esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Common SWIR Imaging Technologies
### Multispectral/Hyperspectral Imaging  
Spectroscopy is a measurement approach to characterize biological tissues by interrogating their responses to a spectrum of light or collection of wavelengths [25]. When a sample is imaged continuously across a range of wavelengths, the technique is referred to as hyperspectral imaging. Conversely, if the sample is imaged within discontinuous wavelength intervals, it is called multispectral imaging. Both approaches can yield spectra of the sample in either a reflectance (Figure 3c) or transmittance configuration (Figure 3d). Analyzing the recorded spectrum of each camera pixel allows for getting insight into the chemical makeup of the given part of the tissue [25; 26].  
Figure 3: Schematic of SWIR techniques. (**a**) SWIRF system with wide-field epi-illumination method, (**b**) SWIRF with FMT configuration; hyper/multi spectral imaging with (**c**) reflectance and (**d**) transmittance setting; OCT systems with (**e**) swept source and (**f**) spectral domain configuration.  
In the SWIR range, spectroscopy reveals more distinct features in the spectra of tissue constituents, such as water, lipid, and collagen, compared to shorter wavelengths [4; 27]. Furthermore, incident light in the SWIR range is less affected by scattering events, enabling deeper exploration of tissue surfaces for targeted molecules [25]. These advantages of SWIR spectroscopy offers heightened sensitivity to tissue constituents, facilitating enhanced characterization of variations in tissue composition concentrations resulting from conditions such as atherosclerotic plaque, tumors, and skin burns [4; 26]. Spectroscopic systems hold the potential to serve as a virtual method for histology, where excised tissues are immediately analyzed by intraoperative post-biopsy procedures without the necessity of staining with chemical agents. Additionally, to study brain function in the field of neuroscience, hyperspectral images generated in the SWIR frequency range provide intricate details about oxygenation levels and metabolic activities in different brain regions, offering a comprehensive view of neural processes. These captivating images not only contribute to our understanding of complex brain functions, but also hold promise for unraveling mysteries related to neurodegenerative disorders. It is worth mentioning that one limitation of spectroscopic methods is their inherent lack of depth resolution, which means they provide a broad overview of the chemical composition of tissue beneath the surface without precise discrimination of information at specific depths.
###### Abstract
The authors provide also a formula for the competitive ratio in the case of continuous time. The bi-directional non-preemptive CP with given \(M\) and \(m\) is optimally solved by [5]. The optimal algorithm \(MRPR\)(\(k\)) calculates depending on the number of runs for each run an own reservation price. The competitive ratio of \(MRPR\)(\(k\)) is close to \(\left(\left(\frac{M}{m}\right)^{\frac{2}{3}}\right)^{\left\lfloor\frac{k}{2} \right\rfloor}\). The bi-directional preemptive CP has not yet been optimally solved. Authors in [3] provide the non-optimal online algorithm \(bTH\). Depending on the number of runs, \(k\)\(bTH\) considers the bi-directional preemptive CP as \(k\) separate uni-directional preemptive conversion problems. For each of the \(k\) runs, \(bTH\) applies the algorithm \(uTH\), such that an upper bound \(U\)  
\[U=\ln\left(\frac{\frac{M}{m}-1}{c(uTH)-1}\right)^{k} \tag{3}\]  
and a lower bound \(L\)\[L=T\cdot\left(1-\left(\frac{m\cdot c(uTH)-1}{M-m}\right)^{\frac{1}{T}}\right)^{ \frac{k}{2}} \tag{4}\]  
for even \(k\) can be provided. The authors in [4] provide the algorithm \(bTH^{\prime}\). Analogous to \(bTH\), \(bTH^{\prime}\) applies the uni-directional algorithm \(uTH^{\prime}\), which is based on \(uTH\), on each run \(k\). The competitive ratio of \(uTH^{\prime}\) and \(bTH^{\prime}\) are (see [4])  
\[c(uTH^{\prime})=\ln\left(\frac{\frac{M}{c(uTH^{\prime})\cdot m}-1}{c(uTH^{ \prime})-1}\right)^{2} \tag{5}\]  
and  
