esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Experimental Procedures  
This compensation processing is based on high-precision surface error detection results. We modify the processing program according to compensation algorithm, which was then carried out repeatedly until the form error is within the tolerance. Matlab software was used to simulate and modify the processing program. The challenge here is to obtain form error from measured data of freeform surface for the measured and theoretical surface represented in different coordinate systems. Coordinate offset between the two systems needs to be calculated and the extent of difficulty depends on the degree of freedom of surface, thus it is challenging to match the measured and ideal surface for freeform optics.  
As described above, ultra-precision compensation machining is based on high accuracy alignment of the measured and designed surface. The aim of alignment is to make the measured surface as close to the theoretical surface as possible. To achieve the best alignment, a rough adjustment based on tooling and precise alignment by iterative adjustment is adopted.  
The detection surface and the theoretical surface are represented in different coordinate systems. There are spatial position deviations in six degrees of freedom, as shown in the Figure 1. Surface matching is used to make the measuring coordinate system coincide with the theoretical coordinate system. In the actual matching process, it is more suitable to make the measured surface approximates the theoretical to the maximum extent as far as possible.  
The first step is to apply the fixture for rough positioning. As showed in Figure 2, there is a plane as the mark which is parallel to the plane comprised by axis X, axis Z, and measuring and theoretical coordinate systems are all based on the mark. Thus, the theoretical coordinate system and measuring coordinate system are quite alike.
# A Comprehensive Review of Micro UAV Charging Techniques
###### Abstract
Due to limited battery capacity, UAVs have short flight times. In most cases, the traditional battery swapping method is used, in which a drone makes frequent trips to a charging station where the depleted battery is removed and a fully charged battery is installed. However, this physical battery swapping method necessitates human assistance, affecting UAV operations and causing significant service disruptions in remote areas. Figure 1: Applications of drones in diverse sectors. These charging techniques are divided into two categories on the basis of transmission distance range: near-field transmission and far-field transmission. Table 1 summarizes different charging techniques to charge UAVs. The energy limitation of UAVs is also addressed through motion control functions and optimization algorithms to enhance energy efficiency [5; 6]. However, these efforts cannot fully eliminate these issues as drones are still required to visit charging stations when their batteries are drained. Furthermore, solar-powered drone technology has been proposed to harvest energy from the sun. However, this solar power feature cannot be embedded in small drones. This solar energy harvesting feature also depends on flight weather conditions e.g., drones cannot properly harvest energy at night or on cloudy days, which leads to the UAV's substandard performance.
