esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### SAR Metrics Used in the Damage Assessment  
The basic SAR metrics were stand level variables calculated from pixel level variables for both polarizations. Using stand level metrics instead of pixel level metrics reduces the the speckle effect and the radiometric variation within homogeneous stands. From the pixel level intensities (\(I_{s,i}^{k,p}\)), the following features were calculated for each stand \(s\), for both polarizations \(p\) and for each image \(k\):  
(a) averages,  
\[10\log_{10}\overline{I_{s}^{k,p}}=10\log_{10}\frac{\sum_{i=1}^{n_{s}}I_{s,i}^{ k,p}}{n_{s}},k=1,...,27,p\in\{\text{VV},\text{VH}\}, \tag{1}\]  
where \(n_{s}\) is the number of the pixels on stand \(s\); (b) standard deviations  
\[\sqrt{\sum_{i=1}^{n_{s}}(I_{s,i}^{k,p}-\overline{I_{s}^{k,p}})^{2}/(n_{s}-1)}, k=1,...,27,p\in\{\text{VH},\text{VV}\}; \tag{2}\]  
and (c) intensity-ratios  
\[1/n_{s}\sum_{i=1}^{n_{s}}I_{s,i}^{k_{1,p}}/I_{s,i}^{k_{2,p}},k_{1}=1...,26\; \;\text{and}\;\;k_{2}=2,...,27. \tag{3}\]  
These predictor variables, stand-averaged intensities (Equation (1)), standard deviations (Equation (2)) and different ratio features (Equation (3)), were evaluated and, optionally, instead of the averages, also median and mode stand level intensities.  
The training data and the validation data and their'stand' boundaries concerning the damages consisted of the cutting reports rather than the forest patches really damaged by the storm. A natural question is whether it is relevant to use SAR features calculated from the areas reported to be cut because the cutting area can be larger than the damaged area. The characteristics such as median and those calculated from the quantiles SAR features inside the cutting stands were also tested, in addition to the differences of the features from SAR data before and after the damage. Median is not as sensitive to the outliers as is, e.g., the average.
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 4 QEPAS Sensor Based on Custom QTF  
A commercially available, low-cost QTF (<$1) with a resonance frequency \(f_{0}\) of \(\sim\)32.76 kHz is usually used in the QEPAS sensor system [60; 61; 62]. Typically, the dimension of the two prongs of the QTF is 3.6 mm in length, 0.6 mm in width, and 0.36 mm in thickness, and the gap between the two prongs is 300 \(\upmu\)m [63]. As shown in Equation (1), the signal amplitude of the QEPAS sensor is inversely proportional to the QTF resonance frequency. A QTF with a smaller\(f_{0}\) will result in a longer integration time, which is an advantage for increasing the QEPAS sensor signal level [64; 65; 66].
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 2 Quantum Linear Systems and the Implementation of the Solver
### Implementation of the Circuit Generation  
In all experiments in this paper, the code for the HHL circuit generation comes from a Qiskit-based open-sourced package [55], which only produces the essential parts of the HHL circuit as colored in Figure 1. We made slight modifications to accommodate the changes in Qiskit 0.46. The state preparation for \(|b\rangle\) uses the algorithm in [56] that decomposes an arbitrary isometry into the optimized number of single-qubit and CNOT gates, where isometry refers to the inner-product-preserving transformation that maps between two Hilbert spaces; i.e., the state preparation is a special case of isometries. To construct the unitary operator \(e^{itA}\) in the QPE stage, the code directly uses Quantum Shannon Decomposition to synthesize the matrix exponential in the circuit.
## 2 Study Area and Data  
### Study Area  
The Tarim River Basin is the largest continental river basin in China. It is bordered by the Qinghai-Tibetan Plateau and the Tianshan, Kunlun, and Altyn Mountain Ranges (Figure 1). These mountains act as a block to the inward passage of warm and moist air, which has caused the Tarim River Basin to develop an extremely dry, desert microclimate with limited precipitation and high potential evaporation [21; 22]. Annual precipitation in the mountainous headwater regions can reach ~250 mm, as opposed to 40-70 mm in the downstream regions of the basin. The Taklamakan Desert and the Lop Nur Basin have average annual precipitation of ~12 mm and annual potential evaporation of ~1000-1600 mm; thus, the Tarim River Basin has the highest aridity index in the world [23]. Most snow cover--a key source of fresh water--occurs over the high-elevation parts of the basin, such as the western Tianshan area in the northwest, Pamirs and Karakorum Mountains in the west, Kunlun Mountains in the south, and Tianshan Mountains in the northeast. This limited and unevenly distributed resource makes the Tarim River Basin an ecologically vulnerable environment, which exemplifies the need for improved resource management and climatic and environmental governance.
