esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Findings and Discussion
As proven by [33], their study found that the highest percentage, 50.3%, of the application areas of AI in project management was in construction,a field that shows great promise in enhancing the efficiency and effectiveness of project management practices. Additionally, the survey responses indicate that participants view AI as a potentially valuable tool for enhancing quality control in aviation construction projects. However, the overall neutral response suggests that further refinement and integration of AI technologies are necessary before they can be fully embraced in the industry. Although [1,35] provide examples of AI technologies promoting quality control, it seems that the real applications of these AI tools are not widely spread or incorporated, particularly in construction projects within the aviation sector, due to strict regulations and standards. The neutrality observed in the survey results was expected, given the challenges of integrating such technologies in highly regulated industries like aviation. Finally, based on the participants' responses regarding the use of AI technologies in the handover, commissioning, and warranty phases, it is evident that they recognize AI's potential to enhance these critical processes, which are essential for the successful delivery of aviation construction projects. However, the overall neutral score suggests that, while AI may offer certain benefits, there is hesitancy to fully automate these processes. This highlights the need for a balanced approach, where critical tasks like testing and commissioning may still require human oversight to ensure quality and reliability. It is important to highlight that the areas of handing over, commissioning, and warranty require further exploration. Although [24] addressed the positive impact of AI tools in certain tasks within these areas, there remains a gap in between, which is concerning, especially in the aviation industry. This gap underscores the need for more focused research and application to fully integrate AI tools in these critical stages. Overall, the survey responses suggest that industry stakeholders are optimistic about the positive impact AI can have on the aviation construction sector, while also highlighting the need for further research, development, and the implementation of AI-based solutions in this field. Seeing the full picture, the greatest advantage of integrating AI technologies lies in the ability to use different tools across various areas of a construction project simultaneously. This will enhance and promote the efficiency and cost-saving potential of the construction project management field. Accordingly, the respondents' recognition of the multifaceted benefits of AI highlights the need for an intensive effort by industry leaders, policymakers, and technology providers to accelerate the development, testing, and large-scale deployment of AI solutions in the aviation construction sector.
## 4 Experiments and Discussions
### Sliding Form Controller for Ship Dynamic Positioning System
#### 4.1.1 Sliding Mode Control  
Sliding mode control (SMC) is a control theory based on modern control theory, the main mathematical core of which is the Lyapunov function. The core idea of sliding mode control is to establish a sliding mode surface and pull the controlled system to the sliding mode surface so that the system moves along the sliding mode surface. One of the advantages of sliding mode control is that it disregards external perturbations and uncertain parameters and uses a more \"violent\" approach to control. The essence of sliding mode control is to design the sliding mode surface through systematic errors so that the system can reach a steady state quickly and the robustness of the system can be greatly improved through the adjustment of the switching function [29].
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 3 Results
### Selection of Activation Function  
The activation function endows the neural network with nonlinear capabilities, enabling effective extraction and interpretation of complex data features. Commonly used activation functions include Sigmoid, Tanh, and ReLU. In this study, simulation experiments were performed to evaluate these options. The fusion model was trained using Sigmoid, Tanh, and ReLU as activation functions, with a 9:1 data split. The model performance is evaluated based on the Mean Absolute Error (MAE) and the Coefficient of Determination (R\({}^{2}\)).  
The experimental results are presented in Figures 5-7, respectively. Comparative analysis reveals that the Tanh activation function achieves an R\({}^{2}\) value of 0.62, representing improvements of 0.02 and 0.08 over the Sigmoid and ReLU functions, respectively. Additionally, its MAE reaches 0.0099, reflecting enhancements of 0.02% and 0.06% compared to Sigmoid and ReLU, respectively. These improvements can be attributed to the advantages of the Tanh activation function, particularly its output range and zero-centered properties.  
Figure 5: The impact of Sigmoid activation function on model performance.  
Figure 6: The impact of ReLU activation function on model performance.  
Figure 7: The impact of Tanh activation function on model performance.  