## 3 Results
### Effects of Different Land Use Patterns on Cmpi
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Soil Depth (cm)** & **Treatments** & **LOC/SOC** & **EOC/LOC** & **DOC/LOC** & **MBC/LOC** & **POC/LOC** \\ \hline \multirow{6}{*}{0–20} & SC & 25.05 \(\pm\) 3.8 b & 39.47 \(\pm\) 0.88 a & 1.86 \(\pm\) 0.18 a & 3.64 \(\pm\) 0.34 b & 55.03 \(\pm\) 0.97 b \\  & MC & 25.25 \(\pm\) 2.09 b & 36.64 \(\pm\) 1.15 a & 1.84 \(\pm\) 0.09 a & 5.18 \(\pm\) 0.28 a & 56.34 \(\pm\) 0.79 b \\  & MSR & 37.76 \(\pm\) 5.89 a & 25.19 \(\pm\) 3.74 b & 1.26 \(\pm\) 0.19 b & 3.95 \(\pm\) 0.77 b & 69.6 \(\pm\) 3.57 a \\  & GB & 37.36 \(\pm\) 1.56 a & 22.59 \(\pm\) 0.74 b & 1.25 \(\pm\) 0.01 b & 5.19 \(\pm\) 0.25 a & 70.96 \(\pm\) 1 a \\  & FB & 31.9 \(\pm\) 1.22 a & 22.4 \(\pm\) 0.78 b & 1.47 \(\pm\) 0.06 b & 5.94 \(\pm\) 0.1 a & 70.18 \(\pm\) 0.72 a \\ \hline \multirow{6}{*}{20–40} & SC & 23.33 \(\pm\) 1.74 b & 32.4 \(\pm\) 1.73 a & 2.02 \(\pm\) 0.17 a & 3.85 \(\pm\) 0.08 c & 61.74 \(\pm\) 1.97 c \\  & MC & 22.02 \(\pm\) 2.13 b & 23.81 \(\pm\) 2.08 b & 2.14 \(\pm\) 0.1 a & 3.72 \(\pm\) 0.12 c & 70.34 \(\pm\) 2.11 b \\  & MSR & 29.77 \(\pm\) 3.93 a & 20.04 \(\pm\) 1.45 c & 1.59 \(\pm\) 0.13 b & 2.94 \(\pm\) 0.22 d & 75.43 \(\pm\) 1.78 a \\  & GB & 33.26 \(\pm\) 0.36 a & 18.36 \(\pm\) 0.22 c & 1.41 \(\pm\) 0.04 b & 4.66 \(\pm\) 0.04 b & 75.57 \(\pm\) 0.22 a \\  & FB & 31.87 \(\pm\) 0.37 a & 18.06 \(\pm\) 0.85 c & 1.47 \(\pm\) 0.03 b & 5.14 \(\pm\) 0.06 a & 75.33 \(\pm\) 0.91 a \\ \hline \multirow{6}{*}{40–60} & SC & 20.92 \(\pm\) 0.71 b & 23.41 \(\pm\) 1.3 a & 2.24 \(\pm\) 0.09 a & 4.32 \(\pm\) 0.15 a & 70.03 \(\pm\) 1.28 b \\  & MC & 20.71 \(\pm\) 1.43 b & 24.65 \(\pm\) 0.93 a & 2.27 \(\pm\) 0.06 a & 4.29 \(\pm\) 0.1 a & 68.8 \(\pm\) 0.82 b \\ \cline{1-1}  & MSR & 30.63 \(\pm\) 5.06 a & 17.51 \(\pm\) 2.37 b & 1.56 \(\pm\) 0.21 b & 2.38 \(\pm\) 0.25 c & 78.56 \(\pm\) 2.73 a \\ \cline{1-1}  & GB & 31.66 \(\pm\) 0.96 a & 18.54 \(\pm\) 0.64 b & 1.48 \(\pm\) 0.02 b & 2.69 \(\pm\) 0.04 b & 77.29 \(\pm\) 0.69 a \\ \cline{1-1}  & FB & 33.42 \(\pm\) 0.89 a & 17.34 \(\pm\) 0.81 b & 1.4 \(\pm\) 0.04 b & 2.42 \(\pm\) 0.11 c & 78.84 \(\pm\) 0.92 a \\ \hline \end{tabular} Note: Table organic carbon (LOC)/soil organic carbon (SOC) is the ratio of LOC and SOC, easily oxidizable organic carbon (EOC)/LOC is the ratio of EOC and LOC, dissolved organic carbon (DOC)/LOC is the ratio of DOC and LOC, microbial biomass carbon (MBC)/LOC is the ratio of MBC and LOC, and particulate organic carbon (POC)/LOC is the ratio of POC and LOC. The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize–soybean rotation (MSR), grass belt (GB), and forest belt (FB). Different letters mean significant differences among different land use patterns in the same soil layer at 0.05 level. \(n=3\). The same is below.  
\end{table} 
Table 2: Ratios of LOC fractions under different land use patterns. significantly higher than SC, MC, and MSR. Specifically, SC, MC, and MSR were lower than GB by 36.60%, 36.34%, and 42.29%, respectively, while FB and GB were 67.15% and 57.09% higher than MC.
