esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 6 Conclusions  
This paper proposed a DWT_LSTM prediction method based on the divide and conquer strategy. The DWT was used to decompose original wind power data into sub-signals, the main information. The independent LSTM was designed to approximate the temporal dynamic behaviors of the sub-signals respectively.  
With the proposed prediction method, the main information that was contained in the approximated wind power sub-signal and the minor information contained in detailed wind power sub-signals can be identified by different LSTMs more efficiently. Also, the temporal relationship contained in the input sequence data can be learned through independent LSTMs. The experimental  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Step** & **Algorithms** & **MAE/MW** & **MAPE/\%** & **RMSE/MW** \\ \hline \multirow{4}{*}{4} & DWT\_LSTM & 19.85 & 5.77 & 29.73 \\  & DWT\_RNN & 20.08 & 6.75 & 29.89 \\  & DWT\_BP & 22.94 & 6.76 & 34.37 \\  & LSTM & 25.96 & 7.65 & 39.83 \\  & RNN & 26.47 & 8.62 & 39.84 \\  & BP & 26.71 & 8.71 & 39.96 \\ \hline \multirow{4}{*}{5} & DWT\_LSTM & 23.96 & 7.11 & 35.83 \\  & DWT\_RNN & 24.20 & 7.20 & 36.22 \\ \cline{1-1}  & DWT\_BP & 26.67 & 8.48 & 40.01 \\ \cline{1-1}  & LSTM & 29.27 & 8.61 & 44.10 \\ \cline{1-1}  & RNN & 30.14 & 10.01 & 44.13 \\ \cline{1-1}  & BP & 30.26 & 10.06 & 44.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._results show that the proposed prediction method has the best prediction accuracy compared with the benchmarks.  
In future work, the optimizer algorithm of LSTM can be further improved to enhance the learning ability for time series prediction. In addition, DWT_LSTM is an accurate forecasting method that can be used as a reference for the prediction of other renewable energy power generation.  
[PERSON], [PERSON] and [PERSON] proposed the main idea. [PERSON] and [PERSON] completed the experiments. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] provided guidances to this paper. The manuscript was written through contributions of all authors.  
This research was funded by the Science and Technology Project Foundation of Southern Power Grid Co., Ltd. of China under Grants no. GDKJXM20161900, and the Joint Research Fund of Chinese Ministry of Education under Grant no. 6141A02033514, and the Natural Science Foundation of Hunan Province of China under Grant no. 2016 JJ1019, and the National Natural Science Foundation of China under Grants no. 61622311.  
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions.  
The authors declare no conflict of interest.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Experimental Results  
#### 4.3.1 Indoor Experimental Results  
Because of their uneven or dim lighting and pillars, manmade indoor parking structures are one of the most challenging settings. Figure 22a shows the B5 parking structure at the National Taipei University of Technology. Figure 22b shows the detection process. In this figure, the first parking space has very few edges. In combination with the high region growing ratio, this space was classified as vacant. The second space was occupied by a parked car; thus, a large number of edges were detected. In addition, the region growing ratio was relatively low. Thus, this space was classified as occupied. The third space was vacant, but because the angle between the car in the second parking space and the camera was not absolutely perpendicular, part of that car was projected into the third parking space. This resulted in an increased number of edges. However, with the assistance of road features, the space was correctly classified as vacant. Table 1 lists the quantified data for these results. Figure 22c shows the detected results.  
Figure 21: Experimental process.
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 3 Model Predictive Intersection Control Design
### Energy-Optimal Intersection Control Design  
Another priority of the intersection management may be to minimize the total energy consumption of the autonomous vehicles. Here, the conditions listed in Section 3.2 along with collision avoidance and dynamic constraints must also be considered. However, when ordering vehicles and defining accelerations, energy optimality is a fundamental design element. Hence, the main difference from the time-optimizing algorithm is that the vehicles at the intersection are sorted not by travel time but by their kinetic energy \(E_{i,kin}=0.5m_{i}v_{i}^{2}\)\(i\in[1...n]\). This calculation assumes that each autonomous vehicle, in addition to its initial position and speed, sends information to the coordinator about its mass \(m_{i}\)\(i\in[1...n]\).  
