esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Lipase Adsorption in PST Microspheres
Therefore,  
Figure 4: CLSM images and fluorescence distribution of lipase adsorbed in gigaporous microspheres at different time point ((a) 2 min; (b) 8 min; (c) 30 min; (d) 40 min). Figure 5: CLSM images and fluorescence distribution of lipase adsorbed in mesoporous microspheres at different time point ((a) 2 min; (b) 8 min; (c) 30 min; (d) 40 min). it tended to be monolayer adsorption in this stage theoretically. Since both kinds of microsphere were based on P(ST-DVB) [poly(styrene-divinylbenzene)], which allowed physical adsorption of lipase, the only difference is the curvature of pores in the stage of monolayer adsorption. The curvature of gigapores was much lower, and the surface of gigapores was flatter than that of mesopores. According to the effect of curvature to proteins [27], when the value of curvature was bigger, the contact area and the deformation were larger. Additionally, the structure of the enzyme molecules cannot be well maintained. Conversely, the flatter surface was beneficial for retaining the molecular structure. This explained the difference of specific activity in gigaporous and mesoporous particles before the time point of 2 min. For lipase immobilized in gigaporous particles, from the time point of 2 to 8 min in Figure 3c, their specific activity increased rapidly to 3.86 U/mg. While the specific activity of lipase immobilized in mesoporous particles did not have significant change. The main reason of the difference is also due to the pore structure. From the time point of 2 min, multilayer adsorption occurred in gigapores with the large enough space. Multilayer adsorption effectively maintained the enzyme activity. The adsorption of lipase in mesoporous microspheres was still inclined to be monolayer adsorption because the mesopores restricted the distribution of lipase molecules (the pore size is 14.7 nm; the molecular size of CALB is 6.92 \(\times\) 5.05 \(\times\) 8.67 nm\({}^{3}\)[28]). Since the monolayer adsorption of lipase was dominant in mesoporous microspheres, the enzyme activity and the specific activity were obviously lower than those in the gigaporous ones. After the time point of 8 min, though the adsorption mass still increased in the gigaporous microspheres, the enzyme activity had little change, and the specific activity decreased. This was presumably due to the fact that with more enzyme adsorbed in the pores, the cross-section of the pores might be reduced; thus, the diffusion resistance for the substrate and the product was increased [29].
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 2 Materials and Data
### Reconstructed Landsat 30-m NDVI Data  
Around 35% of fine-spatial-resolution Landsat data are missing because of cloud cover and long revisit periods [14]. Before we optimized the model coefficients, we used the following steps to reconstruct spatially and temporally continuous Landsat NDVI data (Figure 2).  
Figure 1: Workflow for generating fine-spatial-resolution GPP by the EC-LUE model.  
We chose the surface reflectance data of Landsat Collection 2 Tier 1. We set a data selection rule to minimize the scan line corrector (SLC)-off effect in Landsat 7 Enhanced Thematic Mapper Plus (ETM+): (i) from 1984 to 2002, we selected all the available Landsat data from Landsat 5 and Landsat 7; (ii) from 2003 to 2011, we only used data from Landsat 5; from 2011 to 2013, we chose Landsat 7 data; (iii) after 2013, we only used Landsat 8 data.  
1. We extracted the cloud-free pixels by using the cloud mask in the Landsat surface reflectance product.
2. We used the cloud-free pixels' reflectance to derive the NDVI. Before we calculated the NDVI, we inter-calibrated the red and near-infrared reflectance in Landsat 5 Thematic Mapper (TM), Landsat 7 ETM+, and Landsat 8 Operational Land Imager (OLI). The TM and ETM+ have similar band settings and research has shown that their NDVIs are consistent [15]. However, the reflective wavelength differences between ETM+ and OLI still affected the NDVI values [16]. Therefore, we used the coefficients in [16] to normalize the red and near-infrared band reflectance in OLI and ETM+. Next, we derived the NDVI from all the inter-calibrated cloud-free surface reflectance. After calculating the NDVI for each image, we generated the NDVI at each 16-day interval and set the highest NDVI at each pixel during this period as the 16-day interval NDVI.