## 3 Multivariate Statistical Techniques and Data Treatment
### Data Treatment and Multivariate Statistical Methods
#### 3.2.1 Principal Component (PCs)/Factor Analysis (FA)  
PCA reduces the dimensionality of the data set by explaining the correlations amongst a large number of variables in terms of a smaller number of underlying factors without losing much information [42; 43]. The original variables of PCs produce loadings that have correlation coefficients with PCs. The PCs' formula was taken from [33; 36]:  
\[\mathcal{Y}\mathsf{mn}=\mathcal{Z}\mathsf{m}1\mathcal{X}\mathsf{1}\mathsf{n}+ \mathcal{Z}\mathsf{m}2\mathcal{X}2\mathsf{n}+\mathcal{Z}\mathsf{m}3\mathcal{ X}3\mathsf{n}\ldots+\mathcal{Z}\mathsf{mi}\mathcal{X}\mathsf{in} \tag{3}\]  
where \(z\) is the component loading, \(y\) is the component score, \(x\) is the measured value of a variable, \(\mathsf{m}\) is the component number, \(\mathsf{n}\) is the sample number, and \(\mathsf{m}\) is the total number of variables.  
Meanwhile, FA attempts to extract a lower-dimensional linear structure from the data set and extracts the new group of variables known as varifactors (VFs) via rotation along the PCA axis. In FA, the basic concept is borrowed from [33; 36]:  
\[\mathcal{Y}\mathsf{mn}=\mathcal{Z}\mathsf{p}1P1\mathsf{m}+\mathcal{Z}\mathsf{ p}2P2\mathsf{m}+\mathcal{Z}\mathsf{p}3P3\mathsf{m}+\ldots+\mathcal{Z} \mathsf{pr}P\mathsf{rm}+e\mathsf{pm} \tag{4}\]  
where \(y\) is the measured value of the variable, \(z\) refers to the factor loading, \(p\) is the factor score, \(\mathsf{m}\) is the sample number, \(\mathsf{n}\) is the variable number, \(r\) is the total number of factors, and \(e\) is the residual term accounting for errors or other sources of variation.  
In this study, PCA was employed for qualitative determination of pollution sources.
## 2 Materials and Methods  
### Study Group  
The clinical study took place in the Ophthalmology Clinic of St. Spiridon Hospital and Oftaprof Clinic in Iasi, during October 2018 and December 2019. The groups included patients with sleep apnea syndrome associated with glaucoma and dry eye syndrome. Neural network modeling was performed using the Neurosolutions commercial simulator. This can determine the number of hidden layers, the number of neurons in each layer, the different types of the activation function, and the driving algorithms. The built-in databases gather information on a group of patients with primitive open-angle glaucoma and normal-tension glaucoma, who have been associated with sleep apnea syndrome in various stages of severity (73 eyes).  
The study was approved by the Ethics Commission of Sf. Spiridon University Hospital Iasi, approval no. 49/9.12.2018 in compliance with the ethical and deontological rules for medical and research practice. The study was conducted in accordance with the Helsinki Declaration and with several published principles.  
All patients underwent the following determinations, according to a standardized protocol: the best corrected visual acuity (Snellen optotype), the Schirmer I test (without anesthetics); intraocular pressure (with the Goldmann applanation tonometry), OCT (optical coherence tomography Cirrus HD OCT Zeiss) with the evaluation of the optic nerve, macula, and measurement of the retinal nerve fiber layer, determination of the visual field (Humphrey perimeter--C24-2 program), measurement of the central corneal thickness (Tomey ultrasonic pachymeter), and the corneal hysteresis with ORA (ocular response analyzer, Reichert technology). Moreover, the respiratory functional parameters (apnea-hypopnea index--AHI) and body mass index were recorded. The age of the patients, sex, background data, and associated general conditions (hypertension, obesity, diabetes, etc.) were also recorded.  
The data included in the database were divided as follows: 65 out of the 73 data points were used in the neural network training stage and 8 were kept for the testing stage. In total, 21 parameters were selected as input parameters for neural models, including: age of patients, BMI (body mass index), systolic and diastolic blood pressure, intraocular pressure, central corneal thickness, corneal biomechanical parameters (IOPcc, HC, CRF), AHI, desaturation index, nocturnal oxygen saturation, remaining AHI, type of apnea, and associated general conditions (diabetes, hypertension, obesity, COPD). The output parameters were: c/d ratio, modified visual field parameters (MD, PSD), OCT parameter (ocular computer tomography, ganglion cell layer thickness, linear CDR, and average GCL.  