The iterative computation of the vehicle ordering by the intersection coordinator are as follows:  
* The maximum speed \(v_{i,max}\)\(i\in[1...n]\) is calculated and an additional limit on the maximum speed is defined as \(v_{i,max}\leq v_{i,0}\). Here, the difference compared to the time-optimized algorithm is that only the negative acceleration \(a_{i}\) can be assigned for vehicles.
* Entry time \(t_{i,ent}\) and exit time \(t_{i,fin}\in[1...n]\) are defined based on (4) and (7). If no conflict is detected, vehicles adapt the previously calculated accelerations to cross the intersection.
* In the event of a conflict between two or more vehicles, the energy optimization algorithm is evaluated as follows: vehicle with the maximum kinetic energy \(\max(E_{i,kin})\)\(i\in[1...n]\) gets priority and does not change its acceleration. The algorithm then reduces the acceleration of the other vehicles in an iterative manner similarly as listed in the time-optimized solution, with the difference that the priority is always chosen based on kinetic energy.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 3 System Models and Problem Statement
### Problem Statement  
Based on the literature review, it is observed that most of the works are based on a static sink, random mobility and grid mobility, which lead to the inefficiency of the clustered topologies of their schemes. To prove this issue, we use the spiral mobility pattern based on optimized clustering technique, which provides stronger and more stable network. To verify our proposed models, we use simulations to prove how different ranges of clusters create variation in the network topology. Moreover, our concern in this study is to describe the effects of sink mobility patterns on the network lifetime. Specifically, the objective of this study is to seek solutions of these problems:  
* What will be the features of mobile sink that can maximize the network lifetime?
* Which is a suitable mobility pattern based on the comparative results?
* If we compare the controlled mobility pattern with random mobility, how much improvement in the lifetime of the network can be achieved?
* Why is the grid mobility pattern not suitable for large-scale networks?
## 2 Materials and Methods
### Evaluation of Black Spot Disease Infection Levels  
The phenotypic evaluation method refers to the rating system previously described [31], but with modifications. The leaves were evaluated according to the following rating system: 0--no lesions and no host reaction observed; 1--a lesion with a diameter of 0-0.2 cm appears at the inoculation hole; 3--a lesion with a diameter of 0.2-0.5 cm appears at the inoculation hole; 5--a lesion with a diameter of 0.5-1.0 cm appears at the inoculation hole, or a yellowing area less than 0.5 cm around the lesion; 7--a lesion with a diameter of 1.0-1.5 cm appears at the inoculation hole, or an existing yellowing area spread to 0.5-1.0 cm around the lesion; 9--a lesion with a diameter of 1.5-2.0 cm appears at the inoculation hole, or a yellowing area more than 1.0 cm around the lesion. The black spot susceptibility index (BSI) was calculated following the formula BSI (\(\%\)) = (\(\sum\)_n_X_/9N) \(\times\) 100, where \(X\) represents different disease severity level, \(n\) represents the leaves per level, and N represents the number of total leaves evaluated for each line. We further classified different plant resistance standards depending on the BSI. High disease resistance (BSI-1), 0-11.11%; disease resistance (BSI-2), 11.12-33.33%; disease tolerance (BSI-3), 33.34-55.55%; susceptible (BSI-4), 55.56-77.77%; high susceptibility (BSI-5), 77.78-100%. BSI-1 shows the strongest resistance to black spot disease, while BSI-5 represents the weakest resistance to black spot disease.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 4 Conclusions and Outlook  
In situ neutron radiography investigations can provide information that cannot be obtained by other methods. The kinetics of chemical reactions can be studied because illumination times of a few seconds are often suitable to obtain relevant data. A high spatial resolution in the range of some hundreds or even tens of micrometers is suitable for studying processes depending on length parameters such as diffusion processes or inhomogeneous reactions influenced by inhomogeneous mechanical stress fields in a sample.  
In the framework of the study of hydrogen-related processes in zirconium alloys (hydrogen uptake, diffusion, redistribution), new insights into the kinetics were obtained by in situ neutron radiography experiments. The rapid hydrogen uptake at the beginning of the oxidation of zirconium alloys in steam and the early beginning of the breakaway oxidation in a nitrogen/steam mixed atmosphere are examples.  
