esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow
## 2 Kinematic Bicycle Model for Autonomous Vehicle  
The block diagram of the path tracking control of the autonomous vehicle can be seen in the Figure 1.  
Firstly, the motion planner calculates a reasonable trajectory (\(Z_{ref}\)) according to the current vehicle state information (such as vehicle speed, position, etc.) and the current environment information. In this article, a fixed trajectory is regarded as the target trajectory calculated by the motion planner. The same approach is widely used in the paper [3, 12]. Secondly, NMPC gives the vehicle's control commands (such as front wheel angle, vehicle speed, etc.) based on a series of reference paths in the limited time domain. Finally, the vehicle selects the control command (\(u_{0}\)) at the next moment as the input of the vehicle model.  
The vehicle kinematics model is shown in Figure 2 below. Here, \(L\) represents the wheelbase of the vehicle, \(\beta\) represents the side slip angle of the center of mass, \(\theta\) represents the heading angle of the vehicle, \(\delta_{f}\) represents the front wheel transfer, and the default rear wheel rotation angle is 0; ignoring the lateral sliding of the vehicle, ignoring the slip angle of the vehicle, assuming \(\delta_{f}\) is small, the nonlinear continuous time equations that describe a kinematic bicycle model can be expressed as follows [3]:  
\[\dot{X}=v*\cos(\theta) \tag{1}\]  
\[\dot{Y}=v*\sin(\theta) \tag{2}\]  
\[\omega=\frac{v*\tan\left(\delta_{f}\right)}{L} \tag{3}\]  
where \(\omega\) is the yaw rate of the vehicle, and \(L\) is the wheelbase of the vehicle. The equation above is continuous, which should be discretized.  
Figure 1: Simplified architecture of vehicle trajectory following.  
Figure 2: Kinematic bicycle model for autonomous vehicle.  
\(Z=[X,Y,\theta]\) is taken as output of the nonlinear vehicle model, and \(u=\left[v,\delta_{f}\right]\) is treated as input of the nonlinear vehicle model, the equation above can thus be expressed as follows:  
\[\dot{Z}=f(Z,u) \tag{4}\]  
The Continuous State Space Equation above cannot be used in MPC, so the equation is discretized as follows:  
\[Z(k+1)=A_{k,t}Z(k)+B_{k,t} \tag{5}\]  
In the Equation (5), \(A_{k,t}=\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}\);\(B_{k,t}=T\begin{bmatrix}v\cos\theta\\ v\sin\theta\\ v\tan\delta_{f}/L\end{bmatrix}\); \(T\) is the sample time, which can be set as 10 ms or 20 ms.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 1 Introduction  
In recent years, wireless sensor network technology has been widely used in environmental monitoring, health care, urban temperature detection, agricultural production and other fields [1; 2; 3; 4; 5; 6]. The main power supply for sensor nodes still relies on chemical battery, which offers limited energy storage. Further, as the sensor nodes are widely distributed, it would be difficult to replace and maintain the battery.  
Specifically, energy harvesting technology converts other forms of energy into electrical energy, which is then collected to power the circuit or device [7]. Several forms of energy exist in the environment, such as mechanical energy, thermal energy, nuclear energy, solar energy, chemical energy, etc. [8]. Among them, mechanical energy is the most widely distributed.  
According to the different principles, mechanical energy harvester can be divided into three categories, i.e., electromagnetic energy harvester, electrostatic energy harvester and piezoelectric energy harvester (PEH) [9; 10; 11; 12; 13]. Comparing electromagnetic and electrostatic ones, PEH has a higher energy density and is suitable to harvest vibration energy [14].  
Generally, PEH can be classified into three groups: (1) macroscale; (2) MEMS scale; and (3) nanoscale. The size of a PEH affects a variety of parameters such as its weight, fabrication method, achievable power output level, and potential application areas. For example, nanoscale PEH can be motivated by rubbing, pressing or pulling [15; 16; 17]. It is suitable to harvest energy from human motion. As to larger scale PEHs, they are mainly motivated by ambient vibration, especially vibration with fixed frequency.  
