esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 2 Refocusing Model Conforms to Central Projection
### Central Projection Refocusing Model Based on Plenoptic Disc Data  
The plenoptic disc data is proposed by [PERSON] et al. (2018) in 2018, which is used to improve the calibration accuracy of the plenoptic camera. As shown in Figure 3a, \((F_{u}^{S},F_{v}^{S})\) represents the central projection coordinates of \(F\) in \(ouv\) with regard to the center of the main lens. \(|R|r_{mi}\) stands for the maximum pixel distance between \((F_{u}^{S},F_{v}^{S})\) and the center of micro-image which contains the projection point of \(F\), with \(r_{mi}\) denoting the pixel radius of micro-image. The vector \((F_{u}^{S},F_{v}^{S},R_{F})\) is called the plenoptic disc data of \(F\), while \((F_{u}^{S},F_{v}^{S})\) and \(R_{F}\) are called the plenoptic disc centre, and plenoptic disc radius, respectively. The \(R_{F}\) can be calculated by  
\[R_{F}=b(b-B-F_{z})/(F_{z}B), \tag{3}\]  
with \(F_{z}\) stands for the \(Z\)-coordinate of \(F\) in \(OXYZ\). The relation between \(R_{F}\) and \(v_{F}\) is  
\[R_{F}=bv_{F}/(b-B-v_{F}B) \tag{4}\]  
By the help of plenoptic disc data, the calculated focal stack will conform to the central projection with regard to the center of main lens, as shown in Figure 3b. Given the \((F_{u}^{S},F_{v}^{S},R_{F})\) of \(F\), the projected point \((p_{u},p_{v})\) on raw image can be obtained by  
\[\begin{bmatrix}1&0&(p_{u}-i_{u})\\ 0&1&(p_{v}-i_{v})\end{bmatrix}\begin{bmatrix}F_{u}^{S}&F_{v}^{S}&R_{F}\end{bmatrix} ^{T}=\begin{bmatrix}i_{u}\\ i_{v}\end{bmatrix}. \tag{5}\]  
In Equation (5), \((i_{u},i_{v})\) denotes the central coordinates of the micro-image with regard to \((p_{u},p_{v})\) in \(ouv\). As illustrated in Figure 3b, the rendered refocused image based on Equation (5) can make full use of the light recorded by the sensor at any refocus plane. This can be done simply by restricting the central projection coordinates \((F_{u}^{S},F_{v}^{S})\) other than the orthographic coordinates \((F_{u},F_{v})\) within the range of raw image. Besides, given any imaging point \(Q\), the pixel coordinates \((G_{s},G_{t})\) of \(G\) in \(o^{\prime}st\) is  
\[(G_{s},G_{t})=d(Q_{u}^{S},Q_{v}^{S}), \tag{6}\]  
with \((Q_{u}^{S},Q_{v}^{S})\) representing the central projection coordinates of \(Q\) in \(ouv\). When \(d\) is fixed, the coordinates \((G_{s},G_{t})\) among different refocused images are strictly the same. What is more, the use of central coordinates of micro-images in Equation (5) makes the calculated results in accordance with the theoretical model without the help of camera calibration.  
To sum up, the central projection refocused images have better characteristics than the traditional one, which can provide support for the accurate feature extraction.  
Figure 3: Illustration of proposed refocusing algorithm. (**a**) Illustration of plenoptic disc data. (**b**) Illustration of central projection refocusing model.
