esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The different thermal expansion coefficients of the filler particles and resin matrix induce internal stresses in the material during continual temperature changes. Resulting cracks and gaps damage the mechanical properties over time, accelerating aging. Additionally, in materials with nano-zirconia filler, zirconia may undergo a low-temperature degradation with a phase transition from tetragonal to monoclinic phase at oral temperature, overlapping the abovementioned degradation [29; 30; 31]. It was demonstrated that a definite water content of enamel and dentine structures is mandatory for their physical characteristics and biomechanical behavior, providing stability and robustness. Water absorption induces a greater risk of decomposition of the molecular structure on the microscale, and is considered to change the physical properties and the lifetime of dental restorative materials. Accordingly, the impact of water content on dental materials represents a challenge, as well as their susceptibility to degradation in water and aging [32; 33; 34]. Polymer networks can be strongly altered by the wet oral environment [35]. There is still limited information regarding the effect of aging on the optical stability of these materials [36; 37; 38; 39; 40; 41; 42] Color preservation of esthetic dental restorative materials in the oral environment represents besides longevity a concern in the use of esthetic dental restorative materials. Restorations should reproduce the optical properties of natural teeth and maintain this natural appearance over time. These are directly related to optical harmony with natural teeth and to the color stability of the material. Spectrophotometers are currently used to evaluate the color change (\(\Delta\)E*) of dental materials by quantifying the color coordinates in the CIELAB color space: L*, a* and b*. The L* coordinate is an indicator of brightness, the a* coordinate indicates the red-green component (negative a* represents greenness and positive a* indicates redness), and the b* coordinate corresponds to yellowness or blueness (negative b* indicates blueness and positive b* represents yellowness). L* coordinates are the most important parametersin the optical appearance of restorations, because the lightness has the greatest effect on esthetics [43; 44; 45]. The study aimed to conduct a surface analysis of resin matrix ceramic materials related to finishing and thermocycling, and to assess their consequences on optical properties. The study included hybrid materials that were tested after hydrothermal aging for changes in the optical properties such as translucency and opalescence, and as well for surface and mechanical properties. The null hypothesis is that the optical and surface properties of the hybrid ceramic included in this study are influenced by the hydrothermal aging.
# Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation
## 2 Model Derivation  
This section introduces the model derivation of the topology optimization method for anisotropic materials with smooth fiber orientation. Firstly, taking the single-layer fiber-reinforced composite as the research object, the mathematical model is established with the unit density and the unit fiber angle in the design domain as the design variables. Then, the fiber angle constraint function is created, and the smoothing mechanism of the constraint function to the fiber angle is introduced. The fiber angle is updated by the MMA algorithm. In addition, for the problem of structural stiffness reduction caused by the constraint function, the fiber angle is adaptively filtered to further standardize the reasonable distribution of the fiber angle, and the stiffness of the overall structure is improved while smoothing the fiber.
## 2 Recent Evolution in Actor-Critic Frameworks
### Hybrid Structure-Based Frameworks
#### 2.3.5 Graph Convolution Neural Network (GCNN)-Based  
This type of framework utilizes the graph convolution neural network (GCNN) as the state embedding layer to represent the dynamic state information such as channel state in GU2U or U2 GU links, task attributes, GUs location, and UAVs location in a multi-relational graph-structured data [53]. Additionally, CNN-based pooling in GCNN helps to reduce the large-dimensional state information to the low-dimensional vector, which is suitable for feeding to the FCN-based actor network for action generation. Moreover, the graphical relationship integrates each agent state information with the neighboring agent state information, which enhances decision-making via multi-agent interaction and facilitates extracting key features inherent in graphical structural data. Thus, a notable advantage of this type of framework is to effectively capture and leverage the complex relationships and spatial dependencies among various devices in UAVSNs.  
