esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels
### Band MMSE Block Equalizer
#### 3.2.2 Computational Complexity  
Speaking of the complexity of the banded block equalizer, we assume the CFR matrix \(\mathbf{H}_{F}\) is known to the receiver in this paper. Step 1 requires \(\mathcal{O}(NlogN)\) operations to obtain the receive signal vector in the frequency domain. In step 2, in order to construct \(\mathbf{R}\), \(\mathbf{E}=\mathbf{B}\mathbf{\Lambda}\) should be computed first. Since \(\mathbf{B}\) can be directly constructed from \(\mathbf{H}_{F}\), so that the computational complexity is that of the product of \(\mathbf{B}\) and \(\mathbf{\Lambda}\), which is \(QN+N-Q^{2}+Q\) complex multiplications (CM). Then we need to compute \(\mathbf{E}\mathbf{E}^{H}\). The computation of \((\mathbf{E}\mathbf{E}^{H})_{i,j}\) requires \(2Q+1-|i-j|\) CM and \(2Q-|i-j|\) complex additions (CA). Since \(\mathbf{E}\mathbf{E}^{H}\) is symmetric and banded, by ignoring small terms in the complexity calculation, at most \((2Q^{2}+3Q+1)N\) CM and \((2Q^{2}+Q+1)N\) CA, or \((4Q^{2}+4Q+2)N\) flops in total. Then the band LDL\({}^{H}\) algorithm will be applied to obtain \(\mathbf{D}\) and \(\mathbf{L}\). The complexity of the band LDL\({}^{H}\) algorithm is totally \((4Q^{2}+6Q)N\) flops [21]. Hence, the computational complexity of step 2 is \((8Q^{2}+11Q+3)N-Q^{2}+Q\). In step 3, we apply the forward and backward substitution for solving the two triangular systems. The computational complexity of each system is \(8 QN\). Therefore, the total computational complexity of step 3 is \((8Q+1)N\). In step 4, the computation of \(\mathbf{E}^{H}\mathbf{y}\) needs \((2Q+1)N-Q^{2}-Q\) CM and \(2Q-Q^{2}-Q\) CA, which lead to totally \((4Q+1)N-2Q^{2}-2Q\) operations. Then we perform the IFFT algorithm on \(\mathbf{E}^{H}\mathbf{y}\), which requires \(\mathcal{O}(NlogN)\) operations.  
Therefore, except for the FFT and IFFT operations in step 1 and step 4, the computational complexity of the band MMSE equalizer is \((8Q^{2}+23Q+5)N-3Q^{2}-Q\) in total. Because the computational complexity of FFT and IFFT is \(\mathcal{O}(NlogN)\), the complexity of the band MMSE equalizer is \(\mathcal{O}(NQ^{2})\).
## 4 Discussion
### Applicability of ML to Clinical Practice
The applications are unlimited in diagnosis, research, drug development and clinical trials. Despite the large amount of digitized data, predictive models that are built from medical records are mainly based on traditional linear models and rarely consider more than 20 or 30 variables. However, a key advantage of ML is that researchers do not need to specify which potential predictive variables to consider and in which combinations [195]. An important issue to consider when applying ML in the clinical practice is the consistency of data from heterogeneous sources. Each health system may collect patient data differently for similar purposes. For this reason, before applying ML, it is necessary to standrise the data. This would avoid data overfitting and the difficulty of applying the same technique to other data sets. The problem of bias is also important. This problem arises when there is poor coverage of training data, leading to errors when applying to minority groups. In general, in clinical practice it is interesting to have different and large data sources to highlight the specific features of each group of patients. Finally, the comprehensibility of the algorithms is another key element. A compromise between performance and the interpretability has to be established. Models with better performance (e.g., DL) are often difficult to explain, while models, such as linear regression or decision trees, are more understandable.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 1 Introduction
### Related Works
#### 1.2.3 Network Obstructions  
Recent studies on large vehicle obstruction effects on VANET communication are considered in [12; 13; 14; 15; 16; 17; 18]. For single large vehicle obstruction effects, ref. [12] presents measurements of the impact of vehicle size on the Doppler spreads in the V2V radio channel at 5.9 GHz. In contrast, authors in [16] present an analytical model for the power contributions from diffraction around a truck. They show that without a strong line-of-sight component, packet delivery depends on other propagation mechanisms that are statistically more likely to add destructively so that the signal falls below the noise threshold of the receiver vehicle. The work in [18] focuses on the directional analysis of large vehicle obstructions in V2V channels. They show that not only do large vehicles cause additional attenuation of signal energy but also significantly affect the angular distribution of multi-path components in three-dimensional space and bring larger angular dispersion.  