## 2 Numerical Methods
### BPM Model for Noise Source or Generation  
The original BPM noise model was developed by [PERSON] et al. [14] which is able to predict airfoil self-noise. The model was based on scaling the wind tunnel experimental data using the NACA 0012 airfoil with chord lengths varies from 2.5 cm to 61 cm. Here, the BPM model was integrated with the BEM method [15] in order to deal with the complicated flow for wind turbine. The required boundary layer thickness data in the BPM model is obtained from the Xfoil computation so that this data varies with the airfoils rather than fixed with NACA 0012. A general expression of the BPM model is shown in Equation (8)  
\[\text{SPL}=10 log_{10}\left(\frac{\delta M^{n}lD}{r^{2}}\right)+G_{1}(St)+G_{2}( Re)+G_{3}(\delta)+C \tag{8}\]  
In the equation, it is shown that rotor aerodynamic noise is mainly a function of parameter \(\delta\) which represents boundary layer thickness. The boundary layer thickness depends on airfoil shape, Reynolds number, AOA, turbulence level etc. \(l\) is the spanwise length of an airfoil segment. \(G_{1}\), \(G_{2}\), and \(G_{3}\) are related to the Strouhal number \(St\), Mach number \(M\), Reynolds number \(Re\) and boundary layer thickness parameter \(\delta\). Detailed descriptions of noise mechanisms can be found in references [14; 15], ANSource level of rotor generally depends on the blade shape and operational conditions, such as rotational speed, wind speed, yaw, tilt, etc. Instead of combining the engineering BEM model, the BPM noise model is directly integrated into the NS equations with the AD model. The BPM model is a passive subroutine that can be activated anytime with the inflow velocity and AOA provided from AD simulations. The trailing edge boundary layer thickness is interpolated from the existing database prepared with Xfoil through a wide range of Reynolds numbers and AOAs. To take clean and rough blade surface conditions into account, the boundary layer thickness database is created under different surface roughness cases, such that wind turbine noise under free transition and fully turbulent flows can be modelled. A fully-turbulent boundary layer thickness database is utilized to calculate the noise of rotor whose blade surface is rough, and free transition database is applied for a rotor with a clean surface.
## 3 Methodology
### Modeling
#### 3.4.1 Random Forest (RF)  
The random forest algorithm proposed by [PERSON] [31] is an ensemble learning method that combines multiple individual decision trees to create a powerful predictive model. Each decision tree in the random forest is constructed by recursively partitioning data based on the most informative features. At each node of the trees, the algorithm  
Figure 2: The ensemble learning procedure.  
evaluates the Gini impurity and entropy of each attribute and selects the attribute that provides the most significant reduction in uncertainty.  
The Gini impurity and entropy were calculated using the following formulae:  
\[\text{Gini Index}=1-\sum_{n=1}P_{n}^{2}\]  
\[\text{Entropy}=-\sum_{n=1}P_{n}\log_{2}(P_{n})\]  
where \(P_{n}\) represents the probability of class \(n\).  
Algorithm 1 provides the comprehensive pseudocode for the random forest classifier employed in this research. It outlines the essential steps and hyperparameter choices that were used for constructing the random forest model in this research. These hyperparameters included bootstrap, criterion, max depth, min samples split, n estimators, min samples leaf, class weight, and random state.  
```
Training dataset: \(D\) \(n\_estimators\): [100, 200, 300] \(max\_features\): ['sqrt', 'log2', None] random_state: [42, 123] \(min\_samples\_split\): [2, 5, 10] \(min\_samples\_leaf\): [1, 2, 5] \(max\_depth\): [5, 10, 15] class_weight: [None, 'balanced\"] bootstrap: [True, False] criterion: [\"gini\", \"entropy\"]
```  
**Ensemble** prediction: \(E(X)\) procedureRandomForest(\(D\), \(n\_estimators\), \(max\_features\), \(random\_state\), \(max\_depth\), \(min\_samples\_split\), \(min\_samples\_leaf\), \(class\_weight\), \(bootstrap\), \(criterion\)) for\(t=1\) to \(n\_estimators\)do \(D_{t}\) is a bootstrap sample from \(D\)  Build a decision tree on \(D_{t}\) using \(max\_features\) endfor \(E(X)\leftarrow\) Majority vote of predictions from all trees on input \(X\) return\(E(X)\) endprocedure ```
**Algorithm 1** The random forest pseudocode  
The bootstrap hyperparameter allowed sampling with replacement, facilitating the creation of distinct subsets of the training data for each tree in the forest. By evaluating the split quality based on the criterion hyperparameter, which determined the impurity measure, the model could effectively make decisions at each tree node. The max depth hyperparameter restricted the depth of each decision tree to prevent overfitting and enhance generalization. Additionally, the min samples split hyperparameter specified the minimum number of samples required to split a node, contributing to the optimal tree structure. Controlling the number of decision trees in the forest was achieved through the n estimators hyperparameter. Meanwhile, ensuring the reproducibility of results was facilitated by the random state hyperparameter [32,33].