## Discussion
### Aroma Characteristics of Lingtou Dancong Oolong Tea  
Lingtou Dancong, also known as Baiye Dancong, like Fenghuang Dancong, is a protected geographical indication product (PGI) in Guangdong province, China. The aroma of oolong tea from Guangdong province differs from those of other oolong teas because of the varieties used and the processing methods. Tea tasting experience and existing literature indicate that the floral and honey fragrances are key sensory characteristics of Dancong oolong tea [2; 21]. Other Dancong oolong teas have a prominent floral fragrance, while Lingtou Dancong has a unique honey fragrance [2; 21]. The literature indicates that linalool and linalool oxides are important substances for forming the honey fragrance [2; 22]. However, linalool is a vital odor compound, not only in Lingtou Dancong oolong tea, but also in other oolong teas, green tea, black tea, and even dark tea with a higher degree of fermentation [7; 23; 24; 25]. Therefore, linalool and its oxides are important and universal substances in tea, rather than the signature aroma of Lingtou Dancong oolong tea. The oolong tea \"Oriental beauty\" is widely known for its honey fragrance, which is largely conferred by hotrienol [26]. Hotrienol, which is present in honey and has a honey and fruit aroma, is a very unstable thermal product that can be converted from diols or linalool [27; 28]. Studies have shown that the hotrienol content is higher in Lingtou Dancong oolong tea than in other Dancong oolong teas, and that hotrienol is not present in Rougui teas with different degrees of baking and fermentation [2; 29; 30]. In our study, we detected higher contents of both hotrienol and its precursor, linalool, in high- and medium-altitude teas than in low-altitude teas. This result suggests that hotrienol in tea might be converted from linalool, whose content might be related to altitude. (E)-\(\beta\)-Damascenone (OAV = 316-357) and _cis_-citral (OAV = 4.17-8.04) were also identified as key honey odorants with high OAVs, high sensory intensity (Tables S3-S5), and the highest FD (>512) among all odor compounds(Table 2). Notably, (\(E\))-\(\beta\)-damascenone was not detected in any of the tea products made from Shuixian, Huangmeigui, and Zimudan (three varieties suitable for making oolong tea) [23]. _cis_-Citral was not detected in Dahongpao, Tieguanyin and Dongdingwulong teas [31]. Therefore, hotrienol, (\(E\))-\(\beta\)-damascenone, and _cis_-citral may be the key components of the special honey aroma of Lingtou Dancong oolong tea. However, further aroma recombination experiments are required to test this hypothesis.
## 2 Materials and Methods
### Hyperspectral Imaging System  
In this study, a hyperspectral fluorescence imaging system was used to acquire the HSIs of the rice samples. It consisted of two UV-A excitation lights, a sample translation stage, and the acquisition part of the hyperspectral fluorescence image (Figure 2). The latter was equipped with a slit for the line scan, a high-sensitivity electron multiplying charge coupled device (EMCCD; MegaLuca R, ANDOR Technology, South Windsor, CT, USA) with \(1004\times 1002\) pixels, an image spectrograph, and a C-mount lens (F1.9 35 mm compact lens, Schneider Optics, Hauppauge, NY, USA). EMCCD has a pixel size of \(8\times 8\)\(\upmu\)m, is cooled down to \(-20\)\({}^{\circ}\)C via thermoelectric cooling and can capture 14-bit images at a frequency of 12.5 MHz. The image spectrograph and C-mount lens were fixed in front of the EMCCD. The UV-A excitation lights were vertically tilted at 15\({}^{\circ}\) to excite the samples. The HSI was captured in the spectral range of 420-780 nm, with intervals of 4.8 nm. The image field-of-view was determined using a 25 \(\upmu\)m \(\times\) 18 mm (width \(\times\) length) slit size. The line scan image, acquired through the slit, was spectrally irradiated on the EMCCD surface through a diffraction grating to generate a spectral image for each wavelength band. The horizontal and vertical axes of each line scan image included the respective spatial and spectral information.  
In this experiment, an image was obtained three times for each sample using an exposure time of 0.02 s, an amplification value of ten, and a step of 0.5 mm line scan images. Since HSIs are affected by sample conditions such as degree of milling, experiments were performed under the same conditions in this study to minimize these effects. The HSIs of the rice samples were adjusted by using dark reference images to correct for the device noise as well as fluorescent reference images. The fluorescence reference plate was white inkjet paper that exhibited uniform blue fluorescence [13]. The dark reference images were obtained without light source exposure. The HSIs of the rice samples were corrected according to Equation (1).  
\[I(i)=(I_{s}(i)-D(i))/(I_{r}(i)-D(i)) \tag{1}\]  
Figure 1: Photograph of South Korean and Chinese rice samples.  
where \(I\) is the corrected relative hyperspectral fluorescence image, \(I_{s}\) denotes the sample hyperspectral fluorescence image, \(I_{r}\) represents the hyperspectral fluorescence image of the fluorescent reference plate, and \(D\) is the hyperspectral image of the dark reference plate, all at the \(i\)th wavelength.  
The average spectrum and pixel spectrum of the rice samples were extracted from the calibrated HSIs of the rice samples, excluding the container. Then, multivariate analysis was performed using the extracted HSI data [13].