Recent and near future applications of in situ neutron radiography on the zirconium-hydrogen system are the investigation:  
* Of the hydrogen redistribution during the so-called delayed hydride cracking (hydrogen migrates to the stress field ahead of a crack tip, resulting in an embrittlement of this region, the crack growth into this region, and the hydrogen has to migrate to the new position of the stress field until the sample fails);
* Of the redistribution of hydrogen in cladding tubes consisting of two different zirconium alloys (for instance, the D\({}_{\mathrm{x}}\)/D\({}_{\mathrm{4}}\) composite consisting of a Zry-4 bulk and a 150 um thick liner with reduced tin concentration);
* Of parameters influencing the solubility and diffusion rate of hydrogen in cladding tube materials under the conditions of long-term (100 years) dry storage of spent nuclear fuel rods (reorientation of hydrides from a perpendicular to a radial direction under tensile load).  
Several groups worldwide work on these fields. The first results have already been achieved (for instance, [12; 20]). However, all these processes are not yet completely understood and quantified to develop precise models predicting the cladding tube state after long-term storage of nuclear fuel.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11135775/s1](https://www.mdpi.com/article/10.3390/app11135775/s1)]([https://www.mdpi.com/article/10.3390/app11135775/s1](https://www.mdpi.com/article/10.3390/app11135775/s1)): Video S1: Hydrogen diffusion into a Zircaloy-4 solid cylinder, Video S2: Hydrogen uptake during steam oxidation of Zircaloy-4 at 1000 \({}^{\circ}\)C, Video S3: Reaction of Zircaloy-4 in a nitrogen/steam atmosphere at 900 \({}^{\circ}\)C.  
Conceptualization, M.G.; methodology, M.G., A.K., B.S.; investigation, M.G., A.K., B.S.; writing--original draft preparation, M.G.; writing--review and editing, A.K., B.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the program NUSAFE at KIT. No external funding was received.
# FEM-Based Compression Fracture Risk Assessment in Osteoporotic Lumbar Vertebra L1
## 3 Numerical Results and Discussion
### Estimation of the Fracture Risk
#### 3.3.2 Stochastic Models and for the Risk Modelling
\(G_{\beta}\), \(G_{\delta}\), and \(G_{p,E}\) must be adopted. In the case of the Gaussian r.vs., only the means, variances, and covariances are required. The coefficient of variation \(Cvar(G_{\beta})\) of BV/TV can vary in a wide range, for example, from about 18% [18; 33] up to 37% [34] or even up to 42% for the normal bones [26]. There is less literature on the coefficient of the variation of the cortical shell thickness \(\delta\). We found that the coefficient of variation is also big, for example, 24.5% [33] or even 62.4%, according to Reference [34]. For the patient-specific cases, the variability of these parameters should be less. However, since the BV/TV ratio is determined indirectly, the variability of r.v. \(X_{\beta}\) cannot be very low. As it was already mentioned, the r.vs. \(X_{\beta}\) and \(X_{\delta}\) are dependent [33; 34]. In the case of the Gaussian bivariate r.v. \((G_{\beta},G_{\delta})\), the correlation coefficient \(Cor(G_{\delta},G_{\beta})\) is used in the covariance matrix given in Equation (11) and it is determined that \(Cor(G_{\delta},G_{\beta})\) may equal 0.5 [33] or 0.29 [34]. The external load \(X_{p,E}\) acting on the vertebra is a random variable depending mainly on the mechanical loads. For example, according to Reference [28], the ground reaction of a human may vary from 750 N in the rest state and increases up to 2000 N in the squat jump. The ground reaction of the rest state shows that the mass of the investigated human body in Reference [28] is about 75 kg. Then, in the squat jump, the weight of the body increases \(2000/750=2.66(7)\) times. It should be mentioned that not all the weight of a body is imposed on the lumbar vertebra. On the basis of the data given in Reference [35], it is possible to conclude that a lumbar verteb sustains about 60% of the total load. By taking into account the area of the loaded surface of the vertebra under investigation, \(A=1210\) mm\({}^{2}\), we can conclude that, for the person of article [28], the external load varies within an interval \([p_{min},p_{max}]=[0.372,0.992]\) MPa.
