esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Global Spatial and Temporal Variability of Soil Salinity
### China and East Asia  
The area of soil salinization within China's jurisdiction has shown a decreasing trend, whereas certain regions in Southeast Asia have exhibited an increasing trend. In the irrigation zone along the Tianshan Mountains in northern Xinjiang, the proportions of slight, moderate, and severe salinization significantly decreased after 10 years of continuous treatment ([PERSON], 2020). In western Jilin Province, the degree of salinization of saline soils from 1989 to 2019 was monitored for the first time using the EC inversion model ([PERSON], 2020). This study revealed that the total area of saline soils increased between 1989 and 1999, while a decline was observed during the implementation of government policies from 1999 to 2019. A substantial amount of saline soil was converted into grassland and farmland following improvements. Focusing on the Yellow River Delta, upscaling inversion and dynamic monitoring of soil salinization were achieved by establishing conversion coefficients of spectral indices between Landsat and MODIS images based on the upscaling conversion method proposed through land use zoning optimization ([PERSON], 2020). The monitoring results indicated that soil salinity in the Yellow River Delta gradually decreased from the coast to the inland, with salinity experiencing an initial increase followed by a decrease between 2000 and 2020. Mutual transformations occurred among varying degrees of saline soils, leading to an overall decreasing trend in salinization. In contrast, soil salinity levels in some regions of Southeast Asia, particularly southern Bangladesh, have shown an increasing trend. The number of areas affected by salinity has risen over the past few decades, which may be attributed to factors such as climate change, sea level rise, and inappropriate agricultural practices.
## 3 Controller Design
### Control of IPA System (Inner Loop)  
#### 3.1.1 The Fractional-Order PID (FOPID) Controller  
Industrial control systems commonly use a PID controller, which has been extended to include a fractional-order version called the fractional-order PID (FOPID) controller. The fractional PID was first introduced in 1994, and since then, it has achieved a significant position as a robot controller [29]. One of the most evident reasons for its widespread use is owing to its straightforward design and good performance, which includes a quick response with a low overshoot percentage even in the case of slow regulated plants [30]. The FOPID controller seeks to rectify the difference between the desired setpoint and a measured process variable by calculating and displaying a corrective action that can be used to change the process. The following is a description of the FOPID controller's transfer function [31]:  
\[\text{C2(s)}=\frac{\text{U(s)}}{\text{E(s)}}=\text{ }K_{\text{p}}+\frac{\text{K}_{ \text{i}}}{\text{S}^{\lambda}}+\text{K}_{\text{d}}\text{ }\text{S}^{\mu} \tag{11}\]  
The FOPID equation includes five unknown parameters, where \(\text{K}_{\text{p}}\) is the proportional gain, \(\text{K}_{\text{i}}\) is the integral gain, \(\text{K}_{\text{d}}\) is the derivative gain, \(\lambda\) is the fractional-order integral, and \(\mu\) is the fractional-order derivative. Figure 7 illustrates the structure of an FOPID controller.  
As illustrated in Figure 8, the FOPID controller is represented graphically by an order of integrator and a differentiator with an \(x\)-axis representing the integrator and a \(y\)-axis representing the differentiator. The values of \(\mu\) and \(\lambda\) determine how well the traditional P, PI, PD, and PID controllers can be derived from the FOPID controller.
