esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Problem
### Optimization Methods
#### 4.3.2 Subproblem Approximation Method  
The subproblem approximation method is described as an advanced zero-order iteration method, which requires the values of the dependent variables but not their derivatives [29]. As this method is based on approximation of the objective function and all state variables, a certain number of design sets is required. This initial data can be generated by user using one of the available optimization tool. Otherwise they are generated randomly by program. Dependent variables like objective function and state variables are approximated by means of last square fitting. The objective function is in a quadratic form as given by Equation (2):  
\[\hat{f}=a+\sum_{i}^{n}b_{i}\mathbf{x}_{i}+\sum_{i}^{n}c_{i}\mathbf{x}_{i}^{2}+\sum_{i }^{n}\sum_{j}^{n}d_{ij}\mathbf{x}_{i}\mathbf{x}_{j} \tag{2}\]  
where \(\hat{f}\) is approximated objective function, \(a\), \(b\), \(c\) and \(d\) are weighted coefficients of last squares technique, \(\mathbf{x}\) represents design variables vector, \(n\) is the number of iterations, \(i\) stays for the variable number and \(j\) is number of loops. In the next step, a constrain problem is transform to an unconstrained problem by means of the penalty functions. The obtained objective function that is minimize is given by Equation (3):  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**State Variables**} & \multicolumn{2}{c}{**Dynamic Stress**} & \multicolumn{2}{c}{**Safety Factor**} \\  & \(\mathbf{\Delta\sigma}\) **[MPa]** & **k = max (\(\mathbf{\sigma_{\text{vm}}}\))/Re * \\ \hline Constrains & \(<\)360 & \(\geq\)2.0 \\ \hline \end{tabular}  
* max (\(\sigma_{\text{vm}}\)) is the maximum von [PERSON] stress.  
\end{table}
Table 4: State variables.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Design Variables**} & \multirow{2}{*}{\(\mathbf{x1}\)} & \multirow{2}{*}{\(\mathbf{x2}\)} & \multirow{2}{*}{\(\mathbf{x3}\)} & \multirow{2}{*}{\(\mathbf{x4}\)} & \multirow{2}{*}{\(\mathbf{x5}\)} & \multirow{2}{*}{\(\mathbf{x6}\)} & \multirow{2}{*}{\(\mathbf{x7}\)} & \multirow{2}{*}{\(\mathbf{x8}\)} \\ \hline \multirow{2}{*}{Constrains} & 145\(<\) & 110\(<\) & 105\(<\) & 105\(<\) & 92\(<\) & 5\(<\) & 5\(<\) & 1\({}^{\circ}\)\(<\) \\  & \(<\)175 & \(<\)135 & \(<\)175 & \(<\)105 & \(<\)10 & \(<\)10 & \(<\)20\({}^{\circ}\) \\ \hline \end{tabular}  
\end{table}
Table 3: Design variables (in mm).  
\[Q(\mathbf{x},\;p_{k})=f+f_{0}p_{k}\!\!\left(\sum_{i}^{n}X(\mathbf{x}_{i})+\sum_{i}^{m_{1}}G (\hat{g}_{i})+\sum_{i}^{m_{2}}H\!\left(\hat{h}_{i}\right)+\sum_{i}^{m_{3}}W(\hat {w}_{i})\right) \tag{3}\]  
where \(X\) is the penalty function for design variable constrains, \(G\), \(H\) and \(W\) are penalty functions referred to state variables, \(f_{0}\) is the reference objective function and \(p_{k}\) is penalty parameter.
