esa-sceva/satcom-chunk-collection huggingface dataset:
# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
###### Abstract  
Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.  
broad learning system; point cloud; 3D object classification +
Footnote †: journal: Applied Sciences
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
###### Abstract  
Devastating wildfires in Texas, Oklahoma, and Kansas in 2016 and 2017 resulted in significant social, economic, and environmental losses, with the agricultural sector among those severely affected. Several satellite-based indices were evaluated as potential monitoring tools for post-wildfire ecological recovery and management of grasslands. All indices evaluated provided useful information and indicated rapid vegetation recovery from wildfire. The Leaf Water Stress Index (LSWI) and Gross Primary Productivity (GPP) showed a distinct response to the wildfire events, and differentiated between burned and unburned areas throughout the post-wildfire growing seasons better than the Normalized Difference Vegetative Index (NDVI) and Enhanced Vegetative Index (EVI). In particular, the LSWI may provide a useful tool for mapping the footprint of wildfire, with potential utility for organizations that provide post-fire recovery resources. The GPP, which estimates the biomass productivity of vegetation, can provide information to livestock operators to guide the re-stocking of cattle in the aftermath of wildfire. In sum, satellite-based proxies can provide timely information both to characterize a wildfire's footprint and to guide post-fire grazing management in a manner that balances short term needs for forage with long-term productivity and ecological function.  
**Keywords:** NDVI; EVI; LSWI; GPP; vegetative productivity model; prairie; rangeland; ranching; wildlife; disaster response +
Footnote †: journal: Remote Sensing, 2020, 12, 619; doi:10.3390/rs12040619
# Glucose Data Classification for Diabetic Patient Monitoring
## 5 Implementation
### Hardware Installation  
The general scheme of the proposed system is achieved by connecting the blood glucose level sensor with the e-health shield and the Arduino board. The Arduino board is also connected to the GSM module with a TTL (Transistor-Transistor Logic) to RS232 converter. Data rates for serial communication between the microcontroller, the sensor, and the GSM module have a value of 115,200 bps. In addition, the type of data transmitted in the program must be also set to synchronize the transmitter and the receiver. Figure 7 shows the global hardware installation.  
Figure 5: Flow chart of the system.  
Figure 6: Illustration of blood glucose measurement.
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
## 3 Power Allocation in Dual-Hop Networks  
Power allocation (PA) is an important aspect of wireless communications and much research has been done in this regard [17; 18; 31; 32; 33; 34; 35]. All the transmitting terminals, be it multiple antennas, two-hop/multiple-hop relay assisted communications, or multiple-sources/multiple-relays, require different types of strategy to distribute the power to all the transmitting terminals. In multi-hop wireless networks, the instantaneous SNR, \(\gamma_{i}\), at the \(i^{th}\) relay can be calculated by considering the transmit power \(P_{s}\), i.e.,  
\[\gamma_{i}=\frac{P_{s}}{\sigma^{2}}\left|h_{sr_{i}}\right|^{2}. \tag{5}\]  
Figure 2: Post-detection combining with majority voting.  
Energy efficiency (EE), \(\eta\), is the maximum number of information bits transmitted successfully, per Joule of energy consumed at the transmitter. In other words, EE is the amount of data transmitted per unit power. Mathematically, for a single link between \(S\) and relay \(R_{i}\), it can be defined as:  
\[\eta=\frac{\log_{2}(1+\gamma_{i})}{P_{tot}}. \tag{6}\]  
Similarly, the instantaneous SNR, \(\gamma_{d_{i}}\) at \(D\) form a relay \(i\) can be defined as:  
\[\gamma_{d_{i}}=\frac{P_{i}}{\sigma^{2}}\left|h_{r_{i}d}\right|^{2}, \tag{7}\]  
where \(P_{i}\) is the transmit power of relay \(i\) and energy efficiency at \(D\) can be defined as follows:  
\[\eta=\frac{\log_{2}(1+\gamma_{d_{i}})}{P_{tot}}. \tag{8}\]  
In Equations (6) and (8), \(P_{tot}\) is the sum of the normalized powers at the source and all relays, i.e.,  
\[P_{tot}=P_{s}+\sum_{i=1}^{N}P_{i}. \tag{9}\]  
In the following subsection we investigate the transmit power allocation under certain conditions to achieve a target BER. For discussion on energy usage and energy efficiency, a couple of different scenarios are considered where transmit power allocation is performed in different manners. The scenarios are as follows.
