esa-sceva/satcom-chunk-collection huggingface dataset:
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 5 Conclusions  
This paper presents the derivation and numerical results for ISL availability. The performance of availability is shown to be related to the altitude, resources scheduled, number of users, beamwidth, and whether the ISL signals are kept separate or combined with other signals.  
The availability decreases with smaller scheduling satellite. The four-fold coverage expands to 72,000 km if all satellites can be scheduled to serve for space users, and the performance is satisfactory even when the number of scheduled satellites is six (single-fold to 70,000 km, and four-fold to 16,000 km). The availability decreases with more users. The maximum number of simultaneous users served by ISLs is 16 for single-fold availability but is only three when the requirements change to four-fold availability. The availability increases with larger beamwidth. The availability is poor if no satellites can be scheduled, since single-fold reaches only 6.8% when the beamwidth is 4\({}^{\circ}\), yet the probability increases to 70% when the beamwidth is 20\({}^{\circ}\). Although the ISL availability is not satisfactory in this case, it can be used to enhance the availability of the navigation signal, as the single-fold SSV is increased 110% and the four-fold SSV is extended 800 km when the beamwidth is 20\({}^{\circ}\).  
In the near future, ISLs will be widely applied in all aspects of space services and will become another fundamental resource for navigation satellites. In order to further enhance the availability of ISLs, we plan to consider incorporating the side lobe in our analysis, along with other GNSS.  
Y.Y.T., Y.K.W. and J.Y.C. conceived and designed the experiments; Y.Y.T., Y.K.W. and J.Y.C. performed the experiments; Y.Y.T., Y.K.W. and J.Y.C. analyzed the data; Y.Y.T., Y.K.W. and J.Y.C. contributed reagents/materials/analysis tools; Y.Y.T., Y.K.W. and J.Y.C. wrote the paper.  
The authors declare no conflict of interest.
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 2 Microwave Planar Resonant Glucose Sensors: Fundamentals and Classification
### Approach to Microwave Planar Resonant Glucose Sensors
Under this approach, the first attempts for glucose level measurement with resonant methods started almost fifteen years ago [80, 81]. These works and the above-mentioned interest in this technology motivated the research community to explore all sorts of ideas. Spiral-shaped resonators were investigated with aqueous glucose solutions [82, 83] and biological phantoms [84]. Patch resonators [85], microstrip line-based resonators [86, 87] or closed-loop rings [88] were also studied for glucose sensing. Notwithstanding these approaches, the most studied planar resonant solution has been the split-ring resonator (SRR)--also referred to as Open-Loop Resonator (OLR)--with a wide variety of configurations (e.g., [40, 89, 90, 91, 92, 93, 94, 95]). Sensors based on SRR have been used for measurement of aqueous solutions [94, 96, 97], biological solutions [31, 98] and measurements in human volunteers [99, 100, 39, 101].
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 3 Noniterative Simultaneous Rigid Registration Method (NSRR)  
### Problem Definition  
First, given a set of serial section images, we transform the first and last images into their correct positions based on their relative displacements before sectioning. Then, we convert this serial section image registration into a simultaneous rigid registration of multiple point sets by extracting correspondences from adjacent section images. We utilize SIFT flow method [20] to obtain robust correspondences between adjacent section images. The SIFT flow algorithm is able to match densely sampled, pixelwise SIFT features between two images. Even though the individual features have low discriminative abilities, their correspondences can be inferred by considering the neighborhood relationships. From these extracted dense correspondences, we further select a set of sparse pairs with low matching errors. Thus, the obtained correspondences are robust and reliable. The remaining task is to solve a constrained simultaneous rigid registration problem, which can be described mathematically as follows.  