In particular, the NCYC4007 yeast strain, characterized at the molecular level as a _Rlodotorula_ strain, has been highlighted for its high production of DHA and carotenoids (23% and 11% dried weight, respectively) when the commercial Sabouraud medium (SM) was used [9]. This strain has been successfully used for fish larvae nutrition [10]. Additionally, in SM, a marine yeast from the Southern Austral marine ecosystem of Chile (strain NCYC1146), characterized as a high producer of carotenoids, was isolated. However, the genetic identity at the molecular level of this strain must be determined. The strains NCYC4007 and NCYC1146 isolated from seawater seem to be promising candidates for the biotechnological production of DHA and carotenoid pigments. However, a crucial step to use them in that way needs to sort out the problem of the scaling the production using alternatives source of carbon with a lower price than commercial growing media (i.e., Sabouraud media). At the same time, the raising of the production of these substances to an industrial level needs to satisfy that higher economic, environmental and social sustainability can be reached. In bioprocesses, raw glycerol is an important material for the economic viability of diverse fermentation processes. It is employed as alternative source of carbon different from glucose, due to the abundance and low cost. Among these are the production of substances of microbial origins such as 1, 3-propanediol, citric acid, succinic acid, polyhydroxyalka-noates and rhamnids by bacteria [11]; DHA and EPA by _ThrustoMethylrids_[12; 13; 14; 15; 16; 17; 18]; and lipids and carotenoid pigments by yeast isolated from terrestrial ecosystem [16; 17; 18]. Yeast isolated from seawater such as _Yarrowia lipolytica_ has been cultivated using raw glycerol [19], although _R. marina_, a marine yeast member of the _Rlodotorula_ genus, has not been cultivated using this raw substrate [17]. Other _Rlodotorula_ members such as _R. glutinis_ and _R. toruloides_, have been cultivated using raw glycerol [14; 17]; however, they have not been isolated from seawater, and they require freshwater to grow. In the present study, the capability of the marine _Rlodotorula_ strain NCYC4007 and the marine yeast strain NCYC1146 to grow using raw glycerol in seawater and produce DHA and carotenoids was determined. To evaluate the presence of inhibitory substances in raw glycerol, both strains were grown in a medium with analytical grade glycerol and compared with the use of glucose as the main carbon source. The variables of the biomass production, DHA, total carotenoids and kinetic growth parameters were assessed. Subsequently, the fatty acid profile; carotenoid pigments; live microbial biomass and other metabolic parameters (glucose, phosphorus, cholesterol content and enzymatic activity of malate and lactate dehydrogenase) for both strains were characterized. Finally, the genetic characterization at the molecular level of the strain NCYC1146 was determined.
## 2 Phonation and Simulation Model
### Convective Background Flow Source  
We define the Helmholtz number \(\mathrm{He}=f\mathcal{L}/c\) (\(\mathcal{L}\) being a characteristic length scale) and the Mach number \(\mathrm{Ma}=\mathcal{U}/c\) (\(\mathcal{U}\) being a characteristic velocity scale, \(f\) the frequency, and \(c\) the speed of sound). Then, the partial time derivative in Equation (4) scales with the Helmholtz number and the convective operator in Equation (4) with the Mach number.  
Regarding the dimensional analysis of the terms, we investigate the contribution to the aeroacoustic source terms of the material derivative Equation (4). Dimensionless analysis of the right-hand side volume term  
\[\frac{\partial p^{\mathrm{ic}}}{\partial t}+\overline{v}\cdot\
abla p^{ \mathrm{ic}} \tag{8}\]  
in the time-harmonic form yields  
\[\sim-j\,2\pi\,\mathrm{He}\,\bar{p}+j\,\overline{\mathrm{Ma}}\cdot\
abla\bar{p}\,. \tag{9}\]  
With increasing frequency, the first term will play a dominant role and is localized between the VF and the fVF (will be shown in Section 3.2). The second term plays a role at higher Mach number and in regions where the flow pressure gradient is aligned or anti-aligned with the flow velocity. In the case of phonation the second term is large within the glottal gap (between the VFs) and is strongly modulated by the vocal fold motion. Neglecting the second term means that we introduce errors by neglecting sources inside the VF gap, especially at low frequencies where the first term has a minor impact.
## 3 Proposed Methods
### Model Configuration Strategies  
#### 3.2.1 Loss Function  
In the selection of the loss function, this paper combines the characteristics of the pose estimation task, assuming that the set of real poses of a human body is \(\{g_{1},g_{2}\ldots g_{n}\}\), where the parameter \(n\) is the batch size. The anchor pose is denoted as \(\{a_{1},a_{2}\ldots a_{m}\}\), where the parameter \(m\) indicates the number of anchor poses. The set of anchor pose labels is \(\{l_{1},l_{2}\ldots l_{n}\}\), the set of output results corresponding to the regression branch is \(\{y_{1},y_{2}\ldots y_{n}\}\), the set of output results corresponding to the classification branch is \(\{p_{1}^{i},p_{2}^{i},\cdots p_{m}^{i}\mid i\in[1,n]\}\), and the value corresponding to the regression amount is the difference between the true pose and the corresponding anchor pose \(\left\{g_{i}^{{}^{\prime}}=g_{i}-a_{l_{i}}\mid i\in[1,n]\right\}\). The loss function of the regression branch adopts the mean square error loss \(L_{\text{regression}}=\frac{1}{n}\sum_{i=1}^{n}\parallel y_{i}-g_{i}^{{}^{ \prime}}\parallel_{2}^{2}\), and the classification loss adopts the cross entropy loss \(L_{\text{crossentropy}}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=0}^{m}l_{i}n\left(p_{ j}^{i}\right)\), where \(y_{i}\) denotes the output value corresponding to the \(i\)th data regression branch, \(g_{i}^{{}^{\prime}}\) denotes the regression volume of the \(i\)th data, \(l_{i}\) indicates the label value of the \(i\)th data anchor pose, and \(p_{i}^{j}\) indicates the probability that the output of the classification branch of the \(i\)th data belongs to the \(j\)th anchor pose, as shown in Equation (7), which is the loss function used in this model.  
