esa-sceva/satcom-chunk-collection huggingface dataset:
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
## 1 Introduction
A passivity-based control for a quad-rotor transporting a cable-suspended payload is presented in [20]. Here, the problem is also reduced to the longitudinal plane to avoid complex PDEs. Finally, a passivity based controller for a PVTOL (Planar Vertical Take-Off and Landing) is introduced in [21]. On the one hand, observe in these works that it is not possible to use passivity-based strategies directly to control the vehicle's full dynamics because certain passivity properties are not satisfied, i.e., when this methodology is used directly in the classical quad-rotors equations, then only the attitude can be stabilized. Nevertheless, controllers based on PBC can be used if the dynamics are modified, this requires the use of more complex algorithms and high computational cost implementations for just a partial stabilization. On the other hand, IDA-PBC strategy is designed for underactuated systems. However, it requires computing complex PDEs, many of which may have not solution. The design of an IDA-PBC for a quad-rotor has this problem. By avoiding the complex PDEs, we can only control the quad-rotor's attitude. Motivated by the aforementioned considerations, in this work we introduce a methodology to analyze the quad-rotor vehicle using a passivity-based control with unit quaternions. The PBC strategy is obtained from the mathematical model based on Euler-Lagrange formulation using a quaternion logarithmic mapping such that a fully actuated virtual system is proposed, then an attitude trajectory in the quaternion space is used to apply the mathematical restrictions inherent to the quad-rotor. The resulting control scheme does not compute excessive and complex PDEs and can be designed directly from the model and can control the full quad-rotor equations. The proposed approach has no issues with singularities and can be straightforwardly applied in real-time experiments. The paper is organized as follows: A brief survey of the main concepts of unit quaternions and passivity is presented in Section 2. The representation of the quad-rotor dynamics in terms of unit quaternions is described with the logarithmic mapping in Section 3. The quaternion-passivity-based control is developed in Section 4. Numerical simulations are presented in Section 5. Real-time experimental results are shown in Section 6. Conclusions and perspectives are finally given in Section 7.
## 3 Experiments and Discussion
### Force-Charge Signal Characterization  
#### 3.3.1 Sensitivity Measurement  
To measure the sensitivity quantitatively, the experiment to measure the force generated by using the resistance strain gauge (FRS402) and the loudspeaker was designed and carried out. The relationship between the output response of the signal generator and the force generated by the loudspeaker is shown in Table 1.  
The sensitivity of the sensor was measured by changing the size and frequency of the force generated by the loudspeaker. Taking the 5-Hz excitation signal as an example, the output curves of the sensor under different voltages were obtained after power frequency interference filtering processing, which is shown in Figure 7. The response signal of the rising edge of the excitation signal is selected as the output response of the sensor. There are five rising edges in Figure 7a, which averages the corresponding response signal values and the output response amplitude of the sensor under which each voltage can be obtained. Lastly, the output of the sensor is linearly fitted by using the data analysis software ORIGIN, and the force-charge relationship of the sensor is obtained at the excitation frequency of 5 Hz, which is shown in Figure 7b. The slope of the fitting line aligns with the sensitivity of the sensor at 5-Hz excitation frequency. The sensitivity at other excitation frequencies was obtained by the same method, which is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Output Voltage (V)** & 1 & 3 & 5 & 7 & 9 & 11 \\ \hline
**Force (N)** & 0.48 & 0.99 & 1.45 & 1.96 & 2.45 & 2.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relationship between the output response of the signal generator and the force generated by the loudspeaker.  
Figure 6: The output curves of sensors with different adhesives.  
From Figure 7b, it can be seen that the output response of the sensor linearly increased with the rising exerted force amplitude, which is in accordance with the theoretical derivation of the liner relationship between the output response of the sensor and the exerted force mentioned above. The comparison of the data in Table 2 shows that the sensitivity of the sensor increases with the rising of the frequency of the excitation signal. The magnitude of the increase is getting smaller and smaller, and it tends to be stable at about 15 Hz.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 7 Comparisons
### Asymptotic Comparisons  
Table 1 shows the asymptotic comparison between TSKT-ORAM and some existing ORAM schemes. First, we compare TSKT-ORAM with single-server ORAMs, which include T-ORAM, path ORAM, P-PIR and C-ORAM. As we can see, the communication costs of T-ORAM, path ORAM and P-PIR are not constant, while TSKT-ORAM is in practice. Though C-ORAM incurs constant communication cost, it requires expensive homomorphic encryptions, which incurs a huge amount of computations at the server side and results in long data access delay. For example, with C-ORAM, each data access has to incur a delay as long as seven minutes.  
