fusion, and striatal slicing are key techniques in the current stage [3,10,13]. The results have to be calibrated to core and wireline-log interpretation, and outcrop models wherever available for the best outcome. In light of artificial intelligence, machine learning techniques appear very promising [13].  
A workflow for high-resolution subsurface facies and reservoir mapping is proposed in Figure 3. This seismic-sedimentology-guided workflow includes three mandatory steps and two optional steps. The mandatory steps are proven to be key steps by many previous studies (to list a few, [10,12,17-20]). Optional steps are supported by more recent studies (e.g., [21-25]) and are useful for extra-fine-quality interpretation. The following is a detailed description of each step with application examples where appropriate:
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 2 Position and Orientation Selection
### Inverse Reachability Map
Figure 4: Inverse reachability map: (**a**) without obstacles; (**b**) with obstacles. Figure 5a-e represent an inverse reachability map for each of the five target fruits, and Figure 5f shows the integrated inverse reachability map for all the five target fruits. The target fruits are depicted as green circles, and the positions with the highest score in individual maps are marked with black circles. In Figure 5f, the magenta circle denotes the position with the highest score. Figure 6 denotes the appearance of the manipulator reaching each target object at the selected position and the orientation of the end-effector, where the cyan circles denote obstacles. If the positions of obstacles in the orchard environment and the locations of the target fruits are known in advance, this algorithm can be applied to select the position of the mobile manipulator and fruit-harvesting orientation of the end-effector, enabling the harvesting of multiple fruits. Figure 7 shows the result of directly summing the individual maps. As in Figure 5, the highest score is indicated in magenta. As seen in Figure 7d, summing the individual maps without assigning negative values can result in the highest score appearing at locations that are not reachable for some targets. Figure 5: Inverse reachability map: (**a–e**) for each target and (**f**) for the aggregate target. Figure 6: All the postures of the manipulator at the selected position with the selected orientation.
## 2 Methodology
### Method Process  
Applying the porosity and permeability laboratory test data of cores and geophysical logging data, based on the extreme gradient boosting tree (XGBoost) regression prediction method in machine learning, an XGBoost prediction model of porosity and permeability of geophysical logging was established, followed by optimization with K-means method, which was applied to predict the porosity and permeability in the hole section of the study area, both in the longitudinal direction and in the planar direction. The main processes included were as follows, and the flowchart is shown in Figure 2:  
1. Data preparation;
2. XGBoost model establishment: (1) Logging features and label extraction: feature variables suitable for machine learning methods were selected and extracted from the raw data to establish the dataset; (2) Dataset division: the dataset was randomly divided with 80% as the training set and the remaining 20% as the test set; (3) Feature combination optimization: the exhaustive feature variable combination method was used to weaken the problem of multicollinearity between feature variables, and the feature variable combination method of XGBoost method was preferred to provide a better feature variable representation for the model; (4) Hyper-parameter optimization: Random search was used to ensure that the hyper-parameters were searched in a wide range, and the hyper-parameters were manually adjusted in combination with the grid search so as to achieve a fine search of the parameters and to improve the stability and performance of the model; (5) Establishment of machine learning models: porosity and permeability XGBoost models were established through feature combination and hyper-parameter optimization steps; (6) Model evaluation: evaluated the model performance based on MAE and R2;
3. K-means optimization model: Based on K-means clustering, similar input data were grouped to minimize intra-cluster differences and maximize inter-cluster differences, and the performance of the grouped porosity and permeability XGBoost prediction model was established and evaluated;
4. Determine the model: The performance of the models was compared in steps (2) and (3), and the porosity and permeability prediction model that fits the study area was comprehensively determined;Model application: The final model from step (4) was applied to the layer S13 section in the study area to analyze the planar porosity and permeability distribution.