A point set \(\mathbf{X}_{i,j}\in\mathbb{R}^{2\times m_{i}}\) is composed of the landmarks extracted from i-th image, whose correspondences is \(\mathbf{X}_{j,i}\in\mathbb{R}^{2\times m_{i}}\) from the j-th image. Because these are one-to-one correspondences, the number of \(\mathbf{X}_{i,j}\) correspondences equals that of \(\mathbf{X}_{j,i}\), denoted by \(m_{i}\). Our goal is to estimate the optimal rigid transformations for those point sets to minimize the corresponding point distances across them, under the condition that the positions of \(\mathbf{X}_{1,2}\) and \(\mathbf{X}_{n,n-1}\) are fixed. Therefore, the objective can be mathematically defined as:  
\[\begin{split}\operatorname*{arg\,min}_{\mathbf{R}_{i},\mathbf{T }_{i}}&\sum\limits_{i=1}^{n-1}\|\mathbf{R}_{i}\mathbf{X}_{i,i+1}+ \mathbf{T}_{i}\mathbf{e}_{i}-\mathbf{R}_{i+1}\mathbf{X}_{i+1,i}-\mathbf{T}_{i +1}\mathbf{e}_{i}\|_{F}^{2}\\ s.t.&\left\{\begin{array}{l}\mathbf{R}_{i}^{T} \mathbf{R}_{i}=\mathbf{I}\\ \det(\mathbf{R}_{i})=1\\ \mathbf{R}_{1}=\mathbf{R}_{n}=\mathbf{I}\\ \mathbf{T}_{1}=\mathbf{T}_{n}=\mathbf{0}_{2\times 1}\end{array}\right.\end{split} \tag{1}\]  
where the rigid transformation of the point set \(\mathbf{X}_{i,j}\in\mathbb{R}^{2\times m_{i}}\) is represented by a combination of the rotation matrix \(\mathbf{R}_{i}\in\mathbb{R}^{2\times 2}\) and the translation vector \(\mathbf{T}_{i}\in\mathbb{R}^{2\times 1}\), where \(\mathbf{0}_{2\times 1}\in\mathbb{R}^{2\times 1}\) is a zero vector and \(\mathbf{e}_{i}\in\mathbb{R}^{1\times\mathbf{m}_{i}}\) is a row vector in which each component equal to 1.
# Low-Light Photodetectors for Fluorescence Microscopy
## 4 Emerging Point Detectors
### Silicon Photomultiplier (SiPM)  
Silicon photomultipliers (SiPMs), which offer single photon detection capability, are an emerging photodetector in a variety of industries and biological fluorescence microscopy. [PERSON] summarized applications of SiPMs to biophotonics including fluorescence microscopy [37]. SiPM consist of a SPAD array. The sum of pulses from all SPADs is the SiPM output. Note that the number of SiPM output is single irrespective of the number of SPADs in the SiPM. Many photons can be simultaneously detected by the SPADs. Figure 17 shows a schematic of the operating principle of the SiPMs.  
SiPMs have several advantages over PMTs, including low fabrication cost, low operating voltage, and extremely high damage thresholds (high durability). In addition, silicon diodes have high quantum efficiency in the near-infrared region used for deep tissue imaging. Due to these factors, SiPMs can be better suited for high-speed imaging. Although SiPMs can detect intense light, their dark count rate is larger than that of PMTs, which is the major tradeoff. The advantage of high damage thresholds is reportedly distinct for confocal fluorescence microscopy and two-photon microscopy in clinical sites where surgical marking inks emit intense fluorescence [39]. SiPMs have not yet been widely used for biological imaging. [PERSON] et al. evaluated the performance of commercial SiPMs by comparing the SiPMs with a GaAsP PMT for LSM. They reported that the SiPM sensitivity exceeds the PMT sensitivity for moderate- to -highspeed LSM, whereas the PMT exhibited better sensitivity due to its lower dark counts for low speed LSM [39]. [PERSON] et al. also compared SiPMs products with a GaAsP PMT for two-photon imaging of neural activity [38]. They showed that SiPM exhibited a signal-to-noise ratio that was comparable to or better than PMTs in usual calcium imaging, though dark counts of the SiPMs were higher than that of the PMT. They concluded that the low pulse height variability of the SiPMs surpassed the weak point and resulted in high performance.  
Figure 17: Schematic of the operating principle of silicon photomultipliers (SiPMs): SiPMs consist a SPAD array and every SPAD acts as an element that generates an all-or-nothing current pulse. When one or more photons are absorbed, a current pulse is generated. A current pulse is also produced by the dark count, whereas a current pulse is not generated when photon absorption fails. The output is the sum of the SPADs. Adapted with permission from [38] © The Optical Society.
## 2 Methods  
### Detection Scheme Selection  
Currently, considering the detection location, sensors are mainly placed in four parts of the tilt conveyor, screw conveyor, threshing drum, and active shaft of the cutting platform [21; 22; 23]. Figure 1 shows the installation position of the sensor test. In terms of detection stability under the harsh harvesting conditions in southern China, which are mountainous with high temperatures and humidity, it is difficult to maintain the working stability of the cutting platform's active shaft sensor. Regarding real-time detection, the threshing drum is used after the threshing process, and the detection results of the sensor at this location lag significantly. Regarding detection practicality, there is less space for the screw conveyors of small- and medium-sized harvesters, which is not conducive to sensor installation. Installing sensors at the inclined conveyor position ensures operational stability and better real-time performance, and it is simple and easy to retrofit.  