Second, we compare TSKT-ORAM with other multi-server ORAMs, which include MS-ORAM, MSS-ORAM and CNE-ORAM. MS-ORAM and MSS-ORAM both require server-server communication; additionally, MSS-ORAM incurs high storage cost at the client side, while TSKT-ORAM does not require server-server communication and incurs only constant cost at the client. Compared to CNE-ORAM, which requires at least four non-colluding servers, TSKT-ORAM requires only two; also, TSKT-ORAM incurs lower computational costs (which is translated to lower data access delay) and smaller server storage costs, as will be discussed next.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods
### Data  
Aquasafe utilizes data from both satellites, models, and _in-situ_ measurements to acquire meteorological, physical, biochemical, and economic information about fish farms and their surrounding areas (Figure 1a). The use of multiple data sources has the advantage of providing a more comprehensive view of the farming areas. Satellites provide multi-spectral, thermal, and SAR data, which are used to estimate several parameters. _In-situ_ measurements serve to validate the satellite estimations and provide additional information about the farmed species and their growth. Additionally, meteorological data such as winds, rainfalls, and cloud coverage, as well as data about extreme weather events, are also taken into consideration.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 4 Experimental and Analysis
### Analysis of the Number of Parameters of the Model  
To visually measure the super-resolution performance and the number of parameters for different models, Figure 12 illustrates the comparison of different models in terms of the super-resolution performance and the number of parameters. It can be seen from Figure 12 that our GFSR and GFSR+ achieve a better trade-off between the super-resolution performance and the number of parameters compared to other methods. Specifically,  
Figure 11: Visual comparison of super-resolution results of ‘Img_067’ (Urban100) obtained by different SR algorithms with scaling factor \(\times\)4.  
Figure 10: Visual comparison of super-resolution results of ‘86000’ (BSD100) obtained by different SR algorithms with scaling factor \(\times\)4.  
compared with DASR, the proposed GFSR obtains a higher PSNR using only 36% of the parameters.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 3 Statistical Characteristics for End-to-End Instantaneous SNR
### Derivation of the PDF for End-to-End Instantaneous SNR  
Performing the first-order derivative operation with respect to the variable in Equation (27), the PDF of the end-to-end SNR for the FSO-UWOC mixed dual-hop link can be obtained as:  
\[f_{\gamma^{DF}}(\gamma)=f_{\gamma_{1}}(\gamma)+f_{\gamma_{2}}(\gamma)-f_{ \gamma_{1}}(\gamma)F_{\gamma_{2}}(\gamma)-F_{\gamma_{1}}(\gamma)f_{\gamma_{2 }}(\gamma) \tag{28}\]  
By substituting Equations (12), (14), (23), and (25) into Equation (28), the explicit closed-form expression for the PDF of \(\gamma^{DF}\) can be obtained.
## 3 System Architecture and Test Environment
### Deploy Model for the Nvidia Jetson Nano  
The Nvidia Jetson Nano is a small, low-power computer designed for use in embedded systems and other applications requiring a high computing performance within a compact form factor. It is based on the Nvidia Jetson platform and is powered by a quad-core ARM Cortex-A57 CPU and a 128-core Maxwell GPU. It supports a wide range of interfaces and peripherals, including USB, ethernet, and HDMI [21]. The Nvidia Jetson Nano has a maximum power consumption of 10 watts, which makes it an energy-efficient solution for edge computing. It also has a memory bandwidth of 25.6 GB/s, which allows for fast and efficient data transfer between the CPU and GPU. These parameters make the Nvidia Jetson Nano a suitable platform for running deep learning models as it can handle complex computations while maintaining a low power consumption. Additionally, the Nvidia Jetson Nano is equipped with 4 GB of LPDDR4 memory, which provides sufficient memory for running deep learning models and handling large datasets. Overall, the Nvidia Jetson Nano's power consumption and memory bandwidth parameters make it an excellent choice for deploying AI solutions at the cutting edge.