In summary, the Tanh activation function enhances the model's ability to leverage and interpret factors influencing yield, making it the preferred choice for the hybrid model's activation function.
## 3 Proposed Two-Way Cooperative CF Approaches
### Computational Complexity Analysis  
We usually analyze the computational complexity of recommender systems with consideration of two parts: computation time for model construction and that for one rating prediction. Based on a binary user-item matrix whose size is \(n\times m\), the computational complexities of the user-based CF are \(O\big{(}n^{2}mk\big{)}\) for model construction and \(O(k)\) for one rating prediction. The former is to calculate similarities among users, and the latter is to make a prediction using \(k\) neighbors. Likewise, the computational time of the item-based CF can estimated as \(O\big{(}nm^{2}k\big{)}\) and \(O(k)\). It is obvious that our approaches require more computational time for model construction because we employ the statistical learning algorithms. The computational complexities are \(O\big{(}nm\big{)}\) for logistic regression and \(O\big{(}\min\big{(}n^{3},m^{3}\big{)}\big{)}\) for principal component analysis. The CART (classification and regression tree) algorithm has the complexity of \(O\big{(}mn\log n\big{)}\) in the worst case, which means that the depth of a tree is \(n\). If we build \(s\) trees with \(t\) randomly chosen variables at each split, the complexity of random forests becomes \(O\big{(}stn\log n\big{)}\). Notice that the actual times for training prediction models can be reduced by performing PCA because we use a fewer number of input variables than \(m\). Although our methods need more computational times for model construction than the existing CF approaches, their prediction complexity is \(O\big{(}1\big{)}\), which means a constant time complexity because they do not use \(k\) neighbors. As a result of this small complexity for rating prediction, our methods are suitable for online recommendation, as other model-based approaches are. Our numerical experiments in the next section showed that the item-based CF took 0.05 s and our methods took 0.02 s for one rating prediction, although the PCA+LR item modeling and the RF item modeling took 5.29 s and 47.57 s respectively for model construction.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
## 2 \(H_{\infty}\) Control Approach Basics
### The Weighting Functions
The introduction of two  
Figure 5: \(W_{T}(s)\) Weighting Function—Black line: Complementary Sensitivity function; Red line: Weighting function. new supporting functions, \(W_{s,supp}(s)\) and \(W_{t,supp}(s)\), allows fixing such an issue, defining them as:  
\[W_{s,supp}(s)=W_{\rm S}(s)\frac{s^{v+p}}{(s+\lambda)^{v+p}} \tag{10}\]  
\[W_{t,supp}(s)=\frac{1}{T_{p0}} \tag{11}\]  
\(W_{s,supp}(s)\) and \(W_{t,supp}(s)\) will replace \(W_{\rm S}(s)\) and \(W_{\rm T}(s)\) in the formulation of the generalized plant. In (10), \(\lambda\) is chosen as a low-frequency pole substituting the pole at the origin, which makes the function \(W_{\rm S}(s)\) unstable; \((v+p)\) defines the order of the system: \(v\) corresponds to the number of poles at the origin of the controller, and \(p\) corresponds to the number of poles at the origin of the plant. The choice of the \(\lambda\) pole, as well as possible uncertainties in its actual location, do not represent a critical issue. The only requirement is that it has to be a low-frequency pole for the controlled robot. The quality of the dynamic model of an industrial manipulator included in its control architecture is usually more than sufficient to establish the entire frequency range well, by distinguishing low-, middle-, and high-frequency intervals. Moreover, it must be noted that the \(H_{\infty}\) technique is based on the loop-shaping one. So, any tiny discrepancies do not involve significant changes in the system performances, and they can be possibly fixed at run-time if needed, for example, by modifying the controller crossover frequency. In the next sections, we will prove that such a choice of \(W_{\rm S}(s)\) and \(W_{\rm T}(s)\), and consequently of \(W_{s,supp}(s)\) and \(W_{t,supp}(s)\), when applied to the control of an axis of a real industrial manipulator, may lead to good motor responses, but it can cause some link oscillations that cannot be accepted in the industrial context, where precision is the main task. We will then propose a different formulation, allowing significant improvements of the control performances.
