esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Description of the Measured Dataset  
The experimental dataset for this study was collected by simultaneously monitoring ChIF parameters and hyperspectral reflectance in a cold temperate forest at the forestry research facility of Shizuoka University in Nakakawane (35\({}^{\circ}\)04\({}^{\prime}\)N, 138\({}^{\circ}\)06\({}^{\prime}\)E). The dataset includes measurements from leaves situated at various canopy positions and growth stages of multiple tree species. The following tree species were included in the study: _Acer shirasawannu_ [PERSON], _Betula grossa_ Siebold & Zucc, _Carpinus tschonoskii_ [PERSON], _Fagus crentata_ [PERSON], _Stevartia monadelpha_ Siebold & Zucc. and _Pieris japonica_ ([PERSON].) [PERSON] ex [PERSON]. [PERSON]. For measurements, branches with at least three fully expanded and healthy leaves at different canopy heights were marked with a tape measure and collected before dawn using the detached branch method. Sampling was conducted from the aforementioned species between May and October 2022 and August 2023, facilitated by a scaffold tower positioned around the trees. The scaffolding tower was approximately treated as a cube, and samples were collected within a vertical range of 0-18 m. Detailed information on sampling heights can be found in Zhuang and Wang [20]. ChIF and reflectance spectra (350-2500 nm) were obtained using a miniaturized pulse amplitude-modulated photosynthesis yield analyzer (Mini-PAM, [PERSON], Effeltrich, Germany) and a FieldSpec spectrometer (Analytical Spectral Devices, Inc., Boulder, CO, USA), respectively. The present study utilized a total of 477 data points. Therefore, this dataset provides high spatial resolution and encompasses multiple growth stages. A comprehensive description of the experimental area, the measurement procedures, and the calculation of the ChIF parameters can be found in a previous study [20]. Figure 1 illustrates the distribution of ChIF parameters in the comprehensive dataset used in this study.
The control gain matrix of the system with uncertainty is optimized by the convex analysis with solving conditions, which are determined from the subsystems in LMI form. As opposed to the existing optimization method, the proposed subsystem-based method can be used without matching conditions on the faults, providing a novel robust optimization treatment for the system with varying gradual faults and results in better system performance compared with traditional methods. The detailed work of this paper can be summarized as follows:  
1. Typical gradual faults of NT-DRAS are included in the system's state space formulation in the form of uncertainty. In order to remove the system uncertainty, specific subsystems are obtained through convex analysis. 2. A constant, time-invariant controller is designed based on LMI conditions, which are deduced from the subsystems of the original uncertain system. Compared with the traditional optimization method based on the original uncertain system, the proposed subsystem-based optimization technique can result in a more feasible time-invariant control gain under gradual fault conditions. 3. The simulation analysis results are provided and observations are discussed. First, the effects of gradual faults on the system response performance in the time domain are analyzed based on the pole location. Second, comparative analysis is performed on the NT-DRAS under moderate fault conditions with the proposed controller and with the existing guaranteed cost controller. This paper is organized as follows: Section 1 introduces the background of the proposed NT-DRAS and its FTC problem; Section 2 performs the system modeling under normal and fault conditions; Section 3 presents a novel model analysis and control gain optimization methodology; Section 4 gives the specific FTC structure design and control gain solving algorithm; Section 5 presents simulation analysis results; Section 6 gives conclusions and recommends the research content to be conducted in the future. Notation: In this paper, \(\Delta\) specifies parameter error, \(\text{Re}\lambda_{i}()\) is the form of eigenvalue real part, and \(w(t)\in L_{2}[0,\,\infty)\) represents a quadratic differential function.
## 4 Verification and Validation
### Validation of the Stability Improvement
#### 4.2.5 Fluid Viscosity  
When the viscosity decreases, the stability issue of any IB method will become even more stringent, and the IB-LB coupling scheme is not an exception. In this section, we study the low-viscosity stability of the AWI method by simulating an impulsively started filament.  
