esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Dynamics of an Elastic Stop System and Its Application in a Rotor System
## 3 Dynamic Response
### Stability and Frequency Components  
In this paper, the analysis focuses on the curves with the \"bending\" phenomenon. Taking \(\delta=10\) as an example, the amplitude-frequency characteristic of the system is obtained by the shooting method (SM). The stability of the periodic solution under different excitation frequencies is analyzed according to the Floquet theory, as shown in Figure 7, with four bifurcation points, namely Period Doubling, Neimark-Sacker, Branching Point, and Branching Point. The particular parts on the amplitude-frequency curves are marked as A, B, and C to study their frequency spectra, respectively.  
Based on the Ode15s solver in Matlab R17b, the initial displacement and velocity are 0 (if not specified). The time history and frequency spectra of the system at excitation frequencies of 0.95, 0.97, 1.02, and 1.06 are shown in Figures 8-12, where the frequency spectra are normalized by the excitation frequency. When the excitation frequency is located in region A, the frequency spectrum of the system includes \(0.5\omega\) and \((n+0.5)\omega\), in addition to the multiplier frequency \(n\omega\); when the excitation frequency is located between 1.046 and 1.077, the curve between regions B and C cannot be realized, and the response amplitude is located in regions B or C depending on the initial value, that is to say, there can be more than one steady-state response for a given excitation frequency at these unstable points. This bi-stable regime implies the jump phenomena, and the jump point is the branching point.  
Figure 7: Amplitude–frequency characteristics, stability, and bifurcation points of the system with gap = 10: (**a**) amplitude–frequency curves; (**b**) Floquet multipliers.  
Figure 8: Time history and frequency spectra of the system with an excitation frequency of 0.953 (a) time history; (b) frequency spectrum.  
Figure 9: Time history and frequency spectra of the system with an excitation frequency of 0.973 (a) time history; (b) frequency spectrum.  
Figure 11: Time history and frequency spectra of the system with an excitation frequency of 1.06 (initial displacement is \([0,0]\), the initial velocity is \([0,0]\)): (**a**) time history; (**b**) frequency spectrum.  
Figure 10: Time history and frequency spectra of the system with an excitation frequency of 1.02: (**a**) time history; (**b**) frequency spectrum.  
The excitation frequency of 1.06 is used as an example to explore the domain of attraction of the bi-stable periodic solution to the initial displacement, with the intervals of [0-40] and [0-8] for the initial displacements of the two Dofs. The distribution of the domain of attraction is shown in Figure 13, where the amplitude of the steady-state response tends to B is marked as red+, and C is marked as blue circles. As we can see, there exists a clear boundary between the different attractions.
# Classification of Marine Vessels with Multi-Feature Structure Fusion
###### Abstract  
The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result.  
marie vessel classification; feature fusion; structure fusion; linear discriminant analysis; dimensionality reduction +
Footnote †: journal: Applied Sciences
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 3 Panoramic Texture Generation Method
### GAN-Based Panoramic Texture Translator
#### 3.2.1 Generator  
The input face image was converted to an unwrapped texture map using the UV unwrapping and then utilized as input to the generator module. Like many image translation research works, encoder-decoder networks translate the image. In this work, the input unwrapped texture is encoded and decoded into a panoramic texture. The generator G was trained to generate diverse and comprehensive face information from the input texture x. The generator is a \"U-Net\"-like encoder-decoder network. During the learning procedure, generator G was utilized to discover the distribution of functions required to build panoramic textures y from unwrapped textures x. The skip connections share low-level information between input and output. In this work, we adopt the same generator structure as Pix2 pix [32]. Specifically, two down-sampling layers, followed by nine residual blocks and two up-sampling layers. \(L_{1}\) loss was used to reduce the disparity between the panoramic texture and created textures. The loss function is given as:  
\[\mathcal{L}_{L_{1}}(G)=\mathbb{E}_{x,y,z}[\left\|y-G(x,z)\right\|_{1}] \tag{2}\]  
Here, G is the generator, x and y are the coarse input and generated textures, respectively, and z is the dropout noise.