Based on this concept, in [53], a multi-relational graph convolutional state embedding-based actor-critic DDPG framework is applied to jointly control the trajectory of UAVs and the partial task offloading ratio of GUs to minimize task execution latency. The total battery capacity of UAVs, flight angle, velocity of UAVs, and partial task offloading ratio are considered the key constraints. Nevertheless, the algorithms neglected the communication and computing-related resource allocation, which is highly required to ensure optimal performance. Additionally, it requires higher computational complexity to train the two different agent's (GUs and UAVs) GCNN-based actor-critic frameworks in real-time because GUs have very limited resources.
## 3 Validations and Applications
### Numerical Results of Wind-Induced Vibration Responses
In contrast, the aeroelastic analysis method based on the Volterra series nonlinear aerodynamic model and the Newmark-\(\beta\) method established in this study has the shortest calculation time and the least parameters to be identified. However, it should be noted that the wind tunnel test includes auxiliary structures such as railings, which reduces the streamlined degree of the  
Figure 10: Vertical and torsional displacements of the main deck section of GBEB, based on the ROM method under incoming wind speeds of (**a**) 35 m/s, (**b**) 40 m/s, and (**c**) 45 m/s for the prototype bridge, respectively. girder section. Therefore, the experimental result of the flutter critical wind speed is lower than the numerical results. It should be noted that the aeroelastic analysis method developed in this study uniformly utilizes the first-order kernel corresponding to the actual girder wind speed of 40 m/s in the calculation. The aeroelastic analysis is carried out under other wind speeds by assuming that the first-order kernels within a specific wind speed range are equal. Considering that there may be a particular deviation in the wind speed results, this paper only gives the wind speed range and does not calculate the specific flutter critical wind speed.
## 5 Automatic Signal Recognition
### Extended Validation for Mixed Signals
#### 5.3.2 Algorithm and Feature Space Extension
Originally, \(\theta_{\bar{n}}\) was extracted from the Wi-Fi trace, since only Wi-Fi has RSSI at the noise level. Now that the new signal class also contains the noise peak, the process to determine thresholds must adapt to recognize noise inside the mixed traces. Hence, \(\theta_{\bar{n}}=max\{\text{Wi-Fi}.\theta_{\bar{n}}\), \(\text{MIX\_LTE\_Wi-Fi}.\theta_{\bar{n}}\}\), and in the same way, \(\theta_{p}\) is modified to be \(min\{\text{Wi-Fi}.\theta_{p}\), \(\text{MIX\_LTE\_Wi-Fi}.\theta_{p}\}\).
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 4 Discussion
### Comparison to Other Classification Methods  
Three traditional classification methods that use manually extracted features as inputs were used for comparison with the method proposed in this paper. These traditional methods are the ID3 decision tree, support vector machine (SVM), and backpropagation neural network (BPNN). First, 22 statistical features were calculated from the original dataset of the arc sound segment, including 14 time domain statistical features, as shown in Table 4, and the average energy of 8 bands, 0-2 kHz, 2-4 kHz, 4-6 kHz, 6-8 kHz, 8-12 kHz, 12-14 kHz, and 14-16 kHz. The construction process of each vector consisting of statistical features is shown in Figure 11. Then, the statistical feature dataset of the arc sound segment was divided into a training set (70%) and a test set (30%), and the constructed SVM, BPNN, and ID3 decision tree models were trained and tested.  
Ten training tests were conducted separately for each classification method. Figure 12 compares the accuracy of each test for different classification methods, where SVM-linear is an SVM model with a linear function kernel, SVM-RBF is an SVM model with a radial basis function kernel, BPNN-1 is a BPNN with one hidden layer, and BPNN-2 is a BPNN with two hidden layers. The classification accuracies of all methods fluctuate slightly. The classification accuracies of BPNN-1 and BPNN-2 are very low, at only about 45-60%. The accuracies of SVM-linear, SVM-RBF and ID3 decision tree are better than the accuracies of BPNN-1 and BPNN-2, at about 65-75%. In comparison, the classification accuracy of the 1-CTF CNN and 4-CTF CNN are dominant, high, and stable, at about 90%. The satisfying results indicate that more information in the time and frequency domain is contained in the time-frequency spectrum than in the manually extracted features, and that the features adaptively extracted by the multi-channel time-frequency CNN are more informative than the features extracted manually.  