A filament is placed vertically in a square domain with dimensions \(L\times W=500\times 500\). A uniform grid of 120 Lagrangian points is used to discretize the filament. The length of the filament is 120, of which the middle point is tethered to (250, 250). \(\overline{K}_{s}\) and \(\overline{K}_{f}\) of the previous case are applied, whereas the bending rigidity is set to \(\overline{K}_{b}=2.0\times 10^{-3}\). In addition, the wall boundary condition is applied to the four sides of the computational domain. The fluid is initially at rest, and the filament middle point impulsively begins to move along the positive X direction with velocity \(U_{drive}=0.01\). At the initial stage of the simulation, the filament is bent into a \"\(\supset\)\" shape. As the simulation continues, the filament remains in this shape and only minor fluctuations can be detected at both wings. Using both the explicit and AWI methods to simulate this case, with identical sets of parameters, the results show that when other parameters remain unchanged, even if the fluid viscosity is set to \(\
u=1.0\times 10^{-1}\), the explicit method rapidly diverges because of numerical oscillations. However, when the fluid viscosity is set to \(\
u=5.5\times 10^{-5}\) (with an instantaneous maximum Reynolds number of Re = 21,600), the AWI method maintains numerical stability. Figure 15 shows the velocity field and its vorticity contour at \(t=8.6\times 10^{3}\). Both wings flap back and forth during the simulation and three pairs of symmetrical vortices are formed in the wake zone, indicating that the proposed method can improve the stability of the original IB-LB coupling scheme when the fluid viscosity is small.  
Figure 14: Vorticity field obtained with \(S=0.28\) using the AWI approach.  
Figure 15: Velocity field and vorticity contour at \(t=8.6\times 10^{3}\) with the AWI approach.
## 2 A Brief Introduction to Laser-Scanning 3D Model Reconstruction Technology
### A Brief Description of the Laser-Scanning 3D Model Reconstruction Process
#### 2.2.2 Point Cloud Data Acquisition  
After the placement of control points is finalized, the distribution of 3D laser-scanning stations can be strategically planned according to the site conditions, achieving a uniform distribution of scanning stations and target points, as depicted in Figure 4. A 30% overlap between the scanning stations is recommended, and each scanning station should be equipped with a minimum of four target points [29]. Additionally, adjacent scanning stations should share at least three target points with a noticeable height difference. In critical scanning areas, scanning stations are positioned from various angles and locations to guarantee the completeness of the scanning point cloud data. Generally speaking, the greater the distance between the scanner and the object being measured, the lower the scanning accuracy [30; 31]. When using a 3D laser scanner to scan a small volume of equipment, pipes, valves, etc., the distance between the scanner and the measured item should be reduced. For large areas such as corridors and open spaces, the scanning distance can be appropriately increased to reduce the workload. When the density of points is increased, the absolute error of points will be reduced and the accuracy will be higher. However, the density of points is not as high as possible, which will increase the workload.  
Throughout the 3D laser-scanning process, preserving detailed image data is frequently essential to facilitate subsequent point cloud data processing or the reconstruction of 3D models, which involves capturing on-site photographs of buildings, images of equipment, installation drawings, and as-built drawings of the equipment.  
Figure 3: Schematic diagram of a regional control network.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.7 Controls for Thruster  
A vessel may be equipped with one or multiple thrusters to increase the manoeuvre capability. The thruster controls (e.g., start/stop button, load stage levers) are available on the bridge. The thrusters are mostly deployed under the bow and/or aft side of cargo vessels. However, thrusters may be placed in any place of the ship's hull.
## 4 Numerical Demonstrations  
This section presents numerical demonstrations that were conducted on MATLAB R2022a software (version: 9.12.0.1884302) to demonstrate the effectiveness of the proposed fixed-time backstepping controller. The initial conditions of the HMW are given as follows: \(V=2400\) m/s, \(\alpha_{0}=5\) deg, \(\omega_{20}=0\) rad/s. Five scenarios are considered in this article. The designed parameters of the controller and observers are shown in Tables 1 and 2, the simulation step size is set as 0.0001 s.  