# A Robust Control Algorithm of a Descent Vehicle Angular Motion in the Earth's Atmosphere
## 3 Mathematical Model  
In the modeling, we will consider the lander as an absolutely rigid body, symmetric about the longitudinal axis, the center of mass (CM) does not displace along the transverse axis [24]. We introduce the generalized state vector  
\[\mathbf{x}^{T}=\begin{bmatrix}t&\mathbf{x}^{T}_{trj}&\mathbf{x}^{T}_{attt}\end{bmatrix},\]  
where \(t\) - time from the moment of powering on the GAMS; \(\mathbf{x}_{trj}\) - the state vector during the movement of the CM (three coordinates of position and velocity).  
The model of motion of the CM of the lander [25] has the general form  
\[\hat{\mathbf{x}}_{trj}=\mathbf{f}_{trj}\big{(}\mathbf{x}_{trj},\mathbf{F}_{Q_{v}}\big{(}\mathbf{x}_ {trj},\mathbf{x}_{attt}\big{)}\big{)}, \tag{3}\]  
where \(\text{F}_{Q_{v}}\) - resultant of aerodynamic, gravitational and inertial forces. The model of the angular motion of the lander [9; 10] (between the CS \(E_{v}\) and \(Q_{v}\)) consists of the kinematic and dynamic equations [26; 27; 28], as well as the measurement model:  
\[\begin{split}\hat{\mathbf{x}}_{attt}=\mathbf{f}_{attt}\Big{(}\mathbf{x}_{trj},\mathbf{x}_{attt},\mathbf{M}^{tr}_{E_{v}}\Big{)}&=\begin{bmatrix}\mathbf{G}^{ E_{v}+Q_{v}}_{E_{v}}\Big{(}\mathbf{\theta}^{E+Q_{v}}\Big{)}\Big{(}\mathbf{\omega}^{E_{v}}_{ E_{v}}-\mathbf{\omega}^{Q_{v}}_{E_{v}}\Big{(}\mathbf{x}_{trj},\mathbf{\theta}^{E+Q_{v}} \Big{)}\Big{)}\\ \mathbf{J}^{-1}_{E_{v}}\big{(}\mathbf{M}^{err}_{E_{v}}\big{(}\mathbf{x}_{trj},\mathbf{\theta}^ {E+Q_{v}}\big{)}+\mathbf{M}^{gyr}_{E_{v}}\big{(}\mathbf{\omega}^{E_{v}}_{E_{v}}\big{)}+ \mathbf{M}^{gst}_{E_{v}}+\mathbf{M}^{ctr}_{E_{v}}\big{)}\end{bmatrix},\\ \mathbf{y}_{attt}=\mathbf{g}_{att}\big{(}t,\mathbf{x}_{trj},\mathbf{x}_{att} \big{)}=\begin{bmatrix}\tilde{\gamma}\Big{(}t,\mathbf{x}_{trj},\mathbf{\theta}^{E+Q_{v }}\Big{)}\\ \mathbf{\omega}^{E_{v}}_{E_{v}}\end{bmatrix},\end{split} \tag{4}\]  
here \(\mathbf{G}^{E_{v}+Q_{v}}_{E_{v}}\) - the matrix of the kinematic equations of motion of the CS \(E_{v}\) relative to the CS \(Q_{v}\) in the Krylov angles; \(\mathbf{J}_{E_{v}}\) - tensor of inertia of lander relative to CM in basis \(E_{v}\); \(\mathbf{M}^{gyr}_{E_{v}}\), \(\mathbf{M}^{gst}_{E_{v}}\) and \(\mathbf{M}^{ctr}_{E_{v}}\) - respectively, aerodynamic, gyroscopic, disturbing unbalanced and control moments relative to the CM of lander.
## 3 Design Overview
### System Architecture
* ICN edge nodes supporting multipath services can identify NDC data packets with multipath preference fields, and obtain the index of multipath service nodes according to the data scheduling strategy (e.g., Round-Robin) and the source ID field of NDC data packets. The edge nodes obtain the value of the selected index from the local multipath forwarding information tables as the multiple service node NA, and changethe destination IP of the NDC data packet to the IP of the multipath service node and re-encapsulates it as a new packet through the ID/NA collaboration and refactoring function [30]. Edge nodes forward NDC data packets to corresponding multipath service nodes according to the destination IP. * The multipath service nodes can identify NDC data packets with multipath preference fields and obtain the consumer NA from the NMRS query through the destination ID field of the NDC data packet. The multipath service node changes the destination IP of the NDC data packet to the consumer IP through ID/NA coordination and refactoring. The multipath service node forwards the NDC data packet to the consumer according to the destination IP. * In the process of multipath transmission, consumers can increase or decrease the number of NDCs requested simultaneously according to their throughput. When all the NDC required by the consumer is transmitted, the multipath transmission process ends.