# Analysis of Planar Double-Layer Timber Spatial Frames by Using Parametric Tools
## 3 Results and Discussion
### Comparative Analysis
#### 3.2.3 Metrics: Nodes and Bars  
As a guideline, a breakdown by components of the total cost of the system is estimated. These are the approximate average values [44]: bars 30%, nodes 40%, protection and paint 10% and assembly 20%. From these data, it can be deduced that the cost of a space frame is closely related to the type and number of joints (40% nodes+30% bars). Therefore, in the absence of a budget, the number of nodes can be taken as a guide to cost. In such a case, economizing means that the number of nodes per square meter should be kept to a minimum.  
The graphs from Figure 15 show that frames C1 and D1 are the most \"expensive\" ones, both in terms of the number of nodes and number of bars, while A3 and D2, the lightened topologies, are the most cost-efficient ones. Frame A2 is in the middle of both extremes. As the span of the structure increases, the two lines marked on the graphs maintain their outline because the number of modules remains constant. However, numerically speaking, the structure becomes \"cheaper\". The clearest example is to compare the values of the 7 \(\times\) 12.5 and 25 \(\times\) 50 roofs. Without going any further, the A1 mesh needs 2.46 nodes/m\({}^{2}\) to cover 91 m\({}^{2}\), while it only needs 0.2 nodes/m\({}^{2}\) for 1200 m\({}^{2}\).  
By reducing the density of elements with longer bars, not only a greater load-bearing capacity is achieved, but also material and, therefore, cost is optimized. This is the advantage that space frames offer over other structural systems, and that is why they are so competitive in large spans.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 5 Simulation Evaluations
### Performance Comparison
#### 5.3.3 Impact of Charger's Traveling Speed  
Figure 7 shows the performance comparison of NJNP, TSP, WCI, CI and BC with varying charger's travel speed. Figure 7 demonstrates that when \(v\) increases, the two durations and data loss rate significantly decrease. This is because the faster the charger travels, the shorter time it consumes in the travel path, and the more nodes can be charged in a tour. Compared with NJNP and TSP, we can see that WCI illustrates its advantage. For example, when \(v=1\), WCI's total disjointed duration, inactive duration, and data loss rate is 72%, 70%, and 69% of that delivered by the TSP, respectively. When compare with NJNP, the three ratios are reduced to 42%, 34%, and 34%, respectively. Note that CI outperforms WCI on total nodes' disjointed duration when \(v\) is lower. This is because the connectivity critical nodes are charged first in the CI. Consequently, the total disjointed time of active nodes is shorter while the total inactive time of other nodes is longer, which implies the charging effect of CI is inferior to the WCI and TSP.  
Figure 6: Impact of \(L\) on the charging scheduling algorithms: (**a**) Total nodes’ disjointed time; (**b**) Total nodes’ inactive time; (**c**) Network’s data loss rate.
# Laser Sintering of TiO\({}_{2}\) Films for Flexible Dye-Sensitized Solar Cells
###### Abstract  
In this study, laser sintering of TiO\({}_{2}\) nanoparticle films on plastic substrates was conducted in order to improve the incident photon-to-electron conversion efficiency (IPCE) of flexible dye-sensitized solar cells (DSCs). Lasers with different wavelengths (355 nm and 1064 nm) were used to process the TiO\({}_{2}\) electrodes. With an optimized processing parameter combination, the 1064 nm laser can sinter 13 \(\upmu\)m thick TiO\({}_{2}\) films uniformly, but the uniform sintering cannot be achieved by the 355 nm ultraviolet (UV) laser, since the films possess a high absorption ratio at 355 nm. The experimental results demonstrate that the near-infrared laser sintering can enhance the electrical connection between TiO\({}_{2}\) nanoparticles without destroying the flexible plastic substrate, reduce the transmission impedance of electrons and increase the absorption rate of incident light. Furthermore, the charge collection efficiency, fill factor, and short-circuit current have all been improved to some extent, and the solar conversion efficiency increased from 4.6% to 5.7%, with an efficiency enhancement reaching 23.9%.  
