esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 3 Conclusions  
Our review clearly highlights the rapid development of VIPV for passenger cars, with a large fraction of technologies at the prototype stage. Commercial offers within this product category clearly represent the pioneers of the sector.  
Although early prototypes explored the use of high-efficiency cells, silicon-based photovoltaics is currently the dominant technology presenting an optimal tradeoff across performance, price, and reliability. Their drawback is their poor pliability in bidirectional flexion, limiting design options.  
Longer-term perovskite-based photovoltaics will have the potential of combining high efficiency, low cost, and flexibility after 2023 according to ITRPV 2021.  
Glass-glass and glass-backsheet curved PV modules are currently the most common technology in VIPV despite their higher weight (>11 kg/m2) and lack of aesthetics. However, its similarity to standard flat PV modules ensures the availability of qualified materials and, hence, higher reliability and durability potential.  
Flexible PV module technologies have the advantage of a lower weight (0.7 to 6.7 kg/m2), with power densities close to standard flat PV modules. Additionally, this technology is suitable for both new and aftersales market by adding PV modules to existing vehicle parts. We anticipate that the rapid development of dedicated module materials for flexible panels and their qualification in both standard PV and VIPV applications will lead to their increasing market share. We anticipate that these technologies will form the second generation of VIPV modules.  
Ultimately, light and rigid PV modules represent the most promising technology as they have the potential to replace the body parts of a vehicle. This technology has the potential of a lighter weight balance on the vehicle level thanks to full integration compared to flexible added PV functionality. For example, the development of light and curved PV modules using composite materials is in progress [80].
## 6 Extraction Methodology of Bioactive Compounds from _A. leptopus_
### Non-Conventional Extraction Techniques (Green Technology)
#### 6.2.1 Ultrasound-Assisted Extraction (UAE)  
Ultrasound-assisted extraction (UAE), a sustainable and environmentally friendly method, uses high frequency sounds to extract bioactive compounds from plant material. [PERSON] et al. [55] compared UAE with traditional extraction methods--maceration and percolation--to prepare 20% timctures of _A. leptopus_ and found that UAE was more efficient, yielding higher extract quantities in a shorter amount of time. UAE can also be combined with other extraction methods such as S-CO\({}_{2}\) allowing for a more effective extracting method. Compared to traditional methods, extraction times can be significantly reduced and extraction takes place at a lower temperature while still maximizing product yields [56; 57]. [PERSON] et al. studied optimized UAE techniques for the extraction of phenolic compounds from _Camillia japonica_, testing variables such as power, time, and the solvent used. Their optimized method conditions were 62% amplitude, 8 min, and 39% acidified ethanol, which allowed them to obtain a 56% yield [58]. However, optimal extraction conditions are highly dependent on the sample composition.
## 3 Results and Discussion
### Effect on the Physical Characteristics
#### 3.1.2 Effect on the Micro-Morphology  
The variations in the micro-morphology of the sandstone samples under different F-T action were studied using the scanning electron microscope (SEM) images (Figure 7). The results from the SEM images revealed that the F-T action had an important effect on the surface morphology of the tested sandstone with the induction of more defects suffered under increasing F-T cycles. The density of the micro-cracks under F-T action expanded to a considerable extent. In particular, the transition from intergranular to transgranular cracks in the micro-morphology indicated that stronger F-T action can not only affect the cement strength but also decrease the strength of mineral particles. The damage of cement and mineral particles provided favorable conditions for crack development and was embodied in widening the F-T crack aperture. Besides, the variations of micro-morphology in images also explained the increase of porosity in Figure 6.  
Figure 5: Failure patterns of the tested sandstone that was subjected to different numbers of F-T cycles. (**a**) Before loading; (**b**) After loading.
## 3 Results and Discussion
### Effects of OMET Technique on Irrigation Water Quantity  
The OMET growing technique significantly affected the amount of irrigation water as more water was used to irrigate the non-OMET-grown okra (\(p\leq 0.05\)). The cumulative irrigation water increased throughout the weeks, as shown in Figure 5 demonstrating that the OMET system improved water retention. Hence, it enhanced growth and yield in the disposal of lower irrigation water. The results may be attributed to the fact that the OMET growing technique was practiced under an enclosed trough which eliminated water seepage, thus advocating for climate-smart agriculture. The results were similar to those reported by [PERSON] [2], whereby the OMET growing technique improved the growth and yield of Swiss chard under the disposal of lower irrigation. The top plastic of the OMET system reduced the evaporation rate.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### Tdoa  
It is a technique to calculate the distance information between the two nodes. TDOA determines the variance of arriving times (timestamps) between the anchor nodes in the same package from the target. Figure 4 illustrates its basic operation for the TDOA-based method. This method requires at least three anchor nodes with known coordinates to find the object position. The anchor nodes listen to transmissions from the target and compute a position estimated by comparing the variances in the arrival times [62]. This method performs by determining the change in the time between a couple of anchor nodes.  