\[L=L_{\text{regression}}\times\theta_{\text{{loc}}}+L_{\text{crossentropy}} \tag{7}\]  
Among these metrics, the likelihood function of the regression branch obeys a Gaussian distribution with a mean value of the regression branch output and variance of \(\theta_{\text{{loc}}}\), where \(\theta_{\text{{loc}}}\) is a learnable parameter. In the process of model inference, the output result of the regression branch is used as the final output result of \(x_{i}=y_{i}+a_{l_{i}}\), where \(y_{i}\) denotes the output result of the regression branch and \(a_{
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### Satellite Imagery  
RapidEye and PlanetScope (PS) satellite imagery were used for the classifications from both the low (dry season) and high water (end of the rainy season) periods (Table 1). The RapidEye constellation is comprised of five multispectral satellites which acquire five bands from blue to near infrared wavelengths [58]. The orthorectified, atmospherically corrected imagery used here has a pixel size of 5 m. The PlanetScope constellation consists of more than 130 triple-CubeSats (i.e., miniature satellites) with new satellites launched into orbit every 3-4 months [59]. The Dove PS satellites within that constellation acquire four-band multispectral imagery from blue to near infrared wavelengths. Analytic surface reflectance Dove PS images orthorectified to a 3 m pixel size were used [60]. All imagery was downloaded from Planet Explorer ([[https://www.planet.com/explorer](https://www.planet.com/explorer)]([https://www.planet.com/explorer](https://www.planet.com/explorer)), accessed on 29 May 2021) and mosaicked in ENVI 5.5 (L3 Harris Geospatial, Melbourne, FL) prior to analysis (Figure 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Season** & **Dates** & **Constellation** & **No. Scenes** \\ \hline Low water, Pre-dam & May 31 and July 11, 2014 & RapidEye & 13 \\ High water, Pre-dam & February 9, 2018 & Dove PS & 8 \\ Low water, Post-dam & July 5, 7 and 8, 2020 & Dove PS & 24 \\ High water, Post-dam & January 11, 15, 31, February 13, 14, 17, 2020 & Dove PS & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite imagery used in the classifications.  
Figure 5: Flowchart summarizing the datasets and analyses used in the study. The section numbers are provided as reference for each input dataset, process and result.  
In addition, monthly cloud free basemap mosaics generated from the PlanetScope constellation for the period of December 2018 to February 2020 were downloaded for a 57.6 km\({}^{2}\) tile centered on the confluence of the Teles Pires river and the Rio Roquete Creek to illustrate the construction phase and subsequent seasonal change in flooding extent of one of the main small creek tributaries.
# A Hybrid Autoencoder Network for Unsupervised Image Clustering
## 3 Hybrid Autoencoder Network for Image Clustering
### Clustering Criteria  
In this paper, three clustering measures, ACC (unsupervised clustering accuracy), NMI (normalized mutual information), and ARI (adjusted rand index) [25], are used to determine the performance of the clustering results. The higher the clustering measures, the better the performance of clustering. The formulation of the clustering measures is described as follows:  
\[ACC=\max_{m}\frac{\sum_{i=1}^{n}1[l_{i}=m(c_{i})]}{n} \tag{8}\]  
where \(l_{i}\) denotes the ground-truth label, \(c_{i}\) is the cluster assignment produced by the clustering method, and \(m\) denotes all possible one-to-one mappings between clusters and labels:  
\[NMI(\Omega,C)=\frac{I(\Omega,C)}{0.5\times[H(\Omega)+H(C)]} \tag{9}\]  
where \(\Omega\) denotes the grand-truth labels, \(C\) denotes the cluster labels, \(I(\cdot)\) denotes the mutual information function, and \(H(\cdot)\) is the entropy function:  
\[ARI(X,Y)=\frac{2(n_{00}n_{11}-n_{01}n_{10})}{(n_{00}+n_{01})(n_{01}+n_{11})+( n_{00}+n_{10})(n_{10}+n_{11})} \tag{10}\]  
where \(n_{11}\) denotes that images \(i\) and \(j\) are assigned to the same cluster in both \(X\) and \(Y\); \(n_{00}\) denotes that images \(i\) and \(j\) are assigned to different clusters in both \(X\) and \(Y\); \(n_{10}\) denotes that images \(i\) and \(j\) are assigned to the same cluster in \(X\), but to different clusters in \(Y\); and \(n_{01}\) denotes that images \(i\) and \(j\) are assigned to different clusters in \(X\), but to the same cluster in \(Y\).