3. We performed NDVI gap-filling and wrote the quality control tag (QCtag) in the data quality layer. For pixels with NDVI observation, we wrote QCtag as 0 and kept the NDVI data. When the 16-day NDVI value was missing, we filled it by the linear interpolation method of two NDVI values of nearby dates. If the two nearby NDVI values were observed within 48 days, we defined this gap-filled NDVI as short-term gap-filled NDVI, and the QCtag was set as 1, which indicated that the reconstructed NDVI was based on short-term gap-filled data. If the two nearby NDVI values were more than 48 days apart, we defined this gap-filled NDVI as a long-term gap-filled NDVI, and the QCtag was set to 2 to indicate that the reconstructed NDVI was based on long-term gap-filled data. The data description of the gap-filled and reconstructed NDVI is shown in Table 1.
4. We used the Savitzky-Golay filter with a window size = 3, an adaptation strength = 5 to smooth the NDVI data in the time series of each pixel. We were able to derive the time-series-reconstructed 30-meter-spatial-resolution NDVI of any given study area.  
Figure 2: Workflow of reconstructed NDVI.
## 2 Material and Methods
### Airborne Laser Scanning Processing Chain
#### 2.2.2 Validation Data  
Field data were collected by means of cross-sections and square sample plots. The measurements started by defining a one hundred-metre line parallel to the shore. A measuring tape was placed perpendicularly to this line every ten metres. Measurement started from the waterside and finished on the shoreline (i.e., the point at which water and land have the same height). At every metre along the cross-sections, water depth and reed stem height were measured to the nearest centimetre and decimetre, respectively. The frontline was then surveyed as the location in the cross-section where the first reed stem occurs. Plotting the coordinates of shorelines and frontlines in every cross-section in QGIS allowed the aquatic reed extents to be calculated. Based on expert knowledge, one or two square sample plots of 1 square metre each were placed where differences in stem density were identified. The parameter's density (stems/m\({}^{2}\)), stem diameter, number of stems with and without shoots, and the number of green and dried (brown) stems were additionally recorded (Figure 4). Data collected from the square sample plots were used to verify the classification of point clouds. In addition, data from cross-sections (i.e., the height of reed stems) were used as independent data to validate the derived Digital Surface Models (DSM). Trimble Geo XT was the DGPS system implemented for measuring reference points. Post-processing was executed with the software Trimble GPS Pathfinder Office. The base station SOPAC Wettzell Daily with L2 and the Global Navigation Satellite System (GLONASS) capability was selected for differential correction. The coordinate reference system implemented was the Deutsches Hauptdreiecksnetz (DHDN) Gauss-Kruger Zone 4.  
Figure 3: Graphical representation of methods and statistics used for classifying aquatic reeds in point clouds obtained from Green-Light Detection and Ranging (LiDAR).
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### Approximated Distribution of the Total Power Gain  
The sum-capacity given by the first line of Equation (5) can be expressed in terms of the singular values \(\{\sigma_{k}\}\) of the channel matrix \(\mathbf{G}\) and, therefore, if \(\sigma_{1}\geq\sigma_{2}\geq\cdots\sigma_{K}\) are the random ordered singular values of \(\mathbf{G}\), then Equation (5) can be rewritten as [1]  
\[C=\mathbb{E}\left[\sum_{k=1}^{K}\log_{2}\left(1+\rho\sigma_{k}^{2}\right) \right]. \tag{13}\]  
By comparing Equations (5) and (13), it is possible to conclude that  
\[\sigma_{k}^{2}=\|\mathbf{g}_{k}\|^{2}=M\beta_{k}. \tag{14}\]  
The singular values represent the channel gains of the parallel channels after the singular value decomposition (SVD) of the system [32]. Next we present the squared Frobenius norm of \(\mathbf{G}\) when the environment offers favorable propagation, and consequently, \(\mathbf{G}\) is full rank  
\[\|\mathbf{G}\|_{F}^{2}=\sum_{m=1}^{M}\sum_{k=1}^{K}|g_{mk}|^{2}=\text{Tr}( \mathbf{G}^{H}\mathbf{G})=\sum_{k=1}^{K}\sigma_{k}^{2}. \tag{15}\]  
The last term of Equation (15) can be interpreted as the total power gain of the channel matrix if one spreads the energy equally between all the antennas [32]. Therefore, comparing Equations (14) and (15), we clearly see that the singular values have the same distribution as the Friis' free-space path-loss coefficients, \(\beta_{k},\forall k\). The summation of the \(K\) free-space path-loss coefficients, is a random variable that can be approximated by a Gamma distribution as shown in Appendix D.
# Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation
## 2 Methodology
### High-Resolution Parabolic Radon Transform with \(L_{q_{1}}-L_{q_{2}}\) Mixed Regularization
The dual variables and \(\mathbf{z}_{1},\mathbf{z}_{2},\mathbf{m}_{1},\mathbf{m}_{2}\) are alternatively updated as follows:  
\[\mathbf{z}_{1}^{k+1}=arg\underset{\mathbf{z}_{1}}{\min}(\beta\mu\|\mathbf{z}_{1}\|_{q_{1} }^{q_{1}}+\frac{\rho_{1}}{2}\bigg{\|}\mathbf{m}_{1}^{k}-\mathbf{z}_{1}+\frac{\mathbf{w}_{1 }^{k}}{\rho_{1}}\bigg{\|}_{2}^{2}) \tag{14}\]  
\[\mathbf{z}_{2}^{k+1}=arg\underset{\mathbf{z}_{2}}{\min}(\beta\|\mathbf{z}_{2}\|_{q_{2}}^{ q_{2}}+\frac{\rho_{2}}{2}\bigg{\|}\mathbf{m}_{2}^{k}-\mathbf{z}_{2}+\frac{\mathbf{w}_{2}^{k}}{ \rho_{2}}\bigg{\|}_{2}^{2}) \tag{15}\]  
\[\mathbf{m}_{1}^{k+1}=arg\underset{\mathbf{m}_{1}}{\min}\big{(}\big{\|}\mathbf{A}_{1}\mathbf{m }_{1}+\mathbf{A}_{2}\mathbf{m}_{2}^{k}-\mathbf{d}\big{\|}_{2}^{2}+\frac{\rho_{1}}{2}\bigg{\|} \mathbf{m}_{1}-\mathbf{z}_{1}^{k+1}+\frac{\mathbf{w}_{1}^{k}}{\rho_{1}}\bigg{\|}_{2}^{2}) \tag{16}\]  
\[\mathbf{m}_{2}^{k+1}=arg\underset{\mathbf{m}_{2}}{\min}\big{(}\big{\|}\mathbf{A}_{1}\mathbf{m }_{1}^{k+1}+\mathbf{A}_{2}\mathbf{m}_{2}-\mathbf{d}\big{\|}_{2}^{2}+\frac{\rho_{1}}{2} \bigg{\|}\mathbf{m}_{2}-\mathbf{z}_{2}^{k+1}+\frac{\mathbf{w}_{2}^{k}}{\rho_{2}}\bigg{\|}_ {2}^{2}) \tag{17}\]  
\[\min_{\mathbf{m}_{1}\mathbf{m}_{2}}\biggl{\{}\|\mathbf{A}_{1}\mathbf{m}_{1}+\mathbf{A}_{2}\mathbf{m} _{2}-\mathbf{d}\|_{2}^{2}+\beta\mu\|\mathbf{z}_{1}\|_{q_{1}}^{q_{1}}+\beta\|\mathbf{z}_{2} \|_{q_{2}}^{q_{2}}\biggr{\}} \tag{13}\]0  
\[\min_{\mathbf{m}_{1}\mathbf{m}_{2}}\biggl{\{}\|\mathbf{A}_{1}\mathbf{m}_{1}+\mathbf{A}_{2}\mathbf{m} _{2}-\mathbf{d}\|_{2}^{2}+\beta\mu\|\mathbf{z}_{1}\|_{q_{1}}^{q_{1}}+\beta\|\mathbf{z}_{2} \|_{q_{2}}^{q_{2}}\biggr{\}} \tag{13}\]1  
Due to the proximity operator of \(L_{q}\) regularization, \(prox_{q,\eta}(\mathbf{t})\) is defined as  
\[\min_{\mathbf{m}_{1}\mathbf{m}_{2}}\biggl{\{}\|\mathbf{A}_{1}\mathbf{m}_{1}+\mathbf{A}_{2}\mathbf{m} _{2}-\mathbf{d}\|_{2}^{2}+\beta\mu\|\mathbf{z}_{1}\|_{q_{1}}^{q_{1}}+\beta\|\mathbf{z}_{2} \|_{q_{2}}^{q_{2}}\biggr{\}} \tag{13}\]2  
where \(\eta>0\) is a penalty parameter. When \(0<q<1\), it can be updated as [41]  