## 3 Improved Radial Movement Optimization
### Implementation of IRMO  
According to the studies above, one solution of the five-segment trajectory can be determined by an M dimensional vector containing six variables: \(L_{1}\), \(\theta_{1}\), \(R_{2}\), \(L_{3}\), \(R_{4}\), and \(\theta_{2}\). The N trajectories form a standard matrix [XN,M] with N rows and M columns (M = 6). The standard matrix [XN,M] realizes the connection between the IRMO algorithm and the HDD trajectory optimization (shown in Equation (11)).  
\[\left[\text{X}_{\text{N,M}}\ \right]=\left[\begin{array}{ccccc}L_{1,1}^{1}& \theta_{1,2}^{1}&R_{1,3}^{2}&L_{1,4}^{3}&R_{1,5}^{4}&\theta_{1,6}^{2}\\ \vdots&\ddots&&\vdots\\ L_{N,1}^{1}&\theta_{N,2}^{1}&R_{N,3}^{2}&L_{N,4}^{3}&R_{N,5}^{4}&\theta_{N,6}^{ 2}\end{array}\right] \tag{11}\]After setting the ranges of each variable and the constraint conditions, optimization begins with the initial matrix \([X_{\text{N,M}}]\) initiated by Equation (12).  
\[X_{\text{i,j}}=X_{\text{min j}}+(X_{\text{max j}}-X_{\text{min j}})\times\text{ rand}(0,1) \tag{12}\]  
where \(X_{\text{max j}}\) is the upper limit of the j-th variable in \([X_{\text{ij}}]\), and \(X_{\text{min j}}\) is the lower limit.  
The initial central particle \(cp^{1}\) is chosen from the initial \([X_{\text{N,M}}]\) which has the best fitness value. The central particles \(cp^{k}\) will be updated by _Rbest_ and _Gbest_, as shown in Equation (13). \(c_{1}\) and \(c_{2}\) are proportional coefficients that affect the convergence speed and accuracy of the calculation. The values of \(c_{1}\) and \(c_{2}\) are set as 0.5 and 0.4, respectively, in this study.  
\[cp^{k}=cp^{k-1}+c_{1}\times(Gbeat^{k-1}-cp^{k-1})+c_{2}\times(Rbeat^{k-1}-cp^{k -1}) \tag{13}\]  
Following Equation (14), prepositioned particles \([Y_{\text{i,j}}]\) are generated by the previous central particles.  
\[Y_{\text{i,j}}{}^{k}=cp^{k}+(X_{\text{max j}}-X_{\text{min j}})\times rand(-0.5,0.5)\times w^{k} \tag{14}\]  
\[w^{k}=1.009\times exp(-(1/generation+0.03307)^{2}/0.2454) \tag{15}\]  
To calculate the fitness values of \([Y_{\text{i,j}}]\): If the fitness values of \([Y_{\text{i,j}}{}^{k}]\) are better than those of \([X_{\text{i,j}}{}^{k}]\), the \([X_{\text{i,j}}{}^{k+1}]\) will be updated to \([Y_{\text{i,j}}{}^{k}]\), otherwise \([X_{\text{i,j}}{}^{k}]\) will persist to the next generation. The determination of inertia weight \(w^{k}\) uses a Gaussian function (Equation (15)).  
Based on the objective functions given above (Equations (7) and (8)), the design and optimization of the HDD trajectories in this study were implemented by IRMO on MATLAB 2021b. The implementation of the IRMO algorithm for HDD trajectory optimization is shown in detail in Figure 5.  
Figure 4: Principle of updating the central particle [34].
## 4 Machine Learning-Based Label Quality Assurance
### Dataset for Machine Learning-Based Label Quality Assurance Method Verification  
The dataset employed for the machine learning-based label quality assurance method experiments is Microsoft Common Objects in Context (MS COCO) [40]. MS COCO is a widely used dataset containing a diverse collection of images for various tasks, including object detection, segmentation, key-point detection and captioning. For the purpose of these experiments, we focus on the object detection task, where the goal is to identify bounding boxes surrounding objects in the images. The MS COCO dataset comprises a training set of 118,287 images and a validation set of 5000 images, each with labeled bounding boxes. Some labels carry an \"iscrowd = 1\" flag, indicating that large groups of objects are annotated using a single bounding box. We exclude these samples, resulting in a filtered training set of 109,172 samples and a filtered validation set of 4589 samples. The MS COCO dataset contains 80 predefined classes, with each annotated bounding box for the object detection task belonging to one of these classes based on the object it surrounds. The classes include: \"airplane\", \"apple\", \"backpack\", \"banana\", \"baseball bat\", \"baseball glove\", \"bear\", \"bed\", \"bench\", \"bicycle\", \"bird\", \"boat\", \"book\", \"bottle\", \"bowl\", \"broccoli\", \"bus\", \"cake\", \"car\", \"carrot\", \"cat\", \"cell phone\", \"chair\", \"clock\", \"couch\", \"cow\", \"cup\", \"dining table\", \"dog\", \"donut\", \"elephant\", \"fire hydrant\", \"fork\", \"frisbee\", \"giraffe\", \"hair drier\", \"handbag\", \"horse\", \"hot dog\", \"keyboard\", \"kite\", \"knife\", \"laptop\", \"microwave\", \"motorcycle\", \"mouse\", \"orange\", \"oven\", \"parking meter\", \"person\", \"pizza\", \"pottedplant\", \"refrigerator\", \"remote\", \"sandwich\", \"scissors\", \"sheep\", \"sink\", \"skateboard\", \"skis\", \"snowboard\", \"spoon\", \"sports ball\", \"stop sign\", \"suitcase\", \"surfboard\", \"teddy bear\", \"tennis racket\", \"tie\", \"toaster\", \"toilet\", \"toothbrush\", \"traffic light\", \"train\", \"truck\", \"TV\", \"umbrella\", \"vase\", \"wine glass\" and \"zebra\".  