A new group of four patients was included in this study to make predictions with the best performing neural model. It included patients aged 75-88 years with sleep apnea syndrome, hypertension, three of them obese and without diabetes. The output parameters, c/d ratio, MD, PSD, linear CDR, and average GCL, were obtained from the neural model, which were then compared with the experimental values.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of NFTs Formation
#### 2.3.2. Sesquiterpenenes  
Artemisinin (**53**), a sesquiterpene extracted from _Artemisia annua_ L., inhibits the release of TNF-\(\alpha\), IL -1\(\beta\), IL-6, TNF-\(\alpha\), and NO [89]. It is a lipid soluble substance that readily penetrates the BBB into the brain [89]. Nevertheless, the neurotoxicity of this natural product might be an obstacle for further use in clinical research [80].  
Parthenolide (**54**), another sesquiterpene existing in _Chrusanthemum morifolium_ L. [PERSON]. [90], can inhibit the production of IL-6, TNF-\(\alpha\), and ROS [91; 92; 93].  
The sesquiterpene 7\(\beta\)-(3-ethyl-cis-crotonoyloxy)-1\(\alpha\)-(2-methylbutylyryloxy)-3,14-dehydro-Z-notonipetranone (ECN) (**55**), isolated from _Tussilago farfara_ L. widespread in East Asia, exhibits neuroprotective effects against oxidative stress-induced cell damage and dopaminergic neurodegeneration. Its mechanism of action is through potential activation of the Nrf/antioxidant response element (ARE) signaling pathway and decreasing ROS generation in vitro and in vivo [78; 91].  
\(\alpha\)-Cyperone (56), one of the main components of _Cyperus rotundus_ L. essential oil, lactucopicrin (**57**) from _Lactuca virosa_ L. (wild lettuce), _Cichorium titybus_ L. and dandelion coffee, and bakkenolide B (**58**), extracted from _Petasites japonicus_ ([PERSON] & [PERSON].) Maxim. leaves from East Asia, ameliorate the oxidative stress by activation of the Nrf pathway [78; 92; 93; 94].
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 4 Case Study of Seasonal Variation in Light-Use Efficiency and SIF across the Amazon Basin
### Discussion
#### 4.3.2 Improvements in Instrument Design, Data Acquisition and Processing  
Although we obtained evidence of seasonality for both PRI and SIF, current sensors on orbiting platforms limit the determination of SIF and PRI at higher temporal resolution (i.e., weekly or daily basis) due to inherent noise levels. Improvements in sensor design, data acquisition and processing may reduce the high variance reported. Even though the high noise from both MODIS and GOME-2 retrievals over the area does not appear to have a substantial impact on our monthly averages for both PRI\({}_{\text{diff}}\) and SIF (please refer to the figures), inferences drawn from the data and subsequent extrapolations must be done carefully. Additionally, it should be noted that we are looking at averages over multiple years. Extreme drought events, such as the 2005 and 2010 (addressed in detail in [63; 65; 66; 67; 68]) might have more pronounced effects on both PRI\({}_{\text{diff}}\) and SIF estimates. One might consider using sophisticated BRDF models rather than collecting forward- and back-scattered observations as we did. As a result of our choice, vertical \"stripping\" occurred at the edges of the MODIS sampling swath (see Figures 5-7). This limitation will be addressed with the new MAIAC release that will provide BRDF kernels for Bands 11 and 12.  
We were restricted to sampling over monthly periods to obtain sufficient data and to integrate over large areas. There is progress in refining both deficiencies. [PERSON] and collaborators [44] used GOME-2 data acquired at eight-day intervals over several years and plotted results on a 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) grid with a four-fold improvement in spatial resolution compared with Level 3 GOME-2 data.
## 3 Results
### Principal Components Analysis (PCA) and Pairwise Correlations  
The PCA exhibited two interpretable components that together explain 60.3% (eigenvalue 7.2) of the variation in \(L^{*}\), \(l^{p}\) (hue angle), C\({}^{*}\), TAN, C-3-rut, _PAL_, _ANS_, _UFGT1_, and _UFGT2_ in fig peel and TSS, TA, and pH in fig flesh (Figure 5a,b). In the score plot, a good replication of each cracking category and for every SA concentration is apparent. Category C2 is separate from the rest groups and positioned on the two left quarters, in contrast to C0 and C1 positioned on the two right quarters (Figure 5b).  
Furthermore, C0 controls are positioned in the upper left quarter, while all SA-treated fruit in C0 are presented in the upper right quarter of the score plot. However, the 2 mM SA-treated fruit in C2 are found in the lower left quarter and the 1 mM SA-treated fruit in C2 in the lower right quarter, with C1 being placed between C0 and C2 (Figure 5b).  