Figure 10: Time–frequency spectrum of different welding states and the difference.
# Three-Dimensional Human Pose Estimation with Spatial-Temporal Interaction Enhancement Transformer
## 1 Introduction
To accomplish this, STFormer starts with the generation of coarse temporal and spatial representations and then continuously communicates between them to eventually produce a more accurate 3D prediction. This framework more effectively extracts spatial and temporal features and also builds stronger connections between them. Specifically, in the first stage, we propose the the Feature Extraction (FE) module, which consists of two branches: Spatial Feature Extraction (SFE) branch and Temporal Feature Extraction (TFE) branch, which are responsible for extracting the intrinsic spatial structure of each frame and the temporal dependencies between frames. Although spatial and temporal features are extracted in the first stage, there is no information interaction between them. In view of the influence of temporal information on the spatial structure as discussed in the above paragraph, in the second stage, the connection between spatial and temporal information is established through the Cross-Domain Interaction (CDI) module. It consists of two blocks: the Spatial Reconstruction (SR) block, which is responsible for injecting temporal features into the spatial domain, and the Temporal Refinement (TR) block, which uses the reconstructed features to refine temporal features. CDI captures mutual spatio-temporal correlations to construct cross-domain communication, enabling messages to be passed between spatial and temporal representations for better interaction modeling. With the proposed STFormer, temporal and spatial features are explicitly incorporated into the Transformer model. The spatial structure of each video frame is dynamically changed according to the video sequence information, and then, this adjusted spatial structure, in turn, contributes to the temporal representation. As a result, both representations are significantly enhanced to provide poses that are more accurate. The summarization of our contributions are as follows:  
1. To predict 3D human pose more accurately from monocular videos, we designed a spatio-temporal interaction enhanced Transformer network, called STFormer. STFormer is a two-stage method, in which the first stage extracts features independently from the spatial and temporal domains, respectively, and the second stage interacts spatial and temporal information across domains to enrich the representations. 2. In the second stage, we designed the Spatial Reconstruction block and the Temporal Refinement block. The Spatial Reconstruction block injects the temporal features into the spatial domain to adjust the spatial structure relationship; the reconstructed features are then sent to the Temporal Refinement block to complement the weaker intra-frame structural information in the temporal features.
## 4 Numerical Results
### Verification of Convergence  
In order to ensure that the convergence of DD-POD is not limited to a certain division of material or a certain position of fibres in material, we have tried to generate other material with random fibres, change the number of cells or size of cells, and thus verify the convergence of DD-POD.  
For new material with the same size and same division as the previous material but a different position of fibres, the relative errors of potential, current, and power are shown in Table 4.  
For material divided into \(4\times 4\times 4\) cells with the size of cells being \(200~{}\upmu\mathrm{m}\times 200~{}\upmu\mathrm{m}\times 200~{}\upmu\mathrm{m}\), the relative errors of potential, current, and power are shown in Table 5.  
For material with the size \(1200~{}\upmu\mathrm{m}\times 1200~{}\upmu\mathrm{m}\times 1200~{}\upmu\mathrm{m}\) divided into \(3\times 3\times 3\) cells, the relative errors of potential, current, and power are shown in Table 6.  
The convergence of DD-POD is verified as the relative error does not change significantly with the change of material coupling and parameters of modelling.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Items** & \(\mathbf{B}_{x}\)**Imposed** & \(\mathbf{B}_{y}\)**Imposed** & \(\mathbf{B}_{z}\)**Imposed** \\ \hline Potential & 0.04\% & 0.05\% & 0.04\% \\ Current & 0.79\% & 0.84\% & 0.64\% \\ Power & 0.95\% & 1.03\% & 0.72\% \\ \hline \end{tabular}
\end{table}
Table 4: Relative error of DD-POD on new material of size \(800~{}\upmu\mathrm{m}\times 800~{}\upmu\mathrm{m}\times 800~{}\upmu\mathrm{m}\) divided into \(2\times 2\times 2\times 2\) cells.  