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## 3 Results  
Differences in the Proximate Constituents Between the Juices Obtained with Different Processing Systems  
The use of different juice extraction systems had influence on proximate constituents of bergamot juice and the outcomes of this study are summarized in Table 1. Bergamot juice was very acidic and mainly contained citric acid, which contributes significantly to the composition of this parameter. Acidity, as citric acid monohydrate, ranged from 41 g/L for Ep-3 juice to 43 g/L in Ep-1. The content of total soluble solids expressed as degree Brix (%_w/w_) did not exceed 10% in laboratory and industrial juices. Brix values of harsh (EP-3) and soft (EP-1) squeeze juices remained relatively consistent.  
The average content of free sugars ranged from 9 g/L of glucose and fructose and 17 g/L of sucrose both in laboratory than in commercial squeezing juice.  
It was clear that the different methods employed for the extraction of B] did not affect significantly the quality traits of the juice, as no significant differences were detected in total soluble solids, titratable acidity, and sugar contents. Total soluble solids ranged from \(9.0\pm 0.2\%\) to \(9.5\pm 0.4\%\) in BJ obtained by FMC (Ep-2) and hand squeezed (Ep-1), respectively. The greatest contribution to the total soluble solids content was due to citric acid and this fact was also the evidence for the highest acidity value showed in Ep-1.  
The average content of L-ascorbic acid in B] was 415 mg/L and was similar in all juice. The effect of the system of extraction was also unrelated as regards L-ascorbic acid content.  
Although it was interesting to note that harsh squeeze processed juice (EP-3) had considerable higher amount (\(p<0.005\)) of pectic fractions (total and water-soluble pectins) than fresh hand squeezed juices (Ep-1) and soft squeeze processed juice (Ep-2) (Table 1).
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections  
Each category of antifungal drug has defined limitations related to a spectrum of activity, development of resistance, and toxicity. Azoles show potential drug-drug interactions in patients. In most cases, the use of AmB becomes ineffective due to the reactivation of fungal infections posttreatment. Further, the excessive and frequent use of azoles leads to the emergence of resistant fungal pathogens. Echinomodins exhibit a narrow antifungal spectrum, but these are better-tolerated. The currently available antifungal therapies are hindered by high toxicity, limited clinical efficacy, and inconsistency in pharmacokinetic properties. Further, drug interactions and adverse drug effects are expected in combination therapy. In this regard, nanotechnology-based approaches exhibit improved inhibitory activity on fungal pathogens at lower concentrations. Nanocarriers also assist in the targeted delivery of antifungal drugs [23].  
Nanocarriers possess unique optical, electrical, mechanical, or chemical characteristic features, which show advantages over the conventional dosage form [24]. Nanotechnology is involved in the creation of devices and materials [24; 25]. A wide variety of nanocarriers used as drug delivery systems (DDS) for the treatment of fungal diseases include phospholipid vesicles (such as liposomes, transfersomes, ethosomes, transethosomes, etc.); non-phospholipid vesicles (spanlastics and niosomes); nanostructured lipid carriers (NLCs); solid lipid nanoparticles (SLNs); nanoemulsions; polymeric nanoparticles (such as chitosan-based nanoparticles); metal oxide nanoparticles; silver nanoparticles (AgNPs); carbon nanotubes (CNTs); and dendrimers (Figure 2).  
Figure 2: Nanocarriers that can improve therapeutic outcomes in the management of fungal infections.