## 4 Results and Discussion
In contrast, the hybrid model shows residuals that are more evenly distributed around zero (green color sector), indicating better stability and accuracy in capturing fluctuations in engine operation. Figure 9(b) presents the mean of the residuals, illustrating the extent of underestimation and overestimation for each model. The single-architecture models exhibited larger discrepancies between underestimation and overestimation compared to the hybrid model. Among them, the TCN-LSTM model had the smallest difference (34.7827) and the highest prediction accuracy, further demonstrating its effectiveness in minimizing prediction errors. Figure 11 illustrates the regression accuracy of each prediction model, with adjusted R\({}^{2}\) values exceeding 0.9 for all models--indicating generally high prediction accuracy. However, distinct deviations appear in certain sections, likely due to correlations between model structure and data characteristics or suboptimal parameter settings, leading to errors. These deviations were particularly evident during transitional engine states, such as sudden changes in rpm or torque, where the input data distributions shift rapidly. Such conditions are often underrepresented in the training dataset, which may lead to localized prediction biases. In addition, structure characteristics of the hybrid model--such as the temporal sensitivity of LSTM and the local responsiveness of TCN--may contribute to deviations in specific intervals. To address these limitations, future work will involve augmenting the training database with more samples of dynamic operating conditions and exploring  
Figure 9: The evaluation results of each prediction models. Among the single-architecture models, the CNN model demonstrated more consistent deviations, except in sections where all models exhibited large errors. Unlike LSTM and TCN, it did not show a wide range of errors, suggesting greater robustness. In contrast, LSTM and TCN models exhibited high variance even in high-density data sections (400-800), implying that they lack sufficient explanatory power when used individually for CO2 emission predictions. However, the hybrid model exhibited uniformly distributed deviations across all sections, including in sparsely distributed data ranges (0-200), with the exception of common error-prone regions. This suggests that the hybrid model effectively mitigated the weaknesses of the individual architectures. Notably, the TCN-LSTM hybrid model achieved the highest adjusted R\({}^{2}\) value (0.9779), confirming its superior performance in accurately predicting CO2 emissions based solely on engine operation data.
## 1 Introduction  
Urbanization, driven by economic development and labor migration, has dramatically transformed cityscapes worldwide. Cities such as Wuhan in China have expanded both horizontally and vertically, resulting in diverse skylines and complex urban layouts [1]. This vertical dimension, or Vertical Urban Growth (VUG), is increasingly vital as cities face pressures from population growth and land scarcity. However, it presentsunique monitoring challenges compared to horizontal expansion. Urban growth patterns vary worldwide [2]. For example, Indian cities tend to sprawl horizontally [3], while coastal metropolises in China and South Korea expand vertically, showcasing a range of building heights [4]. This diversity is not confined to international borders; it exists even within countries [3].  
Urban expansion has often been described as the outward expansion of urban areas into surrounding undeveloped areas. Remote sensing technologies, such as satellite and airborne systems, have been extensively used to monitor horizontal urban growth and sprawl [5]. Techniques like object-based image analysis [6], machine learning algorithms [7], and change detection [8] have significantly advanced our understanding of urban expansion [9]. However, these predominantly 2D analyses fail to capture the vertical dimension of urban growth, which is critical in densely populated cities like Wuhan, thus leading to a partial understanding of urban dynamics [10]. VUG monitoring is essential for managing infrastructure strain, subsurface geological risks like subsidence, and other environmental impacts associated with increased urban density.
## 2 Methods  
### The MPT System Model  
Figure 1 illustrates a sketch map of a traditional MPT system including sensors and mud pumps [30]. The measurements in such a system are usually carried out by instruments installed in the drill collar, all of which is controlled by electric machines. The generated information signals are subsequently transmitted to the earth's surface, where they are analyzed by a signal processing unit at the ground [31].  
Considering that the drilling speed is usually much slower than the data transmission speed, the MPT system can be considered a linear time-invariant system, and the system model is  
\[r(t)=h(t)*x(t)+n(t) \tag{1}\]  
where \(*\) represents convolution, \(r(t)\) is the output signal, \(h(t)\) represents the impulse response of the mud channel, \(x(t)\) is the original input signal, and \(n(t)\) is the noise during transmission. The goal of channel estimation is to estimate \(h(t)\) as accurately as possible.  
Due to the multipath effect in the transmission of mud signals, the received signals may suffer from ISI and frequency-selective fading.  
