esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Methodology
#### 2.3.1 EC-LUE Model  
The model's input data also includes the vegetation index (NDVI), which describes the biophysical characteristics of the crops, and environmental-meteorological data (including PAR, T, and Td). The following is a detailed description of the model:  
\[\text{GPP}=\text{PAR}\times\text{fPAR}\times\varepsilon_{\text{max}}\times \text{min}(\text{T}_{\text{s}},\text{W}_{\text{s}}) \tag{1}\]  
\[\text{fPAR}=1.24\times\text{NDVI}-0.168 \tag{2}\]  
\[\text{T}_{\text{S}}=\frac{(\text{T}-\text{T}_{\text{min}})\times(\text{T}- \text{T}_{\text{max}})}{(\text{T}-\text{T}_{\text{min}})\times(\text{T}- \text{T}_{\text{max}})-(\text{T}-\text{T}_{\text{opt}})^{2}} \tag{3}\]  
\[W_{\text{S}}=\frac{\text{VPD}_{\text{max}}-\text{VPD}}{\text{VPD}_{\text{ max}}-\text{VPD}_{\text{min}}} \tag{4}\]  
where fPAR is the fraction of incident of the PAR intercepted by the crop, which has a suitable connection with the NDVI; \(\varepsilon_{\text{max}}\) (2.84 g C m\({}^{-2}\) MJ\({}^{-1}\) APAR) [43] represents the light use efficiency under optimum growing conditions; and \(\text{min}(\text{T}_{\text{s}},\text{W}_{\text{s}})\) represents the minimum value between \(\rm T_{s}\) and \(\rm W_{s}\). \(\rm T_{s}\) and \(\rm W_{s}\) indicate the limitation of the temperature and water to photosynthesis, varying between 0-1. The most stressed component determines the final limit in the model. For maize photosynthetic activity, the \(\rm T_{min}\), \(\rm T_{max}\), and \(\rm T_{opt}\) were adjusted to 0 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, and 23 \({}^{\circ}\)C, respectively, as the minimum, maximum, and optimal air temperatures [44]. When the temperature exceeds the minimum and maximum temperature range, \(\rm T_{s}\) is set to 0 [45]. The values of \(\rm VPD_{min}\) and \(\rm VPD_{max}\) are 650 Pa and 4300 Pa, respectively, and they reflect the minimum and maximum values of the VPD. If the VPD is greater than \(\rm VPD_{max}\), \(\rm W_{s}\) is set to 0, and if the VPD is less than \(\rm VPD_{min}\), it is set to 1 [46].
### Dedicated Short-Range Communication  
IEEE 802.11P is especially useful for VANET communications within a 1-km range. Even though the distance is shorter, it provides a variety of safety benefits. Hop-to-hop communication can be used to extend the communication range of a solitary DSRC. Multi-hop communication is not feasible when there is no vehicle within the DSRC range. As a result, most DSRCs have an on-board unit (OBU). In this situation, a fixed roadside unit (RSU) assists in communication between cars when communication between vehicles is impossible. In the United States, the Department of Transportation (DOT) provides a 75 MHz c band between 5.850 and 5.925 GHz radio frequency for DSRC transmission, which is combined with a 10 MHz bandwidth [2; 3].  
DSRC can enable communication for safety and non-safety applications, such as traffic congestion control, navigation, weather forecasting, cruise control, automatic tolling, emergency response system, infotainment, and entertainment for car users. Many automotive manufacturers have started work on the project, keeping safety and environmental standards in mind. To assist the driver in notifying internal and external factors, all modern automobiles are equipped with various sensors and actuators. This information can be shared via vehicle communication protocols, such as DSRC, and updated over the cloud to help other drivers with navigation, road accidents, and traffic congestion avoidance [4; 5].
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 2 Orbit Determination Method
### Model for Orbit Determination  
According to [PERSON]'s second law, kinematics equations for navigation satellites and LEO satellites can be established in an inertial coordinate system. By combining kinematics equations with the observation equations, the normal equations for the combined orbit errors can be set up to obtain the orbit parameters. Then, the orbit parameters should be compared with reference orbit parameters to determine whether it is necessary to perform iterative calculations of the normal equations and correct the initial state parameters. The calculating chart is shown in Figure 3.  