\[c(bTH^{\prime})=c(uTH^{\prime})^{\frac{k}{2}}. \tag{6}\]  
Based on \(bTH^{\prime}\), the authors in [4] provide an improved lower bound for the bi-directional preemptive CP and continuous time. In the literature, there are alternative assumptions on the price sequence. One of these assumptions is that prices are interrelated. That means, two consecutive prices can maximally differ by two given factors, \(\theta_{1}\) and \(\theta_{2}\), with \(0<\theta_{1}<1<\theta_{2}\) (see [10; 11; 12]). Researchers in [12] provide (among other things) an optimal algorithm for the bi-directional preemptive CP with interrelated prices. Some authors have also considered additional constraints. Those in [13], considered the uni-directional non-preemptive CP with decreasing \(M\) over time. Authors in [14] considered the uni-directional preemptive CP, but they divided the wealth into \(u\) equal valued units and it is only allowed to convert one unit per point in time \(t\). Based on this, the authors in [15] considered a problem that is allowed to convert an integer number of units per point in time. Researchers in [16; 17] considered the bi-directional non-preemptive CP with a bounded number of transactions. The authors in [8; 18] show that the CP is a special case of the portfolio selection problem (PSP). In the PSP, wealth can be distributed on \(n\geq 2\) assets. For \(n=2\), the PSP corresponds to the bi-directional preemptive CP. In this work, the upper and lower bounds for the competitive ratio of the bi-directional preemptive CP are given. These bounds hold for a given \(T\), discrete time, and a constant price interval between the price bounds \(m\) and \(M\).
## 2 Navigation Model Based on GNSS
### Measure Equation of the Navigation System  
In the navigation system of high-orbit satellite based on GNSS, the observation is the pseudo-range measurement between the navigation satellites and the user satellite, and the measurement equation is as follows:  
\[\rho_{j} =d_{j}+v_{j},j=1,2... \tag{2}\] \[d_{j} =\sqrt{\left(x-x_{j}\right)^{2}+\left(y-y_{j}\right)^{2}+\left(z -z_{j}\right)^{2}}.\]  
The subscript \(j\) represents the \(j\)-th visible navigation satellite, and \(v_{j}\) is the measurement error, and \(\rho_{j}\) is the pseudo-range from \(j\)-th navigation satellite to the user satellite, and \(d_{j}\) is the geometrical distance from \(j\)-th navigation satellite to the user satellite. Assuming that there are \(m\) navigation satellites, then \(m\) measurement equations can be obtained, and the measurement model can be obtained by combining these \(m\) pseudo-range measurement equations. Let \(\mathbf{Z}\left(t\right)=\begin{bmatrix}\rho_{1},\cdots,\rho_{j},\cdots,\rho_{m} \end{bmatrix}^{\mathsf{T}}\), \(\mathbf{h}=\begin{bmatrix}d_{1},\cdots,d_{j},\cdots,d_{m}\end{bmatrix}^{\mathsf{T}}\), \(\mathbf{v}=\begin{bmatrix}v_{1},\cdots,v_{j},\cdots,v_{m}\end{bmatrix}^{\mathsf{T}}\), the measurement model of the high-orbit satellite navigation system based on GNSS can be expressed as:  
\[\mathbf{Z}\left(t\right)=\mathbf{h}\left(\mathbf{X}\left(t\right)\right)+\mathbf{v}(t). \tag{3}\]
# Data-Completion and Model Correction by Means of Evanescent Regularization
## 2 Correction of the Available Model
### Methods
#### 2.1.2 The Evanescent Regularization  
The evanescent regularization is a regularization technique initially developed to address the Cauchy problem, while ensuring convergence of the quantities of interest [11]. The authors in [12] used the Evanescent regularization to address the solution inside a given domain, while only knowing the solution on the boundary of the domain. In that case, the inverse problem had observable or \"measurable\" degrees of freedom on the boundary, and unmeasurable ones inside the domain. The use of the evanescent regularization is demonstrated to lead to the slow convergence of the unmeasurable quantities of interest, as well as a strong convergence of the measurable ones, towards the exact solution of the problem [13]. The evanescent regularization is also used when the extra boundary conditions are available on a part of the domain, while totally missing on another part of the boundary [11].  