The AOA command is set as \(\alpha_{ref}=5+5\cdot\sin(t)\) deg; the wing morphing law is given as  
\[\chi=\left\{ \begin{array}{ll}\frac{1}{T_{s}}t&0\leq t\leq T_{s}\\ 1&T_{s}<t\leq 10\end{array} \right. \tag{63}\]  
**Scenario 1. ** In this scenario, in the case of symmetrical morphing of left and right wings, comparisons between the proposed observer-based fixed-time backstepping controller of Case1, finite-time backstepping controller (by making \(r_{1}=r_{2}=\frac{5}{5}\)) of Case2, and \begin{table}
\begin{tabular}{c c} \hline \hline
**Signal** & **Values** \\ \hline \(a_{1}\) & 10 \\ \(c_{1}\) & 10 \\ \(b_{1}\) & 0.002 \\ \(a_{2}\) & 20 \\ \(c_{2}\) & 20 \\ \(b_{2}\) & 0.002 \\ \(r_{1}\) & 5/9 \\ \(r_{2}\) & 9/5 \\ \(\tau\) & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Design parameters of the controller. \begin{table}
\begin{tabular}{c c} \hline \hline
**Signal** & **Values** \\ \hline \(\kappa_{1}\) & 4 \\ \(\kappa_{2}\) & 4 \\ \(\varepsilon_{1}\) & 0.015 \\ \(\kappa_{1}\) & 0.8 \\ \(\beta_{1}\) & 1.4 \\ \(x_{3}\) & 4 \\ \(\kappa_{4}\) & 4 \\ \(\varepsilon_{2}\) & 0.015 \\ \(a_{2}\) & 0.8 \\ \(\beta_{2}\) & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Design parameters of disturbance observer. **Scenario 2. ** During the flight process of the HMW, there are strong disturbances. Meanwhile, due to the complex flight environment and limited accuracy of aerodynamic estimation methods, there are often estimation errors in aerodynamic data, resulting in strong aerodynamic uncertainty. Both the disturbance and uncertainty have a significant impact on HMW flight performance. Therefore, in this scenario, strong disturbance and aerodynamic uncertainty are introduced to the HMW dynamics, and comparisons between the three controllers are conducted.
# Nonlinear Optimal Control Law of Autonomous Unmanned Surface Vessels
## 5 Comparisons of Tracking Performance with Respect to Different Control Gains: \(a_{2}\)  
Optimal control gain \(a_{2}\) in Equation (29) is an adjustable parameter. Hence, in this section, trajectory- and waypoint-tracking performances with respect to different control gain \(a_{2}\) are analyzed. From the simulation result in Figure 11, tracking performance for the continuous circular trajectory is demonstrated. This scenario was successfully achieved for different control gains \(a_{2}\). In the continuous case, tracking errors exponentially converged to zero. The observations of tracking errors and control commands, as shown in Figures 12 and 13 indicate that a small \(a_{2}\) resulted in quicker convergence rates in tracking the desired trajectory. Meanwhile, larger control commands could be found. In the discrete case, tracking performance for the discrete waypoints is demonstrated in Figure 14. This scenario was also successfully achieved for different control gains \(a_{2}\). The tracking errors exponentially converged to zero as shown in Figure 15. In Figures 15 and 16, the tracking errors and control commands indicate that a small \(a_{2}\) resulted in quicker convergence rates in tracking the desired waypoints. Meanwhile, larger control commands could be found. These simulation results consisted of the inversely proportional property of the proposed nonlinear optimal control law, as seen in Equations (28) and (29), with respect to control gain \(a_{2}\).  
Figure 10: Histories of velocities of controlled AUSV.  
Figure 11: Tracking histories of controlled AUSV with respect to different control gains \(a_{2}\).  
Figure 12: Tracking errors of controlled AUSV with respect to different control gains \(a_{2}\).  
Figure 14: Tracking courses: waypoint-tracking scenario with respect to different control gains \(a_{2}\).  
Figure 13: Control commands of controlled AUSV with respect to different control gains \(a_{2}\).  
Figure 15: Tracking errors of controlled AUSV with respect to different control gains \(a_{2}\).  