Figure 3: Calculating chart for the normal equations.
## 2 Materials and Methods
### Fuel Properties  
The main properties of the fuels are presented in Table 1, where the properties of the dual-fuel (D-F) mixture are calculated considering the H\({}_{2}\) mass fraction in the fuel. The lower heating value (LHV) of H\({}_{2}\) is 2.76 times higher compared to G100. Consequently, when 5% H\({}_{2}\) by mass is added to the G95H5 fuel mixture, the energy share of H\({}_{2}\) reaches 12.7%, and the fuel's LHV increases by 6.8%. This indicates that under the same engine load, the cyclic mass of fuel supplied is lower in the G95H5 case. Changes in the elemental composition of the fuel resulted in a 27.9% reduction in the carbon/hydrogen (C/H) ratio and an 8.8% increase in the stoichiometric air/fuel (A/F) mass ratio (which is higher than the increase in the LHV). Consequently, for the same engine load, maintaining a stoichiometric mixture (excess air ratio \(\lambda=1\)) with G95H5 required a greater throttle opening due to the increased air demand. The low density and high volumetric displacement of hydrogen in the cylinder further reduce the air intake, necessitating additional throttle opening to maintain \(\lambda=1\). During the WLTC, the required engine power does not reach the maximum specified in the engine design. When operating with G100, the throttle is partially opened, whereas with G95H5, there is room to open the throttle further. However, at maximum throttle opening, the volumetric displacement of hydrogen and the increased air demand for combustion with G95H5 resulted in a smaller cyclic fuel mass compared to G100, leading to a reduction in the engine's maximum brake torque. The significantly reduced C/H ratio of G95H5 suggests lower emissions of CO and CH, as well as reduced greenhouse gas CO\({}_{2}\) during the driving cycle.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 6 Detection and Mitigation
### Detection Mechanisms--Techniques
#### 6.1.8 Social Network Botnets  
Social network-based botnets (SnB) have become a major security issue in the past few years. Their incentives are based on sensitive information stealing, and perform complex communication procedures. Publicly available resources are highly vulnerable and provide an obfuscation layer in the C&C communication for botnets. The study [87] of this new malware method is essential to understand the actual challenges in detecting and mitigating social botnets. Social networks contain information such as sensitive and personal data of both registered and unregistered users. Moreover, it acts as a human-driven communication channel to share, talk and learn. This tool can be helpful in some respects, but it can also be destructive, e.g., propagating the influence of botnets [199].  
To understand the behaviour of social network botnets, [PERSON] and [PERSON] and [PERSON] detail [60] the design and implementation of a Social Network-based botnet called DR-SNBot. The paper presents the necessary framework to deploy a C&C channel on the Sina blog website with a nickname generation algorithm and divide-and-conquer strategy. Compared to [60; 166], it contains strong analysis on how to detect covert SnB in the real world. It is focused on the Stegobot and how to monitor host profile activity from a social network, and by extension, differentiate a normal profile from a Stegobot's one. Profiles are analysed by looking at their number of friends, likes and shares. A Stegobot has predictable patterns and communicates secret messages via carrier images, called \"stego images\", through the content sharing system from social networks. Their strategy is to study statistical correlation and build a classification algorithm using Machine Learning to identify malicious activities and suspicious accounts.
## 2 Materials and Methods  
### Data Acquisition  
In the data acquisition, two types of accelerometers were used. In the first data acquisition, measured by Seppala [20], accelerometer model LIS344 ALH (high performance 3-axis \(\pm 2/\pm 6\) g ultra-compact linear accelerometer from STMicroelectronics) was placed on the chest of test subjects (\(n\) = 7, 32 \(\pm\) 25 months) to measure chest vibrations, while they were sleeping (see Figure 2, sampled at 5 kHz). A consent letter was signed by the parents, giving permission to use their children's data. Strong, skin-friendly tape was used to secure the sensor in its position during the measurement. In addition, the sensor cable was secured with tape to minimize noise. As the measurements did not include ECG to exactly mark the peak of SI', this was done by human visual inspection.  