In the load plot, close positive relationships are indicated between TAN and c-3-rut (positioned very close to each other on the left upper end), between TSS and TA (in the lower left quarter towards the left axis of the load plot), and between \(L^{*}\) and C\({}^{*}\) (near the right axis of the load plot) (Figure 5a). Indeed, TAN was related to c-3-rut with a highly significant, positive relationship (\(r=0.978\)). Highly significant, positive relationships were also observed in the relations of TSS to TA (\(r=0.947\)) and \(L^{*}\) to C\({}^{*}\) (\(r=0.911\)), whereas TA was related to pH with a negative one (\(r=-0.807\)) (Table S1). Besides, _PAL_ is positioned opposite to _ANS_ (Figure 5a,b) and not related to _UFGT1_ and _UFGT2_ (Table S1).
## 3 Numerical Simulations
### Internal Flows  
The lid-driven cavity problem was analyzed using the proposed method. In this problem, the fluid is contained in a square cavity consisting of three rigid walls with no-slip conditions and the lid moves with a tangential velocity on the top boundary. The kinematic viscosity was adjusted for different **Re** values.  
Prior to the main simulations, the effect of the distribution of the calculation points was checked. Four distributions, in which the number of points in each direction was 51, were chosen for the simulation of the cavity flows when \(\textbf{Re}=100\), as displayed in Figure 3. From Figure 4, it can be observed that the velocity distributions along the central planes in the horizontal and vertical directions are similar, but the even distribution concentrated near the wall boundary shown in Figure 3c was closer to those of the experiment [32].  
Simulations were performed for cases with **Re** = 100, 400, and 1000 and 81, 101, and 151 calculation points were uniformly distributed in the horizontal (\(x\)) and vertical (\(y\)) directions, respectively. Contour maps of the velocity component in the \(x\) direction \(u\) are depicted in Figure 5, where typical cavity flow patterns can be observed. Figure 6 shows a comparison of the velocity distributions along the central planes in the horizontal and vertical directions with the experimental results. It can be seen that there is good agreement between the two.  
To show the efficiency of present method, a simple comparison for the computational time was done. Since the computing speed varies greatly depending on various things, numerical simulations of cavity flow when **Re** = 100 was performed by three different solvers, that is, an in-house code, icFoam of OpenFOAM v2106, and the present one, applying only the very basic items. The CPU and operating system of the computing machine were Xeon E5-1660v3 3.0 GHz and Windows 10 pro, respectively. The in-house and present codes were compiled and linked by intel oneAPI 2021 without any optimization option in compiler level. The icFoam was run under the environment of Ubuntu 20.04 in Window Subsystem for Linux 2. Total number of computational grids were 6400 for thegrid-based solvers and that of computational points was 6651 for the present method. Time increment was 0.001 s and total simulation time was 10s. The average of elapsed time for 10 times of executions were compared in Table 1. It can be seen that present method, which responds to the distribution of unstructured computation points, shows a computational speed similar to that of the structured solver and about 30% faster than the unstructured solver, icoFoam.  
Figure 4: Comparison of velocity profiles (**a**) \(u\) and (**b**) \(v\) along the vertical and horizontal center planes for different point distributions (**Re** = 100).  
Figure 3: Distributions of calculation points: (**a**) uniform, (**b**) uniform with randomization, (**c**) variable, and (**d**) variable with randomization (**Re** = 100; 51 \(\times\) 51 points).