\[prox_{q,\eta}(\mathbf{t})_{i}= \begin{cases}0,&|\mathbf{t}_{i}|<\tau\\ \{0,sign(t_{i})\beta\},&|\mathbf{t}_{i}|=\tau\\ sign(t_{i})z_{i},&|\mathbf{t}_{i}|>\tau\end{cases}  \tag{21}\]  
for \(i=1,\cdots,n\), where \(\beta=[2(1-q)/\eta]^{1/(2-q)}\) and \(\tau=\beta+q\beta^{q-1}/\eta\),\(z_{i}\) is the result of \(h(z)=qz^{q-1}+\eta z-\eta|t_{i}|=0\). The exact solutions of \(\mathbf{m}_{1}\) and \(\mathbf{m}_{2}\) denote  
\[\mathbf{m}_{1}^{k+1}=(2\mathbf{A}_{1}^{T}\mathbf{A}_{1}+\rho_{1}\bm
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Specific Attenuation Model for Rain, Fog, Snow, and Gases  
We provide a brief overview of the specific attenuation of electromagnetic wave signals caused by various weather conditions, including rain, fog, and snow, as well as atmospheric attenuation. This topic has been discussed in detail in our previous work [16].
# A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra
## 2 Materials and Methods  
### Simulated Spectra  
Vibrational spectra were simulated by overlapping several Gaussian functions. In particular, a basic simulated vibrational spectrum \(y_{R}\) was built, in the 750-1750 cm\({}^{-1}\) spectral range, by means of 15 component functions \(y_{i}\), as described by the following equation:  
\[y_{R}=\sum_{i=1}^{15}y_{i}=\sum_{i=1}^{15}A_{i}e^{-\frac{(x-x_{i0})}{2\sigma_{ i}^{2}}}\]  
where \(A_{i}\), \(x_{0i}\) and \(\sigma_{i}\) are the amplitude, wavenumber and broadening parameters, respectively, of each \(y_{i}\). The values assigned to such parameters are reported in Table S1. In particular, the relative intensity, spectral wavenumber and broadening of each \(y_{i}\) have been chosen in order to yield a \(y_{R}\) spectrum similar to a typical vibrational spectrum measured from a cellular sample. Such simulated function \(y_{R}\) can be considered to be a model of spectrum measured for a control-like sample. In addition, the \(A_{i}\) values corresponding to the peak centred at 785, 830, 1090 and 1580 cm\({}^{-1}\) have been decreased by 10% with respect to the corresponding values of the control-like spectrum, in order to simulate a basic exposed-like spectrum.  
Starting from the basic spectra, 25 different spectra were obtained for the two types of samples (control-like and exposed-like), by randomizing the values of \(A_{i}\) of each single peak in a \(\pm\)10% range of values reported in Table S1.  