In the second data acquisition, we used a single-axis opto-mechanical accelerometer, which is designed to be used in magnetoencephalography (MEG) and in magnetic resonance imaging (MRI), described in [21; 22]. In addition, ECG was measured using 3 electrodes. Data was recorded from a group of university students (\(n\) = 9, 28 \(\pm\) 5 years old) and a consent letter was signed by each subject, giving permission to use their data. This group was asked to perform certain tasks during the measurements, i.e., Sitting, Breath Hold, Valsalva, and Deep Breathing. In addition to these tasks, the subjects were recorded at rest in a supine position. For both children/infants and adults, the accelerometer was placed on the lowest part of the sternum bone [11].  
We were interested in evaluating the performance of the proposed algorithms during different breathing tasks, designed to induce motion artefacts due to chest movement.  
* _Sitting_: subjects sat on a chair with no back support for two minutes. This position forces them to stabilize themselves and may induce motion artefacts.
* _Breath Hold_: subjects held their breath for 32 s and breathed normally for 88 s. This series of breathing patterns was repeated three times.
* _Valsalva_: subjects blew into a breathing tube for 30 s followed by a 30 s rest. The procedure was repeated three times.
* _Deep Breathing_: subjects inhaled for five seconds and exhaled for five seconds. This task was repeated six times.
###### Abstract  
This paper proposes an adaptive pre-distortion method for mitigating LED nonlinear distortion in Visible Light Communication (VLC)-OFDM systems. The inherent nonlinear characteristics of LEDs disrupt the orthogonality among OFDM subcarriers, causing signal distortion and performance degradation. To overcome this issue while minimizing computational complexity at the transmitter, we introduce a feedback-based nonlinear parameter estimation approach using the Least Squares Method (LSM) and Median Based Method (MBM). These estimated parameters are then fed back to the transmitter, enabling efficient adaptive pre-distortion based on the inverse function of the estimated nonlinear characteristics. This approach reduces computational costs at the transmitter compared to conventional methods requiring high-performance processing. Additionally, we incorporate Frequency Symbol Spreading (FSS) to further enhance robustness against channel impairments such as Rician fading by equalizing the Signal-to-Noise Ratio (SNR) across subcarriers. Simulation results under various channel conditions, including AWGN, Rician fading, and realistic multi-LED lighting scenarios, demonstrate a significant improvement in Bit Error Rate (BER) performance, validating both the effectiveness and practical advantages of the proposed approach.  
VLC; OFDM; LED; pre-distortion; nonlinearity Article
## 3 Results
### Characterization Part
#### 3.1.2 Structural Characterization of PIBMA-AMP DFMAC  
Comparative FTIR/ATR spectra of PIBMA, AMP and PIBMA-AMP are given in Figure 3.  
According to the FTIR/ATR spectrum of PIBMA in Figure 3, peaks at \(\
u\) = 1853 cm\({}^{-1}\) (C=O stretching-anhydride), 1770 cm\({}^{-1}\) (C=O stretching-cyclopentanone), 1474 cm\({}^{-1}\) (C=O bending-dimethyl), 1219 cm\({}^{-1}\) (-O-CH\({}_{3}\) stretching), 1075 cm\({}^{-1}\) (O=C-O-C=O stretching-anhydride) and 909 cm\({}^{-1}\) (-C-H bending) were observed.  
According to the FTIR/ATR spectrum of PIBMA-AMP in Figure 3, broad carboxylic acid peaks at \(\
u\) = 3284 cm\({}^{-1}\) (O-H stretching), alkane at \(\
u\) = 2967 cm\({}^{-1}\) (CH\({}_{3}\) (as)symmetric stretching), cyclobutane at \(\
u\) = 2933 cm\({}^{-1}\) (CH\({}_{2}\) symmetric stretching), a beta-lactam ring peak at \(\
u\) = 1778 cm\({}^{-1}\) (C=O stretching), a carboxylic acid peak at \(\
u\) = 1704 cm\({}^{-1}\) (C=O stretching), three substituted alkene peaks at \(\
u\)= 1649 cm\({}^{-1}\) (C=C str.), a 2\({}^{\circ}\) amide (of \(\beta\)-lactam) at \(\
u\) = 1649 cm\({}^{-1}\) (C=O stretching) and monosubstituted benzene at \(\
u\)= 1497 cm\({}^{-1}\) (C-C stretching) were observed.  