## 1 Introduction
Recently artificial intelligence (AI) techniques have been used to improve the efficiency of traditional methods, such as the P&O [15]. These algorithms not only have a better energy performance [16], they can also help to minimize the problems of the traditional algorithms such as the P&O and the Incremental Conductance, namely the multiple maxima when the panels are partially shaded [17], the inaccurate responses when irradiation is suddenly increased [18] or the overall performance in changing environment conditions [19]. Since the relationship between the generated power of the PV panels and their output voltage is non-linear, fuzzy logic and artificial neural networks are suitable algorithms for the solution of the MPPT in PV panels [20]. The fuzzy logic can be used together with traditional controllers such as the PID, for MPPT control resulting in a hybrid controller that combines the advantages of both methodologies [21]. It can also be used to improve the performance of typical MPPT techniques, such as the Incremental Conductance [22] and P&O [13]. The fuzzy logic algorithms, can be used to improve the performance of artificial neural networks for the MPPT problem and can also be enhanced by other AI based solutions, such as genetic algorithms and particle swarm optimization [23]. The fuzzy logic based MPPT control techniques enable an adaptative step size, allowing better performance and solving the main problems of the traditional methods, namely the multiple maxima and the trade off between the performance in steady state and dynamic environment conditions, improving the performance of the solar system in both conditions. In this work the main objective will be to study a fuzzy logic controller (FLC), with the error function based on the rules of which the P&O adjusts its duty cycle in accordance to a defined fuzzy rule set. In particular its performance will be compared to the traditional P&O under changing environment conditions (namely the solar irradiation, the ambient temperature or both), by how fast it adjusts and reaches the MPP and the oscillations steady state conditions. The remainder of this paper is organized as follows: in Section 2 a description is made of the system implemented in Matlab/Simulink; in Section 3 both the more traditional P&O MPPT algorithm and the fuzzy logic enhanced P&O MPPT algorithm, are described and how they will impact the control of the PV system; Section 4 briefs the results and discussions obtained for both methodologies and finally in Section 5 the conclusions are presented.
# Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine
## 2 Modeling the Dynamics of Semi-Submersible FOWT
### Simplified Dynamic Model for Semi-Submersible FOWT
#### 2.1.4 Damping Force Terms of Platform  
For the body floating in still water, damping forces are mainly composed of two parts, viscous damping and radiation damping. According to [PERSON]'s representation [19], the viscous damping force is proportional to the square of the relative velocity between the fluid particles and the platform, which can be calculated as follows in this case:  
\[\begin{split} F_{\text{sg}}^{\text{viscous}}&=- \sum\tfrac{1}{2}\rho C_{\text{d}}\int_{0}^{L_{\text{Draft}}}\left|\dot{x}_{ \text{sg}}-h\cdot\dot{\theta}_{p}\right|\left(\dot{x}_{\text{sg}}-h\cdot \dot{\theta}_{p}\right)\text{d}h\\ T_{\text{P}}^{\text{viscous}}&=-\sum\tfrac{1}{2} \rho C_{\text{d}}\int_{0}^{L_{\text{Draft}}}\left|\dot{x}_{\text{sg}}-h\cdot \dot{\theta}_{\text{P}}\right|\left(\dot{x}_{\text{sg}}-h\cdot\dot{\theta}_{ \text{P}}\right)h\text{d}h\end{split} \tag{9}\]  
where \(C_{\text{d}}\) denotes the drag coefficients of members of the platform, which are available in the FAST configuration file; \(L_{\text{Drift}}\) denotes the draft of the platform; \(h\) denotes the distance from the integral position to the free surface.  