Each of these 50 single simulated spectra was area normalized, i.e., the intensity value corresponding to each wavenumber value was divided by the total intensity of the spectrum. The basic Raman spectra were calculated by means of SigmaPlot software (version 12.5, Systat Software).
## 3 Joint Optimization of EE-SE
### Quadratic Transform
#### 3.2.2 Optimization of Phase-Shifting Values for Given Transmitting Precoding
In order to illustrate the necessity of jointly optimizing the transmit precoding matrix and the phase shift values for optimizing the system SE and EE, the results of the EE-SE trade-off is provided in a baseline case. In the baseline, the phase shift values at the RIS are given randomly and are not optimized. It can also be observed that under the same transmission power constraint \(P_{\Gamma}\) of the BS, the EE and SE results of the joint optimization are greater than those of the baseline, respectively. It indicates that jointly optimizing the transmit precoding matrix and the phase shift values is beneficial for improving the SE and EE of the system. Figure 4: Average EE-SE trade-offs under proposed algorithm and baseline, with \(a=0.5\). Figure 3: Average EE-SE trade-offs with different \(a\) under different transmit power budgets \(P_{\Gamma}\).
## 2 Historical Data
### Satellite Photography and Multispectral Imaging
Perhaps the best-known and most widely-used multispectral program, Landsat has been running since 1972, sending back nearly 40 years of continuous Earth observations [2, 35]. While originally designed for mapping, land use and natural resources applications, it is not an exaggeration to state that uses of Landsat imagery extend to all areas of polar and cryospheric research. A prime example is the _Satellite Image Atlas of Glaciers of the World_[36]. One of Landsat's greatest strengths is its long history and nearly continuous archive of missions designed to complement and continue each other. In addition, painstaking work has been put into ensuring a radiometrically-consistent dataset across missions [37]. Archival imagery and climate data record are freely available through [[http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)]([http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)). At significantly lower spatial resolution, the perhaps now inaptly named Advanced Very High Resolution Radiometer (AVHRR) was a NOAA multispectral sensor launched in 1978 with a ground resolution of 1.1 km and a swath width of 4000 km. AVHRR sensors have been on many NOAA satellites since and persist to this day. A variety of images and products are available through NOAA's CLASS ([[http://www.class.ncdc.noaa.gov/](http://www.class.ncdc.noaa.gov/)]([http://www.class.ncdc.noaa.gov/](http://www.class.ncdc.noaa.gov/))), NASA's ECHO REVERB ([[http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/)]([http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/))) and NSIDC (the National Snow and Ice Data Center, [[http://nsidc.org/](http://nsidc.org/)]([http://nsidc.org/](http://nsidc.org/))). AVHRR was improved upon by the MODIS sensors (Moderate-Resolution Imaging Spectroradiometer; 250 m to 1 km pixel size), which derive their scientific value from high temporal resolution and high quality radiometry. MODIS data have, for example, been used to measure daily, seasonal and yearly ice sheet albedo trends [38], to identify melt ponds on Arctic sea ice [39] and to track surface features to measure ice shelf velocities [40]. MODIS data has been stretched, stacked and combined into mosaics of Antarctica (MOA; [[http://nsidc.org/data/moa/](http://nsidc.org/data/moa/)]([http://nsidc.org/data/moa/](http://nsidc.org/data/moa/))) [41, 42] and Greenland (MOG; [[http://planet.sr.unh.edu/mog/](http://planet.sr.unh.edu/mog/)]([http://planet.sr.unh.edu/mog/](http://planet.sr.unh.edu/mog/))) [43], and data from multiple times provide multiple snapshots of these ice-covered continents and variables, such as grounding lines, coastline, ice edge and more (see Table 2). Although there are many multispectral imagers that are not listed here, those included in this article are those known to the authors to offer free and open data access.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 3 Preliminaries  
In this section, some necessary preliminary knowledge is introduced, including the OFDM signal model, the CAZAC sequences, and the FFT frequency estimation algorithm.