Figure 2: FTIR/ATR spectra of PMVEMA, AMP and PMVEMA-AMP.  
The characteristic symmetric and asymmetric C=O stretching vibration peaks of anhydride of PIBMA at 1853 and 1770 cm\({}^{-1}\) disappeared. The carbonyl group peak of the beta-lactam ring of AMP shifted from 1770 cm\({}^{-1}\) to 1778 cm\({}^{-1}\). After the PIBMA-AMP conjugation, new peaks at 1778 cm\({}^{-1}\) (-N-C=O) and 1649 cm\({}^{-1}\) (CO-N-H) due to amide formation can be seen in Figure 3.  
According to the \({}^{1}\)H NMR spectrum of PIBMA-AMP in Figure S1b, \({}^{1}\)H NMR (400 MHz, dsmso-d6) \(\lx@sectionsign\): 8.26 ppm (1H, N-H), 7.99 ppm (1H, N-H), 7.30-7.26 ppm (5H, C-H), 5.64 ppm (1H, C-H), 4.86 ppm (1H, C-H), 4.56 ppm (1H, C-H), 3.02 ppm (1H, C-H), 2.97 ppm (1H, C-H), 2.65 ppm (1H, C-H), 1.49 ppm (1H, C-H), 1.12 ppm (6H, CH\({}_{3}\)), 0.96 ppm (2H, CH\({}_{2}\)).
# Image Completion with Hybrid Interpolation in Tensor Representation
## 2 Preliminary
### Image Completion with Tucker Decomposition  
For the \(N\)-th order tensor \(\mathcal{Y}\in\mathbb{R}^{\mathbb{I}_{1}\times\mathbb{I}_{2}\times\ldots \times\mathbb{I}_{N}}\), the Tucker decomposition [58] with the ranks \(J=(J_{1},J_{2},\ldots,J_{N})\) can be formulated as  
\[\mathcal{Y}=\mathcal{G}\times_{1}\mathbf{U}^{(1)}\times_{2}\mathbf{U}^{(2)}\times_{3 }\ldots\times_{N}\mathbf{U}^{(N)}, \tag{3}\]  
where \(\mathcal{G}=[g_{j_{1},\ldots,j_{N}}]\in\mathbb{R}^{\mathbb{I}_{1}\times \mathbb{I}_{2}\times\ldots\times\mathbb{I}_{N}}\) for \(J_{n}\leq I_{n}\) is the core tensor, and \(\mathbf{U}^{(n)}=[\mathbf{u}_{1}^{(n)},\ldots,\mathbf{u}_{I_{n}}^{(n)}]=[u_{i_{n},j_{n}}] \in\mathbb{R}^{I_{n}\times I_{n}}\) for \(n=1,\ldots,N\) is the factor matrix capturing the features across the \(n\)-th mode of \(\mathcal{Y}\). The operator \(\times_{n}\) stands for the standard tensor-matrix contraction along the \(n\)-th mode, which is defined as  
\[\left[\mathcal{G}\times_{n}\mathbf{U}^{(n)}\right]_{j_{1},\ldots,j_{n-1},i_{n},j_ {n+1},\ldots,j_{N}}=\sum_{j_{n}=1}^{J_{n}}g_{j_{1},\ldots,j_{N}}u_{i_{n},j_{n}} ^{(n)}. \tag{4}\]  
The optimization problem for low-rank image completion with the Tucker decomposition can be formulated as follows:  
\[\min_{\mathcal{Z},\mathcal{G},\mathbf{U}^{(1)},\ldots,\mathbf{U}^{(N)}} \frac{1}{2}||\mathcal{Z}-\mathcal{Y}||_{F}^{2}+\Phi(\mathcal{G},\{\mathbf{U}^{(n)} \}),\quad\text{s.t.}\quad\mathcal{Z}_{\Omega}=\mathcal{M}_{\Omega},\ \mathcal{Z}_{\Omega}\geq\mathbf{0},\] \[\forall n,j_{n}:||\mathbf{u}_{j_{n}}^{(n)}||=1,\ j_{n}=1,\ldots,J_{n},\ n=1,\ldots N. \tag{5}\]  
where \(\mathcal{Y}\) is given by model (3), and \(\Phi(\cdot)\) is a penalty function that imposes the desired constraints onto the core tensor \(\mathcal{G}\) and the factor matrices \(\{\mathbf{U}^{(n)}\}\). The projection \(\mathcal{Z}_{\Omega}=\mathcal{M}_{\Omega}\) means that \(z_{i_{1},\ldots,i_{N}}\) is replaced with \(m_{i_{1},\ldots,i_{N}}\) if \(\omega_{i_{1},\ldots,i_{N}}=1\); otherwise, no changes. Assuming \(\forall n:J_{n}<I_{n}\), the tensor \(\mathcal{Y}\) in (3) has a low Tucker rank.  