Thus, the loss function \(\mathcal{L}\) is reformulated in the following form:  
\[\begin{array}{c}\mathcal{L}=\sum\limits_{i=1}^{n_{t}}\left(\sum\limits_{j=1 }^{N_{m}}\left(y_{i}^{s,j}-y_{i}^{s,j}\right)^{2}\right)+\lambda_{1}\left(B^{ m,m}-\hat{B}^{m,m}\right)^{2}+\\ \lambda_{2}\left(B^{m,nm}-\hat{B}^{m,nm}\right)^{2}+\lambda_{3}\left(\mathbf{ y}_{0}^{s,nm}-\mathbf{\hat{y}}_{0}^{s,nm}\right)^{2}\end{array} \tag{8}\]  
with \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) the evanescent regularization parameters. The \(\hat{\cdot}\) quantities are the best estimation of the regularized outputs of the optimization problem, which can be taken as the known values from the previous iteration of the gradient descent algorithm. In fact, the evanescent regularization will improve the convergence of both quantities, with a faster convergence of the measurable quantities of interest. Thus, the regularized optimization problem is reformulated as:  
\[\left(\mathbf{B},\mathbf{y}_{0}^{s,nm}\right)=\begin{array}{c}\underset{ \begin{subarray}{c}\mathbf{B}\in\mathbb{N}^{2}\\ \mathbf{y}_{0}^{s,nm}\in\mathbb{N}\end{subarray}}{arg\min}\ \ \{\mathcal{L}\},\end{array} \tag{9}\]  
To solve the optimization problem defined in Equation (9), the gradients can always be computed numerically, at the expense of dramatically increasing the computation time of the gradient descent algorithm. It is therefore preferable to use an integration scheme with automatic gradient differentiation through time steps, such as the Neural Ordinary Differential Equations (Neural ODE) methods [14]. This will allow a fast and reliable gradient calculation through automatic differentiation and will accelerate the convergence of the optimization algorithm.
## 3 Methods
### Computational Fluid Dynamics
The Kunz model was proved to be accurate and the results were validated with experimental data (Figure 3). However, the pressure diagrams show small discrepancies in the cavitation region that are, in the real bearing, wider than the one predicted by the simulations. This evidence can be probably explained by the calibration of the [PERSON] model whose constants were taken from literature. For the sake of completeness, it should be mentioned that in literature more advanced numerical approaches are available [48]. These include the effect of surface texturing, slip boundary, realistic deformations of the bearing with fluid structure interaction, thermal effects and viscosity variation, shaft misalignment and other effects that on complex systems. However, for the purposes of this paper, the present approach was considered sufficiently representative of the selected bearing. Simulations (Figure 4) were performed for different levels of eccentricity and the results reported in terms of Drag and Lift forces. Figures 5-7 show the forces and the attitude angle for different eccentricities and rotational speeds. Both the CFD and the half-Sommerfeld results are reported. These data are the basis for the calculation of the trajectory and equilibrium position of the journal. Figure 4: Computational fluid dynamics (CFD) results: Pressure contour (Pa). Figure 3: Validation of the present approach based on the [PERSON] (OpenFOAM) model by comparison with experimental data from [PERSON] [3] (**a**), and from [PERSON] [4] (**b**) (\(\overline{p}=p\frac{\omega^{2}}{\rho LD}\)). Figure 5: Total hydraulic force (**a**) Drag (F\({}_{\perp}\)) force (**b**); Lift (F\({}_{\parallel}\)) force (**c**) and attitude angle (**d**) vs. eccentricity; –328 rad/s. Figure 6: _Cont._  
Figure 7 reports the resultant forces and attitude angle obtained by integrating the pressure distributions calculated with the different models. The Half-Sommerfeld solution underestimates the forces, especially for what concerns the drag effects.
# Study on Dynamic Strain Distribution in Rock Broken by Abrasive Slurry Jet
## 2 Experiment  
### Experimental System  
The experimental system consists of an ASJ generating system and a dynamic strain measurement system. The former can provide ASJ with different pressures, while the latter can measure the strain distribution in rock surface in real time.  