On the other hand, the multi-signal needs two different types of signals that have varying propagation speeds to compute its distance to another node. The accuracy of TDOA is due to complex indoor propagation such as multipath transmission and shadowing. The radio signals reaching the receiving antenna by different paths cause multipath transmission. This method needs extra equipment. The ultrasound or audible frequency can be used in this method using the same algorithm [61]. The achievement of the TDOA is subject to synchronization between the anchor nodes and the precision of the taken timestamp.  
Figure 4: TDOA-based trilateration.  
Figure 3: TOA-based trilateration.
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Sensing Applications Based on Radiation Pressure
#### 4.2.4 Inertial Sensing
When an acceleration was applied to the motional proof mass, the slot separation width was perturbed to shift the optical resonance frequency and change the optomechanical stiffness detuning from the driven laser frequency. It was demonstrated that this optomechanical inertial sensor provided an 8.2 \(\upmu\)g Hz\({}^{-1/2}\) velocity random walk at an acquisition rate of 100 Hz and an enhanced optical driving transduction sensitivity of 625 \(\upmu\)g Hz\({}^{-1}\). What is more, an apparatus for detecting gravitational force [144; 145] was provided based on a similar identical scheme in Figure 25b. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Accelerator** & **Device Size** & **Bandwidth** & **Sensitivity** & **Reference** \\ \hline MEMS & Large (mm-scale) & 3.2–20 kHz & \(\sim\)1 mg/Hz\({}^{1/2}\) & [139; 140] \\ Fiber optics & Small (\(\upmu\)m-scale) & 1 kHz & 0.1 \(\upmu\)g/Hz\({}^{1/2}\) & [141] \\ Optomechanics & Ultrasmall (nm-scale) & \(>\)10 kHz & 1–100 ng/Hz\({}^{1/2}\) & [130; 142] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison of sensitivity versus bandwidth of three representative accelerators. Figure 24: Schematic of PCN zipper cavity-based optomechanical accelerometer (the inset box indicates that canonical of accelerometer) [130]. optomechanical oscillator deformed under gravitational force, which caused a shift in resonance associated with the optomechanical sensitivity up to 10 ng/Hz\({}^{-1/2}\)[144]. However, considering practical engineering applications, one large obstacle that should not be underestimated is associated with a complete integration on-chip device. Significant studies and engineering works are needed to achieve a compact integration system that not only contains the optomechanical device in a vacuum, but also the highly efficient light coupling, readout receiver and related frequency modulation.
## 2 Methods
### Photogrammetric Processing of RGB and TIR Images  
The application of photogrammetry for the generation of 3D thermal and orthomosaic models with thermal images presents a large gap with respect to the quality of the representations obtained with RGB images, mainly due to the lack of resolution, contrast, and homogeneity in the TIR images [59]. The standardized methods for photogrammetry with RGB images do not obtain the same results when working with TIR images. The representations with the temperature variable can be obtained by implementing different strategies [60], from the single application of thermal images to generate the model [61], the texturing of 3D models with TIR images [62], or the implementation of four-band images through the fusion of RGB and TIR images [63].  
This study proposes a methodology resulting from the combination of different strategies based on the simultaneous capture of RGB and TIR images with low-cost dual sensors. Based on previous works, the proposal is based on solving the problems in the alignment of the thermal images of the structure from motion (SfM) algorithms used in modern photogrammetry [64] by implementing the optimized external orientation parameters (X, Y, Z coordinates and pitch, yaw, and roll angles) of the RGB images to position the TIR images [46].  
