esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Diurnal Variation
#### 3.3.2 Gross Primary Productivity (GPP)  
Figure 9 compares the multi-year average diurnal cycle of simulated and observed GPP. The diurnal patterns are generally consistent, showing a distinct \"single-peak\" around noon (local time 12:00), aligning with the typical diurnal pattern of photosynthesis. During the daytime (06:00 to 18:00), simulated GPP is underestimated to varying degrees for vegetation types like EBF, DNF, CSH, and OSH. Conversely, the simulations tend to overestimate GPP for MF, SAV, and ENF. The simulations for GRA and WET show the closest agreement with the observations. Nighttime GPP is near zero in both the simulations and observations for most types.  
Figure 10 illustrates the diurnal cycle of GPP simulation bias across the seasons. Diurnal biases are larger in the spring and summer, potentially reaching 800 gC/m\({}^{2}\), whereas autumn and winter biases are mostly within 200 gC/m\({}^{2}\). For vegetation types like ENF, CSH, SAV, GRA, and WET, the simulated GPP is higher than observed in the early morning during the summer (positive bias), but the bias fluctuates complexly after noon, possibly due to photoinhibition or other physiological processes. In the winter, when vegetation activity is reduced, biases are relatively stable and smaller.  
Figure 8: Bias (simulation minus observation) of the multi-year average diurnal variation of ET flux in different vegetation types in spring, summer, autumn, and winter: (**a**) ENF; (**b**) EBF; (**c**) DNF; (**d**) DBF; (**e**) MF; (**f**) CSH; (**g**) OSH; (**h**) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO.  
Figure 10: Bias (simulation minus observation) of the multi-year average diurnal variation of GPP flux in different vegetation types in spring (yellow), summer (blue), autumn (green), and winter (red): (**a**) ENF; (**b**) EBF; (**c**) DNF; (**d**) DBF; (**e**) MF; (**f**) CSH; (**g**) OSH; (**h**) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO.  
Figure 9: Comparison between the observed and simulated GPP flux on a diurnal course in different vegetation types (multi-year average): (**a**) ENF; (**b**) EBF; (**c**) DNF\({}_{\rm i}\) (**d**) DBF; (**e**) MF\({}_{\rm i}\) (**f**) CSH; (**g**) OSH; (h) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO.
## 3 Proposed Method
### Learning the Fault Pattern and Calculating the Probability Based on Random Forests  
Given the class-assigned features, the following step is to handle fault patterns using a machine-learning algorithm. This method is based on the random forests method, which is a well-known ensemble learning model for classification. We first learn the fault pattern of the features. We use a 136-dimension input vector, \(X_{i}\), composed using extended descriptors and the information of each feature, such as its location and class, for a more precise description. The extended descriptors are computed by summing \(d_{x}\) and \(|d_{x}|\) separately for \(d_{y}<0\) and \(d_{y}\geq 0\). Similarly, \(\sum d_{y}\) and \(\sum|d_{y}|\) are split for \(d_{x}<0\) and \(d_{x}\geq 0\). Thus, the number of vector dimensions of the descriptors doubles to 128. After building the learning model by training the properties of the features, we can find the probability \(p_{i}\) that each feature of an inspected image will be classified as a fault, which is calculated using Equation (7). Because the classes of the features are extremely imbalanced, most features are classified as normal. That is, the probability that the features will be estimated as a fault is considerably less than the probability that they will be predicted as normal. Consequently, because it is difficult to detect a fault area using only the results of the random forests, we should consider the density in addition to the map of the probability of being predicted as a fault.
## 3 Results
### Yield Response of Wheat to Inoculation with MR-15  
A decrease in the number of fillers (3.3-2.6) was observed with reduced water field capacity (100-60%) (Figure 9). MR-15 inoculation significantly increased the number of fillers per plant (4-3.6) in both varieties. Without MR-15 application, the spike length was reduced by 8% and 11% under normal and stressful conditions. Inoculation with MR-15 increased the spike length by 9% in V1, but this improvement was non-significant in V2. Non-primed and hydro-primed control plants exhibited 2.3 and 2.6 spikes per plant, respectively. However, at 60% WFC, V1 showed a maximum of 3.6 spikes. MR-15  
Figure 8: Effect of MR-15 inoculation on various membrane permeability parameters ((**A**) Malondialdehydehyde, (**B**) hydrogen peroxide, (**C**) total soluble proteins, (**D**) total sugars) of two wheat varieties grown with or without drought stress (non-primed (NP), hydro-primed (HP), Sehar-06 (V1), GA-2002 (V2), 100% field capacity (Control), 60% field capacity (Stress)). Different letters denote a statistically significant difference.  
priming enhanced spike numbers, but this increase was non-significant compared to the control plants.  