In this study, we consider the MS COCO validation set as a _small set_, which is used as the training and validation set for constructing the label quality assurance model. The MS COCO training set is treated as a test set for evaluating the label quality assurance model. Figure 7 presents a labeled sample from the label quality assurance training set (a subset of the MS COCO validation set):
## 4 Solution Approach
### Objective and Constrains
#### 4.1.2 Subproblem 2 (Related Decision Variable \(y_{sl}\))  
\[Z_{\text{sub1.2}}(\ \mu_{sl}^{2})=\min\ \sum_{s\in S}\sum_{l\in L}(-\mu_{sl}^{2} \big{|}D_{s}\big{|})y_{sl},\]  
**Subject to:**  
\[y_{sl}=0\ or\ 1 \forall s\in S,l\in L\] (LR 1.2) \[\sum_{l\in L}y_{sl}\geq\max\left\{H_{s},\left|D_{s}\right|\right\} \forall s\in S\] (LR 1.6) \[\sum_{l\in L_{s}}y_{sl}=0 \forall s\in S\] (LR 1.7) \[\sum_{l\in L_{s}}y_{sl}\leq 1 \forall s\in S,v\in V-\{S\}.\] (LR 1.8)  
Subproblem 2 can be further divided into \(/S/\!\) independent subproblems. For each multicast group \(s\), here is an algorithm [2] stated as following to solve each subproblem:  
**Step 1:** Compute \(\max\{H_{s},|D_{s}|\}\) for each multicast group \(s\).  
**Step 2:** For each group s, compute the coefficient (\(-\mu_{sl}^{2}D_{s}\)/) for each link, and count the number of the coefficient (\(-\mu_{sl}^{2}D_{s}\)/).  
**Step 3:** If the counting number is larger than \(\max\{H_{s},|D_{s}|\}\), assign the corresponding \(y_{sl}\) to 1, and others are set to 0.  
**Step 4:** If the counting number is not larger than \(\max\{H_{s},|D_{s}|\}\), assign the corresponding \(y_{sl}\) to 1. Then, assign the remaining \(\{\max\{H_{s},|D_{s}|\}\)--the counting number] of smallest positive coefficients' corresponding \(y_{sl}\) to 1, and others are set to 0.
###### Abstract  
The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration.  