## 3 Results
### GridSearch Values for Training the Random Forest Models  
This section shows the values of the random forest parameters obtained from the GridSearch method of Scikit Learn; these are the values with which the best performance is obtained. Table 4 shows the results of GridSearch in the case of the random forest trained with the feature matrix of all years (the first matrix created). Table 5 shows the GridSearch results for the 3-year mode feature matrix (the second matrix created).  
The results shown in Tables 5 and 6 show that there are specific differences in the parameters. Firstly, because the second matrix of characteristics has more columns than the first (355 vs. 123), it needs a greater number of estimators to make the prediction; this can be seen in the number of estimators obtained. In the case of the first matrix, 400 were used, while in the second matrix, 1800 were used. The number of features in each division is also different in each case. For the first matrix, it is given by the logarithm base two of the number of features, while for the second matrix, it is given by the square root of the features. Finally, it can be observed that the depth of each tree is different: for the first matrix, it is 80, while for the second, it is 20. Therefore, the random forest trained with the matrix of the modes of all years has a lower number of estimators but a higher depth with respect to the random forest of the second matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** \\ \hline n\_estimators (number of estimators) & 400 \\ max\_features (number of features in each division) & Log2 \\ max\_depth (maximum tree depth) & 80 \\ min\_samples\_split (minimum number of samples to split node) & 2 \\ min\_samples\_leaf (minimum number of samples in each leaf node) & 4 \\ Bootstrap (sample selection method for the training of each tree) & True \\ \hline \hline \end{tabular}
\end{table}
Table 4: GridSearch results for random forest that receives the matrix created by calculating the mode per year for each variable as input.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** \\ \hline n\_estimators (number of estimators) & 1800 \\ max\_features (number of features in each division) & \(\sqrt{N^{\circ}}\)_Features_ \\ max\_depth (maximum tree depth) & 20 \\ min\_samples\_split (minimum number of samples to split node) & 5 \\ min\_samples\_leaf (minimum number of samples in each leaf node) & 2 \\ Bootstrap (sample selection method for the training of each tree) & False \\ \hline \hline \end{tabular}
\end{table}
Table 5: GridSearch results for random forest receiving the matrix created by calculating the mode every 3 years per variable as input.  
Finally, it is important to mention that the parameters shown in Tables 5 and 6 were used for all random forests implemented.
# Microstructural Changes Influencing the Magnetoresistive Behavior of Bulk Nanocrystalline Materials
## 3 Results
### Medium Ferromagnetic Content: Magnetoresistance Measurements  
Specimens for GMR-measurements were prepared out of the as-deformed and the annealed Cu\({}_{55}\)Co\({}_{45}\) material and the drop in RT resistivity was recorded as a function of applied magnetic field. Furthermore, the specimens were placed i) with the direction of applied current perpendicular to the magnetic field and ii) with the current parallel to the magnetic field. Specimens using the same production route and of varying Co-contents were already investigated under the same conditions and the results are presented in [11]. Therein, a gradual change from isotropic GMR for low ferromagnetic  
Figure 5: HEXRD pattern of the as-deformed and ex-situ and in-situ annealed Cu\({}_{55}\)Co\({}_{45}\) specimens, in addition the lines of pure elements are shown.  
Figure 6: Micrographs of Cu\({}_{55}\)Co\({}_{45}\) in three different microstructural states in tangential view, as-deformed (**a**) and annealed for 1 h at 400 \({}^{\circ}\)C (**b**) and 600 \({}^{\circ}\)C (**c**).  
content to AMR with increasing Co-content is reported. Figure 7 shows that the GMR is not perfectly isotropic for the Cu\({}_{55}\)Co\({}_{45}\), owing to the comparatively high Co-content, indicating some contribution of AMR. Parallel alignment of current flow with the magnetic field gives slightly smaller drops in resistance.  
