esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Drylands cover about 40% of the world's land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people's lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010-2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models' performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (\(\psi\)), the mean absolute percentage difference (\(\left\{\psi|\right\}\)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R\({}^{2}\))).  
drylands; climate classification; GLDAS; FLDAS; machine learning; Google Earth Engine 2020, 12, 3973; doi:10.3390/rs1223973
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) has been recognized as a proxy of gross primary production (GPP) across various terrestrial biomes. However, the effects of low temperature on SIF and GPP among different plant function types (PFTs) have not yet been well-explored. To gain a better understanding of the relationship between SIF and GPP, we investigated the variation in the GPP/SIF ratio in response to low-temperature conditions using satellite and tower-based datasets. Based on the TROPOMI SIF product and FLUXCOM GPP data, we found that the SIF and GPP exhibited consistent seasonal and spatial patterns, while the GPP/SIF ratio differed for different PFTs. The GPP/SIF ratio for forest types was generally higher than 10 gC-d-1-mw-1.nm-sr, whereas the GPP/SIF ratio for grass and crop types was generally lower than 10 gC-d-1-mw-1.nm-sr. In addition, there were noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples that experienced low-temperature stress (below 10 \({}^{\circ}\)C, defined as group A) and those that grew under relatively warm conditions (above 10 \({}^{\circ}\)C throughout the year, defined as group B). The GPP/SIF ratio for group A generally exhibited a \"hump-shaped\" seasonal pattern, and that for group B showed a slightly \"bowl-shaped\" seasonal pattern, which means it is important to consider the effects of temperature on the SIF-GPP relationship. Through linear regression and correlation analysis, we demonstrate that there was a positive correlation between the GPP/SIF ratio and temperature for group A, with a wide temperature range including low-temperature conditions, indicating that, in this case, temperature affected the SIF-GPP relationship; however, for group B--with a temperature higher than 10 \({}^{\circ}\)C throughout the year--the GPP/SIF ratio was not consistently affected by temperature. The response of GPP/SIF to low temperature stress was confirmed by tower-based observations at a C3 cropland (C3 CRO) site and a boreal evergreen needleaf forest (BoENF) site. Although the relationship between the GPP/SIF ratio and temperature differed among PFTs, the GPP/SIF ratio decreased under low-temperature conditions for PFTs. Therefore, the GPP/SIF ratio was not constant and was largely influenced by low temperature for different PFTs, thus highlighting the importance of incorporating temperature into SIF-based GPP estimation.  
solar-induced chlorophyll fluorescence; gross primary production; low temperature; plant function type; satellite remote sensing; tower-based observation +
Footnote †: journal: remote sensing
## 2 Materials and Methods
### Data Processing Method
#### 2.2.2 PPP PWV Calculation Method  
The PPP PWV calculation method is as follows: (1) The Saastamoinen model is used to calculate the station \(ZHD\)[29]. (2) \(ZWD\) is stripped from the \(ZTD\) of real-time PPP inversion. (3) PPP PWV is calculated with \(ZWD\)[30].  
\[ZWD =ZTD-ZHD \tag{3}\] \[=ZTD-\frac{0.0022277.P}{1-0.00266\cos(2\varphi)-0.00028.H} \tag{4}\]  
\[PWV=\frac{10^{6}}{\left(K_{2}^{\prime}+K_{3}/T_{\rm m}\right)\cdot R_{V}\cdot \rho}\cdot ZWD\]  
where \(P\) denotes the atmospheric pressure, the unit is hPa; \(H\) denotes the geodetic height of the station; \(K_{2}^{\prime}\),\(K_{3}\), and \(R_{V}\) are constants, and their values are 16.48 K\(\cdot\)hPa\({}^{-1}\), (3.776 \(\pm\) 0.014) \(\times\) 105 K2\(\cdot\)hPa\({}^{-1}\), and 461 J\(\cdot\)(Kg\(\cdot\)K)\({}^{-1}\); \(\rho\) is the water vapor constant; and \(Tm\) is the atmospheric weighted temperature. Its mathematical expression is:  
\[Tm=\frac{\int_{H}^{+\infty}\frac{e}{T}dh}{\int_{H}^{+\infty}\frac{e}{T^{2}}dh} \tag{5}\]  
where \(e\) is water vapor pressure and \(T\) is absolute temperature, which were obtained from ERA5 data. \(dh\) is the increment of the vertical integration path. The horizontal resolution of ERA5 data is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). After calculating the \(Tm\) at the grid point, the Kriging interpolation method was used to interpolate the \(Tm\) at the station.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Combination of observation & IF \\ Observation & Elevation mask angle & 10\({}^{\circ}\) \\  & Stochastic model & Elevation weighting \\ \hline  & Phase wrapping & Correction \\  & Phase center variation & Igs14.atx \\ Error correction & Atmospheric loading & Leave out \\  & Tide correction & Solid tide, polar tide, and ocean tide \\  & Relativistic correction & Correction \\  & Tropospheric delay & Parameter estimation \\ \hline  & Tropospheric mapping & NMF \\ Parameter estimation & Site coordinates & Constant \\  & Station receiver clock error & White noise \\  & Ambiguity & Float ambiguity \\  & Filtering method & Extended Kalman filter \\ \hline \hline \end{tabular}
\end{table}
Table 2: PPP algorithm processing method.