The run time of the proposed control method with respect to the two testing scenarios is revealed in Table 7. In this study, the central processing unit (CPU) of the calculator used for calculating the proposed nonlinear optimal control law was Intel Core i7-8750H, 2.20 GHz. From Table 7, it is easy to find that the proposed nonlinear optimal control law can be executed in real time. This is because an average run time of 3 and 2 microseconds is spent for each single calculation of the proposed nonlinear optimal control law with respect to the trajectory and waypoint scenarios, respectively.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 3 Experiment and Results  
### Experiment Setup  
Considering the number of physical channels of the equipment used for data acquisition and the structural parameters of the paraboloid, for all experiments, a uniform circular array was set up, where L = 7 microphones, and the distance between the microphones was 18 cm. The other microphone was at the focus of the parabolic reflector, and the circular array and the paraboloid were combined into a whole, as shown in Figure 5. The input signals, with the sampling frequency of Fs = 16 kHz, were transformed into the STFT domain, which used a Discrete Fourier Transform (DFT) length of 256 and 50% overlap. Signals were divided into 16 ms frames with an 8 ms frame shift in time-domain. McRoomSim [23] was used to generate the Room Impulse Responses (RIRs) required to simulate different acoustic conditions. The room property was set to an anechoic chamber with a sound absorption coefficient of 1, which means that there was no reverberation or other noise in the room. To illustrate the independency of the source spatial position, eight different positions were set at different angles. Table 1 shows the configuration used to generate the dataset. For each position in the training stage, 100 speech signals were convolved with the simulated RIRs corresponding to the specific setup, while for each position in the testing stage 60 speech signals were convolved. The proposed system was evaluated on the IEEE database [24]. Each clean utterance was mixed by adding the speech interference to each isolated noise (babble, volvo, destroyer engine, pink, and factory noise) at different SNRs from -18 dB to 7 dB. The speech interference noise was an utterance of WSJ0 in around 10 s. The other five noises were non-stationary and each signal was around 4 min long. Random cuts from the first half of each noise were mixed with each training utterance to create the training mixtures, and cuts of the second half of that were mixed with each testing utterance to create testing mixtures. Acoustic conditions are shown in Figure 5, and different source positions are shown in Table 1 in detail.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 3 Remote Sensing Target Detection
### Small Object Detection in Remote Sensing  
We analyzed the principle of Yolov5 image recognition, taking the commonly used 640\(\times\)640\(\times\)pixel network input as an example (Yolov5s). After several iterations of down-sampling, the largest feature image is 80\(\times\)80, and each field of vision is only 8\(\times\)8. If an image of more than 5000 pixels is input, the smallest object pixel width or height the network can identify is around 62.5. However, the size of many objects on the satellite remote sensing images are smaller than this pixel width limit, making them difficult to identify. If the input image size of the network is forcibly increased, it is easy to exceed the GPU's memory, and this also greatly increases the detection time. More effective methods are required to run the algorithm under the limitation of the device.  
Based on the above analysis, we made the following improvements to enhance Yolov5's capabilities for detecting small targets:  
1. Modification of the model: we added a small detection anchor box, reduced the multiple of the sampling layer as much as possible within the GPU's memory limit, and added a small target feature extraction layer. On this basis, we used a large number of real satellite images for training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Yolov5s** & **Yolov5m** & **Yolov5l** & **Yolov5x** \\ \hline Depth multiple & 0.33 & 0.67 & 1.00 & 1.33 \\ \hline Width multiple & 0.50 & 0.75 & 1.00 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of different Yolov5 networks.  
Figure 6: The structure of FPN and PAN.  
Image segmentation: we developed an image segmentation program that is similar to manually cutting large images into several small pieces, and merges the model's outputs after recognition.  
As shown in Figure 7, our proposed model is modified on the basis of Yolov5m. An initial detection anchor box has been added, and the corresponding novel structure has been modified to make an additional sampling and fusion layer.  
The image segmentation algorithm is implemented as follows: first, we divide the original large-sized image into multiple small slices. The size of a slice should be 640 pixels (as the neural network input is 640 \(\times\) 640), with a 20% overlap reserved between each small slice, to prevent small targets from being missed. Each small image is resized to 640 \(\times\) 640 as a prediction input. We obtain the detection results from the network and concatenate the output of multiple slices into the detection results of the original large-sized image. Figure 8 shows a simple example of the image segmentation detection method.