## 3 Neural Network-Based Bridge Maintenance Cost Prediction Framework
### The Fully-Connected ANN Model  
The fully-connected ANN is an information system constructed by theoretically abstracting, simplifying, and simulating the structure, function, and basic characteristics of the real neural network in the human brain [43]. The BP neural network, currently the most widely used fully-connected ANN, is a multi-layer feedforward neural network trained according to the error backpropagation algorithm. The transfer function used by the neurons of the BP neural network is usually a differentiable function that can realize any nonlinear mapping between the input and the output. Therefore, the BP neural network has a wide range of applications in pattern recognition, risk evaluation, intelligent prediction, etc. [44].  
The BP neural network has arbitrarily complex pattern classification capabilities and excellent multi-dimensional function mapping capabilities. The objective function of it is the square of the network error; the minimum value of the objective function is calculated through the gradient descent method [45]. The detailed calculation process is as follows:  
1. The connection weight value and threshold are initialized based on random values.
2. The output of each unit of the hidden layer and the output layer is calculated according to the parameters selected by the input mode and output mode. In this study, the _ReLU_[46] is used as the activation function of the hidden layer. The _Tanh_[47] is used as the activation function of the output layer. The _ReLU_ can be defined as \[f(x)=\max(0,x)\] (13) The _Tanh_ can be defined as \[f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\] (14)
3. The new connection weights and thresholds are calculated using the Equations (13)-(16). The modification of the neuron threshold: \[\theta_{k}(t+1)=\theta_{k}(t)+\eta_{t}\sigma_{k_{r}}(t=1,2,\ldots,p;k=1,2, \ldots,l)\] (15)\[\theta_{j}(t+1)=\theta_{j}(t)+\eta_{t}\sigma_{j},(t=1,2,\ldots,p;j=1,2,\ldots,m) \tag{16}\]  
where \(\theta_{j}\) and \(\theta_{k}\) are the threshold values of the \(j\)-th node in the hidden layer and the \(k\)-th node in the output layer, respectively; \(\sigma_{j}\) and \(\sigma_{k}\) are errors of the \(j\)-th node in the hidden layer and the \(k\)-th node in the output layer respectively. \(\eta_{t}\) is the learning rate of the \(t\)-th training iteration.  
The update formula of the weight is as follows:  
\[\omega_{jk}(t+1)=\omega_{jk}(t)+\Delta\omega_{jk}(t),(t=1,2,\ldots,p) \tag{17}\]  
\[\
u_{ij}(t+1)=\
u_{ij}(t)+\Delta\
u_{ij}(t),(t=1,2,\ldots,p) \tag{18}\]  
where \(t\) is the number of iterations in the training process; \(\
u_{ij}\) is the weight value updating from the input layer to the hidden layer; \(\omega_{jk}\) is the weight value updating from the hidden layer to the output layer.  
(4) Return to the second step to train the neural network and update the learning input mode continuously until the number of training times reaches the preset value. The basic flow of the above calculation process is shown in Figure 5:
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### The VULA and VULA Algorithm Comparison  
#### 4.7.1 The Measuring Range of Direction Finding  
According to the direction vector of VULA: \(A(\theta)=\left[\begin{array}{cccc}1&e^{iQ}&e^{i2Q}&\dots&e^{(N-1)/Q}\end{array} \right]^{\text{H}}\) (\(Q=\begin{array}{cccc}2\pi f_{a}\frac{vTsin\theta}{c}\end{array}\)), we learn that when the azimuth angle \(\theta_{1}=2\pi-\theta_{2}(\theta_{1}\
eq\theta_{2})\), \(Q(\theta_{1})=Q(\theta_{2})\). This will cause the fuzzy phenomenon of the VULA system. In other words, the measuring range of VULA is \(180^{\circ}\). On the other hand, each of the VULA direction vector in Equation (23) is unique. Thus, the measuring range of VULA is \(360^{\circ}\).