# Mdpj _sciences_
## 1 Introduction  
With the rapid development of the vehicle industry, vehicles have become indispensable tools in modern life, significantly affecting people's quality of life. Maintenance is an essential component of a vehicle's life cycle. It is vital for ensuring the sustainable use and operation of a vehicle. Accurately predicting maintenance demands can significantly reduce costs and downtime, while improving safety and service life. However, current methods predict only part of the maintenance demands, indicating room for improvement. It is important to note that, in real-life scenarios, maintenance predictions are often clustered by the mileage of return trips, which can limit the practical application of even highly accurate prediction models.  
In the development of vehicle maintenance demand, vehicle mileage significantly impacts maintenance demands. Generally, increased mileage exacerbates the wear and aging of vehicle components. As mileage increases, frequently active components such as the engine, suspension, and braking systems are subjected to greater strain, fatigue, and potential damage. Additionally, long journeys may lead to increased fuel consumption, deterioration in lubricant quality, and related complications, thus increasing the maintenance risk factor. Consequently, vehicles require more frequent and varied maintenance as mileage increases.  
In recent years, artificial intelligence techniques have increasingly been employed for real-time fault diagnosis, predictive maintenance, and sensor data processing in vehicles. Studies such as those by [PERSON] et al. [1] and [PERSON] et al. [2] demonstrate the effectiveness of artificial intelligence for maintenance prediction using the deep learning and healthmonitoring of automotive suspensions, respectively. Moreover, [PERSON] et al. [3] highlighted the use of LSTM networks for estimating the remaining useful life of Li-ion batteries, while [PERSON] et al. [4] developed a real-time monitoring and aging detection algorithm for SiC-based automotive power drive systems, further emphasizing the pivotal role of artificial intelligence in vehicle technology advancements.  
Based on the aforementioned research background, this study proposes an artificial intelligence model that accounts for the impact of mileage on vehicle maintenance demand. The primary contributions of this study are as follows:  
* Given that mileage is not in the same vector space as maintenance projects, we propose a mileage representation tailored to maintenance demand prediction.
* To fully leverage the impact of key temporal information on maintenance demand prediction, we introduce a key temporal information learning module that combines LSTM with attention mechanism.
* To enhance the deep integration of mileage and maintenance projects, we propose an information fusion module based on gated unit.
* Extensive experiments with real-world data demonstrate the superior performance of our model compared to contemporary baselines.
# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 3 Critical Parameter Calculation of MUT Intersections
### The Calculation for the Width of the Median U-Turns at MUT Intersections  
The trajectory of vehicles within median U-turns can be approximated as a semicircular arc. [PERSON] et al. combine the circular curvature and the trajectory of vehicle turns to develop the Euler spiral curve for vehicle turning paths. The Euler spiral curve is the transition between a straight line and circular curve [42; 43]. It prevents drivers from turning suddenly when entering a median U-turn. This curve can be applied to the trajectory of vehicles turning around at the median U-turn. The formula for the Euler spiral curve can be described as:  
\[R_{s}L_{s}=A^{2}, \tag{21}\]  
where \(R_{s}\) is the radius of the Euler spiral curve, \(L_{s}\) is the distance between the vehicle and the beginning (or ending) of the Euler spiral curve, and \(A\) is the parameter of the Euler spiral curve.  
Figure 8 shows that the trajectory of the detoured vehicle is approximately a semicircle when the vehicle turns around. This trajectory is divided into three segments: the entering Euler spiral curve segment (orange line), the circular curve segment (blue line), and the exit Euler spiral curve segment (red line). The center of the entering and exit Euler spiral curve segments is at the intersection point of the perpendicular bisectors of the beginning and ending tangents.  
Figure 8: The width of the median U-turn.  
Figure 7: The minimum gap of the median U-turn.  
The width of the median U-turn can be calculated as follows:  
\[E\geq R_{s1}+R_{s1}-d_{L}. \tag{22}\]  
where \(R_{s1}\) is the radius of the exit Euler spiral curve segment. \(R_{s2}\) is the radius of the entering Euler spiral curve segment. \(d_{L}\) is the width of a single lane.