1  
later Sintering of TiO\({}_{2}\) nanoparticles films on plastic substrates was conducted in order to improve the incident photon-to-electron conversion efficiency (IPCE) of flexible dye-sensitized solar cells (DSCs). Lasers with different wavelengths (355 nm and 1064 nm) were used to process the TiO\({}_{2}\) electrodes. With an optimized processing parameter combination, the 1064 nm laser can sinter 13 \(\upmu\)m thick TiO\({}_{2}\) films uniformly, but the uniform sintering cannot be achieved by the 355 nm ultraviolet (UV) laser, since the films possess a high absorption ratio at 355 nm. The experimental results demonstrate that the near-infrared laser sintering can enhance the electrical connection between TiO\({}_{2}\) nanoparticles without destroying the flexible plastic substrate, reduce the transmission impedance of electrons and increase the absorption rate of incident light. Furthermore, the charge collection efficiency, fill factor, and short-circuit current have all been improved to some extent, and the solar conversion efficiency increased from 4.6% to 5.7%, with an efficiency enhancement reaching 23.9%.  
Keywords:1064 nm laser; TiO\({}_{2}\) films; laser sintering; flexible dye-sensitized solar cells +
Footnote †: journal: Applied Physics Letters
## 5 Simulations Results and Discussions
### Case Study 1: Home Is Not Equipped with DGs
#### 5.1.2 PADR  
Figure 6 shows the PADR of the consumer's appliances. The results show that our proposed GWDO algorithm-based ECS decreases PADR by 40.1% when compared with the case of the unscheduled load operation, whereas it is 27%, 11.8%, 32.3%, 16.3%, 23.7%, 19.2%, 5.3%, and 36.8% for cases of using the GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO, respectively. These algorithms are supposed to minimize the overall consumer load. The PSO, WDO, and BFA algorithms shifted more of the load to less electricity pricing time slots, resulting in rebound peaks. These new peaks in the time slots of 19-24 create instability in the power system and result in a penalty from the electricity supplier. However, the GWDO algorithms uniformly distribute the operation of the overall appliance and result in low PADR.
## 2 Material and Methods
### Model Framework  
After preprocessing, the restructured images and their labels were trained and validated through our MCMS attention-based CNN model. The proposed MCMS attention module could be plugged into any existing CNN backbone architecture to enhance feature extraction. The multi-channel attention layer allowed the model to learn the most important channel, while the multi-spatial attention layer helped the model learn the most informative areas. Thus, by learning to assign different weights to different channels and spatial areas, the model was trained to identify more distinguishable patterns from the extracted features, eventually making more reasonable predictions and achieving higher accuracy. Figure 2 presents the overall framework of the proposed MCMS attention-based CNN model.  
Figure 1: An overview of our data preprocessing method. Firstly, we apply image augmentation to the original whole-slide images to enhance the dataset diversity. We further slice each image into smaller tiles in 224 \(\times\) 224, followed by a sorting of these tiles based on the mean pixel value. The top \(N\) (N = 36 in this study) most informative ones are selected to form a new image, which is a 6 \(\times\) 6-tile square image.
## 3 Finite Element Model  
ANSYS Maxwell 2D is a powerful software package that uses the finite element method to solve 2D low-frequency electromagnetic problems, by specifying the appropriate geometry, material properties and excitations for a device or system of devices [24; 25]. The proposed magnetic field problem is solved using the eddy current field solver, which allows computing steady state, time-varying (AC) magnetic fields at a given frequency, here 50 Hz. It also computes current densities, taking into account eddy current effects in solid conductors (including skin and proximity effects), as well as other quantities that can be derived from the magnetic field solution. An adaptive mesh refinement technique is used to achieve the best mesh required to meet the defined accuracy level.  
The quantities that the eddy current field simulator resolves are the magnetic vector potential (\(\mathbf{A}\))--related to magnetic flux density by \(B=\
abla\times\mathbf{A}\)--and the electric scalar potential (\(V\)). A first equation used for this purpose, derived from [PERSON]'s equations, is:  
\[\
abla\times\frac{1}{\mu}(\
abla\times\mathbf{A})=(\sigma+j\omega\varepsilon)(-j \omega\mathbf{A}-\
abla V), \tag{1}\]  
where \(\mu\) is the absolute magnetic permeability, \(\sigma\) is the electrical conductivity, and \(\omega=2\pi f\) is the angular frequency at which all quantities are oscillating and \(\varepsilon\) is the absolute electric permittivity.  