At present, there are still problems that limit the application of PEH: (1) the power loss in rectification circuit caused by the low output voltage [18]; (2) the narrow working frequency bandwidth of the energy harvester [9; 19; 20]; and (3) the mismatch of the frequency between the PEH and the environment [21; 22].  
This article mainly discusses the MEMS scale PEH. The following section introduces a general model of energy harvester and describes the research progress of PEHs from three aspects: piezoelectric materials, piezoelectric modes and energy harvesters' structure. These contents are analyzed in Section 3. Section 4 predicts the future development of PEHs.
## 5 Experiment and Analysis
### Ablation Study for Features  
After obtaining the optimal hyperparameters, we tested our model based on different features in 2016 (test set). The comprehensive NSTD-10 includes macroeconomic indicators, sentiment-derived news scores, and historical stock trading data. The NSTD without Macro excludes macroeconomic indicators while retaining news scores and historical data. The NSTD without News removes news scores featuring macroeconomic indicators and historical data. The NSTD Pure is the most pared-down, solely comprising historical stock trading data.  
Figure 4 shows the year-long trajectory of cumulative returns for each NSTD variant. The NSTD-10 model sets a high standard with an impressive performance, indicating that a 10-day time window robustly captures market trends when combined with a full feature set.  
Figure 3: Comparison with different temperature controllers in 2015 (validation set).  
Figure 2: Comparison with different window lengths in 2015 (validation set).  
Meanwhile, the NSTD without the macro variant experiences a moderate decline in return. This suggests that while macroeconomic indicators contribute to performance, their absence does not drastically diminish the model's efficacy. Conversely, the model without news sentiment maintains a competitive AR close to the full model's, suggesting that excluding sentiment indicators might not significantly impact the model's performance in the presence of other data types. Yet, as shown in Table 2, the altered ratios of Sharpe and Sortino indicate that news scores play a role in refining risk-adjusted returns. Notably, the NSTD Pure model demonstrates a significant decrease in AR and the Sharpe ratio, emphasising the collective value of integrating both macroeconomic and sentiment indicators into the model. This variant's underperformance highlights the complexity of market dynamics and the importance of a diversified information set for effective market analysis. It is evident that the comprehensive NSTD-10 model, with its full suite of indicators, excels, underscoring the importance of a multifaceted approach to capturing the full spectrum of market signals. However, the model does not predict the recurrent end-of-month drops in \(P_{t}/P_{0}\), which indicates a potential area for model refinement to accommodate such cyclical patterns.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## Materials and Methods
### Optimizing the Georeferencing  
Each point in the sparse cloud has four accuracy attributes: the reconstruction accuracy (RA), the reprojection error (RE), the projection accuracy (PA) and the image count [35]. In particular, the RE is suggested as the quality measure of tie points [20; 36]. It is the deviation of the positions of identified features in the original image from positions of the same features in the calculated 3D space. The removal of points with a high error and the subsequent optimization of the camera positions can improve the georeferencing. However, by removing too many points in the individual sparse clouds, images no longer align and the checkpoint error increases. An iterative approach is used to find the optimal RE threshold for the dataset by filtering the sparse cloud using different RE threshold values in order to minimize the checkpoint error (Figure 2). This method was applied to each flight of the two multi-temporal datasets.  
The initial pointclouds are the direct results from the feature identification and matching algorithms from Metashape. In order to account for the inherited randomness of these processes and the slight  
Figure 2: Overview of the workflow and reproducibility analysis. The georeferenced sparse point cloud is iteratively filtered until the checkpoint error reaches its minimum. A surface model and orthomosaic can either be retrieved through (**a**) the dense point cloud or (**b**) the creation of a mesh. For the reproducibility analysis (**c**) the whole photogrammetric process is repeated x times, leading to x orthomosaics from the same image source. Pixel-wise correlation analysis between pairs of orthomosaics leads to n correlation layers and n binary layers based on a correlation coefficient threshold of >0.95. The final reproducibility layer is the sum of all binary layers.  
differences of the checkpoint error due to the manual alignment of the GCP, the optimization was repeated five times and the standard deviation of the checkpoint error was calculated.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Propagation Models
#### 3.1.4 Nakagami Fading Model  
It is primarily a fading model that accounts for signal strength variation caused by multi-path fading. As the model is not very suitable as a path loss model for simulations, it can be used with other loss models for a better performance result. It has continuous probability distribution for the varied environments, including gamma distribution [45].  