# Statistical and Clustering Analysis of Safety Profiles of FDA-Approved Protein Kinase Inhibitors
###### Abstract  
Protein kinases are vital regulators in cell biology, modifying protein functions through phosphorylation. Protein kinase inhibitors (PKIs), which impede these kinases' activities, have broadened their therapeutic scope from cancer treatment to autoimmune and viral diseases. Despite their clinical success, challenges such as drug resistance and tolerability of adverse events persist. This article used the Protein Kinase Inhibitor Database (PKIDB) and major pharmacovigilance databases to assess the adverse events (AEs) linked to FDA-approved protein kinase inhibitors. Methods were adopted to statistically identify outliers and construct a prototypical profile to generalize AE occurrences. A total of 66 inhibitors were analyzed, revealing substantial variability in the safety profiles. Cluster analysis identified distinct patterns of AEs based on kinase target classes, highlighting the importance of tailored therapeutic approaches. Statistical methods such as Tukey's fences were used to identify drugs with unusual safety profiles, offering insights into both frequent and rare severe reactions. Real-world evidence complements clinical trial data, capturing AEs that might not be fully identified pre-approval.  
pharmacovigilance; mechanism-based side effects; target-based drug design; drug-related side effects; target-specific safety profile; PKIDB; adverse effects; adverse events +
Footnote †: journal: Applied sciences
# Modeling and Control of an Articulated Multibody Aircraft
## 3 Control System Design
### Linear Quadratic Integral (LQI) Control  
Since the aim of the controller is to track a non-zero reference input, integral control is also added to improve the tracking performance of the LQR controller, hence the term linear quadratic integral (LQI) control. LQI is, therefore, an augmented version of the LQR controller that designs an optimal controller, using full state feedback to optimize the quadratic cost function. Additional states are introduced into the system as the integrals of the error \(e\), between the reference command and the actual output of the system. For the output equation in Equation (54), the LQI state feedback control law is given by  
\[\mathbf{u}=-\mathbf{K}[\mathbf{x};\mathbf{x}_{i}]=-\mathbf{K}\mathbf{z} \tag{60}\]  
where \(\mathbf{z}\) are the states of the augmented system and \(\mathbf{x}_{i}\) are the additional integral states. Upon introducing the integral states, the state-space representation of the augmented system becomes  
\[\begin{bmatrix}\hat{\mathbf{x}}\\ \hat{\mathbf{x}}_{i}\end{bmatrix}=\begin{bmatrix}\mathbf{A}&0\\ -\mathbf{C}&0\end{bmatrix}\begin{bmatrix}\hat{\mathbf{x}}\\ \hat{\mathbf{x}}_{i}\end{bmatrix}+\begin{bmatrix}\mathbf{B}\\ -\mathbf{D}\end{bmatrix}\mathbf{u} \tag{61}\]  
Equation (61) above implies that  
\[\begin{cases}\mathbf{z}&=\hat{\mathbf{A}}_{aug}\mathbf{z}+\hat{\mathbf{B}}_{ aug}\mathbf{u}\\ \mathbf{y}&\mathbf{C}\mathbf{x}+\mathbf{D}\mathbf{u}\end{cases} \tag{62}\]  
Hence, the general cost function for the augmented system is the given by  
\[\mathbf{J}=\int_{0}^{\infty}(\mathbf{z}^{\prime}\mathbf{Q}\mathbf{z}+\mathbf{ u}^{\prime}\mathbf{R}\mathbf{u})dt\]  
where \(\mathbf{z},\mathbf{u},\mathbf{Q}\), and \(\mathbf{R}\) represent the augmented state vector, control vector, state weight matrix and input weight matrix, respectively. For the augmented system, \(\mathbf{Q}\) and \(\mathbf{R}\) are design parameters that can be adjusted to meet the design requirements of the controller.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### Complexity Analysis
Obviously, the longer the codeword, that is, the larger the \(p\) value, the more the processing speed of the hardware is improved, thereby greatly improving the real-time performance of the communication system.
## 7 Conclusions
**Abbreviations**  
ANN Artificial Neural Network  
CEEMD Complementary ensemble empirical mode decomposition  
CNN Convolutional Neural Network  
DFA Detrended Fluctuation Analysis  
DT Detection Rate  
DWT Discrete Wavelet Transform  
EEG Electroencephalograph  
FFac Form Factor  
FF-ANN Feedforward Artificial Neural Network  
FFNN Feedforward Neural Network  
FFT Fast Fourier transform  
FInfo Fisher information  
FIR Finite Impulse Response  
FMQAS fuzzy measure-theoretic quantum approximation of an abstract system  
GMM Gaussian mixture model  
HComp Hjorth Parameters: Complexity  
HFD Higuchi Fractal Dimension  
HMob Hjorth Parameters: Mobility
## 5 Simulations and Discussions
### Performance Evaluation Considering Feedback Corrections  
To analyze the effect of feedback corrections, the following two MPC controllers are utilized for the trajectory tracking of the test road, as shown in Figure 8.  