Under a reduced water supply, both varieties showed a reduction in the 100-grain mass when MR-15 was not applied. The maximum 100-grain mass was achieved by V2 in non-primed (5.7 g) and hydro-primed plants (5.2 g) at 100% WFC. At 60% WFC, a significant reduction in the 100-grain mass was observed in both varieties. MR-15 significantly increased the 100-grain mass in both varieties under normal (46% and 12%) and stress (60%, 14%) conditions.  
Total grains per plant were significantly reduced by stress in both varieties (12% and 39%). MR-15 application exhibited a significant increase in total grains at 100% WFC (66%) and 60% WFC (64%). Overall, the total plant yield was significantly boosted by MR-15 application, increasing by up to 200% under normal conditions and 150% under stress conditions.
# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 2 Materials and Methods
### Convolution Layer
#### 2.3.2 Trunk Extraction Network Principle and Introduction  
In this research, the backbone extraction network selected is ResNet50, a residual network constructed by several blocks. The basic residual block is shown in Figure 5.  
Figure 5: Basic residual block.  
The basic residual network (Figure 5) shows that the structure can be split into the main area and the side area. First, the preprocessed graphite ore images is input as a feature matrix into the residual structure for feature extraction, it is designated as 256-d. With the next convolution layer, with the dimension of 1 \(\times\) 1 \(\times\) 64, the number of channels becomes 64; after the activation function ReLu, a 3 \(\times\) 3 \(\times\) 64 convolution layer is passed, and the number of channels remains unchanged at this time; in a 1 \(\times\) 1 \(\times\) 256 convolutional layer, at this time, the number of input channels becomes 256, and then adding the outputs of the main branch and sub-branch leads to obtain the output of the residual block. This can reduce the number of parameters, reduce the amount of calculation, and effectively improve the network's depth. The final output of this residual block is shown in Equation (1).  
\[H(x)=F(x)+x \tag{1}\]  
In Equation (1), \(x\) represents the input of the basic residual block. \(F(x)\) represents the network map before summation. The exact structure of ResNet50, composed of basic residual blocks, is shown in Table 2.  
As shown in Table 2, the primary step entails acquiring the input image via the 7 \(\times\) 7 \(\times\) 64 convolutional layers, Conv1, followed by the application of the 3 \(\times\) 3 max pool and a sequence of convolutional blocks from Conv2_x to Conv5_x. Subsequently, the final output is obtained through a 1 \(\times\) 1 Average pool, FC, and Softmax [23]. In comparison with other networks, ResNet50 evades the gradient disappearance and gradient explosion issues that arise during the deep learning process and resolves the network degradation problem prevalent in deep learning training processes. For this experiment, the 50-layer network architecture of the ResNet series was selected to yield superior results.