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 6 Results and Discussion
### Performance for Network
As the parameter \(V\) increases, the network average energy efficiency first increases rapidly and then gradually converges to the optimal value. This trend confirms Theorem 2 that network energy efficiency increases at the rate of \(\mathcal{O}(1/V)\). This is because the network needs to consume more energy to keep the queue stable. Meanwhile, when the computation tasks are fixed, the network energy efficiency is highest on sunny days and lowest on cloudy days, because the HAP harvests more energy on sunny days and the network can process more data to improve the network computation. This is because HAP harvests more energy on sunny days and can process more data to increase network computation, while cloudy weather is less conducive to energy harvesting, and the volatility of solar energy harvesting in different time slots of the HAP is greater due to cloud cover, so the network needs to consume more energy to keep the queue stable, and therefore its network energy efficiency is lower than that of cloudy weather. Figure 6 illustrates the effect of the control parameter \(V\) on the network queue backlog. The average queue length increases with the value of the control parameter \(V\), verifying that the time-averaged energy queue backlog increases at the rate of \(\mathcal{O}(V)\) as stated in Theorem 2. It is clear from Figure 6 that under certain conditions of available energy, as the computing task increases, the energy required for local computing or remote offloading at  
Figure 4: The performance of energy efficiency under different algorithms with network bandwidth. Figure 5: System energy efficiency vs. control parameter \(V\). terminals is insufficient, and tasks that cannot be processed in time will be buffered in the data queue, increasing the network queue backlog. When the network computing task is certain, the queue backlog is the largest in cloudy weather, which is because the volatility of solar harvesting by HAP in different time slots is larger in cloudy weather, which requires HAP to consume more energy to guarantee the queue stability, and it is difficult to compute and process the terminal data in time, causing the network queue backlog to increase.
## 3 Results and Discussion
Taking into account Equation (19), this leads to a weak dependence of the waveform of the photovoltage  
Figure 1:6 The oscillograms of the photovoltage pulses arising in a thin CuSe/\(t\)-Se film with circular polarization of exciting radiation and angles of incidence \(\alpha=10.5\), 58.5, 64.5, and 82.5\({}^{\circ}\) (black circles), and their approximations shown by red lines according to Equation (19). pulse on the angle \(\alpha\) for linearly polarized laser pumping. It should be noted that the transverse photocurrent arising in the medium due to the PDE or CPGE also consists of the circular and linear contributions. Therefore, the temporal profile of the transverse photocurrent pulses of the PDE or CPGE generated in the nonlinear optical medium when pumped by short laser pulses of circular polarization can also depend in a complex way on the angle at which the light falls on the surface of the absorbing medium. Good agreement between the experimental data and the calculated dependences confirms that the angular dependence of the waveforms of transverse photocurrent pulses in a thin CuSe/Se film under circularly polarized femtosecond laser pumping originates from the transformation of circular polarization into elliptical polarization upon refraction of light in a semitransparent CuSe/Se film and the interaction of LPC and CPC having different relaxation times in the film structure. As mentioned above, despite the large number of publications on the topic of generation of polarization-sensitive transverse photocurrent in various materials, arising by various mechanisms, such a phenomenon has not been observed before. It is possible that this is due to the fact that in many studies of the transverse photocurrent, cw laser radiation or nanosecond laser radiation has been used (see, for example, [33, 67, 68, 69]). The results obtained in this work can be used in various applications in optoelectronics. For example, the experimental setup presented in the inset to Figure 5 can be used for the fast direct detection of the circular polarization state of light. If at angles of incidence \(0<\alpha<58.5^{\circ}\) (for example, at \(\alpha=45^{\circ}\)) the photocurrent pulses generated in a CuSe/\(t\)-Se thin film have a positive polarity, then this means that the incident radiation is right-hand polarized (looking towards the light source). If, under the same experimental conditions, the photocurrent pulses have a negative polarity, then the incident radiation is left-hand polarized. This method of determining the state of circular polarization does not require the use of optical elements. Further, it is obvious that this photovoltaic property of a CuSe/Se thin film can be used to determine the fast and slow axes of a quarter-wave plate without using a reference quarter-wave plate and an optical light polarization.