## 1 Introduction  
### Research Background  
The sustained growth in global vehicle ownership has significantly amplified the demand for efficient vehicle maintenance and repair services [1]. Concurrent advancements in mechatronics and embedded electronics have driven unprecedented improvements in automotive automation capabilities but simultaneously introduced increasingly sophisticated architectures that demand advanced diagnostic methodologies [2]. Consequently, traditional experience-based maintenance paradigms struggle to meet the escalating requirements for rapid and precise fault identification in modern automotive systems. This technological gap underscores the critical need for intelligent diagnostic systems integrating multi-modal sensing, cognitive electronics, and cloud-enabled data analytics to address the growing imperatives of globalized automotive ecosystems--marked by intensifying international competition, and Industry 4.0-driven transitions toward automated, digitized, and networked manufacturing infrastructures [3, 4, 5].  
Rotational speed serves as a critical parameter for the comprehensive assessment of engine performance and a holistic indicator of vehicular operational integrity [6]. Dynamicvariations in rotational speed enable predictive diagnostics of an engine's operational states while facilitating accurate power output estimation through thermodynamic correlation models [7; 8]. Furthermore, the real-time monitoring of rotational speed fluctuations allows for the early detection of incipient mechanical faults--such as misfire events, bearing wear, or timing chain degradation--during engine operation. This ability is critical for preemptive failure mitigation, significantly reducing unplanned downtime and enhancing operational safety across automotive and industrial powertrain applications [9]. Current mainstream methodologies face three primary limitations: (1) contact-based sensors utilizing magnetoelectric/Hall effects are constrained by mechanical installation restrictions and electromagnetic compatibility issues; (2) OBD signal analysis methods relying on crankshaft/camshaft signals are limited by proprietary vehicle communication protocols; (3) non-contact detection techniques based on vibration/acoustic signals are susceptible to environmental noise and multi-source vibrational interference. In application scenarios, such as annual vehicle inspections and rapid fleet diagnostics, the existing technologies struggle to simultaneously satisfy the tripartite requirements of measurement speed, implementation cost, and operational efficiency, significantly impeding the development of large-scale digital operation and maintenance frameworks. Studies by SAE International [10] reveal a 17% failure rate for conventional methods in the field diagnostics of heavy-duty commercial vehicles, underscoring the urgent need for novel non-invasive measurement architectures.
## Appendix A  
Definition of the Kennaugh Matrix and its elements for quad-polarized (A1), HH/VV-polarized (A2) and HH/HV- or VV/VH-polarized data (A3) is reported in the following and based on [31].  
\(S_{\rm XX}\) and \(S_{\rm YY}\) refer to the complex signals of the co-polarized channels. \(S_{\rm XY}\) refers to the complex signal of a cross-polarized channel.  