Problem (5) can be solved by performing iterative updates with the Tucker decomposition in each step. The Tucker decomposition can be computed in many ways, depending on the constraints imposed on the estimated factors. If the nonnegativity constraints are used (as specified), any nonnegative least square (NNLS) solver can be applied in the alternating optimization scheme. Neglecting the computational cost of using an NNLS solver and the cost of computing the core \(\mathcal{G}\) in (3), the total computational complexity for approximating the solution to (5) in \(K\) iterations can be roughly estimated as \(\mathcal{O}(K\sum_{n=1}^{N}J_{n}\prod_{p=1}^{N}I_{p})\).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Stages** & **AST (U/L)** & **ALT (U/L)** & **CHOLESTEROL (mg/dL)** & **TG (mg/dL)** \\ \hline Baseline & 26.66 \(\pm\) 1.40 & 25.33 \(\pm\) 1.23 & 144.22 \(\pm\) 4.58 & 91.01 \(\pm\) 4.86 \\ \hline Exercise\({}_{\text{only}}\) & 29.22 \(\pm\) 0.98 \({}^{\text{a}}\) & 28.88 \(\pm\) 1.26 \({}^{\text{a}}\) & 138.66 \(\pm\) 5.28 & 67.77 \(\pm\) 5.32 \({}^{\text{a}}\) \\ \hline Exercise\({}_{+\text{supp}}\) & 24.77 \(\pm\) 0.52 \({}^{\text{x}}\) & 24.66 \(\pm\) 1.45 \({}^{\text{x}}\) & 136.33 \(\pm\) 5.29 & 68.55 \(\pm\) 5.02 \({}^{\text{a}}\) \\ \hline  & \(\eta_{p}\)\({}^{2}\): 0.33 & \(\eta_{p}\)\({}^{2}\): 0.32 & \(\eta_{p}\)\({}^{2}\): 0.02 & \(\eta_{p}\)\({}^{2}\): 0.65 \\ \hline \end{tabular} Comparison of other stages according to the Baseline stage; a: \(p<0.05\). Comparison of Exercise\({}_{\text{only}}\) and Exercise\({}_{+\text{supp}}\) phases; \(x\): \(p<0.05\); \(\eta_{p}\)\({}^{2}\): partial eta squared.  