The proposed method (Figure 1) is based on the processing of RGB images following the conventional workflow standardized for SfM photogrammetry software. First, the images are aligned by identifying homologous points through scale invariant feature transform (SIFT) algorithms, enabling the generation of sparse point clouds. The optimization of the orientation of the images and georeferencing of the model is achieved through GCP registration, which subsequently obtains a dense point cloud using multi-view stereo (MVS) algorithms [65]. Finally, once the point cloud is obtained, the SfM photogrammetry software enables the generation of 3D models, meshes, orthomosaics, and textures. Then, using the thermal images, the model generated with the RGB images is taken as a basis. The TIR images are oriented on it using the optimized orientation parameters of the homologous RGB images to project the radiometric information on the model. Thus, the thermal 3D model is generated, which includes the temperature for each represented point and enables acquisition of a thermal orthoimage that shows the distribution of the surface temperatures of the landfill.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 4 Simulation Results and Discussions
### Performance of Block Error Rate  
In Figure 7 we show the block error rate (BLER) of the proposed system, to evaluate the performance of the proposed algorithm from another perspective. To do so we first set the target  
Figure 5: Transmit Power versus the harvest energy \(E_{c}\) for the proposed algorithm (\(\sigma^{2}=-112\) dBm and \(K=12\), the RNs are evenly divided into 3 groups, with the corresponding BER target of each group \(=10^{-2}\), \(10^{-4}\) and \(10^{-6}\)).  
Figure 6: Transmit Power versus the harvest energy \(E_{c}\) for the proposed algorithm (\(\sigma^{2}=-112\) dBm and \(K=4\,,8\,,16\)).  
BER to be \(10^{-6}\) and get the power allocation profiles of different RNs with the proposed algorithm. Then we perform the simulations on BLER with such power allocation profiles by vary the SINR from 0-15 dB. To compare, we also illustrate the BLER performance of the system with equal power allocation. It can be observed from the figure that the BLER of the proposed system decreases as SINR increases, indicating the efficiency of the proposed system on cancelling the interference. Moreover, the system with our proposed algorithm outperforms the one with equal power allocation. And the performance gap becomes larger when SINR is higher. It reveals that the SWIPT aided OFDM-IDMA system with the proposed algorithm performs well especially in the high SINR regime.
## 3 Results and Discussion
### XRD Analysis
The present results are in good agreement with those reported for different ZnO doping with rare earth's [2,12] and transition metals (Na\({}^{+}\)[10] and Mg\({}^{2+}\)[26]). Crystal imperfections induces lattice strain \(\epsilon\) which can be determined from the Williamson-Hall expression [27]:  
\[\beta\cos\theta=\frac{k\,\lambda}{D}+4\,\epsilon\sin\theta\]  
The term \(\beta\)cos\(\theta\) (\(y\)-axis) is plotted against 4 sin\(\theta\) (\(x\)-axis) in Figure 3 and \(\epsilon\) is calculated from the slope. As summarized in Table 2, the lattice strain \(\epsilon\) increases with further Na doping, revealing the high imperfections in the lattice induced from the doping process. However, the reduction of the crystallite size \(D\) can be explained from the growth inhibition of the ZnO crystal by Eu\({}^{3+}\) doping. Because of the crystallite size reduction, the specific surface area of the ZnO NCs will increases which is benefit for the photocatalytic activity. A TEM image of ZnEuNa4 sample is shown in Figure 4a. It shows the formation of quasi-uniform spherical nanoparticles with diameters in 15-25 nm range, which is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **ZnO** & **ZnEu** & **ZnEuNa1** & **ZnEuNa2** & **ZnEuNa3** & **ZnEuNa4** & **ZnEuNa5** & **Zn** \\ \hline \(D_{\text{D-Sr}}\) (nm) & 27.49 & 18.65 & 15.94 & 15.42 & 15.2 & 15.30 & 15.22 & - \\ \hline \(\epsilon\) (10\({}^{-3}\)) & 1.81 & 1.60 & 1.55 & 2.01 & 2.52 & 3.13 & 2.90 & 1.49 \\ \hline \(D_{\text{W-H}}\) (nm) & 47.63 & 25.06 & 20.26 & 21.19 & 22.83 & 26.20 & 24.57 & 24.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Crystallite size and strain of undoped and doped ZnO NCs. Figure 3: Williamson Hall plots of undoped and doped ZnO NCs. in good agreement with the XRD analysis. Energy dispersive X-ray spectroscopy (EDS) mapping on 2 \(\times\) 2 \(\mu\)m\({}^{2}\) area was provided for the sample ZnEuNa4, to determine the compositional elements of the ZnEuNa4 sample (Figure 4b). As expected, the spectrum showed the presence of Zn, O, Eu and Na elements, in the atomic % 48.29, 47.1, 0.96 and 3.65, respectively.