\[p(x;m,\omega)=\frac{2m^{m}}{\Gamma(m)\omega^{m}}x^{2m-1}e^{-\frac{m}{\omega}x^ {2}} \tag{4}\]  
Here, \(m\) parameter denotes the fading depth and \(\omega\)--the average power received. The probability density function (pdf) at a given distance is computed by modifying gamma distributions. The signal follows gamma distribution as a stochastic model with average power fading and severity with amplitude \(x>=0\). For \(m=1\), the Nakagami becomes the Rayleigh model.
## 2 Materials and Methods
### Construction of an App for Deploying Heritage Data and Data Capture  
In this first phase, the scope of the application was defined according to the proposed research objectives. From the initial ideas about implementation within the context of MAR-MPN, the general requirements necessary to obtain, from the computer field, the required tool to verify the research objectives were determined. The primary goal was to design a mobile application that enables the display of and interaction with heritage and territorial data in a dynamic, portable environment. Additionally, this application is intended to be adaptable across various disciplines, to involve spatially referenced content, and to be applicable at multiple geographic scales, from local to continental.  
Key aspects of the system include:  
* Input data--the application integrates heritage, spatial, and educational datasets derived from previous analytical stages. This includes structured data definitions, covering valid formats, storage methods, interoperability standards, and additional multimedia components such as AR models, imagery, textual descriptions, and spatial metadata;
* Output data--information is presented through a graphical user interface (GUI) optimised for mobile and location-aware contexts, ensuring that heritage content is delivered in a way that aligns with the specific site or scenario. Furthermore, the system utilises mobile device sensors to capture usage patterns, spatial positioning, and temporal interactions, allowing for a contextually relevant digital experience.  
Figure 1 represents the app, which is divided into five components, and their interaction. A sixth component is established outside the app and where spatial analyses are performed:  
* Sensors and communication: this component includes the camera, GPS, gyroscope, WiFi, and mobile data (4G/5G), which collect information about the user's environment and location, allowing the MAR and MPN to function correctly;
* Map server: component that stores the geospatial database;
* Augmented reality (AR): the app processes and displays information about heritage buildings and their location on the device;
* Location database: contains the location data and spatial references of the data of the heritage buildings;
* Internal storage: the device stores the time and location data in a comma-separated values (CSV) format;
* Spatial analysis: the complementary software GIS (ArcGIS Pro 3.4) generates spatial indicators and maps, which allow the interpretation of the data in order to analyse and understand the relationships between the heritage resources, the time of consultation, and their location.  
In this way, the app collects data from the environment through its sensors, contextualizing the information with multimedia, AR, and MAR resources. In addition, the map server is accessed to obtain location and time data, which are stored internally within the device for later analysis through GIS.
## 2 Methods
### Improved Morlet Wavelet Transform
#### 2.1.1 Wavelet Theory  
Assuming \(\varphi\in L^{2}\cap L^{1}\), \(\hat{\varphi}(0)=0\) generates a function group as follows \(\left\{\varphi_{a,b}\right\}\):  
\[\varphi_{a,b}(t)=|a|^{-\frac{1}{2}}\varphi\bigg{(}\frac{t-b}{a}\bigg{)} \tag{1}\]  
Equation (1) is a continuous wavelet, where \(a\in R-\left\{0\right\}\), \(b\in R\), and \(\varphi\) is a basic wavelet. If \(\varphi\) is a double-window function, then \(\varphi\) is called a window wavelet function.  