# A Deep Neural Network Model for Speaker Identification
## 2 Materials and Methods
### Background
#### 2.1.4 Convolutional Neural Network (CNN)  
According to previous research, CNNs possess strong adaptability and gradually have become the main research tool in the field of image and speech [43; 44]. In the study of speaker recognition, the spectrogram [45] gives a large amount of information including the personality characteristics of the speaker, and dynamically shows the characteristics of the signal spectrum change. Due to this feature of the spectrogram, it provides an effective method for researchers, thus feature vectors need to be obtained through the spectrogram. Although speech is a time-varying signal with complex correlations at a range of different timescales, the spectrogram provides a good solution. We use the spectrogram as the input of the CNNs, the spectrogram contains the identity information of the speaker and is a two-dimensional signal. At the same time, CNNs can provide translation invariance in time and space, so we can obtain the voiceprint features in the spectrogram space without destroying the time sequence. Therefore, this study proposes to use the spectrogram as the input of the convolutional neural network.  
The convolutional layer of CNN contains multiple feature maps (Feature Map) [46]. The convolution kernel is essentially a weight matrix, the input is locally filtered through the convolution kernel. The CNN layer can effectively extract the structural feature from spectrogram and reduce the complexity of the model through weight sharing.
## 3 Proposed Method
### Fsnd-Enn Principle
#### 3.1.3 Evolutionary Process  
The evolutionary process includes selection, crossover, and variation. Selection eliminates individuals that do not perform well. During the selection process, individuals in the population are randomly and repeatedly selected \(N\) times, and the genes of the selected individuals are used as genotypes that can be passed on to the next generation (the same genotype may be selected multiple times), where \(N\) is the number of individuals in the population. The probability that an individual is selected is determined by the proportion of its fitness value in the total fitness value of the population. The population size is \(M\), and the probability that individual \(i\) with fitness \(f_{i}\) is selected is  
\[P_{i}=\frac{f_{i}}{\sum_{i=1}^{M}f_{i}}. \tag{5}\]  
Obviously, the higher the fitness of an individual, the greater its probability of passing a gene to the next generation. The selected genotypes are used as individuals of the parent generation for the next step, crossover, and mutation.  
In the crossover process, parent individuals are traversed, and one is randomly selected to form \(N\) crossover combinations to generate offspring, where \(N\) is the parent population size. The crossover combination randomly generates a binary mask whose length is that of the individual. If the mask is 1 (0), the offspring will inherit the genetic information of parent 1 (2) in the crossover combination. During the mutation process, the genetic information of the offspring is coded with a small probability to select the position of an individual for variable transformation, 0 becomes 1, and 1 becomes 0. The offspring of these three genetic processes constitute the next-generation population, and genetic evolution cycles until the optimization criterion is satisfied. In Figure 5, a segment of genetic code of length 8 is used as an example. If the mask is 000101010, then bits 4, 6, and 7 in parent 1 and bits 1, 2, 3, 5, and 8 in parent 2 are selected to form the next generation of genes. If position 5 is selected as the variant, the final gene code of the offspring is 111011110. The black part of the offspring code in Figure 5 is inherited from parent 1, the white part is inherited from parent 2, and the red part is the variant.  
Figure 4: Gene translation.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Spatial Resolution  
The spatial resolution of an imaging system describes the size of the smallest discernible feature within an image. It is an important performance indicator that helps users determine if the imagery is suitable for a particular analysis [35; 36; 37].  