Figure 1: Sketch map of an MPT system.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 2 Background
### Spatio-Temporal Data and the Automatic Identification System (AIS)
#### 2.1.1 Data Fitness
In previous studies [26], the data compilation approach for acquiring information relevant to the vessel population was based on the generation of vessel position'snapshots' in a specified time window. As the duration of this window increases, the amount of information gained in terms of distinct vessels, newly detected by the available sensors, tends to decrease. On the other hand, coastal reception from the terrestrial receivers is only possible if a vessel is within the line of sight or approximately 40 nautical miles in ideal conditions, which is affected by bad weather and other conditions [27]. Besides its limitations, AIS remains the go-to source of data for understanding maritime activities and for this several works have focused on identifying the issues [27; 28; 29] and developing techniques that improve the existing AIS by offering better tracking accuracies and guarantees. **Definition 1** (Incomplete Trajectory). : _Given the sparse spatial data \(p_{0}(x_{0},y_{0},t_{0})\), \(p_{1}(x_{1},y_{1},t_{1})\), \(p_{3}(x_{3},y_{3},t_{3})\), \(p_{5}(x_{5},y_{5},t_{5})\), \(p_{7}(x_{7},y_{7},t_{7})\), \(p_{8}(x_{8},y_{8},t_{8})\), \(p_{15}(x_{15},y_{15},t_{15})\) of a moving ship, consisting of its time stamped locations, the resulting trajectory can be defined as:_  
\[\sum traj_{2}=p_{0}p_{1},p_{1}p3,p_{3}p_{5},p_{5}p_{7},p_{7}p8,\underline{p_{8 }p_{15}}\]  
_It should be noted that \(traj_{1}\
eq traj_{2}\)._  
Such inaccuracies of sparse trajectories, can easily lead to faulty analysis and conclusions regarding vessel movement, if not dealt with appropriately. In the following sections, two techniques for improving the quality of sparse trajectories are described.
## 5 Sensor Signal Feature Extraction
### Fractal Analysis Signal Features Extraction  
An innovative signal processing approach based on fractal analysis was applied to carry out feature extraction from the segmented thrust force and torque signals.  
Fractals were introduced by the mathematician [PERSON] to describe the length of Britain's coastline [30]. Nowadays, a fractal is seen as an object which owns a self-affine pattern or singularity. Several applications of fractal analysis were presented in the literature. In metrology, fractal analysis has been investigated and effectively applied to define the roughness of surfaces [31]. As a matter of fact, mathematicians found that rough surfaces have a self-affine behaviour and used the fractal dimension to estimate its roughness [32]. On the other hand, only few research articles show interest in applying this analysis technique in machining, such as for the analysis of sensor signals detected during machining process monitoring.  
In this research work, the objective of fractal analysis is to quantify the changes in complexity and shape of the drilling sensor monitoring signals along the tool life. Using regularization fractal analysis, based on convolutions of sensor signals with kernels of different types (rectangular kernel and Gaussian kernel), two sets of curves were built [33]. Two examples of regularization analysis graphs obtained using the rectangular kernel and the Gaussian kernel are displayed in Figure 16 where the different colours refer to the different hole numbers. It can be observed that the regularization curves are influenced by the tool wear level (Figure 16a): they become increasingly bumpy, with higher and more marked arches as the hole number grows.  
In order to adapt the analysis to the machining process under study, the regions to extract the fractal parameters were selected based on the tool rotational speed. Two regions, Region 1 and Region 2, were identified in the regularization analysis graphs for the rectangular kernel (Figure 16a) and a third region, Region 3, for the Gaussian kernel (Figure 16b). The boundaries of Regions 1 and 3 were set so as to keep low \(a\) values and hold a sufficient number of points for linear regression to extract the slope from the graph curve. The boundaries of region 2 were positioned in order to extract information from the graph arches.  
For each region, three fractal parameters were extracted: the slope, \(D\), which represents the fractal dimension quantifying the signal complexity; the \(y\)-intercept, \(G\), quantifying the signal ruggedness; and the \(R\)-square, \(R^{2}\), quantifying the auto-scale regularity in the selected region.  
Figure 15: Frequency peaks of the thrust force vs. hole number (6000 rpm–0.15 mm/rev) [27].