Figure 7 depicts the highest GMR-effect for the specimen annealed at 300 \({}^{\circ}\)C, in both directions. Except for the 150 \({}^{\circ}\)C annealed specimen in parallel orientation, there is the general trend of increasing GMR-effect up to 300 \({}^{\circ}\)C followed by a decrease. As expected, the 600 \({}^{\circ}\)C annealed specimen shows minimal GMR, due to the large Co-grains. Furthermore, its saturation occurs at smaller magnetic fields, pointing at an enhanced susceptibility.
## 3 Results
### Spatial Distribution of Water Deficit- and Heatwave-Driven Flash Droughts  
To further dissect the evolution of flash drought phenomena within the Songnen Plain, this analysis bifurcates the events into water scarcity- and heatwave-driven events,  
Figure 3: Spatial distribution of the (**a**) number and (**b**) average duration of flash drought events in the Songnen Plain during 2002–2022.  
delving into the spatiotemporal evolution of these distinct drought typologies. Figure 4 illustrates the spatial variances in the frequency and mean duration of both drought categories from 2002 to 2022, unveiling pronounced disparities. The water scarcity droughs exhibit a high frequency and widespread distribution, encapsulating a spatial motif of peripheral prevalence yet central scarcity. Notably, over 80% of the northeastern regions near Suihua and Qiqihar were afflicted by water scarcity droughs. Conversely, heatwave-driven droughs were more localized, with certain areas registering 8-10 events. Regarding duration, water scarcity droughs averaged around 26 days, occasionally extending to 45 days, whereas heatwave-driven droughs averaged 28 days, diminishing to approximately 22 days in western Jilin. Thus, the Songnen Plain is a battleground for both water deficit- and heatwave-driven droughs, with the former being more frequent but the latter enduring longer.
## 3 Methodology
### Graph Optimization
#### 3.2.2 Optimizing Graph Edges  
When constructing the initial high-quality phrase graph, the weights of the edges between the nodes are simply set to 1. In practice, however, the weights of the edges between the nodes are closely related to the following factors: (1) the cosine similarity  
Figure 2: High-quality phrase graph. The time in the figure represents the publication time of the news document where the phrase was found, and the time range is determined by the corpus used, from the earliest published news in the corpus to the latest.  
of node-to-node phrase embedding and (2) the influence of time between nodes. A key event in a news article is usually discussed in large bursts by a number of articles, and the heat of that key event decreases more rapidly over time. However, the effect of time information on the nodes is not presented as a conventional linear relationship. Therefore, this paper constructs a nonlinear exponential function \(I(u,v)\) to simulate this law, as shown in Equation (9).  
\[I(u,v)=e^{(1+ConsSim(t_{u},t_{v}))} \tag{9}\]  
where \(CosSim\) denotes cosine similarity, \(u\in[1,M],v\in[1,M]\) denotes two nodes in the graph, and \(t_{u},t_{v}\) is the corresponding time embedding.  
The weights of the edges are closely related to the cosine similarity of the phrase embedding between nodes, in addition to the influence of time between nodes. Therefore, taking these factors into account, the cosine similarity of phrase embedding between nodes and Equation (9) are combined to design the calculation of edge weights \(w_{u,v}\) in high-quality phrase graphs, as shown in Equation (10).  
\[w_{u,v}=I(u,v)*\max(CosSim(\hat{n}_{u},\hat{n}_{v}),0) \tag{10}\]  
where \(\hat{n}_{u},\hat{n}_{v}\) are the deep semantic embedding features corresponding to the two nodes \(u,v\).