In [17], channel measurements in an urban scenario for three test cases, non-obstruction, small-vehicle obstruction, and large-vehicle obstruction, are compared and analyzed for the performance of path loss characteristics. The joint effects of radio environment and traffic flow on link connectivity are investigated to represent the obstruction probability and inter-vehicle connectivity probability. Here, the authors considered highway moving obstacles following a light-of-sight blocked model [15]. The works in [12; 14] use geometric and stochastic shadowing of multiple big vehicles on multiple lanes to understand their impact on V2V communications.  
In [12], hardcore repulsive Poisson point process (HRPPP) was proposed to model the locations of vehicles, while [14], instead considers the big vehicle selective relaying strategy to enhance V2V communication. It can be observed that none of the works investigates the combined effect of obstruction and channel fading. The works that come close to giving a generalized connectivity analysis for VANETs is [19]. However, the closed-form generalized connectivity probability was not given for the Nakagami-\(m\) fading channel, which we provide in this work, and for other fading channels. Moreover, this paper further gives generalised connectivity expressions with big vehicle obstruction effect for all fading channels considered. While [20] analyzed the impact of the radio channel modeling on the connectivity performance of a VANET with incomplete random communication range statistics, it ignored generalized inter-vehicle distribution, a modal considered in [19].
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 3 Results and Discussion
### The Quantitative Relationship of Precipitation and Temperature to Soil Moisture  
In the study we found that temperature and precipitation play an important role in the process of the evolution of soil moisture. In order to present the ability of constraints to temperature and precipitation to affect soil moisture, the multiple regression analysis method is used to analyze the quantitative relationship of precipitation and temperature to soil moisture in different depths of soil throughout the year, as shown in Table 3. It is clear that precipitation and temperature in spring, summer and autumn have positive correlations with soil moisture in different depths, but the correlations are negative in winter. In each season and at each layer of soil moisture, the effects of temperature and precipitation on soil moisture are varied. Among them, in autumn the temperature and precipitation have a significant effect on different depths of soil moisture, which is not the case for winter. The results also indicate that precipitation, temperature and soil moisture are consistent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Regression Coefficient** & **0–10 cm** & **10–40 cm** & **40–100 cm** & **100–200 cm** \\ \hline \multirow{2}{*}{**Spring**} & Temperature & 0.992 & 0.787 & 0.998 & 0.951 \\  & Precipitation & 0.999 & 0.682 & 0.975 & 0.987 \\ \hline \multirow{2}{*}{**Summer**} & Temperature & 0.736 & 0.620 & 0.289 & 0.401 \\  & Precipitation & 0.663 & 0.536 & 0.190 & 0.305 \\ \hline \multirow{2}{*}{**Autumn**} & Temperature & 0.999 & 0.985 & 0.999 & 0.999 \\  & Precipitation & 0.989 & 0.999 & 0.986 & 0.980 \\ \hline \multirow{2}{*}{**Winter**} & Temperature & \(-0.128\) & 0.062 & \(-0.067\) & \(-0.141\) \\  & Precipitation & \(-0.908\) & \(-0.812\) & \(-0.880\) & \(-0.913\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression coefficients of temperature, precipitation, and soil depth in different seasons.  
Figure 11: The correlation coefficient of precipitation and temperature with soil moisture in different seasons: (**a**) correlation coefficient of precipitation; (**b**) correlation coefficient of temperature.
# Extracellular Vesicles for Regenerative Medicine Applications
## 2 Extracellular Vesicles (EVs)
### Microvesicles  
Microvesicles are formed through the outward budding of the cell membrane [45]. Microvesicles range in size from 100 nm to 1 \(\upmu\)m in diameter and are thought to be involved in cell-to-cell communication, both locally and systemically [29; 31; 32; 33; 35]. Unlike exosomes that have distinctive marker proteins, there are no specific marker proteins for microvesicles. The biogenesis of microvesicles occurs through the trafficking of molecular cargo to the plasma membrane where contractile machinery induces budding and ultimately the pinching off of these vesicles [46; 47]. Since microvesicles are produced from the outward budding of the cell membrane, the proteins found on the surface of microvesicles are similar to the surface proteins of the parent cell. The intracellular cargo of microvesicles is diverse and contains proteins including, but not limited to, cytoskeletal proteins, heat shock proteins, integrins, and proteins involved in cell fusion machinery (SNAREs and tethering proteins) [35; 48; 49; 50; 51; 52; 53; 54].