\begin{table}
\begin{tabular}{c c c} \hline
**Relative Error** & **Tolerance** & \(\mathbf{10^{-4}}\) & \(\mathbf{10^{-5}}\) \\ \hline Potential & 0.01\% & 0.004\% \\ Current & 0.13\% & 0.06\% \\ Power & 0.14\% & 0.07\% \\ \hline \end{tabular}
\end{table}
Table 3: Relative errors of DD-POD with \(\mathbf{B}_{x}\) imposed for the convergence condition based on current on the material of size \(800~{}\upmu\mathrm{m}\times 800~{}\upmu\mathrm{m}\times 800~{}\upmu\mathrm{m}\) divided into \(2\times 2\times 2\) cells.
# Optimization of the Housing Shape Design for
## 2 Noise and Vibration Measurement of the Gearbox
### Order Analysis  
To analyze the noise source of the gearbox and set the frequency range of interest, the noise and vibration should be measured. The main excitation frequencies in the gearbox are determined by the rotational speed of the shaft, number of gear teeth, and rolling element characteristics of the bearing. Therefore, the noise and vibration levels should be analyzed in the frequency domain according to the rotational speed of the input shaft [14]. This process is called order analysis. A tachometer is used to measure the rotational speed, and a microphone and an accelerometer are used to measure noise and vibration. The information of the rotational speed can be used to calculate the GMF defined in Section 2.2. By comparing the measurement result of noise and vibration with GMF, it is possible to analyze the noise source. The accelerometer was attached to the housing where the bearing was mounted. This makes it possible to measure structural vibrations caused by gear excitation forces. The radiated noise caused by structural vibration of the gearbox housing was measured by placing the microphone in the same row as the gearbox. The measurement results of noise and vibration were analyzed in the frequency domain through fast Fourier transform (FFT). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Rotational Speed, rpm** & **Shaft Frequency, Hz** & **Gear Ratio** \\ \hline Input shaft & 2650 & 44.2 & Stage 1: 3.29 \\ \hline Mid shaft & 806.5 & 13.4 & Stage 2: 3.76 \\ \hline Output shaft & 214.4 & 43.6 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Rotational speed of the shaft at the rated speed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GMF and Its Harmonics, Hz** & & \\ \hline  & **1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline Stage 1 & 618.3 & 1236.7 & 1855.0 & 2473.3 \\ \hline Stage 2 & 282.3 & 564.6 & 846.8 & 1129.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gear mesh frequency (GMF) at the rated speed. To operate the gearbox under the same conditions as the actual operating conditions, speed control was performed on the input shaft, and the torque was controlled on the output shaft using the dynamometers. The torque was measured using a torque meter mounted on the output shaft. As a result of checking the measured value with a torque meter, the load was set equal to the rated condition of the motor. The configuration of the order analysis test is shown in Figure 3 and listed in Table 3.  
The noise and vibration levels measured using a microphone and an accelerometer are shown in Figure 4 as the sound pressure level and acceleration in decibels. Since the order analysis process is based on rotational speed information, the horizontal axis of the contour graph represents the frequency and the vertical axis represents the rotational speed. Considering that the rated speed of  
Figure 3: Test layout for order analysis.
## 3 Methods and Data  
The overall flowchart of the methodology is shown in Figure 2. A three-step process is used: (1). Estimating the volume of the forests in Japan using SAR and converting these data to tree age information data; (2). Developing a detailed land cover (forest cover) map to allocate the precise forest types across the land area of Japan; and (3). Utilizing the obtained data from the preceding steps together with the CO\({}_{2}\) sequestration rate per unit area to estimate the sequestration values by each forest type and age.