## 3 Results and Discussion
### Construction of the CDI
#### 3.1.2 Comparison of Temporal Variation of Different Drought Indices  
The characteristics of the temporal variation in the different drought indices from 1982 to 2020 (Figure 5) were highly similar, showing a decreasing trend followed by an increasing trend. The UB curve of the MK test shows that the trend of each index turned around 2000. All indices showed a decreasing trend, except for VHI, which showed a non-significant (\(p<0.05\)) increasing trend. This is probably because NEC is located in the middle and high latitudes, where the lack of heat resources is one of the factors limiting the growth and development of crops, and warming, combined with relatively sufficient moisture, instead promoted crop production. The scPDSI had the best correlation with SMCI (r = 0.81), followed by SPEI (r = 0.75) and the weakest correlation with VHI (r = 0.44). The scPDSI was less similar to the temporal variation characteristics of the CDI by VHI than SMCI, but still significantly correlated (r = 0.76, \(p<\) 0.01). The SPEI, VHI, SMCI, and scPDSI have been used to reflect the spatial and temporal variation of agricultural drought characteristics in NEC, and several studies are consistent with our findings [1; 50; 51; 52; 53; 54; 55]. [PERSON] et al. [51] found that the moment of trend shift of SPEI12 in NEC was around 2000, after which the drought trend eased, and the drought area showed a decreasing trend. After 2000, the annual precipitation in NEC increased, and the average annual temperature decreased, while it also showed a trend of greening and productivity growth [52; 56]. Because of the significant decrease in ET0, NEC has experienced significant climatic wetting, which could alleviate the agricultural drought crisis [55]. Ecological improvements from forest and cropland protection policies [56] and the seasonal dependence of the Atlantic sea surface temperature (SST) and atmospheric circulation [57] have improved climatic conditions in NEC, resulting in a decreasing trend of agricultural drought hazard in the last 20 years.  
Figure 5: Temporal variation of different drought indices in the growing season of crops in NEC from 1982 to 2020 (a–e).
## 5 Results and Discussion
### Epstein Square Ring Loss Calculation Results
#### 5.2.1 Epstein Square Ring Loss Cloud Map
Moreover, the error between the calculated value and the experimental value of the electromagnetic characteristics of the Epstein square ring of the FBA alloy is 8.12% at a frequency of 200 Hz and a range of 0.1-1.5 T. When the \(B_{\text{m}}\) is 0.1-0.9 T at a frequency of 400 Hz, the error between  
Figure 15: Comparison between the calculated and experimental values of the Epstein frame loss of the 0.10 mm UTGO silicon steel. the calculated value and the experimental value is relatively small, with a maximum error of 4.40%. When the \(B_{\rm m}\) is 1.0-1.3 T, the error is large, with a maximum error is 8.81%. It can be seen from the above results that the mathematical models of the electromagnetic properties adopted by different magnetic materials will be affected by nonlinearity and produce large errors. Especially under high-frequency and high-flux conditions, the smaller hysteresis loop further increases the influence of the nonlinearity. The established electromagnetic characteristics model can thus accurately calculate the magnetic field and the loss of the Epstein frame within the allowable error range.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 3 2D Permutation-Aided OTFS Secure Transmission
### Design Of 2D Permutation-Aided OTFS Secure Transmission
When the input signal \(s(t)\) passes through the wireless channel, it will be affected by the channel delay and Doppler shift, as follows:  
\[r(t)=\int\int h(\tau,\
u)s(t-\tau)e^{j2\pi\
u(t-\tau)}d\tau d\
u+w(t), \tag{12}\]  
where \(w(t)\) denotes the white Gaussian noise, \(h(\tau,\
u)=\sum_{p=1}^{L}h_{p}\delta\big{(}\tau-\tau_{p}\big{)}\delta\big{(} \
u-\
u_{p}\big{)}\). * _OTFS_ demodulation By using the Wigner transform, which obtains the mutual ambiguity function and then performs sampling, the received signal \(r(t)\) in the time domain is converted into the TF domain signal, denoted by \(Y[n,m]^{{}^{\prime}}\), as follows: \[Y[n,m]^{{}^{\prime}}=\int g_{rx}(t-\tau)e^{-j2\pi\
u(t-\tau)}r(t)dt\mid_{\tau= nT,\
u=m\triangle f},\] (13) where \(g_{rx}(t)\) is the reception pulse shape. Assuming that the transmit pulse \(g_{tx}(t)\) and the receive pulse \(g_{rx}(t)\) are ideal, we have \(\int e^{-j2\pi m\triangle f(t-nT)}g_{rx}(t-nT)g_{tx}(t)dt=\delta(m)\delta(n)\). Since the TF domain signal is multiplied by the conversion matrix before the Heisenberg transform, the corresponding inverse transformation must be performed after the Wigner transform to obtain the correct DD domain signal \(Y[n,m]\). Then, by using the SFFT, the signal \(Y[n,m]\) in the TF domain is transformed into the signal \(y[k,l]\) in the DD domain: \[y[k,l]=P_{SFFT}\Bigg{(}\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}Y[n,m]e^{-j2\pi\big{(}\frac{n}{N}-\frac{m}{M}\big{)}}\Bigg{)},\] (14) where \(k\in\{0,\cdots,N-1\}\) and \(l\in\{0,\cdots,M-1\}\). Further, \(P_{SFFT}(\cdot)\) denotes the SFFT transform after the FFT matrix and IFFT matrix de-permutate.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 3 The FireBIRD Main Payload
### Bi-spectral Sensors of TET-1 and BIROS
#### 3.1.4 Radiometric Processor of the BIROS Bi-Spectral IR Sensor
This improved correction is based on a variant of the principle of Entropy Maximization (MEM) and can reconstruct defects in images based on the knowledge of the so-called Point SpreadFunction (PSF) of the MIR and TIR sensor heads at least partially from the surroundings of the disturbance. This takes place without leaving larger artifacts at, e.g., edges with strong contrasts, as was partially still the case with the radiometric processor used in the TET-1 mission. In some BIROS data takes, this even applies to the improved reconstruction of hot spots.