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 3 Performance Analysis
### Diversity-Multiplexing Trade-off
#### 3.2.1 DMT of TDBC-IR
_Case I_: when \(\frac{1}{3}<\alpha\leq\frac{1}{2+r}\), \(\frac{1}{\alpha}-1-r\geq 1\) and \(\Sigma_{2}\doteq\rho^{-d_{1}(r)}\) with \(d_{1}(r)=2-\frac{\alpha r}{1-2\alpha}\) (see the left part of Figure 3 for calculation of \(d_{1}(r)\), which is a simple linear programming problem). _Case II_: when \(\frac{1}{2+r}<\alpha<\frac{1}{2}\), \(\frac{1}{\alpha}-1-r<1\) and \(\Sigma_{2}\doteq\rho^{-d_{1}(r)}\) with \(d_{1}(r)=\frac{1}{\alpha}-1-r\) (see the right part of Figure 3 for calculation of \(d_{1}(r)\)). Concluding the above process, we have  
1. when \(0<\alpha\leq\frac{1}{3}\), \(P_{out}\doteq\rho^{-2(1-r)^{+}}\);
2. when \(\frac{1}{3}<\alpha\leq\frac{1}{2+r}\), for \(0\leq r\leq 0.5\),  
\[P_{out}\doteq\left\{ \begin{array}{ll}\rho^{-2(1-r)^{+}},&\text{if }\frac{1}{3}< \alpha\leq\frac{2}{5}\\ \rho^{-(2-\alpha r/(1-2\alpha))^{+}},&\text{if }\frac{2}{5}<\alpha\leq\frac{1}{2+r}. \end{array} \right.\]  
For \(0.5<r\leq 1\), \(P_{out}\doteq\rho^{-2(1-r)^{+}}+\rho^{-(2-\alpha r/(1-2\alpha))^{+}}\doteq \rho^{-2(1-r)^{+}}\);
3. when \(\frac{1}{2+r}<\alpha<\frac{1}{2}\), for \(0.5<r\leq 1\),  
\[P_{out}\doteq\left\{ \begin{array}{ll}\rho^{-2(1-r)^{+}},&\text{if }\frac{1}{2+r}< \alpha\leq\frac{1}{3-r}\\ \rho^{-(1/\alpha-1-r)^{+}}.&\text{if }\frac{1}{3-r}<\alpha\leq\frac{1}{2}. \end{array} \right.\]  
For \(0\leq r\leq 0.5\), \(P_{out}\doteq\rho^{-(1/\alpha-1-r)^{+}}\). With the above results, the DMT of TDBC-IR, i.e., \(d_{TDBC-IR}=-\lim\limits_{\rho\rightarrow+\infty}\frac{\log P_{out}}{\log\rho}\), is readily obtained by comparing \(\Sigma_{1}\) and \(\Sigma_{2}\).
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 4 Results and Discussion
### L/D = 5.0, 5.5 and 6.0  
#### 4.2.1 Rotation Response  
In this section, the rotation responses at \(L/D=5.0\), 5.5 and 6.0 are investigated. The complete response results are shown in Figure 14. The specific analyses are as follows:  
Figure 14: Complete rotation response of FIR system at \(L/D=5.0\), 5.5 and 6.0: (a) Arc length response; (b) Frequency response.  
(1) On the whole, in the entire test flow velocity range, the rotation responses of these three cases showed the typical VIV response law and there was no re-growth region. The \(A^{*}\) first increased and then decreased sharply, as well as the \(f^{*}\) increasing continuously with increase of \(U_{r}\), which meant there was entering of the initial branch, the upper branch and the lower branch of VIV, in turn. Furthermore, the rotational oscillation responses of \(L/D\) = 5.0, 5.5 and 6.0 were characterized by longer arc length. The maximum \(A^{*}\) of all three cases was more than 0.85, among which \(L/D\) = 6.0 was as high as 0.92, exceeding the \(A^{*}\) of the VIV amplitude response of a single cylinder in the previous study [52]. It could be seen that when the distance between the two cylinders of FIR system increased from 4.5\(D\) to 5.0\(D\), the rotation angle \(\theta\) would increase significantly, which would greatly improve the oscillation level (arc length \(A\)).  
(2) By carefully comparing the oscillation characteristics of these three cases, it could be seen that, in each branch of VIV, the \(A^{*}\) showed a slight upward trend with the increase of \(L/D\), implying that the case with \(L/D\) = 6.0 had a larger synchronization interval than the other two cases. Additionally, further analysis of the rotation response of \(L/D\) = 5.0, 5.5 and 6.0 showed that, when 4.0 \(<U_{r}\) < 5.5, the rotational oscillation was in the upper branch of VIV, and \(f^{*}\) increased in this flow velocity range. Continuing to increase \(U_{r}\), the growth rate of \(f^{*}\) would slow down, and in the entire test velocity range, \(f^{*}\) did not exceed 1.0. The reason might have been that the ratio of the moment of inertia \(J^{*}\) of \(L/D\) = 5.0, 5.5 and 6.0 was still small (\(J^{*}\) = 1.745-2.113). Even if the oscillation entered the nonlinear resonance zone when 4.0 \(<U_{r}\) < 5.5, the vibration frequency was not locked, but still showed an increasing trend. When \(U_{r}\) > 5.5, the rate of growth slowed, which was consistent with the research results of [PERSON] [53].  
In general, when the spacing ratios of the two tandem-coupled cylinders were 5.0, 5.5 and 6.0, the oscillation responses all showed the typical VIV law and were characterized by high oscillation levels. Additionally, the vibration structure with \(L/D\) = 6.0 was more suitable for flow-induced motion power generation, because of its the longer arc length and the wider range of synchronization intervals.