Generally, the radiation damping term is represented by the convolution integral form [18]:  
\[F_{j}^{\text{radiation}}=-\int_{0}^{t}K_{ij}(t-\tau)\hat{q}_{j}(\tau)\text{d} \tau,j=1,2,...,6 \tag{10}\]  
where \(F_{j}^{\text{radiation}}\) denotes radiation damping force for the \(j\)th degree of freedom (DOF); \(K_{ij}\) denotes the \((\text{i},\text{j})\) component of the wave-radiation-retardation kernel; and \(\hat{q}_{j}\) denotes the \(j\)th DOF velocity.  
After a series of attempts, it is found that these two damping estimation formulas are very computationally expensive for optimization calculation, which is discussed in Section 3. Furthermore, the values of the radiation damping terms for most of modes are considerably smaller than those of the viscous terms. Hence, following the previous study [12], the total damping force is simply approximated by the following equations:  
\[\begin{split} F_{\text{sg}}^{\text{damp}}&=-D_{ \text{sg}}^{\text{sg}}\cdot\big{|}\dot{x}_{\text{sg}}\big{|}\dot{x}_{\text{sg }}-D_{\text{p}}^{\text{sg}}\cdot\big{|}\dot{\theta}_{\text{p}}\big{|}\dot{ \theta}_{\text{p}}\\ T_{\text{p}}^{\text{damp}}&=-D_{\text{sg}}^{\text{p}} \cdot\big{|}\dot{x}_{\text{sg}}\big{|}\dot{x}_{\text{sg}}-D_{\text{p}}^{\text {p}}\cdot\big{|}\dot{\theta}_{\text{p}}\big{|}\dot{\theta}_{\text{p}}\end{split} \tag{11}\]
# A Near-Far-Field Model for Bubbles Influenced by External Electrical Fields
## 5 Coupling Near-Field and Far-Field
### Bubble Formation: Experiment 2  
In the following, we couple the near-field and far-field computations.  
We have the following setting, see Figure 15.  
We apply the following parameters:  
* Input-parameters of the near-field bubbles:
* \(r_{1}(0)=0.01\), \(z_{1}(0)=0\), \(r_{1}(L)=0\), \(L=1\), \(\frac{\Delta p_{1}}{\sigma_{1}}=0.8\),
* \(r_{2}(0)=0.01\), \(z_{2}(0)=0\), \(r_{2}(L)=0\), \(L=2\), \(\frac{\Delta p_{2}}{\sigma_{2}}=0.8\).
* We compute the bubbles based on the near-field code and we obtain the ellipse-diameters \(a_{bubble}\), \(b_{bubble}\).
* We initialize the two ellipses for the far-field computations given as:  
The numerical results of near-field bubble formation are given in Figure 16.  
The numerical results of the near-far-field coupled bubble transport code, which is given in Figure 17.  
The coupling of the formation and transport of the bubbles is done with ordinary and partial differential equations. Such a coupling closes the gap between the different scale-dependent models of a near-field and far-field simulation, see [37]. Based on decoupling such systems of mixed ordinary and partial differential equations, we were able to compute each separate part with the optimal numerical solvers. We apply higher order near-field and far-field solvers, which accelerate the computational time, see also [25; 37].  
Figure 16: The computation of the bubble formation (near-field) for two bubbles.  
Figure 15: (**left**) Bubble formation with the ordinary differential equations (ODEs). (**right**) Bubble transport with the partial differential equations (PDEs) (level-set equations).