# Triangular Mesh Surface Subdivision Based on Graph Neural Network
## 3 Mesh Subdivision Based on Graph Neural Network
### Graph Neural Network Structure  
The network structure consists of three parts: the static graph convolutional layer, dynamic graph convolutional layer, and fully connected layer, as shown in Figure 5. The static graph convolutional layer aggregates features from each node's neighbors and updates their features. Local patches covering a specific mesh area are generated for each mesh vertex and converted into the corresponding graph structure as input to the first static graph convolutional layer. Dynamic graph convolution is based on dynamic graph construction, and the KNN algorithm is used to calculate the neighbors of each node to dynamically generate the graph structure in the process of graph convolution. The KNN algorithm does not depend on the graph's topological connection relationship. Still, it dynamically calculates the k nodes closest to the current vertex according to the feature vector of each node. The dynamic graph does not depend on the original graph structure, so it connects non-adjacent graph nodes to aggregate the information of non-adjacent nodes. The dynamic graph connects the non-adjacent graph nodes, enriches the receptive domain of the graph nodes, and enables the overall network to capture the dependencies between non-adjacent nodes while learning the relationship between adjacent nodes. In this way, it learns the global features of the mesh by stacking. The fully connected layer is located at the end of the graph neural network and is used to predict a vertex displacement vector for each mesh vertex. The algorithm uses L2 loss to calculate the difference between the positions of each vertex of the prediction mesh and the fine mesh as a loss function.  
Figure 4: The algorithm flow of graph neural subdivision. It consists of an input coarse mesh, mesh upsampling, and extracting local patches based on each vertex. It is then further transformed into a graph representation, is fed to the graph neural network to obtain the vertex displacement vector corresponding to each vertex, and finally updates the position of each vertex to obtain a subdivided mesh once. Then, it will be subdivided multiple times until the termination condition is reached.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 5 Polarimetric Analysis  
Polarimetric mode provides valuable information on scene materials, geometry and the nature of the electromagnetic scattering mechanism involved. However, polarimetric images are often acquired at coarser resolutions. Up to now, polarimetry has been used for classification and analysis purpose, or in combination with interferometry, mainly trough coherence optimization for estimating different scattering phase centers in a given resolution cell, or for increasing the number of permanent scatterers selected to estimate deformations. Today, studies on benefits of polarimetry over urban areas have been limited, mainly by the lack of real polarimetric data at high resolution. Here, our data set includes two PolInSAR Tandem-X acquisitions in a stripmap 2 \(\times\) 3 m resolution. Thus, the dataset corresponds to four polarimetric images. Only one temporal baseline is available, and therefore, analysis is limited to first investigations. This study concerns first the benefit of polarimetry for repeat-pass interferometry, then the polarimetric analysis of buildings. One of the polarimetric image is represented in the Pauli basis in Figure 8 on the left, and one of the corresponding interferogram acquired in the repeat pass mode is shown on the right.