In signal \(f\), the continuous wavelet transform \(W_{f}(a,b)\) of \(f\) is defined as  
\[W_{f}(a,b)=\big{\langle}f,\varphi_{a,b}\big{\rangle}=|a|^{-\frac{1}{2}}\int_{R}f( t)\overline{\varphi\left(\frac{t-b}{a}\right)}dt \tag{2}\]  
\(\varphi\in L^{2}\cap L^{1}\) and satisfies  
\[C_{\varphi}=\int_{R}\frac{\big{|}\overset{\wedge}{\varphi}(\omega)\big{|}^{2 }}{|\omega|}d\omega<\infty \tag{3}\]  
If \(f\) is continuous at \(t\), then there is a wavelet reconstruction formula for signal \(f\):  
\[f(t)=\frac{1}{C_{\varphi}}\iint\limits_{R^{2}}W_{f}(a,b)\varphi_{a,b}(t) \frac{da}{a^{2}}db \tag{4}\]  
In the above discussion, \(\overset{\wedge}{\varphi}(\omega)\) is the Fourier transform of \(\varphi(t)\), and \(\overline{\varphi(t)}\) is the conjugate of \(\varphi(t)\).
# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 3 Results and Discussion
### Comparison with Previous Methods
#### 3.3.2 Qualitative Evaluation  
Figures 5 and 6 show the qualitative results for the CT-DRIVE and IOSTAR datasets, respectively. We can see that: (1) The tree-wise results based on the estimated vessel topology clearly show the improvement on thin or terminal vessels compared to the pixelwise prediction results, and (2) the proposed method gives more spatially consistent results along the vessel structure. The abrupt class changes either on each segment or between connected segments are suppressed in the results by the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Acc** & **Se** & **Sp** & **Time (s)** \\ \hline [PERSON] et al. [13] & N/A & 80.0 & 80.0 & - \\ [PERSON] et al. [9] & 87.4 & 90.0 & 84.0 & - \\ [PERSON] et al. [10] & 91.7 & 91.7 & 91.7 & 131.3 \\ \hline Ours & 91.4 & 90.9 & 91.8 & 8.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances of different AV classification methods on the CT-DRIVE dataset. Accuracy (Acc), sensitivity (Se), specificity (Sp), and average per-image computation time are presented.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Thick**} & \multicolumn{3}{c}{**Thin**} & \multicolumn{3}{c}{**All**} \\  & **Acc** & **Se** & **Sp** & **Acc** & **Se** & **Sp** & **Acc** & **Se** & **Sp** \\ \hline Pixelwise & 77.3 & 77.0 & 77.7 & 60.4 & 65.2 & 55.5 & 70.7 & 72.3 & 69.2 \\ Segment-wise & 82.4 & 83.1 & 81.6 & 67.3 & 71.3 & 63.2 & 76.5 & 78.4 & 74.6 \\ Tree-wise & 82.9 & 83.1 & 82.7 & 69.4 & 73.8 & 64.9 & 77.6 & 79.3 & 75.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances of the proposed AV classification method on thick and thin vessels for the IOSTAR dataset. The boundary for the thick and thin vessels is a width of three pixels. The performances of the pixelwise prediction and using a segment-wise voting are also presented for comparison.  
Figure 5: Two example results from the CT-DRIVE dataset. The six images for a single case are, in row-major order beginning from the top-left, the original input image, estimated topology of the proposed method, GT annotation of the topology, pixelwise AV classification result, tree-wise classification result of the proposed method, and AV GT annotation. Different colors in the topology GT represent separate trees. In each result, correctly classified arteries and veins are marked as red and blue, respectively. Centerlines incorrectly labeled as veins are in green, while those incorrectly labeled as arteries are in yellow.
## 2 Study Subjects and Monitoring System Configuration
### Study Subjects  
The VLBI antenna is a complex structure built with various components and materials. Even though it is designed to resist deformation, every structure is bound to suffer some deformation due to its own weight and because the rigidity of its components deteriorates over time. Therefore,the points on the structure where displacements are likely to occur must be selected and must be continuously monitored.  
The methods for measuring the absolute 3D positions of multiple points on a structure, based on a stationary reference point where no displacement occurs, include total station observation and close-range photogrammetry.  
The total station is a modern surveying instrument that can accurately measure coordinates within a range of 3 km. It is the most appropriate method for accurate measurements of static displacement.  
Close-range photogrammetry with multiple digital cameras can measure the static displacement of an antenna's main reflector with high accuracy when combined with precise calibration. Its position measuring accuracy is lower than that of the total station, but this shortcoming can be addressed by analyzing the displacement trend through continuing measurements over a long period of time.  