Figure 1 shows the ASJ generating system composed of a control cabinet, motor, tank, pump, abrasive tank, nozzle, and cutting table. High polymer solution in the tank was pressured by the pump driven by the motor and mixed with abrasive particles in the abrasive tank, finally forming the ASJ through the nozzle. The control cabinet can control the start-stop of the system and display the pressure and the motor speed in real time. The cutting table is a computer numerical control (CNC)machine tool, which can control the traverse speed accurately and adjust the distance between rock and nozzle.  
As shown in Figure 2, the non-contact strain measurement system consists of a high-speed camera, strong light source, and analysis software (DIC-3D, designed by University of Science and Technology of China (Hefei, China) and Southeast University (Nanjing, China)). Before the ASJ impacts rock, a picture of feature points on rock surface must be taken by the high-speed camera, which will be regarded as the reference image. Then, pictures of feature points on the rock surface were taken in real time by the high-speed camera in the process of the ASJ impacting the rock. Finally, analysis software was used to calculate the displacement of the same feature point between the experimental pictures and the reference image, obtaining the dynamic strain distribution on the rock surface.  
The approach has two advantages. One is that the lack of contact between the measuring element and measured object can simplify the strain measuring process. The other is that a strain data of the whole measured surface can be obtained instead of data of a point. However, the approach also has a shortcoming as its accuracy will decrease when it is used to measure a strain less than 10 \(\upmu\)m.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Recent Methods of Mitigating Spectral Leakages in Spectrum Beyond Aliasing  
Aliasing is an effect that causes different signals to be indistinguishable when sampled. In other words, signals with different frequencies may have the same samples over the given time intervals. Aliasing occurs whenever a signal is sampled at a rate below its Nyquist rate, and so when decomposing a time series into the frequency domain, two or more different sinusoids identically fit the same set of samples. Figure 2a shows an example of two aliased sinusoids that are indistinguishable from the samples in black diamonds. The two sinusoids are a sine wave of 4 Hz, shown in red, and a sine wave of 36 Hz, shown in blue, that differs from the first sine wave by the sampling frequency of 32 Hz. Mitigating the aliasing effect is crucial in many areas of research, such as interpolation of seismic data that are coarsely sampled [25]. Additional information or assumptions must be provided to overcome the aliasing effect and identify the true signals. For instance, the gradients in Figure 2b, i.e., the derivative of the sinusoids, has different values at sample positions and can be used to identify the true sinusoid.
## 4 Discussion
### Analysis of GPP Estimation Accuracy Based on Improving SIF
[71] conducted a study on the spatiotemporal distribution of vegetation GPP in China from 2007 to 2018 based on SIF data, indicating that the annual average values for the desert vegetation area (based on latitude and longitude division) range from 0 to 0.1 gC/m\({}^{2}\) (0.034 mw/m\({}^{2}\)/nm/sr in this study); and [PERSON] [72] conducted a comparative study on the high-temperature stress mechanisms of crops based on SIF data, showing that the annual values for various crops range from 0.22 to 4.42 gC/m\({}^{2}\) (0.26 mw/m\({}^{2}\)/nm/sr in this study).
## 3 Results and Discussion
### Focusing and Long Wavelength Envelopes  
In Figure 7, we investigate focusing effects of the slab. The dispersive behaviour of the periodic slab at \(f=3.3\) MHz is exploited, exhibiting negative group velocity across most of the first Brillouin zone. In Figure 7c, the slab is insonified with a Gaussian beam oriented at \(\alpha=6^{\circ}\) incidence. The instantaneous displacement shows a clear negative refractive behaviour of the incident beam. In Figures 7d,e, a similar behaviour is observed, where an image develops firstly within the interior of the slab (magnified in the inset of Figure 7d) and, at a later time, in the transmission region beyond the slab of metamaterial.  
Figure 7: Scattering of a shear-dominated Gaussian beam at an angle of incidence \(\alpha=6^{\circ}\) and a point source at (0, 40 mm), both with centre frequency \(f=3.3\) MHz. Panels (**a,b**) show the IFC diagram (and its magnification); the red asterisk represents the intersection point of the Gaussian beam propagation direction and the isofrequency contours. Panels (**c–e**) show the magnitude of the displacement field at different times in the FE simulation, with the total time being \(T=120\) μs. The inset of panel (**d**) is a magnification of the centre of the slab.  