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
[PERSON] [25] analyzed the influence of the number and distribution of GCPs on the planimetric accuracy reached in the final QuickBird- and IKONOS-corrected panchromatic image using different orthorectification methods. [PERSON] and [PERSON] [26] assessed the attainable geometric accuracies of RFMs applied to IKONOS and Quickbird images when user adjustments are introduced. [PERSON] et al. [27] compared results recorded for IKONOS (Geo Ortho-kit) and QuickBird basic imagery, applying two different sensor models: (1) a 3D rational function refined by the user with zero- or first-order polynomial adjustment and (2) the 3D Toutin physical model. The influence of the number of GCPs on the geometric accuracy reached with RFM applied to GeoEye-1 images was analyzed by [PERSON] et al. [28]. This paper describes orthorectification that was conducted on WorldView-2 (WV-2) imagery using several algorithms which are available in common commercial and free software. Both rigorous and empirical approaches are considered and positional accuracies of the results are compared and discussed in order to define the best algorithm or the best combination. To our knowledge this, is the first work that compares several methods (including polynomial functions and rational functions, with variable number of GCPs) on WV-2 imagery. The paper is organized as follows. Section 2 describes the data and methods; various algorithms are applied to WV-2 imagery regarding the area around Vesuvius. Section 3 presents and discusses the results. Finally, Section 4 draws some conclusions.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Initiator of Communication
#### 3.4.2 Destination  
Contrary to the first category, those routing protocols establish the routing paths following the request of sinks (or base stations) when they want to send packets to the sensor nodes. Hence, the communication starts from the sinks.
## 2 Materials, Experimental Setup, and Procedures
* Microwave irradiation was carried out until the concentration of pyrolysis gas components decreased at powers of 840 W, 1760 W, and 2200 W. After the maximum concentrations were reached, the magnetron was turned off. Further, the release of pyrolysis gas continued without energy supply from an external source. * After the experiment, the solid pyrolysis product was weighed. Before starting the next experiment, the reactor and all gas channels were purged with compressed air. In one experimental set, 3-5 experiments were performed under identical conditions. * Gross errors were excluded and experimental results were averaged. Calculation of average concentrations was performed using the trapezoidal method. The details of the method are given in [30]. The basis of the method was that the area under the concentration trend was approximated by rectangular trapezoids with a step of 1 s. Next, the area of each trapezoid was determined. Then, the ratio of the sum of the areas of all trapezoids to the time of emission of the gas component was calculated. After averaging the results, confidence intervals (error bars) were determined. To date, approaches to pyrolysis in microwave reactors can vary significantly in different studies. Previously, it was found (for example, in [27; 31; 32]) that the time of exposure to microwaves significantly affects the yield of pyrolysis products. Depending on the objectives of the research and the characteristics of the equipment, the time of exposure to microwaves can be a variable factor. In the present study, the magnetron worked for a limited time in the experiments. Since the experiment was carried out on a laboratory scale with a limited mass of raw materials, this was expedient. The main criterion was the reduction in CO\({}_{2}\) concentration. Verification tests showed that the time of exposure to microwaves is sufficient for the material to self-heat and further release of the pyrolysis gas components occurs due to the thermal energy accumulated in the layer. In addition, the factor of the magnetron operation time was further considered when calculating the pyrolysis efficiency index, when the consumed energy was one of the main influencing factors (Section 3). Figure 3 shows an example of the CH\({}_{4}\) emission trend during microwave pyrolysis. The increase in concentration began at the start of the experiment. The time of the CH\({}_{4}\) release was about 1863 s. The integral area under the curve was 4278.4 s\(\cdot\)%. Therefore, the average value of the methane concentration in this experiment was 2.3%. Figure 3: CH\({}_{4}\) emission trend during microwave pyrolysis of sawdust.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 3 Transmission Error Frequency Analysis
### Characteristic Frequencies of Transmission Error  
The TE of an RV reducer is influenced not only by the fundamental motion characteristic frequencies of its primary components--gear shaft, planet gear, crankshaft, cycloidal gear, pin gear, and flange--but also by their interactions. This is because TE ultimately manifests as a change in the meshing state of the gears, particularly affecting the engagement between the cycloidal and pin gears. Therefore, an analysis of each key component is required.