## 3 Results  
### Characterization of Spatiotemporal Variations  
#### 3.1.1 Temporal Trends  
The time series analysis of WUE from 1982 to 2018 (Figure 2) revealed that both annual and growing season WUE exhibited an increasing trend in the YRB. Between 1982 and 2018, there were similar fluctuating characteristics observed in both the annual WUE and the growing season WUE. The annual WUE (\(0.56\times 10^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\), R\({}^{2}\) = 0.5478) and growing season WUE (\(0.44\times 10^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\), R\({}^{2}\) = 0.5171) exhibited a significant increasing trend. The annual WUE variation rate was \(0.48\times 10^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\) (R\({}^{2}\) = 0.15) and \(0.41\times 10^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\) (R\({}^{2}\) = 0.1985) in 1982-2000 and 2000-2018, respectively. The growing season WUE revealed a gradual increasing trend at the rate of \(0.39\times 10^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\) (R\({}^{2}\) = 0.1479) and \(0.29\times 10^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\) (R\({}^{2}\) = 0.1465) in 1982-2000 and 2000-2018, respectively. The average WUE of the YRB from 1982 to 2018 was 0.93 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, with the highest value of 1.10 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, which the highest value appeared in 2018, and the lowest value of 0.72 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, which the lowest value appeared in 1982. According to the time series trend of Figure 2, it was found that the ecosystem WUE of the YRB from 2000 to 2018 (Mean value: 0.99 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) is higher than that before 2000 (Mean value: 0.87 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O).  
Table 1 revealed the WUE of the upstream, midstream and downstream throughout the year, growing season and different seasons in the YRB. During 1982-2018 in the upstream of the YRB, the WUE was 1.08 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, 1.36 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O,  
Figure 2: Temporal trends of WUE in 1982-2018. (**a**) Annual; (**b**) Grow.  
0.29 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O in spring, summer, fall, and winter, respectively. Among them, the autumn has the highest WUE. The order of magnitude is: autumn > summer > spring > winter. The autumn WUE in the midstream and downstream of the YRB was higher than that in other seasons, which was 2.38 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O and 2.42 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O, respectively, and the downstream was higher than that in the midstream. Compared the WUE of the upstream, midstream and downstream at different seasonal scales, it was found that the WUE in autumn was significantly higher than that in spring, summer and winter at different geographic zones, and the WUE was the lowest in winter.
## 3 Fusion Precomputed AES Encryption Algorithm
### Precomputation  
Anticipation refers to calculating and storing the results of some computation ahead of time, before they need to be used, so that they can be quickly accessed and used subsequently. This approach saves computational resources and time, and improves the operational efficiency of the program.  
Figure 3 is a list of common computation processes:  
The common cryptographic computation process is shown in the left side of Figure 3. For the received message, the relevant computation is performed for each message in real time, and there may be repeated computations for duplicate data. The precomputation process is shown in the right side of Figure 3, where the resources required for computation are advanced and stored in the data structure, after which only lookup operations are required without computation operations as a way to reduce the time overhead.
# Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles
## 6 Polymer Coated MSNs for Bone Related Disorders  
MSNs also find applications in treating bone related diseases. Recently, lithium doped silica nanospheres coated with PDA was prepared by [PERSON] et al. to improve the bioactivity and osseointegration ability of polyetheretherketone (PK) orthopaedic implant. Nanospheres were coated on PK surface by direct immersion and coated PK demonstrate excellent cell proliferation suggesting bone tissue formation and enhanced osseointegration in vivo. The improvement in PK performance after nanosphere coating was attributed to the change in the surface characteristics and release of Si and Li ions, which supported the osteogenic activity of osteoblasts [60].  
In another study, [PERSON] et al. developed phospholipid coated MSNs to provide additional lubricating effects during treatment of osteoarthritis via nanoparticles injected directly to joint capsules. Tribiological test results suggested that coated MSNs provide effective lubrication via hydration lubrication mechanism owing to the amphiphilic nature of phospholipids. The release kinetics of nanoparticles were studied using rhodamine B as a model compound and a sustained release was observed from lipid coated MSNs compared to uncoated particles. Cytotoxicity assay on MC3T3-E1 cells suggests the low toxic behaviour of coated MSNs, advocating the use of developed particles in biomedical applications [61].