## 4 Experimental Results
### Ablation Experiments
#### 4.6.1 Evaluation of the Encoder-Decoder Module  
As shown in Table 2, the encoder-decoder module significantly outperforms the original SSD [16], by improving the AP by 3.32%. The SSD [16] built the pyramid starting from high up in the network without using low-level feature maps. However, the low-level feature maps contain high spatial resolutions and rich detailed features, which is essential for pedestrian detection in low-resolution IR images. Our method proposed a new decoder module to effectively combine the high-resolution low-level detailed features with the low-resolution high-level semantic features, which can increase context information that helps to boost the pedestrian detection rate in low-resolution IR images. Figure 9a,b illustrate visual comparisons of the original SSD detection results versus the encoder-decoder detection results. It is clear that the SSD produces more false positives and false negatives.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**KMU Test Set**} & \multicolumn{2}{c}{**CVC09 Test Set**} \\ \cline{2-5}  & **Speed (s/f)** & **Precision** & **Speed (s/f)** & **Precision** \\ \hline Histogram-of-oriented-gradients (HOG) + Support vector machine (SVM) [44] & 0.09 & 8.7\% & 0.09 & 12.3\% \\ Hotspot + SVM [23] & 0.08 & 10.8\% & 0.08 & 9.1\% \\ Oriented center-symmetric local binary (OCS-LBP) + Cascade random forest (CaRF) [21] & 0.06 & 92.4\% & 0.06 & 63.9\% \\ You only look once version 2 (YOLOv2) [20] & 0.02 & 66.5\% & 0.02 & 47.1\% \\ YOLOv2 + Adaptive Boolean-map-based saliency (ABMS) [30] & 0.02 & 80.5\% & 0.02 & 58.5\% \\ Attention-guided encoder-decoder convolutional neural network & 0.03 & 97.5\% & 0.03 & 87.68\% \\ (AED-CNN) (ours) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the computation times and average precisions (APs) for the Keimyung University (KMU) and Computer Vision Center (CVC)09 test sets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Average Precision on KMU Test Set** \\ \hline Single shot multibox detector (SSD) [16] & 92.04\% \\ Encoder-decoder module & 95.36\% \\ Encoder-decoder module + attention module (AED-CNN) & 97.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the ablation experiments on KMU test set.
# Three-Dimensional, Time-Dependent Analysis of High- and Low-\(Q\) Free-Electron Laser Oscillators
## 2 Numerical Formulation  
Simulations of FEL oscillators have appeared in the literature by including optical propagation algorithms to existing FEL simulation codes, as well as self-contained FEL oscillator codes [103, 175, 176, 177, 178, 179, 180, 181, 182]. In this paper, we describe the use of an existing FEL simulation code to treat the interaction within the undulators and to link that to a code specifically designed to propagate the optical field through various resonator configurations. Such optics codes are sufficiently general to be able to treat a variety of resonator designs, and many may have been originally created to deal with other types of lasers. In this simulation environment, the FEL code hands off the optical field at the resonator exit to the optics code, which then propagates the field around the resonator and back to the undulator entrance, after which it is handed off to the FEL code for another pass through the undulator.  
Among the earliest applications of this method was to simulations of the IR-Demo [64, 65], as described in Section 4.1, and the 10-kW Upgrade experiments at JLab [183], which employed the MEDUSA FEL code [184] and OPC [185] and successfully reproduced many of the essential features of the experiment. OPC has also been interfaced with the GENESIS FEL code [186] and the PUFFIN FEL code [187] to study VUV FEL oscillators [188, 189]. In this paper, we discuss the linkage of the MINERVA simulation code with OPC.  
As will be described below, while MINERVA represents the optical field as a superposition of Gaussian modes with two independent polarization directions, OPC propagates the field on a grid. This necessitates the mapping of the Gaussian modes in MINERVA at the undulator exit to the grid supported by OPC, and the decomposition of the optical field in OPC at the undulator entrance back into Gaussian modes for both polarization components.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 7 Toward a Fog-Based Real-Time Big Data Pipeline
### Building a Smart City Data Pipeline
#### 7.1.1 Data Ingestion  
This phase concerns the acquisition of data streams from various sensors and devices deployed in smart city facilities. These data streams can be in various data formats such as Extensible Markup Language (XML), JavaScript Object Notation (JSON), and text and can be stored in traditional relational database management system (RDBMS) data repositories or NoSQL databases. Additionally, to enable data persistence and consumption by multiple applications, publish-subscribe messaging systems like Apache Kafka must be supported.