## 2 Materials and Methods
### Establishment of the Femoral Finite Element Model  
Four groups of femoral micro images were respectively imported into the MIMICS software to reconstruct four geometric femoral models, and then the four geometric models were optimized and respectively imported into the ABAQUS software to establish four femoral finite element (FE) models using the C3D8 element, as shown in Figure 1b [17]. Since the apparent fracture of the rat femur under three-point bending load occurred in the middle cortical bone region, as shown in Figure 2, the fracture simulation object in this study is the cortical bone structure. To simulate the experimental boundary conditions, rigid columns were established above and below the femur model, where the relative positions between the three rigid columns and the femur were consistent with the experiment. The interaction between the upper rigid column and the femur was set to frictionless contact, and the interaction between the lower rigid column and the femur was set to TIE contact [18]. The compressive displacement load was applied to the upper rigid column while constraining all the degrees of freedom of the lower rigid columns to achieve the bending condition. Because the static FE analysis was conducted in this simulation, the compressive displacement was the static load, and the maximum displacement load was set to 1 mm. The schematic diagram of the boundary condition in the simulation can be seen in Figure 1b.  
The elastic moduli of the cortical bone tissue in the three-month-old Wistar rat femur have been measured by our previous nanoindentation test, where the average value of the transverse elastic modulus is 8769 MPa and the longitudinal elastic modulus is 10,383 MPa [19]. The Poisson's ratio was set to 0.3 according to the literature [20]. Meanwhile, the critical failure strain and critical energy release rate in the three-month-old Wistar rat femoral cortical bone material had also been obtained from previous studies, where the critical failure strain in compression was 4.35%, in tension was 2.61%, and the critical energy release rate was 0.16 N/mm [10; 21; 22]. Therefore, all the material input parameters required to perform the fracture simulation using the three numerical methods were known.
## 3 Materials and Methods
### Details of the Testbed
Arduino Uno's communication with all four components and supporting accessories;
2. Sim808 Module for GPS location-based information retrieval and GSM (Global System for Mobile Communication) utilization for cellular network use;  
Figure 2: Communicational passage for IoT network. * Esp8266, which helps the WiFi network communication to initialize the communication between the IoT device with the cloud network;
* The Load Cell Sensor and HX711 Module help generate the sensor data based on an object's weight over the sensor. The HX711 module converts the analog signal from the sensor and amplifies the voltage output from the load sensor so that Arduino Uno can read the output. Algorithm 1 outlines the procedure utilized for the communication of cloud network with developed IoT devices. ```
1:Connect the SIM808 module to the Arduino Uno using the appropriate pins and connectors
2:Configure the SIM808 module with the appropriate settings and credentials for the cloud network
3:Use the Arduino Uno's serial communication functions to establish a connection with the SIM808 module
4:Use the SIM808 module's API to obtain the data that you want to transmit
5:Use the Arduino Uno's networking functions (such as WiFiClient or EthernetClient) to establish a connection with the cloud network
6:Use the Arduino Uno's HTTP or MQTT libraries to send the data to the cloud network
7:Disconnect from the cloud network and the SIM808 module when the transmission is complete
```  
**Algorithm 1** Transmitting data from SIM808 to cloud network  
For simulation, both the weight and GPS sensors are programmed to continuously provide the data to the cloud server, hence helping with the optimal level of data generation and storage.
## 4 Concept
### System Operation  
In the \"System operation\" area of influence, the CPPS is used for its intended purpose in production and produces data.  
The sensors initially provide raw data in the form of individual signals or signal bundles that have been calibrated for the intended use during \"realization of data acquisition\". However, data acquisition can usually only represent a slice of reality. That is, some effects such as unforeseen deviations from the intended use of the CPPS, defects in the CPPS, or interference from the environment may affect the data quality in such a way that error-free preprocessing and analysis of the data are not possible. Difficulties may also arise in correctly interpreting the analysis results. Therefore, additional potentially influential events must be logged. For example, maintenance work on the shop floor can influence system behavior, knowledge of which is helpful for data interpretation with regard to anomalies or the causes of errors. In short, the generated data result from the system behavior of the CPPS and potentially from superimposed influences, errors, and disturbances.