## 5 Conclusions  
In the study, the effects of different initial jet pressures, cone angles, and nozzle diameters on the jet structure are investigated in the context of red tide management. The lateral distribution of jet velocity and variation characteristics of TKE and gas holdup are analyzed. The cluster analysis method is used to research the jet impact diameter. The following results can be obtained:  
1. Along the lateral direction of the exit, the velocity gradient at the gas-liquid boundary flattens, and the uniformity of velocity distribution inside the jet continuously increases. When the initial pressure and nozzle diameter are constant, the lateral distribution of jet velocity exhibits two distinct changes due to the influence of cone angles (105\({}^{\circ}\) and 110\({}^{\circ}\), 115\({}^{\circ}\) and 120\({}^{\circ}\)).
2. When the jet pressure and nozzle cone angle are constant, the peak value of turbulence kinetic energy (TKE) at the jet exit initially decreases and then increases. Along the central axis, the TKE of the flow field has no consistent change pattern. The  
Figure 7: (**a**) Hotspot map of cluster distance distribution; (**b**) spectrum graph clustering results.  
corresponding TKE fluctuates sharply at two positions when the nozzle diameter is 1 mm, indicating an extremely unstable flow field structure.
* Along the axial direction, the gas holdup inside the jet increases under the effect of entrainment. The gas hollows display a typical \"double valley\" feature in the cross-section at the jet exit. With the increase in initial pressure, the variation in gas holdup on the central axis of the jet is highly similar. The degree of entrainment at the jet outlet continuously decreases, and the entrainment range remains small.
* When the initial pressure is 0.36 MPa, the cone angle is 115\({}^{\circ}\), and the nozzle diameter is 2 mm, the obtained jet equivalent diameter has the least similarity with other operating conditions and can be classified as a separate category. Under the condition that it can effectively guide the actual jet parameter adjustment, all working conditions can be divided into two or three categories.  
Conceptualization, Writing--original draft, B.Z.; Data curation, Formal analysis, C.Z.; Investigation, Resources, J.L.; Software, Writing--review and editing, Validation, H.W.; Visualization, X.L.; Supervision, Funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 52001196).  
Not applicable.  
The research data have been presented in the current paper.  
All authors would like to express their sincere thanks to the editor and reviewers for valuable comments.  
The authors declare no conflicts of interest.
## 8 Implementation and Experiments
### \"Home-Made Deep Learning\" Project--A High-Level View  
The repository for the _hmdl_ project is placed at [[https://github.com/pklesk/hmdl](https://github.com/pklesk/hmdl)]([https://github.com/pklesk/hmdl](https://github.com/pklesk/hmdl)). At this opportunity, we would like to give great credit to the _Numba_ compiler ([[https://numba.pydata.org](https://numba.pydata.org)]([https://numba.pydata.org](https://numba.pydata.org))). Using numba.jit and numba.cuda amenities, important parts of hmdl were compiled into fast machine code (CPU and GPU, respectively). The main module is implemented in the hmdl.py file. The UML diagram in Figure 12 provides a high-level view of hmdl and the classes it involves.  
The abstract class Layer is the backbone of the hierarchy, and there are five specialized classes that inherit from it. They represent frequently applied layer kinds discussed in former sections: Conv2D, MaxPool2D, Dense, Flatten, and Dropout. Other new extensions can be introduced. There are some pieces of information common to all layers, defined within the Layer class, in particular input_output_delta_backward_input_delta_delta_backward_output_weights_, and gradient_. All of those should be understood as tensors stored as numpy arrays of numpy.float32 numbers. To be precise, weights_ and gradient_ are short lists of numpy arrays. This allows for independent access to proper weights and biases. The dimensionality of arrays is implied by the layer kind. For example, for the Conv2D or MaxPool2D layers, the input_ tensor is a 5-dimensional array, where the successive dimensions represent indexes of the data example (within a batch), image row, image column, input channel, and output channel. As regards Dense layers, their input_ and output_ are 2-dimensional arrays (indexing only data examples and neurons).  