L vRT; HVRT; ESS; parameter identification; multi-algorithm fusion analysis; MT 6040 simulator +
Footnote †: journal: Applied sciences
## 4 Experimental Results and Analysis
### Comparison between the Proposed Method and Other Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Images**} & \multirow{2}{*}{**Number of Thresholds (K)**} & \multicolumn{6}{c}{**Optimal Threshold Values**} \\ \cline{2-7}  & & **Proposed** & **PSO** & **BF** & **ABF** & **NMS** & **RCCA** \\ \hline  & 2 & 55, 97 & 98,166 & 108, 174 & 108, 174 & 108, 173 & 107, 170, 174 \\ \#102 & 3 & 25,64,97 & 113, 145, 180 & 103, 148, 189 & 98, 168, 189 & 94,12, 189 & 94, 142, 190 \\  & 4 & 25,62, 86, 118 & 84, 124, 156, 189 & 78, 122, 164, 200 & 90, 127, 164, 198 & 2, 64, 119, 173 & 1, 63, 120, 174 \\  & 5 & 25,62, 86, 112, 140 & 99, 128, 174, 194, 218 & 81, 113, 114, 115, 117, 220 & 82, 114, 185, 182, 128 & 9, 62, 108, 147, 190 & 1, 62, 104, 145, 189 \\ \hline  & 2 & 54,100 & 109, 162 & 105, 165 & 105, 164 & 106, 163, 164 & 106, 166, 163 \\ \#112 & 3 & 276, 111 & 104, 136, 226 & 73, 140 & 7, 123, 175 & 3, 9, 49, 145 & 1, 70, 142 \\  & 4 & 25, 61, 91, 111 & 63, 103, 153, 206 & 54, 171, 156, 192 & 58, 105, 164, 182 & 4, 6, 313, 178 & 1, 65, 223, 172 \\  & 5 & 18, 49, 76, 94, 111 & 56, 128, 135, 187, 213 & 48, 112, 137, 161, 200 & 47, 108, 142, 171, 197 & 2, 44, 79, 131, 175 & 1, 49, 95, 139, 183 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Reconstruction of Ancient Lake after Peat Excavation--A Case Study about Water Quality
## 2 Materials and Methods  
### Study Site  
Nienawiszcz is a village in the Wielkopolska (Greater Poland) region (Poland) with areas covered by forests, arable fields, meadows, waters and peat bogs. The peat-lake studied was localized at 52\({}^{\circ}\)40\({}^{\prime}\)31.6\({}^{\prime\prime}\) N, 16\({}^{\circ}\)59\({}^{\prime}\)05.0\({}^{\prime\prime}\) E southwest from the village and was constructed in consecutive steps. The geology of the watershed is the effect of Baltic ice deglaciation in the Holocene and in the site of an ancient lake, peat and gyttja developed. To the depth of about 120 m, there are tertiary and quaternary mineral deposits. The lowest are tertiary formations of silt and fine sands with lignite layers, which are the lowest parts of the Miocene deposit. Above them there is a layer of sands with a thickness of 10-20 m, locally overgrown with muddy sediments. These deposits are covered by a layer of clay and silt sediments with a coal layer of 10-30 m in thickness. Within it, there are local sandy layers--silty sands, sometimes sandstones. Another layer consists of Miocene and Pliocene clays with strongly differentiated thickness [18]. The subquaternary layer is strongly differentiated and is located at a height of approximately 30-70 m above sea level. The geology of study site is presented in Figure 1.  
All works on the post-peat lake were carried out in three steps:  
1. Identification of the soil and water conditions of the area, physicochemical analyses of peat;
2. Exploitation of the peat and the simultaneous construction of the reservoir (the construction was carried out in two stages--during the period 2004-2007, when peat was exploited in the northeastern part of lake, which after being separated by a causeway in 2007 was flooded with water (A) (Figure 2); and the remaining part of the reservoir (B) (Figure 2) during the period 2007-2012 was drained by pumping out the water in order to exploit the dry peat),
3. From July 2012 to October 2015, parts A and B were connected by the removal of the causeway and filling the whole reservoir with water, further analysis of water quality and plants species structure.  
Figure 1: Stratigraphic column of Nienawiszcz reservoir before peat excavation.  
Figure 2: Sketch map of studied post-peat reservoir Nienawiszcz (III) after connection of both parts (**A**,**B**) and small post-peat reservoirs I and II, finished in years 1999 and 2000, respectively.
## 5 Analysis of Experimental Data
### Earth Fault Testing in TT Configuration  
The ground fault test results are illustrated in Figure 13, showcasing the recorded waveform of the fault process in its entirety (a) and a magnified view (b). Channel 1 displays the residual current waveform of measurement point 1, while channels two and three exhibit the residual current waveforms of measurement points 2 and 3. During the test, a current transformer (with the load being resistance) is employed to measure the residual current, with the unit of measurement in millivolts (mV).  
Figure 12: Schematic of the test case.  
The initiation of the fault is indicated in Figure 13a, revealing that post fault, there is minimal change in the residual current amplitude at measurement point 1. Subsequently, upon the introduction of saltwater at the fault location, arc fault characteristics manifest, as depicted in Figure 13b. Notably, the residual current at measurement point 1 exhibits a distinct current arc-extinguishing (zero breaks) feature at the zero-crossing point. Figure 14 illustrates the computation of the residual current within the closed section established by the three measurement points.  