## 3 Results and Discussion
### Kinetic Parameters Determination
In addition to the lower catalytic efficiency, the immobilized enzyme showed a higher affinity for the substrate and it is important to take into account the good reusability presented by the immobilized ASNase, thus proving that carbon nanotubes are a good matrix for immobilization of this enzyme Analogous results were reported by [PERSON] et al. [5] and [PERSON] and [PERSON] [57] where the \(v_{max}\) of immobilized ASNase was also lower than that of free ASNase and the enzyme affinity towards its substrate also increased after immobilization. The Hill coefficient, \(n\), represents the degree of cooperation among the enzyme subunits and the substrate molecules number that can attach to the enzyme complex. Equation (1) is simplified to the Michaelis-Menten equation when \(n\) equals 1. Positive cooperativity (homotropic regulation) is indicated by a value of \(n>1\), which means numerous substrate molecules attach to the enzyme concurrently. A value of \(n<1\) shows negative cooperativity; the attachment or catalysis of the second substrate molecule is hampered by the first's binding. In this study, the determined Hill coefficient values above 1 (Table 4) indicate that both free and immobilized ASNase forms have allosteric regulation with positive cooperativity.
## 2 Discussion
### Sonoluminescence Using Therapeutic Ultrasound  
The ability to achieve multibubble sonoluminescence using therapeutic ultrasound of a relatively low intensity was also demonstrated in [40], where it was also shown that the intensity of the so-generated sonoluminescence light could be high enough to activate a photodynamic therapy (PDT) agent (Figure 4). The latter results are particularly important for the field of photodynamic therapy, where it has been suggested that sonoluminescence and the reactive oxygen species (ROS) associated with bubble collapse could be employed to activate drugs developed in the framework of sonodynamic therapy (SDT) [77; 78]. STD has demonstrated promising results for the treatment of aggressive and resistant tumour cell, and it has several advantages over PDT. For example, while PDT is clinically approved for the treatment of superficial lesions that can be reached with an endoscope [45], a large penetration depth of ultrasound into biological tissues, compared with that of light, enables using SDT in hard-to-reach places in a living body.  
However, it is noteworthy that in the experiment in [40], the authors had to use photomultiplier tubes (PMTs) to measure the intensity of sonoluminescence light. PMTs are a class of vacuum tubes that are extremely sensitive detectors of light due to their high gain, low noise, ultra-fast response, and large area of collection [79]. PMTs can also be configured to detect individual photons to enable photon-counting measurements, where individual photons are counted as pulses corresponding to each detected photon, which is in contrast to conventional photodetectors, which generate analogue signals proportional to the photon  
Figure 4: Sketch of the operation of a sonoluminescence-activated photosensitive drug. Note that the use of ultrasound waves enables employing this approach both for diagnostic and therapeutic applications, thereby increasing the range of applications of photosensitive drugs, which is currently restricted by a limited penetration of light in the biological tissues and bodily fluids. Reproduced from [40] under the terms of a Creative Commons license.  
Figure 3: Single-bubble sonoluminescence emission spectra obtained in degassed water compared with the sonochemiluminescence spectra from an aqueous lumininol solution. All spectra are plotted as a function of the driving acoustic pressure. Reproduced from [60] with the permission of John Wiley and Sons. Copyright 2012.  
flux. While PMTs are more complex and expensive compared with semiconductor-based photodetectors, they remain essential for applications requiring high-sensitivity detection of imperfectly collimated light, which is the case of many sonoluminescence experiments, especially when the amplitude of ultrasound waves is not high. Although the use of PMTs is not a limiting factor in fundamental research studies, in practice, it is desirable to increase the intensity of sonoluminescence light to a level detectable with technically simple and less expensive than PMTs semiconductor photodetectors.
# A Review of Innovative Electromagnetic Technologies for a Totally Artificial Heart
## 4 Linear Electromagnetic Actuators
### Single-Mover MM-LOA  
According to the stator configuration, the following arrangements have been proposed:  
* E-shaped stator cross section with a pair of coils supplied by opposite currents [62], [70; 71; 72; 73; 74];
* Single or dualrrents [75; 76; 71]; C-shaped stator cross section, the latter with coils supplied by opposite cu
* Stator with multiple coils to allow multiphase or fault-tolerant supply [77; 78; 79].  
The latter configurations can benefit from the smooth force profile and emergency operation in case of a partial winding fault; however, the mitigation of the radial and cogging forces possibly requires slotless stator and quasi-Halbach PM magnetization, resulting in winding oversizing and higher losses due to the high equivalent air-gap.  