## 3 Results and Discussion
### Climate Relationship with NDVI  
From the spatial scale, AMT, AMP, and NDVI exhibited variations in 1982, ranging from \(-\)3.87 to 9.79 \({}^{\circ}\)C for AMT (Figure 2A), \(-\)1 to 0.97 for NDVI (Figure 2B), and 33.36 to 559.69 mm for AMP (Figure 2C). Furthermore, AMT was significantly and negatively correlated with NDVI in Inner Mongolia (\(R^{2}\) = 0.38, \(p\) \(<\) 0.0001, Figure 2D). In contrast, a significant positive relationship between AMP and NDVI was observed (\(R^{2}\) = 0.60, \(p\) \(<\) 0.0001, Figure 2E). Vegetation status is one of the most important and sensitive indicators of climate change [26]. The northeast part of Inner Mongolia is semi-humid, with annual precipitation of more than 500 mm [27], which is mainly affected by the East Asian monsoon [21]; therefore, a high NDVI value in the northeast of Inner Mongolia was observed. In contrast, the low temperature and the insufficient rainfall in the western region of Inner Mongolia would explain the low NDVI value (Figure 2B). Most of the western region belongs to desert steppe area and there are many bare lands with sparse vegetation cover [27]. Thus, the western region of Inner Mongolia is windy and dry with little rain, coupled with sufficient sunshine and strong evaporation, as a result of lower NDVI [21; 23].
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 3 Flight Control Analysis of Pull-Up Process
### Elastic Deformation  
Apart from the low Reynolds number, elastic deformations are influencing factors on the pull-up process. The fuselages of large-scale aircraft utilize carbon-fiber composites when they are manufactured. Hence, the fuselage has obvious elastic deformations and aeroelastic effects due to the special material and its large scale. Such characteristics have negative impacts on the pull-up process. For instance, the authors in [19] proposed a nonlinear trim and flight load analysis method based on the structural dynamic model of large flexible aircraft established by the CR(co rotation) theory and by considering geometric nonlinear effects. Another work pointed out that when the flexible aircraft with a high aspect ratio had elastic deformations or wing torsion, the attack angle of local wing profile increases, thus resulting in flow separation and the occurence of dynamic stall [20].  
Based on aircraft aerodynamic data combined with numerical simulations, ground ballooning tests, and flight test data, our previous work involved an in-depth study on the longitudinal control of solar-powered UAVs by considering the elastic deformation of fuselages [22]. First, the aerodynamic force on the horizontal stabilizer is obtained by comparing the elastic deformation of the fuselage in ballooning tests and static load tests; them, static loading tests were carried out. Then, the pitch moment coefficient distribution of the aircraft considering the elastic deformation of the fuselage is obtained by calculating the longitudinal moment balance of the entire aircraft. The influence of flight speeds on the pitch moment coefficient provided by the horizontal stabilizer and the influence of flight speeds and elevator deflection angles on the control surface effect are analyzed. Finally, a complete dynamic model including an elastic structural dynamic model is established; the pitch angle(theta) and the value of elevator control surface (de) are compared with the rigid model, as shown in Figures 2 and 3, respectively.  
It can be seen from Figure 2 that the aircraft's pitch angle decreases by about 0.5\({}^{\circ}\). Figure 3 shows that the elastic deformation of the aircraft fuselage and horizontal stabilizer leads to an increase in the trim elevator angle by 13.5\({}^{\circ}\). The effect of elastic deformations will seriously weaken the efficiency of the elevator control surface, and considering the effect of elastic deformations on the aircraft's longitudinal alignment and compensating the horizontal stabilizer angle are necessary.  
Figure 3: The value comparison of the elevator control surface between rigid and elastic models.  
Figure 2: The comparison of pitch angles between rigid and elastic models.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 4 Simulation Results and Analysis
### Simulation Results
#### 4.2.5 Features Visualization  
To observe the feature extraction abilities of different methods more intuitively, the features extracted can be visualized by using the t-distributed stochastic neighbor embedding (t-SNE) [59] to reduce the extracted features to two dimensions. The feature map and silhouette coefficient (SC) are shown in Figure 12.