Additionally, one should notice the properties named activation_ and activation_d_ present in the Layer class. They are function pointers (or None values), representing the activation function and its derivative, respectively. We apply Python's getattr(...) mechanism to obtain suitable pointers from string names specified by the user, e.g., \"relu\", \"sigmoid\", \"softmax\", etc.
## 2 Materials and Methods
### Kinematic Model of the Robotic Antenna
#### 2.1.1 The Gough-Stewart Platform Kinematics  
Let the closed kinematic chain for each limb be in accordance to Figure 4, where the vectors \(l_{i}\in\mathbb{R}^{3}\) represent the corresponding spacial directions from base point \(b_{i}\) to articulated point \(a_{i}\), \(\forall\ i=\{1,\ldots,6\}\) (_w.r.t._ frame \(\Sigma_{0}\)).  
Then, the absolute position of \(a_{i}\) is \(p_{i}=b_{i}+l_{i}(t)=d(t)+R(t)a_{i}\in\mathbb{R}^{3}\)[14], which yields  
\[l_{i}(d(t),R(t))=d(t)+R(t)a_{i}-b_{i} \tag{1}\]  
whose unit vector (in the direction of each limb) becomes  
\[\lambda_{i}\triangleq\frac{l_{i}(t)}{q_{i}(t)}=\frac{d(t)+R(t)a_{i}-b_{i}}{q_ {i}(d,R)} \tag{2}\]  
Since the pair \((d,R)\in SE(3)\) has six DoF [16], six Equations (1) are sufficient to solve either for \(q(t)=(\ldots,q_{i}(t),\ldots)^{T}\in\mathbb{R}^{6}\) or for the pair \((d,R)\). Considering a minimal attitude parametrization \(\mathbf{\theta}(t)\in\mathbb{R}^{3}\)[17], of the set of Euler angles, then \(R(\mathbf{\theta})\in SO(3)\), such that the _pose_\(z(t)\triangleq(d(t),\theta(t))^{T}\in\mathbb{R}^{6}\), where \((d(t),\theta(t))\) spans the operational space (\(\mathcal{OS}\)) of the platform: \(z\in\mathcal{OS}\subset\mathbb{R}^{6}\). Let the set of limb extensions span the configuration space (\(\mathcal{CS}\)) of the robot: \(q\in\mathcal{CS}\subset\mathbb{R}^{6}\), which leads to the following mappings [18]:  
\[\text{FK:}\qquad\qquad\mathcal{CS}\mapsto\mathcal{OS}:\qquad \qquad\qquad(R,d) =(R(q),d(q))\] \[\text{IK:}\qquad\qquad\mathcal{CS}\mapsdot\mathcal{OS}:\qquad \qquad\qquad q =q(R,d)\]  
Figure 3: Schematics of a variable-length strut GS platform.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 1 Introduction
The Sentinel-1 satellites provide an exceptional combination of high spatial and high temporal resolutions for dual polarization synthetic aperture radar data (6 days of temporal resolution and a 10 m \(\times\) 10 m pixel spacing). The availability of a dense time series enables the derivation and analysis of temporally backscattered signals. This study concentrates on the use of Sentinel-1 temporal backscatter signatures of rice cultivation to accurately map rice areas. We present a new approach for mapping rice areas using a Sentinel-1 SAR time series over the Camargue region of south-east France. First, the temporal behavior of SAR backscattering derived from Sentinel-1 images was analyzed over all existing crop types. Then, we took advantage of the exclusive temporal profile of the rice crop in VH polarization and the VV/VH ratio, compared to other crop types in the area, to identify a rice plot. Numerous metrics were derived using the Gaussian fitting of the VV/VH ratio and the linear fitting of the VH time series that were found to best discriminate rice crops from other crop types. Finally, we applied a decisional tree and a Random Forest (RF) classifier using the derived metrics to map the rice areas in the Camargue region. After a detailed description of the study site and dataset in Section 2, Section 3 presents the proposed methodology. The results and validation are presented in Section 4. A discussion is presented in Section 5 and finally Section 6 presents the main conclusions.