## 1 Introduction
This also concentrates on controlling the key characteristics, productivity, and enhancing the quality of resources for its community members [8]. Air quality and climate-change issues are essential research areas in smart cities. One research study [9] includes a viewpoint on the opportunities for highlighting urban air-quality management (UAQM) concerns by employing an intelligent urban model in 'Smart urban computing.' This research explains the responsibility of Intelligent urban computing inUAQM and offers a collaborative platform for smart urban computing environments for air pollutants processes. Smart cities enable AI, ML, and IoT-based systems such as embedded linked devices, IoT sensors, smart traffic-control systems, smart streetlamps, and smart roads to gather and investigate statistics. These urban areas utilize this gathered information to optimize transportation systems, utility services, facilities, and creatures that interact with different platforms such as smart buildings, smart health care systems, smart automobiles, smart agricultural production, and Industry 4.0. This work presents an ATM based on ML and IoT design and implementation. The key contributions include:  
1. The proposed ATM system utilizes the architecture and smart traffic signal to avoid congestion. 2. We introduce a completely deterministic adaptive technique for effective and close traffic monitoring and a congestion-control system at major regional intersections on any sequence of events. 3. One critical advantage of the proposed ATM structure is its ability to integrate with any adaptive method without requiring changes to the architectural style. The complete research article is organized into various sections. Section 2 discusses existing work and covers a comparative analysis. In Section 3, materials and methods are covered; in Section 4, simulation experimental results and comparison of the results are covered. In the last section, five conclusions and future work are discussed.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## Materials and Methods
### Multivariate Data Analysis
#### 2.5.2 Feature Wavelengths Extraction  
There reportedly exist certain redundant characteristic variables among hundreds of spectral wavebands, which might slow down the modeling speed and influence the model' performance [17; 21]. Therefore, three common variable selection algorithms, successive projections algorithm (SPA), principal component analysis loading (PCA loading), and random frog (RF), were applied to extract representative spectral variables in this study.  
SPA is a novel variable selection method that aims at eliminating the redundant information of raw spectral data to the biggest extent. As a forward selection algorithm, it is put forward to achieve the minimum of vector space collinearity by successive orthogonal projection [22; 23]. The number of eventual feature variables could be decided, according to the minimal root mean square error of validation (RMSEV) in multiple linear regression calibration.  
For PCA loading, the first few PCs contain a large proportion of spectral information, as previously mentioned in section of the exploratory classification analysis. During the transformation process, it is exactly the liner regression coefficient of original variables that plays the dominant role. Consequently, by loading as many and large proportion PCs as possible, the feature wavelengths with finger characteristics could be chosen without defect and omission [24].  
RF is also a frequently-used variable selection strategy. It is based on the idea of the reversible jump markov chain monte carlo method and model cluster analysis in the iterative manner. Besides, implementing RF must be attached to the specific model [25; 26]. In this study, the partial least square was decided by reason of strong capacity in handling correlated data. Concisely, the variable subset was initialized firstly, followed by generating a random number. Next, propose the candidate data set by choosing the nearest integer to a random number. Afterwards, confirm the variable subset by root mean square error of cross-validation and achieve the fixed number of iterations (which be set as 100). Lastly, the indicator of the importance of variables could be regarded as the final criterion by calculating selection probability of each variable.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 2 Theoretical Background  
Ultrasound refers to sound waves with frequencies higher than the upper audible limit of human hearing, typically above 20 kHz. These high-frequency waves propagate through a medium via mechanical vibrations of particles, transferring energy without net transport of the medium itself [24]. Ultrasound is extensively used for non-destructive testing (NDT) and imaging because of its ability to penetrate materials and reflect from interfaces where properties such as density or acoustic impedance change [8]. The fundamental parameters influencing ultrasound behavior include the following:  
* **Frequency \(f\) and Wavelength \(\lambda\):** Higher frequencies provide better resolution but suffer from greater attenuation. These quantities are linked by the relation \[\lambda=\frac{c}{f}\;,\] (1) where \(c\) is the sound speed in the considered medium.
* **Acoustic Impedance (\(Z\)):** Defined as \(Z=\rho c\), where \(\rho\) is the medium density. It determines the reflection and transmission of ultrasound at material boundaries.
* **Attenuation:** Loss of wave energy due to absorption, scattering, and other mechanisms.