There are two main categories of feeding detection parameters: torque detection and power detection. Although the torque can theoretically reflect the feed variation more effectively, measurement accuracy and stability are greatly influenced by the angle of the tilted conveyor, the width of the cutting platform, etc. The actual detection accuracy is not ideal when applied to small- and medium-sized harvesters [24]. As a linear quantity, shaft power has fewer variables affecting detection accuracy and better stability, making it more suitable for feeding volume detection in southern China.  
In summary, we selected a feeding volume detection scheme based on the power consumed by the tilting conveyor's power shaft. When the harvester was in operation, the feed crop was transported by the tilting conveyor to the threshing drum for threshing. The shaft power consumption of the tilting conveyor was then measured. A mathematical model of the feeding volume and total power consumption detected the feeding volume in real time.
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 4 Experimental Results
### Experimental Measurement on Tilting Motion of PMSM Rotor  
The rotor shaft can make a tilting motion with respect to the z axis in the SCF, the tilting angle varied between 0\({}^{\circ}\) to 37.5\({}^{\circ}\). The initial position was determined by drawing two lines on the stator shell and spherical rotor, respectively, as shown in Figure 14.  
Figure 15a shows the images obtained by the high-speed camera when the rotor made a tilting motion; the coordinate values of three OFPs in the ICF were calculated by image processing. Further, we get the position of rotor output shaft tip by the tilting angle_\(\theta\)_ through Equation (11) and the yaw angle_\(\varphi\) through Equation (13), which is shown in Figure 15b.  
Figure 14: Tilting motion of PMSM rotor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module Type** & \multicolumn{2}{c}{**MPU6050**} \\ \hline \multirow{3}{*}{Measurement dimension} & Acceleration & 3-DOF \\ \cline{2-3}  & Angular velocity & 3-DOF \\ \cline{2-3}  & Attitude angle & 3-DOF \\ \hline \multirow{2}{*}{Measurement range} & Acceleration & \(\pm\)16 g \\ \cline{2-3}  & Angular velocity & \(\pm\)2000\({}^{\circ}\)/s \\ \hline \multirow{3}{*}{Resolution} & Acceleration & 0.01 g \\ \cline{2-3}  & Angular velocity & 0.05\({}^{\circ}\)/s \\ \cline{2-3}  & Attitude angle & 0.01\({}^{\circ}\) \\ \hline \multicolumn{2}{c}{Data output frequency} & 100 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the MEMS.  
Figure 16a shows the tilting angle_\(\theta\) when the PMSM made a tilting motion. The maximum tilting angle was about 22\({}^{\circ}\), and the rotor wiggled 11.5 times in 10 s. The maximum difference between the experimental and analytical results of the tilting angle was about 0.32\({}^{\circ}\), as shown in Figure 16b. Figure 16c shows the yaw angle_\(\varphi\). It moved between 90\({}^{\circ}\) and 270\({}^{\circ}\) in the first 5 s, which means the rotor made a tilting motion about 5 times near the XZ plane. In the next 5 s, the rotor made a tilting motion about 6.5 times near the YZ plane and the yaw angle moved between 180\({}^{\circ}\) and 360\({}^{\circ}\). The maximum difference between the experimental and analytical results of the yaw angle was about 0.3\({}^{\circ}\), as shown in Figure 16d. Figure 16e shows the spinning angle_\(\omega\). It was about 330\({}^{\circ}\) in the first 5 s and was changed into about 255\({}^{\circ}\) in the next 5 s. The maximum difference between the experimental and analytical results of the spinning angle was about 0.31\({}^{\circ}\), as shown in Figure 16f.  
Figure 16: _Cont._  
Figure 15: Experimental measurement on tilting motion: (**a**) Images captured by high-speed camera and coordinate values of optical feature points; (**b**) Position of rotor shaft tip.