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 1 Introduction
All devices such as smart phone, tablet, connected cars, smart phone, smart building and 4G LTE are based on EPC. In the past, EPC solution is realized through an exclusive system. Today, mobile telecommunication service providers pursue an alternative model to support rapidly increasing network demand. If virtualized EPC (vEPC) solution is used, mobile telecommunication service providers can expand at a cost-effective way using a standard bulk server instead of a purpose-built system. It is impossible to continue to use a purpose-built system in line with the network traffic volume increase. Service providers must expand network capacity and introduce new services while avoiding the high purchase cost of the purpose-built equipment that cannot be used for multiple purposes. That is to say, EPC is one of the most attractive alternatives that can lead to changes. In the 4G LTE network, EPC suppliers provide signaling, management, control and account management function that are essential to all IP convergence sound and data network traffic. The function element that EPC provides is (MME) Mobile Management Entity. In the past, many service providers have used a purpose-built system for EPC, but the expansion of EPC to accommodate traffic increase by a purpose-built system seems to require higher cost. It is an appropriate time for change. In case of areas where 4G LTE is still impossible, the service providers of relevant areas have an opportunity to substitute the exclusive system before investment. Some service providers in other areas of the world are ready to supplement the existing EPC solution or to expand EPC in order to accommodate traffic increase. Therefore, this paper suggests the vEPC model for server operation and virtualization to save energy, and carries out test bed. The test bed is produced with the Java Android application. A system that efficiently reduces the network server operation cost based on the NFV technology is presented in this study. The performance analysis has shown that the cost was significantly reduced by 24% compared to the operation cost of the existing network server. It is expected that the technology used in this research will be foundational to the future sustainable computing. Once virtualized EPC (vEPC) solution is executed, service providers can control costs and also can increase subscribers, traffic and access volume.
## 3 Results
### Luminescence Lifetime  
The lifetime of the charges in their excited state is determined for sample A10 by measuring decay curves at the maximums of PL emission at 325 and 346 nm. The results are shown in Figure 9. The IRF signal is also presented in the figure for comparison. The excitation is performed using a pulsed source with emission at a wavelength of 265 nm. Both curves could not be fitted with a single exponential decay curve, and a double exponential curve is used to fit the curves using Equation (1).  
\[y=A_{1}\cdot exp(-x/t_{1})+A_{2}\cdot exp(-x/t_{2}) \tag{1}\]  
where, \(A_{1}\), \(A_{2}\) are the amplitudes of individual exponential, and \(t_{1}\) and \(t_{2}\) are decay constants. The results are combined in Table 2.  
Generally, when the electron is excited to a higher quantum state from where it is expected to come back to the ground state accompanied by the emission of photons due to electron-hole recombination, the intensity of emission is written as \(I=I_{o}\exp(-t/\tau)\). The decay curve in such a case can be fitted with monomolecular or first-order decay kinetics. On the contrary, when 'n' number of electrons move to the conduction band, leaving behind 'n' number of empty centers, the rate of recombination, 'r' is given by \((-\mathrm{dn}/\mathrm{dt})=\mathrm{.n}^{2}\), and the decay curve can be fitted with hyperbolic or second order decay kinetics.  
Figure 8: (**a**) PL excitation spectrum while monitoring the maximum at 346 nm, and (**b**) PL emission spectra monitored upon excitation at 225, 254, and 275 nm. The letter X in the figure denotes the excitation wavelength.  
When looking into our results, there are at least two different pathways of electron relaxation from the excited state to the ground state, as is evident from the lifetime of \(\sim\)3 ns and \(\sim\)11 ns. The results also suggest that the decay follows second-order kinetics. When the decay results are compared upon excitation at 325 and 346 nm, we observe that the relaxation pathways are similar in the two cases.
## 4 Wireless Transmission in WBANs
### Network Protocols
From the above, the former is conducive to interoperability and interconnection among WBAN devices, and the latter can provide network-level technical support for the implementation of telemedicine, which is significant for remote continuous monitoring. 5. With the development of the Internet of Things, the increasing number of WBANs and the mobility of WBANs, interference is becoming more challenging. For a single WBAN, intra-BAN interference can be effectively avoided by using TDMA techniques, but multiple WBANs interfere with each other when they are co-located (i.e., inter-WBAN interference). Figure 4 describes the different types of interference in WBANs [109] and the parameters that cause inter-BAN interference.