## 4 Simulation Experiment
### Experimental Results
#### 4.2.3 INS + LEO2 + Unbiased Altimeter and No Altimeter Scenario Experiment under Adjacent Tracks  
According to the parameter settings in Tables 1-3, in adjacent orbits, the experimental results of the INS + LEO2 switching + unbiased altimeter algorithm and the INS + LEO2 switching algorithm in adjacent orbits are shown in Figure 6. Similarly, as a comparison, we also included the standard INS + LEO2 switching navigation and positioning algorithm, and the standard INS + LEO2 switching + altimeter navigation and positioning algorithm as a reference.  
From Figure 6 we can see that, in addition to effectively suppressing the divergence of INS, the INS + LEO2 + unbiased altimeter algorithm in adjacent orbits can also suppress the error divergence problem of the INS + LEO2 switching algorithm under a longer switching time, especially when the switching time is 90 s. The final trajectory error statistical results of the two algorithms at 60 and 90 s are shown in Figure 7.  
From Figure 7 we can obtain that, with the aid of the altimeter, when the switching time is 60 s, comparing with the INS + LEO2 switching algorithm, the INS + LEO2 switching + unbiased altimeter navigation and positioning algorithm in longitude, latitude and altitude's errors improve by 50.50%, 37.55%, and 99.70%, respectively, and the corresponding standard deviation performance values improve by 59.93%, 54.77% and 94.25%, respectively. It can be seen that due to the addition of the altimeter, the new algorithm has the most obvious improvement in height error, this result is consistent with the analysis result in Section 2.1.2, and we will not analyze it in detail here.
## 5 Discussions
### Findings and Contributions
#### 5.1.4 CPU and Memory Usage  
This paper does not explicitly evaluate the CPU and memory usage in ESP8266. The CPU and memory usage in the ESP8266 are analyzed, revealing significant variations between active and sleep modes, which directly affect power consumption. The CPU is at its maximum capacity, managing tasks with Wi-Fi enabled, consumes approximately 80-100 mA of power in active mode. In sleep modes, the CPU either decreases activity or shuts down to conserve energy. The Wi-Fi module is also shut down while the CPU is still operational. During deep sleep, the CPU shuts down, consuming only 10-20 \(\upmu\)A, and performs with limited performance until it resumes operation.  
Due to its constrained 4 KB flash memory and 64 KB RAM, optimal memory management is crucial for the ESP8266 to prevent performance bottlenecks, memory overflow, and system crashes. Excessive RAM utilization leads to higher power consumption due to frequent memory access. Deep sleep mode facilitates energy conservation, but crucial data must be saved in non-volatile memory or transferred to the cloud before sleep. The balancing of CPU and memory usage and energy efficiency needs to be deeply experimented with and discussed in future work.
# Active Electromagnetic Clutch for Crankshaft Decoupling from a Belt Drive System
## 4 Clutch State Estimation
### Inductance Variation Method  
The nominal variation of the inductance as a function of coil current and air gap is plotted in Figure 5. By knowing the inductance at a fixed current, the position of the clutch can be estimated. However, note that the monotonic behavior of the inductance with air gap variation is only guaranteed when the coil current is below 0.6 A.  
Figure 5 suggests that the use of the inductance to estimate the air gap is only valid when a small coil current is applied. Thereby, the clutch control unit (CCU) can apply a persistent voltage disturbance that produces a coil current below 0.6 A. When the clutch is closed or open, the disturbance due to back-EMF is not present (\(\mathrm{d}g/\mathrm{d}t\approx 0\)) and (3) reduces to the following:  
\[v_{c}=L(i_{c},g)\frac{\mathrm{d}i_{c}}{\mathrm{d}t}+i_{c}R(\Theta). \tag{4}\]  
Figure 4: Numerical static FEM simulation variables as a function of the air gap and coil current. (**a**) Coil flux linkage. (**b**) Electromagnetic force.  