As we can see, the right side of (1) consists of a complex conductivity, \(\sigma+j\omega\varepsilon\), multiplied by the complex value of the electric field strength, i.e., \(\mathbf{E}=-j\omega\mathbf{A}-\
abla V\). Therefore, the result is the complex current density, \(\mathbf{J}\), which is the sum of three components:  
* \(\mathbf{I_{s}}=-\sigma\
abla V\), the source current density due to the differences in electric potential;
* \(\mathbf{I_{e}}=-j\omega\sigma\mathbf{A}\), the induced eddy current density due to time-varying magnetic fields;
* \(\mathbf{I_{d}}=j\omega\varepsilon(-j\omega\mathbf{A}-\
abla V)\), the displacement current density due to time-varying electric fields.  
Since the total current (\(I_{T}\)) flowing in any conductor that is connected to an external source is specified when setting up the problem, a second equation used by the eddy current module to solve for \(\mathbf{A}\) and \(V\) is:  
\[I_{T}=\int_{S}\mathbf{J}\mathbf{S}\mathbf{S}=\int_{S}(\sigma+j\omega\varepsilon)(-j\omega \mathbf{A}-\
abla V), \tag{2}\]  
which basically states that the total current in a conductor equals the integral of \(\mathbf{J}\) over the cross-sectional area of the conductor, \(S\).  
Because \(\mathbf{B}\) is assumed to lie in the \(xy\) plane, \(\mathbf{A}\) has only a component in the \(z\) direction. Therefore, the eddy current module will solve only for \(\mathbf{A_{z}}(x,y)\). Additionally, \(\mathbf{E}\) has a \(z\) component only, which means that \(V\) is constant over the entire cross section of a conductor. Therefore, it is not necessary to solve for \(V\) at every node.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 4 Healthcare Data Protection Legislation and Framework
### Major Healthcare Initiative
#### 4.1.6 The Privacy Act (Australia)  
It entails the set of principles of Australian legislation to protect the personal information of Australian citizens. These principles refer to the usage, storage, and disclosure of personal information. Moreover, individuals have the right to the access and correction of their personal information. This law also includes data security, data quality, and cross-border data flow policies [75].  
Healthcare data is a subset of personal information and needs extra security policies and protection. All organizations that provide a health service and hold health information (other than an employee record) are covered by the Privacy Act, whether or not they are small businesses. In certain circumstances, the Privacy Act permits the handling of healthinformation and personal information for health and medical research purposes, where it is impracticable for researchers to obtain individuals' consent.
## 3 Numerical Modeling of the Filling Phase
### Rheological Models
#### 3.2.6 Cross  
The Cross model accounts for the effects of shear rate and temperature on viscosity. It is comparable to the Carreau model and acquires both Newtonian and shear-thinning behavior [6]. The shear-thinning behavior is represented by the generalized Cross equation [54], which is a well-established alternative to the Bird-Carreau-Yasuda model [37]. The mathematical formulation of this model is expressed in the following equation [30].  
\[\frac{\eta-\eta_{\infty}}{\eta_{0}-\eta_{\infty}}=\frac{1}{1+(K\dot{\gamma})^ {1-n}} \tag{21}\]  
The parameters utilized to characterize the rheological behavior of the fluid are the zero shear rate viscosity \(\eta_{0}\), the infinite shear rate viscosity \(\eta_{\infty}\), the time constant \(K\), and the Power-Law index \(n\). These parameters characterize the fluid's shear thinning behavior. If the viscosity is negligible at infinite shear rate, the Cross model can be expressed in the following form [51].  
\[\eta_{\dot{\gamma}}=\frac{\eta_{0}}{1+\left(\frac{\eta_{0}\dot{\gamma}}{\tau^{ \ast}}\right)^{1-n}} \tag{22}\]  
Here, \(\tau^{\ast}\) represented the critical shear stress at the transition from the Newtonian plateau. The defining characteristics include the Power-Law index \(n\) and the value \(K=\frac{\eta_{0}}{\tau^{\ast}}\).  
The following equation can be employed for the purpose of determining the zero shear rate viscosity, with respect to the effects of temperature within the framework of the WLF approach [55; 56; 57]. This can be considered analogous to the temperature shift factor [55; 56].  
\[\eta_{0}=D_{1}\exp\biggl{(}\frac{A_{1}(T-T_{\rm s})}{A_{2}+T-T_{\rm s}}\biggr{)} \tag{23}\]  
In this equation, \(D_{1}\) represents the viscosity at the reference temperature \(T_{\rm s}\), \(A_{1}\) serves as a descriptor of the temperature, and \(A_{2}\) serves as a descriptor of the polymer pressure dependence of the viscosity [5].