Two important metrics used when assessing spatial resolution are Ground Resolvable Distance (GRD) and Ground Sampling Distance (GSD). The GRD is the smallest distance between two spatially distinct point features such that they can be distinguished in the image. The GRD can be measured or computed in several ways [36]. The GSD is the ground projected distance from one spatial pixel to the next in the image, the pixel-to-pixel distance. Due to imperfect optics, the GRD may be larger than the GSD [36]. Thus, a smaller GSD in value does not guarantee that smaller features are resolvable. The GRD, however, considers all effects influencing the spatial resolution. The theoretical GSD for HYPSO-1 is discussed in more detail in [5].  
In this section, we estimate the spatial resolution in units of pixels by estimating the FWHM of the Line Spread Function (LSF). The estimated FWHM is used to quantify the GRD. In addition, using direct geo-referencing, the GRD is computed from the pixel level spatial resolution and compared to the simulated GSD from [5].
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 6 Complexity Considerations  
By lowering complexity and attaining equivalent performance to the DNN approach outlined in prior work [2], CNN-DPD achieves a significant improvement. Table 4 depicts the difficulty of creating a DPD model, which is mostly determined by the number of actual multipliers necessary, as multipliers use the majority of the hardware resources. MSA DPD (220 multiplications) is significantly less complicated than CPWL, as seen in Table 4. (880 multiplications). More complex versions can be constructed by increasing the memory depth \(Q\) and nonlinearity order \(K\) in the Volterra series. However, the computational  
Figure 5: EVM performance comparison (**a**) for CNN and No DPD case for flexible 5G NR waveforms. (**b**) 5G NR performance DPD efficacy in terms of EVM for proposed CNN DPD method vs GMP method and without DPD for varying RF input power.  
complexity must be considered. This means that while choosing the DPD model and its complexity, a reasonable trade-off between complexity and performance may be made.  
For a comprehensive review of CNN and Volterra techniques in the manner of complexity, Table 4 summarizes the complexities for the methods. CNN complexity is a challenging problem to solve, however as shown in this work, it can be reduced by employing a minimal number of \(N\) and \(K\).  
Indeed, with the higher modulation format and higher bandwidth similar to multiple LTE carriers or 5G new radio (NR) waveforms as discussed, they would lead to higher complexity of DPD operation due to stronger PAPR. Concomitantly, the elevation in bandwidth will lead to an overall increase in the base-band memory of the system model. Nevertheless, the evaluated models are still valid. However, higher values of the Q and K will be indispensable as compared to the considered case.  
In Table 5, the values of MSE and EVM @5 dBm are summarized for the proposed method.
## 4 Fault Identification Method Based on IPSO-WNN  
WNN is a type of neural network that uses an excitation function error back propagation and wavelet cardinality as the hidden layer node. This paper chooses wavelet neural network to realize fault identification because it fully combines the good time-frequency localization property of wavelet transform and the self-learning property of neural network.
## 4 Discussion
### Simulation of Forest NPP  
In the study area, northern Chinese larch plantation covered the largest area, so in the study of forest NPP, most of the research objects were northern Chinese larch. There were many factors affecting forest NPP, such as age, initial planting density, stand structure and so on. [PERSON] et al. [42] found that the productivity of northern Chinese larch was different at different ages, with a minimum of 3.22 m\({}^{3}\) h\({}^{-2}\) a\({}^{-1}\) and a maximum of 5.72 m\({}^{3}\) h\({}^{-2}\) a\({}^{-1}\). [PERSON] et al. [43] found that the annual average net productivity of coniferous and broad-leaved mixed forests in different site types was different. The lowest value appeared in the forest types with poor site and coniferous broad-leaved ratio of 2:1, which was 4.36 t h\({}^{-2}\) a\({}^{-1}\), and the maximum value appeared in the forest types with better site and coniferous broad-leaved ratio of 1:2, which was 9.58 t h\({}^{-2}\) a\({}^{-1}\). The result of this study was that the NPP of the forest in the study area was 564.71 g C m\({}^{-2}\) a\({}^{-1}\), which was consistent with the above results.  
NPP simulation results of coniferous forest, broad-leaved forest and coniferous broad-leaved mixed forest by different researchers using different models were summarized, as shown in Table 4. The NPP range of coniferous forest simulated by different researchers was 345-585 g C m\({}^{-2}\) a\({}^{-1}\), the broad-leaved forest was 624-870 g C m\({}^{-2}\) a\({}^{-1}\), and the coniferous and broad-leaved mixed forest was 423-928 g C m\({}^{-2}\) a\({}^{-1}\). The simulation results of this study were within the above ranges. The simulation of vegetation NPP can be affected by tree species composition, different years, geographical location, model adoption, data sources and simulation accuracy.