The system can be considered a resistive-inductive circuit in which the inductance changes with the coil current and air gap, while the resistance is temperature-dependent (\(\Theta\)). For a short time frame, both the inductance and the resistance are assumed to be constant. Rewriting (4) in the Laplace form yields the following:  
\[\frac{i_{c}(s)}{v_{c}(s)}=\frac{1}{sL+R}. \tag{5}\]  
For a practical implementation in the CCU, a linear difference equation is obtained using [PERSON]'s discretization method:  
\[a_{1}i_{c}z^{-1}+a_{0}i_{c}=b_{1}v_{c}z^{-1}+b_{0}v_{c}, \tag{6}\]  
where \(b_{0}=b_{1}=T_{s}\) is the sampling time and  
\[a_{0} =2L+RT_{s} \tag{7a}\] \[a_{1} =RT_{s}-2L. \tag{7b}\]  
At time instant \(k\), and using \(N\) past measurements of the coil current \(i_{c}\) and voltage \(v_{c}\), expression (6) is arranged as follows:  
\[\underbrace{\left[\begin{array}{cc}i_{c}k&i_{c}k-1\\ i_{c,k-1}&i_{c,k-2}\\ \vdots&\vdots\\ i_{c,k-N+1}&i_{c,k-N}\end{array}\right]}_{A}\left[\begin{array}{c}a_{0}\\ a_{1}\end{array}\right]=\underbrace{\left[\begin{array}{c}b_{1}v_{c,k-1}+b_ {0}v_{c,k}\\ \vdots\\ b_{1}v_{c,k-N}+b_{0}v_{c,k-N+1}\end{array}\right]}_{b}+\mathbf{\omega}. \tag{8}\]  
The vector \(\mathbf{\omega}=[\omega_{1},\cdots,\omega_{N}]^{\intercal}\) has been added to lump the uncertainty due to the measurement error. Since \(A\) and \(b\) are known, the minimum mean square error is used to obtain the coefficients \(a_{0}\) and \(a_{1}\). Then, the equation system in (7) is solved to obtain \(L\) and \(R\).  
Finally, the estimated inductance is used to evaluate the clutch state. For instance, when the clutch is correctly closed (\(g=2.5\,\mathrm{mm}\)) and the excitation current is below \(0.6\,\mathrm{A}\), it is seen from Figure 5 that the inductance is confined to approximately \(90\,\mathrm{mH}\). Different inductance values would indicate a clutch malfunction.  
Figure 5: Coil inductance as a function of the air gap, for different coil current values.
## 3 Strength Test
### Establishing Fatigue Equations Based on the Weibull Distribution of Interval Parameters
#### 4.3.4 Example of Calculation Analysis
The effect of accuracy and stability of test equipment and the representative problem of test sampling and errors caused by materials, structures, the environment, and loads can be considered in the S-N fatigue equations characterized by interval parameter fatigue equation. Interval parameter fatigue equation increases the accuracy of the fatigue life prediction for the cement-treated aggregate base material. By using the concept of the interval numerical value in mathematics and replacing the point value of design parameters with interval values, the problems of accuracy and stability of test equipment and the representative problem of test sampling were solved, and the errors caused by the materials, structure, construction, environment, and load were reduced, making the fatigue life interval more reasonable and scientific compared to the point value life. This is the objective required to characterize the complex nature of parameters and is in agreement with situation in which designs comprise bias and construction comprises errors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Curing** & **Cement** & **Point Parameter Fatigue** & **Interval Parameter Fatigue Equation** \\
**Times (days)** & **Content (\%)** & **Equation** & **Equation** \\ \hline \multirow{2}{*}{28} & 3.5 & \(lgN_{i}=10.8943-10.0056(\sigma/\text{S}_{\text{f}})\) & \(lgN_{i}=[10.3,\ 11.5]-[9.5,10.5](\sigma/\text{S}_{\text{f}})\) \\  & 4.5 & \(lgN_{i}=11.5796-10.6162(\sigma/\text{S}_{\text{f}})\) & \(lgN_{i}=[11.4,11.8]-[10.5,10.8](\sigma/\text{S}_{\text{f}})\) \\ \hline \multirow{2}{*}{90} & 3.5 & \(lgN_{i}=10.5094-9.5126(\sigma/\text{S}_{\text{f}})\) & \(lgN_{i}=[10.4,10.9]-[9.2,9.9](\sigma/\text{S}_{\text{f}})\) \\  & 4.5 & \(lgN_{i}=10.8334-9.6826(\sigma/\text{S}_{\text{f}})\) & \(lgN_{i}=[10.8,10.9]-[9.5,9.9](\sigma/\text{S}_{\text{f}})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Standardization fatigue prediction model for cement content of 3.5% and 4.5%.