In Figure 14, in the absence of faults, the residual current amplitude remains below 10 mA. However, following the fault occurrence, the fault residual current surges to 126 mA, representing a noticeable increase. The discernible change in the residual current amplitude post fault highlights the reliable detection of earth faults based on the residual current value within the closed section.
## 3 Results and Discussion
### The Relationship Between Surface-to-Volume Ratio and Sensitivity
Based on the FE-SEM analysis, about 70% of the nested nanorods were exposed by successful removal of the sacrificial Al\({}_{3}\)O\({}_{2}\) layer. For the other 30%, the remaining surface cap stood in the way. It is worth noting that the ALD wrap-around coating worked on 100% of all nanorods. However, on 30% of the nested nanotube structures, the ALD surface capping layer was not removed, due to insufficient rod height. It is also apparent that the process of ALD synthesis of wrap-around sacrificial films followed by ALD ZnO sensing film coatings can be continued to create even more complex nested ZnO thin-film nanostructures by adding additional nested films. Each new coaxial ALD ZnO film will further improve the surface-to-volume ratio and the device sensitivity. Depending on the application requirements, the device sensitivity can be increased to the desired sensitivity by adding n-numbers of additional free-standing coaxial ZnO films.
## 4 Methodology of NOx Prediction  
_Multi-Layer Perceptron (MLP) Network_  
One of the most popular ANNs frequently used in engineering, medicine, and mathematical modelling applications is a Multi-Layer Perceptron (MLP) network [68; 69]. A Multi-Layer Perceptron (MLP) network is a type of neural network that consists of multiple layers of interconnected nodes (or neurons) arranged in a feedforward manner. It is a supervised learning algorithm used for classification and regression tasks. The structure of an MLP neural network is composed of input units, hidden units, and output units (see Figure 4). Inputs are multiplied by weights and added to bias conditions to form a weighted sum. The activation level of each unit is calculated by applying a transfer function to the weighted sum and bias condition. The MLP is deployed in a layered feed-forward topology in which information flows in only one direction from the input layer through the hidden layers to the output layer. This structure is commonly referred to as a Feed-Forward Neural Network. Each layer has several processing units and is fully interconnected with weighted connections to units in the subsequent layer. The MLP transforms \(n\) inputs to \(l\) outputs through some nonlinear functions [70; 71; 72].  
In our case, multiple inputs (LHD working parameters) and only one output (NOx value) are considered.  
\[\begin{split} x_{t}&=A_{t-1}x_{t-1}+q_{t}\\ y_{t}&=H_{t}x_{t}+r_{t}\end{split} \tag{1}\]  
Based on a dataset with input parameters obtained from the LHD monitoring system, this ANN was trained. Then, tests were conducted to validate the accuracy of the NOx prediction.  
The ANN created for solving the NOx emission prediction problem consists of 1 input layer, 1 hidden layer, and 1 output layer. For inputs, 11 parameters are used, and the output is only \(1-\)NOx emission level. In the hidden layer, 20 neurons are used. Input data are normalized to the range \([-1,1]\), and the transfer functions used are hyperbolic tangent sigmoid for the hidden layer and linear for the output layer. As a loss function, MSE was chosen. Data are split using random sampling into 3 datasets--training data (70%), validation data (15%), and test data (15%).  
\begin{table}
\begin{tabular}{c c c c} \hline No. & Parameter & Description & Units \\ \hline  & **ENGNOX** & NOx Emissions & ppm \\ \hline
1 & **ENGCOLT** & Coolant temperature & \({}^{\circ}\)C \\
2 & **ENGOLLT** & Oil Temperature & kPa \\
3 & **ENGRPM** & Engine rotations & rpm \\
4 & **ENGTS** & Engine acceleration & \% \\
5 & **FUELUS** & Fuel consumption & L/h \\
6 & **GROILP** & Gear oil pressure & kPa \\
7 & **GROILT** & Gear oil temperature & C \\
8 & **HYDOILP** & Hydraulic oil pressure & MPa \\
9 & **INTAKEP** & Intake air pressure & kPa \\
10 & **INTAKET** & Intake air temperature & \({}^{\circ}\)C \\
11 & **SPEED** & Vehicle speed & km/h \\
12 & SELGEAR & Selected gear & \(-4...0..4\) \\ \hline \end{tabular}
\end{table}
Table 4: Parameters of LHD operation taken from the onboard monitoring system for NOx emission analysis.