In Figure 8, we show scattering of waves by a rough microstructured slab (described in Section 2.4), and its smooth counterpart, for which a long-wavelength enveloped wave mode phenomenon is observed. Figure 8c,d show the scattering effects at the same time point within the simulations. Apart from a small difference in phase, due to the finer mesh for the rough crosses leading to a smaller time step, the results for the smooth and rough cases are almost indistinguishable. It can clearly be seen that the long-wavelength envelope within the slab is around ten unit cells in length, but is only marginally affected by the roughness. Similarly, the beam refraction at the interface (consistent with the IFC diagrams in Figure 8a,b) is observed for both the smooth and rough crosses. This example of comparing rough and smooth crosses is one of five individual cases that was studied in this work, providing preliminary insights for future investigation of the practical implementation of the Swiss cross-shaped holes metamaterial. These initial results indicate that the wave effects are likely to be unaffected by perturbation from smooth-edged crosses that would result when manufacturing the structures for future experimental validations. However, Monte Carlo analysis over a wide range of roughness and frequencies, similar to previous studies [25], will reveal more comprehensive understanding.
# Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia
## 4 Results and Discussion
### Charging Stations  
#### 4.2.1 Required Station Numbers  
A comprehensive analysis of Hail City's projected automotive landscape in 2030 was conducted, drawing on market data and estimates from several trustworthy sources. By 2030, Hail City's population is expected to reach 582,152, according to [42]. According to [45], Hail City is anticipated to account for 2.3% of Saudi Arabia's car market. Furthermore, data in [46] indicate that 53.8% of the market comprises the private automotive category. A more accurate estimate, which accounts for average family sizes and projected population increases, suggests that there may be close to 304,060 automobiles overall.  
\begin{table}
\begin{tabular}{l} \hline Southern to northern \\ Shortest distance (black) = 37.77 km \\ Shortest period (red) = 0.564 h \\ \hline Southern to middle \\ Shortest distance (black) = 16 km \\ Shortest period (red) = 0.324 h \\ \hline Southern to southern \\ Shortest distance (black) = 19.35 km \\ Shortest period (red) = 0.291 h \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._The key regions were required to have the maximum number of charging stations in the scenario where private charging was not adopted, represented as 0% PC. The NSs and influencing factors were calculated. Specifically, zone 4 required the most significant NSs--seven--while zones 6 and 7 required only six stations. This pattern aligns with the expected rise in demand for public charging stations if private charging options are unavailable. However, when the model incorporated a 60% PC rate, there was a discernible decrease in the requirement for public charging stations, indicating a substantial uptake of private charging options. This scenario demonstrated that the NSs in zone 4 was reduced to four station, while the NSs in zones 6 and 7 were reduced to 3 and 4, respectively. Table 4 illustrates the required NS' in zones 4, 6, 7, 8, and 9 at different private charging rates, while other zones needed only two stations in all cases. These results highlight the critical influence that private charging infrastructure has on the need for public charging stations. Notably, at least two stations are maintained in every zone, irrespective of the PC rate, to guarantee operational reliability and accommodate maintenance requirements. This baseline guarantees that EV consumers consistently have access to public charging alternatives, even in areas with lower demand.
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 4 Models
### 3D U-Net Model  
The 3D U-Net model used for the full 3D and the slab-based setup comprises nine convolutional step blocks (see Figure 2a). Each convolutional step block contains two 3D convolutions of size \(3\times 3\times 3\) followed by the ReLU activation and a dropout layer with a rate of 20% between them to reduce overfitting. The contracting path consists of four convolutional step blocks, each followed by max-pooling 3D of size \(2\times 2\times 1\). The expanding path also consists of four step blocks, each followed by 3D upsampling of size \(2\times 2\times 1\). The feature maps outputted by upsampling operation are concatenated with the corresponding ones from the contracting path. Finally, the last convolutional step block is applied, followed by a 3D convolution of size \(1\times 1\times 1\). A sigmoid activation function is used to produce a final probability map.  
To adapt the model to the box-based setup, the size of max-pooling and upsampling operations was changed from \(2\times 2\times 1\) to \(2\times 2\times 2\) to adapt the model to smaller input resolutions. Additionally, in the expanding path, the number of filters was reduced by half. Finally, the dropout rate was set to 25% to facilitate training.