Figure 1 shows the Space Geodetic Observation Center (SGOC), operated by the National Geographic Information Institute in South Korea. The features identified include the VLBI antenna, pillars (four points), reference point, and Global Positioning System (GPS) permanent observatory.  
To monitor the static displacement of the VLBI antenna surface, two fixed cameras for surveying and one rotating camera for state observation were installed at the SGOC. The program developed in this study acquired measurements of the feature points on the VLBI antenna surface. We used the total station installed on one pillar close to the camera installation position and two fixed survey cameras installed at the SGOC.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 5 Simulation Model, Setup and Results
### Network and Mobility Model  
The sensor network comprising \(N\) nodes is represented by a graph \(G\left(V,E\right)\), where \(V\) represents a set of sensor nodes (vertices) and \(E\) represents a set of communication links (edges). Two nodes, \(u\) and \(v\), communicate with each other if they are within communication range (\(Rc\)). This property is known as a Unit Disk Graph (UDG) of graph theory. However, since our work is proposed in the context of sensor-based opportunistic networks, we consider the \"double range property\" [24] in our experiment, defined as \(Rc=n\times Rs\) where \(n\geq 2\) and \(Rs\) refers to the sensing range.  
We consider a 4.0 km\({}^{2}\) (approximately 2 km \(\times\) 2 km) network size as it is a realistic area around disaster zones where rescue personnel need to be connected for recovery operations. Moreover, the mobility speed of rescue personnel is assumed to be about 5 m/s or 300 m/min, considering the scenario that rescue personnel are moving slow or walking in a certain region of that area to perform the operation.  
The random mobility of the nodes was generated using mobility generator BonnMotion [25], which places each node randomly in a position in the simulation area of size \(2000\times 2000\) m\({}^{2}\). The pattern of mobility is the Random Scenario (RS) provided by the mobility scenario generator BonnMotion [25]. The generated mobility patterns pause each node at its current location for a certain period before it moves to another randomly generated location at a random speed.  
The IEEE 802.15.4 standard Zigbee protocol is used in the low data rate and powered sensors. We consider that sensors are used to create the opportunistic network in the simulation. We consider using the libelium Waspmote [26] that considers all parameters and specifications mentioned in [26] in our simulation. Table 1 lists the simulation parameters and their respective values.
## 3 Inherent Optical Properties (IOPs)
### General Definitions
#### 3.1.1 Rayleigh Scattering Phase Function  
When the size \(d\) of the scatterers is small compared with the wavelength of light (\(d<\frac{1}{10}\lambda\)), the Rayleigh scattering phase function gives a good description of the angular distribution of the scattered light. The Rayleigh scattering phase function for unpolarized light is given by  
\[p(\cos\Theta)=\frac{3}{3+f}(1+f\cos^{2}\Theta) \tag{9}\]  
where the parameter \(f=\frac{1-\rho}{1+\rho}\), and \(\rho\) is the depolarization ratio, attributed to the anisotropy of the scatterer (molecule) [6; 7; 8]. Originally this scattering phase function was derived for light radiated by an electric dipole [9]. Since the Rayleigh scattering phase function is symmetric about \(\Theta=90^{\circ}\), the asymmetry factor is \(g=0\). If the Rayleigh scattering phase function is expanded in Legendre polynomials, the expansion coefficients \(\chi_{\ell}\) [see Equation (117) below] are simply given by \(\chi_{0}=1\), \(\chi_{1}=0\), \(\chi_{2}=\frac{2f}{5(3+f)}\), and \(\chi_{\ell}=0\) for \(\ell>2\).  
Using \(\rho=0.0286\) for air at 500 nm [10], we get \(f=\frac{1-\rho}{1+\rho}=0.944\), and using \(\rho=0.039\)[11; 12] for water, we get \(f=\frac{1-\rho}{1+\rho}=0.925\). Hence, for Rayleigh scattering, the scattering phase function moments become:  
* \(\chi_{0}=1\), \(\chi_{1}=0\), \(\chi_{2}=0.0957\) and \(\chi_{\ell}=0\) for \(\ell>2\) for air, and
* \(\chi_{0}=1\), \(\chi_{1}=0\), \(\chi_{2}=0.0943\), and \(\chi_{\ell}=0\) for \(\ell>2\) for water.