\end{table} 
Table 1: Serum biochemistry levels. \begin{table}
\begin{tabular}{c c c c c} \hline
**Stages** & **Cu** & **Mg** & **Mn** & **Fe** & **Zn** \\ \hline
**Baseline** & 748.55 \(\pm\) 21.03 & 18,626.31 \(\pm\) 206.01 & 5.70 \(\pm\) 0.25 & 2883.90 \(\pm\) 123.88 & 1270.26 \(\pm\) 22.08 \\ \hline
**Exercise\({}_{\text{only}}\)** & 776.47 \(\pm\) 19.92 & 17,798.75 \({}^{\text{a}}\) & 4.85 \(\pm\) 0.24 & 1680.52 \(\pm\) 62.34 \({}^{\text{c}}\) & 885.84 \(\pm\) 24.97 \({}^{\text{c}}\) \\ \hline
**Exercise\({}_{+\text{supp}}\)** & 797.64 \(\pm\) 18.47 & 18,689.53 \(\pm\) 337.32 \({}^{\text{x}}\) & 4.69 \(\pm\) 0.26 & 1591.20 \(\pm\) 63.24 \({}^{\text{c}}\) & 1090.22 \(\pm\) 18.30 \({}^{\text{b}}\) \\ \hline  & \(\eta_{p}\)\({}^{2}\): 0.02 & \(\eta_{p}\)\({}^{2}\): 0.04 & \(\eta_{p}\)\({}^{2}\): 0.34 & \(\eta_{p}\)\({}^{2}\): 0.90 & \(\eta_{p}\)\({}^{2}\): 0.39 \\ \hline \end{tabular} Comparison of other stages according to the Baseline stage. a: \(p<0.05\); b: \(p<0.01\); c: \(p<0.001\). Comparison of Exercise\({}_{\text{only}}\) and Exercise\({}_{+\text{supp}}\) phases. x: \(p<0.05\); z: \(p<0.001\); \(\eta_{p}\)\({}^{2}\): partial eta squared.  
\end{table} 
Table 2: Serum mineral levels (ppb).
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 3 Proposed Method  
This section describes the features included and the proposed approach for PM2.5 value forecasting over 48 h. We collected temporal and spatial features to forecast the PM2.5 value. Temporal features were used for preliminary forecasting and then spatial features were used to improve the forecast.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 3 Finite Element Model  
### Finite Element Mesh and Boundary Condition  
In this study, the finite element software ABAQUS [31] was used. A three-dimensional finite element mesh was built as shown in Figure 2. The finite element mesh in this model had a length of 1200 mm, width of 990 mm, and depth of 750 mm. The square basement excavation (on plan) was the same as that in the centrifuge model test, with a length and width of \(300\times 300\) mm (equal to \(18\times 18\) m in the prototype). Preliminary calculations checked the influence of the mesh density (13,540-86,590 elements in a structured or unstructured mesh, respectively) and of the element type (triangle or quadrilateral elements with linear interpolation). The influence of these factors on the results has been shown to be small. However, for precise and quick convergence, 86,950 elements in a structured mesh with quadrilateral element type was adopted. Based on the principle of the excavation amount being equal to the square excavation amount recorded in the test, a circular excavation was conducted with a  
Figure 1: Layout of the proposed centrifuge model test [7].  
diameter of 339 mm (equal to 20.3 m in the prototype). The basement excavation depth was 150 mm, which is equal to 9 m in the prototype. The embedment depth of the diaphragm wall was 225 mm, which is equal to 13.5 m in the prototype. The sand and the diaphragm wall were simulated by an eight-node brick element, and the tunnel was simulated by a four-node shell element. To consider the friction boundary condition, pin supports were adopted both at the vertical sides and at the base of the model. Interface elements were adopted at the soil-diaphragm wall and at the soil-tunnel interfaces. Each interface element used is described by zero-thickness slip element, assigned with the Coulomb friction law. The friction coefficient (\(\mu\)) and limiting relative displacement (\(\gamma_{\mathrm{lim}}\)) at which slippage occurs are controlled by two input parameters for each slip element. The interface friction coefficient, \(\mu\), is derived from \(\mu=\mathrm{tan}\delta\), where \(\delta\) is the interface friction angle, which is taken as 20\({}^{\circ}\) (i.e., 2/3 of the critical friction angle of soil). The limiting displacement of 5 mm is assumed to achieve full mobilization of the interface friction. It is noted that the initial compaction due to air pressure was not taken into account.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 4 Use of USVs in Environmental Disasters with Contaminants
### Chemical Hazards
#### 4.1.1 Oil Spill Detection and Monitoring
[108] test two USVs, C-Enduro and C-Worker--capable of reaching approximately 3 knots and powered by using a combination of solar-, wind-, and diesel-powered engines--during ten days in PAM tasks.