## 2 Preliminaries on Mittag-Leffler Functions, Their Properties and Available Fractional Operators
### Mittag-Leffler Functions and Related Kernels
#### 2.1.2 Two-Parameters Mittag-Leffler Function  
The two-parameter of the Mittag-Leffler type is defined as a series expansion  
\[E_{\alpha,\beta}(z)=\sum_{k=0}^{\infty}\frac{z^{k}}{\Gamma(ak+\beta)},\quad z \in\mathbb{C},\quad\alpha>0,\quad\beta>0,\quad\beta\in\mathbb{C} \tag{17}\]  
For \(\beta=1\) we get the classical one-parameter Mittag-Leffler function \(E_{\alpha,1}(z)\) defined by (12).  
From the definitions (12) and (17) it follows that [30; 31; 33]  
\[E_{1,1}(z)=\sum_{k=0}^{\infty}\frac{z^{k}}{\Gamma(k+1)}=\sum_{k=0}^{\infty} \frac{z^{k}}{k!}=e^{z} \tag{18}\]  
\[E_{1,2}=\sum_{k=0}^{\infty}\frac{z^{k}}{\Gamma(k+2)}=\sum_{k=0}^{\infty}\frac{ z^{k}}{(k+1)!}=\frac{1}{z}\sum_{k=0}^{\infty}\frac{z^{k+1}}{(k+1)}=\frac{e^{z}-1}{z} \tag{19}\]  
The corresponding Laplace transforms are [30; 31; 33]  
\[\mathcal{L}\Big{[}t^{\beta-1}E_{\alpha,\beta}(-\lambda t^{\alpha})\Big{]}= \frac{s^{\alpha-\beta}}{s^{\alpha}+\lambda}=\frac{s^{-\beta}}{1+\lambda s^{- \alpha}} \tag{20}\]\[\mathcal{L}\Big{[}t^{\alpha k+\beta-1}E_{\alpha,\beta}^{(k)}(-\lambda t^{\alpha}) \Big{]}=\frac{k!s^{\alpha-\beta}}{\left(s^{\alpha}-\lambda\right)^{k+1}},\quad k =0,1,2,\ldots \tag{21}\]  
\(E_{\alpha,\beta}(-z)\) is completely monotonic [34; 35] for any \(\alpha\in(0,1]\), precisely if and only if \(\alpha\in(0,1]\) and \(\beta\geq\alpha\)[36].  
The integral of \(E_{\alpha,\beta}(t)\) is  
\[\int\limits_{0}^{t}E_{\alpha,\beta}(\lambda t^{\alpha})t^{\beta-1}dt=t^{\beta }E_{\alpha,\beta+1}(\lambda t^{\alpha}),\quad\beta>0 \tag{22}\]  
Applying the Riemann-Liouville fractional derivative to (17) we get [33]  
\[{}_{0}D_{t}^{\mu}\Big{[}t^{\alpha k+\beta-1}E_{\alpha,\beta}^{(k)}(\lambda t^ {\alpha})\Big{]}=t^{\alpha k+\beta-\mu-1}E_{\alpha,\beta-\mu}^{(k)}(\lambda t ^{\alpha}) \tag{23}\]  
When, \(k=0\), \(\lambda=1\) and \(\mu=m\) is an integer we get [33]  
\[\frac{d^{m}}{dt^{m}}\Big{[}t^{\beta-1}E_{\alpha,\beta}(t^{\alpha})\Big{]}=t^{ \beta-m-1}E_{\alpha,\beta-m}(t^{\alpha}),\quad m=1,2,3,..... \tag{24}\]  
For \(m=1\) we have  
\[\frac{d}{dt}\Big{[}t^{\beta-1}E_{\alpha,\beta}(t^{\alpha})\Big{]}=t^{\beta-2 }E_{\alpha,\beta-1}(t^{\alpha}) \tag{25}\]  
When \(\beta=1\) (one parameter Mittag-Leffler function), then (25) yields  
\[\frac{d}{dt}[E_{\alpha,1}(t^{\alpha})]=\frac{E_{\alpha,0}(t^{\alpha})}{t} \tag{26}\]  
In this context, the first derivative of \(E_{\alpha}(-t^{\alpha})\), defined by (13), and the first derivative of \(E_{\alpha,\beta}(-t^{\alpha})\) are related as follows [30]  
\[\frac{d}{dt}[E_{\alpha,\alpha}(-t^{\alpha})]=t^{-(1-\alpha)}E_{\alpha,\alpha}( -t^{\alpha})=-\frac{d}{dt}E_{\alpha}(-t^{\alpha}) \tag{27}\]  
For \(\lambda\
eq 1\) we have [33]  
\[\frac{d}{dt}[E_{\alpha,1}(\lambda t^{\alpha})]=\frac{E_{\alpha,0}(\lambda t^ {\alpha})}{t} \tag{28}\]  
That is  
\[\int\limits_{0}^{t}\frac{d}{dt}[E_{\alpha,1}(\lambda t^{\alpha})]=\frac{E_{ \alpha,0}(\lambda t^{\alpha})}{t} \tag{29}\]
## 3 Blind Demodulation of CDS-SS Signals Based on Particle Filters
### Blind Demodulation of CDS-SS Signals under the White Gaussian Noises Condition
One cycle of the PF chaos fitting includes two steps:  