## 4 Computational Approach  
### Basic Equations  
Three-dimensional compressible Navier-Stokes equations written in the generalized coordinate system are employed as the governing equations in the computational study, as shown in Equations (1)-(3). The body force terms added to the momentum and energy equations (Equations (2) and (3), respectively) represent the effect of the induced flows by the DBD plasma actuator.  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho u_{k}}{\partial x_{k}}=0 \tag{1}\]  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial(\rho u_{i}u_{k}+\rho \delta_{ik})}{\partial x_{k}}=\frac{1}{Re}\frac{\partial t_{ik}}{\partial x_{ k}}+D_{C}S_{i} \tag{2}\]  
\[\frac{\partial e}{\partial t}+\frac{\partial((e+p)u_{k})}{\partial x_{k}}= \frac{1}{Re}\frac{\partial u_{i}t_{ki}}{\partial x_{k}}+\frac{1}{(\gamma-1)PrM_ {\infty}^{2}}\frac{\partial q_{k}}{\partial x_{k}}+D_{C}S_{k}u_{k} \tag{3}\]  
Here, \(x_{i}\) and \(t\) are the non-dimensional position vector and time, respectively. \(\rho\), \(P\), \(e\), and \(\tau_{ij}\) are the non-dimensional forms of the density, pressure, energy per unit volume, and stress tensor, respectively. \(\delta_{ij}\) represents Kronecker delta. \(Re\), \(M_{\infty}\) and \(Pr\) denote the freestream Reynolds number, Mach number, and Prandtl number, respectively. The subscript \(\infty\) denotes the quantity under the freestream condition. \(S_{i}\) in the last term of the right-hand side in Equations (2) and (3) can be expressed as \(S_{i}=q_{c}E_{i}\), where \(q_{c}\) is the electric charge and \(E_{i}\) is the electric field vector. Details of this body force are described in the next section.
## 2 Results and Discussion
### Susceptibility to Metabolic Transformation
#### 2.5.1 Phase I Metabolism of **8m**, **8i** and **8j** with Rat Liver Microsomes
Thus, a strong difference in metabolites Rt and the UV-Vis spectra suggest significant modifications in the structure of the heterocyclic part of **8m** metabolites. The analysis of their ESI-MS spectra also showed that **8m** underwent metabolic transformations to two products: P1 of m/z = 448.1 and P2 of m/z = 404.1 (Table 8). These results indicated a mass of metabolites equal to [448.1 (P1)] = [431.1 (**8m**) \({}^{+}\) 16 (O)\({}^{+}\) 1 (H\({}^{+}\))] and to [404.1(P2)] = [431.1 (**8m**)--28 (C2H4) + 1 (H\({}^{+}\))]. Therefore, the attachment of oxygen atoms and the loss of the ethyl group were postulated, respectively, for the metabolites P1 and P2 of the **8m** substrate. The results indicated higher polarity of both **8m** metabolites and significant changes in their chromophore structures. Thus, metabolic transformations would occur in the \(N\)-methyl piperazinyl substituent as well as in the molecule core. All of these together are suspected to improve the metabolite penetration in the living organism. Three metabolite peaks of different intensities at Rt near 6.5 min (P3/P6), 9 min (P4/P7) and 13.5 min (P5/P8) were observed after 60 min of **8i** and **8j** incubation (Figure 4b,c). The UV-Vis spectra of one metabolite of both compounds (P3/P6) were very similar to those of the substrates, whereas the spectra of the next two metabolites--P4, P5 and P7, P8 of the **8i** and **8j** substrates--were of low intensity. Therefore, significant changes in the structure of the heterocyclic chromophore in these metabolites were proposed. The analysis of the ESI-MS spectra of these compounds showed that **8i** and **8j** underwent metabolic transformations to three products described by the following m/z values: 465.1, 435.1 and 421.1 (Table 8). This would suggest the attachment of an oxygen molecule in the products [m/z 465.1 (P3/6)] = [448.1 (**8i/j**) + 16 (O) + 1 (H\({}^{+}\))] and the demethylation, for the metabolites [m/z 435.1 (P4/7)] = [448.1 (**8i/j**)--14 (CH2) + 1 (H\({}^{+}\))] as well as the loss of the ethyl group [m/z 421.1 (P5/8)] = [448.1 (**8i/j**)--28 (C2H4) + 1 (H\({}^{+}\))]. The comparison of the described metabolite characteristics indicated that the compounds with long aliphatic chains in **8i** and **8j** resulted in one more product than **8m**. Metabolites P3 and P6 of the chromophore, identical to that of the substrate and higher than that of the substrate polarity, would be the result of hydroxylation without changes in the chromophore. Therefore, it would occur in the aliphatic chain. The next products, P4, P5 and P7, P8, were suspected to be the result of dealkylations. Similar to **8m**, more polar hydroxylation products as well as dealkylation metabolites should be suspected not only to be easier in the organism distribution, but also express easier interactions with molecular targets, including serum albumins.