## 4 Analysis of the Entire Collapse Process
### Analysis of the Collapse Mechanism
#### 4.2.3 Time-History Analysis of Collapse  
To analyze the response coupling characteristics of the typical weakness (Section 4.2.1) in the entire collapse process, Figure 20 shows the displacement time history of the typical transient maximum displacement response node (key weak node). Among them, Node16385(A), Node18433(B), Node20481(C), Node22529(D), and Node23809(E) are the maximum displacement response nodes at transient t = 0.2 s, t = 0.4 s, t = 0.4 s, t = 1.4 s, and t = 3.4 s. The comparative study found that the maximum displacement position of the cooling tower first appears at 167.90 m (Node23809(E)). With the progression of collapse, it moves down to 124.48 m (Node16385(A)). Then, the maximum displacement position moves quickly to the tower's top rigid ring area after the rigid ring is broken. In the deformation section of the throat (0 s-2.2 s), the maximum displacement occurs in the throat area. The displacement of each key weak node increases with the degree of collapse. The displacement growth rate of the throat area is higher than that of the rigid ring area. In the transitional period (2.2 s-4.4 s), the displacement growth rate of each key weak node slowed down and the maximum displacement shifts from the throat to the windward point of the rigid ring, while the displacement growth rate of the rigid ring area exceeds the throat area.  
Figure 21 shows the time history of the strain energy per unit volume during the entire collapse process. From the perspective of structural energy dissipation, it is found that during the initial small deformation elastic stage (0-2.2 s), the unit volume strain energy is close to 0. There is no way to bear the extreme wind loads by energy consumption. In the plastic development phase of the tower (2.2-8.6 s), the unitary volume of strain energy begins to accumulate at Node A, which has a certain energy consumption effect. After complete collapse (\(\geq\)8.6 s), each unit bears a certain amount of energy dissipation, but due to the release of gravitational potential energy the ring is completely collapsed. Comprehensive analysis indicates that increasing the duration of the plastic development phase can improve the ability to resist continuous collapse.  
Figure 20: Time history of typical maximum displacement response nodes during the collapse process. (**a**) Response measurement point. (**b**) Displacement time history curve.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 5 Performance Test
### Effect of the Pitch Amplitude  
As for the pitch amplitude of the wave in Figure 10, the output power of the system increases significantly as the pitch amplitude increases. However, the excessive amplitude will rapidly reduce the energy conversion efficiency. At the same time, this will also make the precession angular displacement reach the limit position, causing system instability. In addition, excessive platform counter torque is not conducive to energy generation.  
Figure 9: Effect of the PTO damping coefficient: (**a**) output power; (**b**) conversion efficiency; (**c**) flywheel precession angular displacement; (**d**) platform counter torque.
## 6 Experimental Results and Evaluations
### Experimental Setup  
In our study, we use Python's chainer framework to create deep learning model architecture. Also, we considered the large number, length, and complexity of the source code to develop our proposed model. Before the start of training, we defined several hyperparameters for the experiment to obtain better results. First, we determine the number of hidden layers and epochs. Then the number of neurons was determined based on the number of hidden layers. Thus, the neurons were equal to the defined number of hidden layers. For example, If hidden layers \(h_{l}=100,200,300,400,\) and so on. Thus, the neurons at each hidden layer will be equal to the number of hidden layers, such as \(n\_units=h_{l}\)  
Figure 4: Word embedding and encoding process for source code.  
where \(n\_units\) = neurons at each layer and \(h_{l}\) = number of hidden layers. Dropout was used to regularize the LSTM network performance to avoid overfitting. To obtain better training accuracy dropout ratio was set to 0.5 [41]. We optimized the LSTM network using the Adam optimization algorithm [42]. Particularly, optimizer smoothing the model learning by binding together loss function and model parameters in order to produce better training accuracy. The learning rate or step size of our network was \(l=0.001\). The network weights were updated based on the value of \(l\) during training. A higher (lower) value of \(l\) makes initial learning faster (slower). The values of \(\beta_{1}\) and \(\beta_{2}\), the exponential decay rates for the first- and second-moment estimates, were set to 0.001 and 0.999, respectively. It is often effective to reduce the learning rate when training is running. Without exponential decay, the loss function cannot start again to diverge after decreased a certain point. The value of \(\varepsilon\) (\(=1e^{-8}\)) was used to prevent division by zero in the implementation. We trained our network with various numbers of hidden layers (50, 100, 150, 200, 250, 300, and 400). The corresponding models are called the 50-layer model, 100-layer model, and so on. We evaluated the performance of all models to determine the optimal number of hidden layers.