**Step 1**: Do PF chaos fitting and estimate \(\hat{b}_{k+1}\) according to Equation (23) and \(\hat{x}_{k}\). The state equation is \(\hat{b}_{k+1}\!=\!\hat{b}_{k}+w_{k}^{(2)}\), and the measurement equation is \(z_{k+1}\!=\!\hat{b}_{k+1}(g(\hat{x}_{k})+\beta)+v_{k+1}^{(2)}\). **Step 2**: Do PF chaos fitting and estimate \(\hat{x}_{k+1}\) according to Equation (22) and \(\hat{b}_{k+1}\). The state equation is \(\hat{x}_{k+1}\!=\!g(\hat{x}_{k})+w_{k}^{(1)}\), and the measurement equation is \(z_{k+1}\!=\!\text{sgn}(\hat{b}_{k+1})\hat{x}_{k+1}+v_{k+1}^{(1)}\)
# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 1 Introduction
Which factors are important when designing FHM programs that combine terrestrial and remote-sensing data? 2. Which remote sensors and systems are suitable for monitoring which FH indicators? 3. Which new technologies and current developments are relevant for the design of future FHM programs? In the introduction, we provide an overview of the current close-range FHM programs to identify potential links between in situ observations and RS data. Section 2 reviews the trends and new technologies for close-range RS approaches. Section 3 analyses air- and space-borne RS systems for estimating and monitoring FH indicators. In Section 4, we describe approaches of physical and empirical models followed by conclusions in Section 5.
## 2 Risk Modeling Techniques
### Stress Testing  
Stress testing involves simulating extreme conditions to evaluate how different assets or systems perform under stress. Stress testing is applicable to some areas in the electrical engineering sector, such as the printed circuit board assembly (PCBA) process. Moreover stress testing assesses the risks associated with critical infrastructure (CI). The CI performance changes over time due to aging, hazards, or events, impacting functionality. The grading system accounts for this, making stress tests periodic. Passing with grade AA or A keeps risk objectives until the next test, with intervals set by the regulator. Most CIs get grade B or C, indicating high risk. To promote upgrades, stricter objectives or shorter test intervals are used, aiming for grade A or CI termination. Redefining risk objectives requires identifying the characteristic point of risk, which guides future grading. This point is the highest risk above the ALARP region on the F-N curve. If grade B is obtained in the first test (ST1), the B-C boundary will be adjusted in the next test (ST2) to reflect risk levels beyond the ALARP region, ensuring risk equity across cycles. This ensures risk equity over two cycles and can be expressed as follows [22]:  
\[R_{\text{ST1}}-R_{(A-B)}=R_{(B-C),ST1}-R_{(B-C),ST2} \tag{27}\]  
\(R_{(A-B)}\) is the A-B boundary, \(R_{(B-C),\,ST1}\) and \(R_{(B-C),ST2}\) are the B-C boundaries in ST1 and ST2, and RST1 is the risk measure in ST1. The left side of Equation (27) represents the risk beyond the ALARP region in ST1. If grade C is given in ST1, the B-C boundary and the period until ST2 are reduced. The B-C boundary is set to the A-B boundary, which is the maximum reduction. The reduced period until ST2 (\(t_{cycle,\textit{trafficed}}\)) ensures risk equity over two cycles and is calculated as follows:  
\[t_{cycle,\textit{trafficed}}=\left[t_{cycle,\textit{initial}}\cdot\frac{R_{(B-C), ST1}-R_{(A-B)}}{R_{ST1}-R_{(A-B)}}\right] \tag{28}\]  
where \(t_{cycle,\textit{initial}}\) is the initial duration between two stress tests.  