* MPC controller without feedback corrections, setting \(k_{1}=0,k_{2}=0\), denoted as Controller V;
* MPC controller with feedback corrections, as proposed in this work, denoted as Controller VI.  
The simulated results are shown in Figure 10. The lateral track errors with/without feedback corrections are shown in Figure 10a. It can be seen that although both controllers can limit the lateral track error within 0.15 m, the tracking performance of Controller VI is obviously better than that of Controller V. The comparative simulation results on regularized ZMP, front wheel angle and sideslip envelope are shown in Figure 10b-d, respectively. It can be seen that the regularized ZMPs of both controllers coincide nicely with the LTR. The optimal front wheel angle inputs generated by both MPC controllers satisfy the actuator saturation limits. Both controllers are able to prevent excessive sideslips during trajectory tracking.  
It can also be seen from Figure 10a that the lateral tracking error of Controller VI is very similar to that of Controller V in the straight lane section, and the main deviations appear in the curve section. These differences are most probably caused by the model mismatch in the curve sections, as the small angle assumption for (1)-(3) may lose validity. Moreover, the equivalent tire cornering stiffness coefficient \(\tilde{C}_{\ast\ast}\) may differ from that at \(\alpha_{\ast}=0\), leading to parameter uncertainties. The integration of the feedback corrections module in the MPC controller is able to mitigate the tracking errors caused by these model mismatches and parameter uncertainties, leading to better performance of Controller VI  
Figure 9: Comparison of lateral track error and regularized ZMP. LTR, load transfer ratio.  
Thus, we can conclude that using feedback corrections can compensate, to some extent, the unmodeled dynamics and parameter uncertainties.
# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 1 Introduction
### Convolutional Neural Network (CNN  
CNNs are a type of artificial neural network usually used for classification and computer vision tasks. Therefore, CNNs are considered efficient tools for medical imaging classification. In addition to input and output layers, CNNs include three main types of layers: convolutional, pooling, and fully connected. The convolutional layer is the main part of the CNN; it incorporates input data, a filter, and a feature map. The pooling layer or down-sampling layer seeks to reduce the number of parameters in the input. In the fully connected layer, a neuron applies a linear transformation to the input vector through a weight matrix [19]. Figure 3 illustrates the general structure of the CNN, assuming an input image size is 28 by 28, and a target task is classifying images into one of 10 classes [20].  
The CNN model is affected by many factors, including the number of layers, layer parameters, and other hyperparameters of the model, such as the optimizer and loss function. The loss function is used to calculate the difference between the predicted value and the actual value. An optimizer is a function that modifies the weights and learning rate of the DL model to reduce the loss value and increase accuracy. With classification problems, especially when there are more than two classes, the categorical_crossentropy function is the best choice to calculate loss value [21]. Root mean square propagation (RMSProp), and adaptive moment estimation(Adam), are the most commonly used optimizers [22; 23].
# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 2 Materials and Methods
### Yield Prediction with Machine Learning
\[ME=\frac{\sum_{i=1}^{n}y_{i}-x_{i}}{n}, \tag{10}\]  
\[MAE=\frac{\sum_{i=1}^{n}\left|y_{i}-x_{i}\right|}{n}, \tag{11}\]  
\[MAPE=\frac{\sum_{i=1}^{n}\left|\frac{y_{i}-x_{i}}{y_{i}}\right|\times 100}{n}, \tag{12}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-x_{i}\right)^{2}}{n}}, \tag{13}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-x_{i}\right)^{2}}{\sum_{i=1}^{n}\left( y_{i}-\overline{y}\right)^{2}}, \tag{14}\]  
where \(MAE\) is the mean absolute errors, \(MAPE\) is the mean absolute percentage error, \(ME\) is the mean error, \(R^{2}\) is the \(R\)-squared, \(RMSE\) is the root mean square of errors, \(x\) is the measured yield value, \(y\) is the predicted yield value, \(n\) is the number of observations, \(i\) is the iteration of observations, and \(\overline{y}\) is the mean value of predicted yield. All procedures were carried out in \(R\) with an rminer package [61; 62]. The results show that random forests [63] provided the most accurate results. Therefore, random forests was used for further tuning and analysis (see Results section).