The most common stator configurations are those with C or E shapes, combined with different types of movers, differing in the magnetization pattern, pole number, and yoke assembly. Figure 8 shows two typical configurations with the corresponding force profiles determined at constant total ampere-turns, with the outer radius, air-gap, stroke length, and ohmic losses being the same. Regarding the E-shaped stator with the interior PM (IPM) mover, the C-shaped stator with surface PM mover (Figure 8a) provides a higher peak force but suffers from higher thrust unevenness. Furthermore, the PM and mover masses are 20% and 80% higher, respectively. The optimized IPM mover provides \(\sigma_{f}\approx 0.47\) N/cm\({}^{3}\).  
Considering additional mover PM configurations, the E-shaped configuration seems more favorable, as it limits the leakage flux along the entire stroke and yields a force density higher than that of the C-shaped one. The adoption of a quasi-Halbach type provides an even more uniform thrust profile. The application of ferromagnetic rings to the mover ends increases the average thrust, due to the higher magnetic flux [74]. On the other hand, such a design increases the mover mass and the coil inductance.  
Figure 8: Examples of MM-LOA and related force performances; (**a**) C-shaped stator with surface PM mover [71]; and (**b**) E-shaped stator with IPM mover [73; 71] (outer radius: 32 mm, air-gap: 1 mm, and stroke: \(\pm 5\) mm). Republished with permission of IEEE.
# Three-Dimensional, Time-Dependent Analysis of High- and Low-\(Q\) Free-Electron Laser Oscillators
## 2 Numerical Formulation
### Performance of the MINERVA FEL Code  
To illustrate the performance of the MINERVA FEL code, we briefly compare the predictions of this code with experimental data from two SASE FEL experiments. We consider the \"Sorgente Pulsata ed Amplificata di Radiazione Coerente\" (SPARC) experiment, a SASE FEL located at ENEA Frascati [197], and the Linac Coherent Light Source (LCLS), which is a SASE FEL at the Stanford Linear Accelerator Center [198].
## 3 Results and Discussion
### Microbial Community Composition
[50] showed that in biofilm colonized mainly by _Marinobacter_ spp. (present strongly in SCD), a very high electron transfer rate was obtained. These data suggest that the _Marinobacter_ genus is probably a promising candidate for the design of halotolerant bioanodes. Regarding experiments carried out with real wastewater and SCD as a source of inoculum, the biofilms were mainly colonized by _Crenia_ spp. (45%), _Caenispirillum bisanense_ (10.5%), _Clostridium_ sp. (10%), _Desulfovibrio_ sp. (7%), _Caenispirillum_ sp. (3%), _Idiorarina sesoinensis_ (6%), and _Marinobacter_ sp. (5%). Anthose treated with SSM, biofilms were colonized by _Crenia marismortui_ (25%), _Desulfovibrio_ sp. (18%), _Caenispirillum_ sp. (16%), _Crenia_ spp. (15%), _Idiorarina seonensis_ (10%), _Marinobacter_ sp. (4%), and _Halocella cellulosityca_ (4%). Comparing with the bacterial diversity of the communities detected in the biofilms formed in the STWW we notice that the species belonging to _Crenia_, _Idiorarina_, and _Desulfovibrio_ genus are only present in the enriched biofilms of the RTWW. According to previous literature, these microorganisms are considered as sulfate-reducing bacteria, and this ability has led them to be used for the remediation of contaminated soils. Additionally, microbial communities belonging to the genus _Desulfovibrio_ are well known for theipotential to efficiently process azo dyes [51; 52]; it is also interesting to note that _Desulfovibrio_ possesses an abundance of membrane-bound multiheme cytochromes that are involved in direct extracellular electron transfer [29].