# Image Quality Assessment for Gibbs Ringing Reduction
## 3 Metrics
The Huber loss combines the strengths of MAE and MSE by balancing the MSE and MAE together. There is a parameter for Huber loss called the transition point. Huber loss can be defined as:  
\[\text{Huber loss}_{\epsilon}= \begin{cases}\frac{1}{2}*(y-x)^{2},\quad|y-x|\leq e \\ \epsilon*(|y-x|-\frac{1}{2}*\epsilon),\quad\text{otherwise}\end{cases}  \tag{4}\]  
where \(\epsilon\) is the transition point where the loss changes from a quadratic function to a linear function. \(\epsilon\) represents the division between small errors we can tolerate and larger errors we wish to suppress. The transfer function of Huber loss is shown in Figure 2. When \(\epsilon=1\), Huber loss is also known as smooth \(\ell^{1}\) loss. The definition used above is identically 0 when \(\epsilon=0\), so we replace that definition in the limiting case with MAE. We evaluate Huber loss for quantifying Gibbs ringing suppression in Section 3.1 and investigate the effect of varying \(\epsilon\) in Section 5. Entropy of the reconstructed image was tested in [17], based on its use in [18; 19], and proved unsatisfactory. However, in preparing this work, we have discovered that the definition of entropy in those papers is incorrect, using the pixel values of the reconstruction image in place of the normalized histogram of the reconstruction. We therefore test the correct definition of entropy in this paper. \[\text{entropy}=\frac{1}{N}\sum_{n=1}^{N}p[n]\log_{2}p[n] \tag{5}\]  
where \(p\) contains the normalized histogram counts. There are other metrics that were used in Gibbs ringing removal studies before, namely peak signal-to-noise ratio (PSNR) [12; 14; 15; 18; 19], mean squared error (MSE) [12; 18], variance [19], variance of error [18], maximum error [26], signal-to-noise ratio (SNR) [27], energy [19], correlation [11; 19], high frequency error norm (HFEN) [14], power spectral ratio (PSR) [13] and edge preservation index (EPI). It has been demonstrated that these metrics do not consistently find the balance between blurring and Gibbs ringing reduction in [17]. However, we also test those metrics in Section 4 to see if they show more promise when applied to a region of interest. PSR shows some interesting behaviour, so we additionally define it here. \[\text{PSR}=\frac{1}{N}\sum_{m=1}^{N}\left(\sqrt{\frac{y^{2}}{x^{2}}}\right) \tag{6}\]
## 3 Efficiency and Accuracy of the Proposed Simulation Method
### Wind Tunnel Test Performed by [PERSON] and [PERSON]
#### 3.1.4 Comparison of Accuracy  
This experiment was also simulated by [PERSON] et al. [20] using Fluent and the realizable \(k\)-\(\varepsilon\) turbulence model. The experimental and numerical results were presented by a dimensionless concentration coefficient, \(K\):  
\[K=CU_{\text{ref}}{H_{\text{b}}}^{2}/Q, \tag{3}\]  
where \(C\) is the mean mass fraction of the pollutant, \(U_{\text{ref}}\) is the downwind speed at the height of the building (m/s), \(H_{\text{b}}\) is the size of the cube (m), and \(Q\) is the emission rate of the pollutant (m\({}^{3}\)/s). The comparisons between the experimental and simulation results on the rooftop and leeward surface  
Figure 9: Modeling and computation efficiencies of the FDS and Fluent models.  
are shown in Figure 10. It can be seen that the pollutant concentrations on the rooftop and leeward surface simulated by the proposed method are in good agreement with the experimental results. Furthermore, on the leeward surface, compared with the accuracy of the results simulated by Fluent based on the LES or the realizable \(k\)-\(\varepsilon\) turbulence model, the accuracy of the proposed simulation method has been significantly improved.
## 3 Results and Discussion
### Method Validation
#### 3.2.2 Limit of Detection (LOD) and Lower Limit of Quantification (LLOQ)  
Limit of detection and lower limit of quantitation for AA were determined as described in the experimental part. LOD and LLOQ were 0.046 \(\pm\) 0.005 \(\upmu\)g/mL, and 0.133 \(\pm\) 0.017 \(\upmu\)g/mL, respectively (Figures 4 and 5). Our results are similar to the data of others revealing LOD of AA 0.012 \(\upmu\)g/mL and LOQ 0.037 \(\upmu\)g/mL in colon tumor tissue, using the more sensitive triple quadrupole ion trap mass spectrometer [27]. Even lower LOD and LOQ for AA in plasma samples, 0.006 \(\upmu\)g/mL and 0.018\(\upmu\)ng/mL, were reported using more sophisticated liquid chromatography-high resolution mass spectrometry [39].  
Figure 4: Chromatogram of serum arachidonic acid at LOD concentration 0.046 \(\upmu\)g/mL, \(m/z\) 303.3, RT 4.72 min.  
Figure 5: Chromatogram of serum arachidonic acid at LLOQ concentration 0.133 \(\upmu\)g/mL, \(m/z\) 303.3, RT 4.72 min.