\[\begin{array}{c}{\bf K}_{quad}=\left[\begin{array}{cccc}\text{K0}&\text{K4}& \text{K5}&\text{K6}\\ \text{K4}&\text{K1}&\text{K9}&\text{K8}\\ \text{K5}&\text{K9}&\text{K2}&\text{K7}\\ \text{K6}&\text{K8}&\text{K7}&\text{K3}\end{array}\right],\text{ with }\\ \text{K0}=0.5\Big{(}|S_{\rm XX}|^{2}+2|S_{\rm XY}|^{2}+|S_{\rm YY}|^{2}\Big{)} \\ \text{K1}=0.5\Big{(}|S_{\rm XX}|^{2}-|S_{\rm XY}|^{2}-|S_{\rm YY}|^{2}+|S_{ \rm YY}|^{2}\Big{)}\\ \text{K2}=\Re(S_{\rm XX}S_{\rm YY}\ast-S_{\rm XY}S_{\rm XY}\ast)\\ \text{K3}=-\Re(S_{\rm XX}S_{\rm YY}\ast-S_{\rm XY}\ast^{*}S_{\rm XY})\\ \text{K4}=0.5\Big{(}|S_{\rm XX}|^{2}-|S_{\rm XY}|^{2}+|S_{\rm XY}|^{2}-|S_{ \rm YY}|^{2}\Big{)}\\ \text{K5}=\Re(S_{\rm XX}S_{\rm XY}\ast+S_{\rm XY}S_{\rm YY}\ast)\\ \text{K6}=\Im(S_{\rm XX}S_{\rm XY}\ast+S_{\rm XY}S_{\rm YY}\ast)\\ \text{K7}=\Im(S_{\rm XX}S_{\rm YY}\ast+S_{\rm XY}S_{\rm XY}\ast)\\ \text{K8}=\Im(S_{\rm XX}S_{\rm XY}\ast-S_{\rm XY}S_{\rm YY}\ast)\\ \text{K9}=\Re(S_{\rm XX}S_{\rm XY}\ast-S_{\rm XY}S_{\rm YY}\ast)\\ \end{array} \tag{18}\]  
\[\begin{array}{c}{\bf K}_{HH/VV.}=\left[\begin{array}{cccc}\text{K0}&\text{ K4}&\text{0}&\text{0}\\ \text{K4}&\text{0}&\text{0}&\text{0}\\ \text{0}&\text{K2}&\text{K7}\\ \text{0}&\text{0}&\text{K7}&\text{0}\end{array}\right],\text{ with }\\ \text{K0}=0.5\Big{(}|S_{\rm XX}|^{2}+|S_{\rm YY}|^{2}\Big{)}\\ \text{K4}=0.5\Big{(}|S_{\rm XX}|^{2}-|S_{\rm YY}|^{2}\Big{)}\\ \text{K3}=-\Re(S_{\rm XX}S_{\rm YY}\ast)\\ \text{K7}=\Im(S_{\rm XX}S_{\rm YY}\ast)\\ \end{array} \tag{19}\]  
\[\begin{array}{c}{\bf K}_{XHH/HV\ or\ VV/VH.}=\left[\begin{array}{cccc} \text{K0}&\text{0}&\text{K5}&\text{K6}\\ \text{0}&\text{K1}&\text{0}&\text{0}\\ \text{K5}&\text{0}&\text{0}&\text{0}\\ \text{K6}&\text{0}&\text{0}&\text{0}\end{array}\right],\text{ with }\\ \text{K0}=0.5\Big{(}|S_{\rm XX}|^{2}+2|S_{\rm XY}|^{2}\Big{)}\\ \text{K1}=0.5\Big{(}|S_{\rm XX}|^{2}-2|S_{\rm XY}|^{2}\Big{)}\\ \text{K5}=\Re(S_{\rm XX}S_{\rm XY}\ast)\\ \text{K6}=\Im(S_{\rm XX}S_{\rm XY}\ast)\\ \end{array} \tag{20}\]  
Conversion of Kennaugh Matrix to 3 \(\times\) 3 Coherency Matrix (**T**) of quad-polarized (19) and 2 \(\times\) 2 T of HH/VV-polarized data (17) is defined according to [31; 42] as:  
\[\begin{array}{c}{\bf T}_{quad}=\left[\begin{array}{cccc}\text{T}_{11}& \text{T}_{12}&\text{T}_{13}\\ \text{T}_{21}&\text{T}_{22}&\text{T}_{23}\\ \text{T}_{31}&\text{T}_{22}&\text{T}_{33}\end{array}\right]=\\ =\left[\begin{array}{cccc}0.5(\text{K0}+\text{K1}+\text{K2}-
## 1 Introduction
### Research Differentiation and Contributions  
This study proposes an innovative approach that integrates 3D object generation, texture mapping, and data optimization, providing differentiated contributions from existing research:  
1. Optimization of 3D Object Generation Processing Speed: The system generates precise 3D objects by integrating Shapefile-based 2D spatial data (buildings, roads, administrative district) with high-resolution Digital Elevation Model (DEM) data. It generates structured triangular meshes using Delaunay triangulation [12], which is combined with the Douglas-Peucker algorithm to enhance computational speed and optimize data size [13].
2. Enhancement of Texture Mapping Quality: This study applies the WIT technique using orthoimages. The proposed technique enhances texture alignment accuracy by refining UV mapping precision and improves visual quality by minimizing lighting inconsistencies and masking artifacts [14; 15].
3. Optimization of Vector-based Spatial Information Data Processing: This study performs memory usage reduction through efficient data compression and algorithm optimization in large-scale datasets. By implementing CityGML Validator and parallel processing technologies, it reduces resource consumption during data conversion and validation processes [16; 17].  
This research presents CityGML 3.0-validated three-dimensional models, establishing a comprehensive foundation for practical implementation across multiple domains, including smart city development, disaster management systems, and urban planning initiatives.