## 3 Material and Test Procedures
### Test Procedures  
Tensile and compressive tests were performed using the MTS810 hydraulic test machine. A displacement control of 1 mm/min and \(-\)2 mm/min were applied to conduct static tensile and compressive tests, respectively. The compressive tests were conducted according to ASTM D6484 [30]. The standard suggests that these procedures perform the open-hole composite compressive tests without buckling the specimen. The designed test fixture according to the standard is shown in Figure 4. The support parts of the fixture were assembled using bolts. The bolts were fixed at 7 N-m according to the method given in the standard. The test configuration for the tensile and compressive tests is presented in Figure 4. During the test, a CCD camera was used to capture the images of the specimen until the test was completed. The captured images were used to perform DIC. The DIC  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Symbol** & **Units** & **Value** \\ \hline Longitudinal modulus & \(E_{1}\) & GPa & 147.7 \\ Transverse modulus & \(E_{2}\) & GPa & 8.52 \\ Shear modulus & \(G_{12}\) & GPa & 4.59 \\ Poisson’s ratio & \(v_{12}\) & - & 0.3 \\ Longitudinal tensile strength & \(X_{t}\) & MPa & 2737 \\ Transverse tensile strength & \(X_{c}\) & MPa & 1600 \\ Longitudinal compressive strength & \(Y_{t}\) & MPa & 51.32 \\ Transverse compressive strength & \(Y_{c}\) & MPa & 201.08 \\ Shear strength & \(S_{12}\) & MPa & 81.0 \\ Fiber tensile fracture energy & \(G_{f}\) & kN/m\({}^{2}\) & 180 \\ Fiber compressive fracture energy & \(G_{fc}\) & kN/m\({}^{2}\) & 100 \\ Matrix tensile fracture energy & \(G_{mtt}\) & kN/m\({}^{2}\) & 0.30 \\ Matrix compressive fracture energy & \(G_{mc}\) & kN/m\({}^{2}\) & 1.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties for T700/Epoxy composite material.  
Figure 3: Geometry and dimensions for open-hole specimen (unit: mm).  
analysis was conducted using the VIC-2D software program. Through the DIC method, the strain field of the specimen surface that varies during the test can be obtained. Strain measurement was conducted using strain gauges as well as the DIC method. The strains measured by the gauge and DIC were compared. The reason for performing strain measurements is to compare the results with the PFA results. The strain gauge data sampling, load data sampling, and image capture rates were set to 100 Hz.
## 4 Results
### Data-Driven Modeling Results
#### 4.2.3 Classification and Regression Trees (CART)
The output of the CART model is presented as a decision tree, which effectively captures and represents the data intervals described in Figure 8. This decision tree serves as a graphical representation of the model's decision-making process. It illustrates a sequence of decisions made by the model to ultimately arrive at its prediction. To utilize the decision tree, the estimation process commences at the initial node and follows the branches to subsequent nodes, adhering to the rules written on the leaves (see Figure 8 for reference). This sequential process continues until the final node is reached, which corresponds to the model's ultimate prediction. The decision tree offers a straightforward and interpretable way to understand how the model analyzes the input data and makes informed predictions based on specific conditions and thresholds. It is important to notice that Figure 8 is based on the normalized values. Tables 8 and 9 offer a comprehensive description of the CART (Classification and Regression Trees) model, providing valuable insights into its structure and predictive capabilities. Table 8 presents an overview of the CART's architecture, encompassing details such as the number of terminal nodes, split variables, values for each node, parent and son nodes, and the corresponding predicted values. On the other hand, Table 9 delves deeper into CART's intricacies, presenting the specific rules and predicted values for each terminal node. Figure 8: The proposed tree for prediction of \(\mathrm{q_{u}}\). By referring to these tables, readers can gain a better understanding of the influential variables and their relationships with the target variable. The CART model assists in identifying critical decision points and the potential outcomes associated with each decision,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nodes** & **Objects** & **Split Variable** & **Values** & **Parent Node** & **Sons** & **Predicted Values** \\ \hline
1 & 60 & & & & 2; 3 & 0.37 \\ \hline
2 & 44 & D & \(\leq\)0.28 & 1 & 4; 5 & 0.28 \\ \hline
3 & 16 & D & \  
facilitating improved model accuracy and enhanced predictions. Utilizing the insights from CART can contribute to better decision making in various domains, such as foundation engineering or other fields where predictive modeling is applicable. These tables serve as valuable references, empowering readers to explore the model's inner workings and leverage its interpretability for informed and accurate predictions.