Risk modeling techniques are essential for organizations to effectively identify, assess, and manage potential risks. By utilizing these methods, companies can make informed decisions, minimize losses, and adapt to unexpected challenges, ensuring their operations remain resilient, sustainable, and reliable.
# Sparse Multiuser Receiver Design in Large Scale Array System
## 5 Simulation Results  
In this section, we numerically evaluate the performance of the analog matrix designing method and the multi-user receiver algorithm based on GAMP.
## 2 Materials and Methods  
The bitumens used in this work are three commercial ones having penetrations ranging between 70 and 220 dmm (one penetration unit = 0.1 mm, Table 1). Penetration value test on bitumen is a measure of hardness or consistency of bituminous material. A 70/100 grade bitumen indicates that its penetration value lies between 70 and 100. Penetration value is the vertical distance traversed or penetrated by the point of a standard needle into the bituminous material under specific conditions of load, time and temperature. This distance is measured in one tenths of a millimeter. The two soft bitumens having the same class of penetration (160/220) have similar physical properties, while they have different origins and are processed by different techniques. They were selected to ascertain whether their chemical compositions are equal or different [22]. In the literature, it has been shown that, while two bitumens have the same softening point and penetration grade, they may show significant difference from the chemical point of view [22,23]. In addition, a 70/100 paving grade bitumen was also investigated to understand the effects of different bitumen processing on bitumen fundamental chemical properties.  
The diagram shown in Figure 1 illustrates the main methodology followed in this research. In the first phase, \({}^{1}\)H-NMR spectra were recorded on untreated bitumen samples and on asphaltene and maltene fractions separated from the three bitumens using \(n\)-pentane as a solvent, with a slightly modified method compared to the commonly used one. The separation procedure is explained later.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measured Properties**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Paving Grade**} & \multicolumn{2}{c}{**Paving Grade**} & \multicolumn{1}{c}{**Industrial**} \\ \cline{5-6}  & & & **70/100** & **160/220** & **160/220** \\ \hline Penetration at 25 \({}^{\circ}\)C & EN 1426 & 0.1 mm & 70–100 & 160–220 & 170 \\ Softening point (R\&B) & EN 1427 & \({}^{\circ}\)C & 43–51 & 35–43 & 35–43 \\ Flash point & EN 2592 & \({}^{\circ}\)C & \(\geq\)230 & \(\geq\)220 & 271 \\ Solubility & EN 15292 & \% (m/m) & \(>\)99 & \(\geq\)99 & \(\geq\)99 \\ Mass change at 163 \({}^{\circ}\)C & EN 12607-1 & \% & \(\leq\)0.8 & \(\leq\)1 & \(\leq\)1 \\ Retained penetration & EN 1426 & \% & \(\geq\)46 & \(\geq\)37 & \(\geq\)37 \\ Increase in softening point & EN 1427 & \({}^{\circ}\)C & \(\leq\)9 & \(\leq\)11 & \(\leq\)12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fundamental physical properties of bitumens.  
Figure 1: Research scheme.  
In the second phase, the \({}^{1}\)H-NMR spectroscopy was applied to the separated zones extracted from each bitumen by preparative TLC.
This work further develops the previous theory, and the developed theory applies to LRM/LRMM/LRRM calibrations. This theory constructs the T-matrices of error boxes by using single-port measurements, and newly introduces three wave ratios to express the error boxes in the algorithm. Existing calibration methods, e.g., LRRM [12], also allow the reduction of calibration unknowns to three by utilizing a two-port standard measurement, but consequently a perfect thru or a known transmission line is often required for the convenience of analytical derivation [21; 22]. In this theory, a unified mathematical framework is developed for LRM/LRMM/LRRM calibrations, and the line standard L can be an arbitrary known two-port standard. Although the early work [23] can also use the arbitrary known two-port standard, the formulas for the LRRM calibration were not described in sufficient detail. As will be shown in this paper, by using the proposed theory, LRM, LRMM, and LRRM calibration algorithms can be simply derived. This paper is organized as follows. Section 2 explains the new general calibration theory. Section 3 gives the analytical derivations of the LRM/LRMM/LRRM calibration techniques using the new theory. In Section 4, the proposed theory is validated by experimental results. Some conclusions are drawn in Section 5.