_Applied_  
_sciences_  
_Article_  
**Mechanism and Compensation of Measurement Error Induced by Thermal Deformation of Digital Camera in Photo Mechanics**  
**[PERSON] 1\({}^{\copyright}\)**, [PERSON] 2, [PERSON] 1\({}^{,}\)\({}^{\star}\)\({}^{\copyright}\) and [PERSON] 3\({}^{,}\)\({}^{\star}\)\({}^{\star}\)**  
1 School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China; [EMAIL_ADDRESS]  
2 Flexible Optical Measurement Technology Center, Institute of Flexible Electronic Technology of THU, Jiaxing 314006, China; [EMAIL_ADDRESS]  
3 School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China  
*Correspondence: [EMAIL_ADDRESS] (Q.M.); [EMAIL_ADDRESS] (S.M.)  
Received: 3 April 2020; Accepted: 7 May 2020; Published: 15 May 2020  
**Abstract:** A thermal-induced measurement error induced by thermal deformation of digital camera in photo mechanics methods that has the same magnitude of the error as that in the resistance strain measurement method reduces the accuracy of high-precision measurement substantially and must, therefore, be compensated. Starting from the underlying mechanism of a thermal-induced measurement error, we investigated the image error introduced by thermal behaviours of digital cameras widely used in photo mechanics. We experimentally determined the relationships between the thermal behaviours and temperature, derived the relationship between the image error and measurement error of photo mechanics methods, and eventually established a physical model to explain the underlying relationship between the thermal-induced measurement error and temperature. Furthermore, based on the investigation of the underlying mechanism, we introduced three types of temperature compensation methods for photo mechanics, namely the model compensation method, the preheating method, and the compensation specimen method. We experimentally demonstrated the feasibility of these compensation methods. The model compensation method only needs the data regarding the environmental temperature during operation of the digital camera to implement the correction of the measurement results and enhance the measurement accuracy of photo mechanics methods. The preheating method is suitable for indoor photo mechanics measurements wherein the environmental temperature is almost constant, which ensures that the appearance of thermal balance of the digital camera after a period of self-heating. The compensation specimen method reduces the effective resolution of the images and it also requires that the image error forms in the region of tested specimen are the same as that in the region of the compensation specimen.  
**Keywords:** photo mechanics; digital camera; thermal-induced measurement error; temperature compensation
# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
## 3 Methods
### Multilayer Feature Fusion Point Transformer
#### 3.3.1 Local Feature Encoder  
The local feature encoder, essentially aligned with the encoder in the PointTransformer (PT), consists of a stack of Multilayer Perceptron (MLP) modules, Transition Down (TD) modules, and Point Transformer (PT) modules. This configuration is designed to extract local features from point cloud data and aggregate local information using a self-attention mechanism. The stacking of these modules allows the network to utilize a wider range of contextual information. Specifically, the module begins by expanding the point cloud features through the MLP, then employs the attention mechanism of the Point Transformer (PT) module to calculate the relationships between a point and several surrounding points. Following this, the Transition Down (TD) module performs downsampling, as depicted in Figure 12a. This process not only enriches the features but also reduces the number of points, facilitating subsequent operations such as upsampling and skip connections.  
Figure 11: PointTransformer with multi-layer feature fusion.  
Figure 12: Up-and-down sampling modules. (**a**) Transition Down unit; (**b**) upsampling unit.  
The PT module's structure and the Point Transformer layer schematic are displayed in Figure 13a. Comprising two linear layers and one PT layer, the module forms a PT block using residual connections, as illustrated in Figure 13b. The PT layer, central to the Encoder, calculates relationships among neighboring points within the local domain of each point, obtaining attention vectors. These vectors are then used to aggregate features of neighboring points:  
\[y_{i}=\sum_{x_{j}\in\left(i\right)}\rho\big{(}\gamma\big{(}\varphi(x_{i})-\psi \big{(}x_{j}\big{)}+\delta\big{)}\big{)}\odot\big{(}\alpha\big{(}x_{j}\big{)}+ \delta\big{)}\big{)} \tag{1}\]  
\[\delta=\theta\big{(}P_{i}-P_{j}\big{)} \tag{2}\]  
In the Point Transformer network, \(y_{i}\) represents the output feature, and \(\varphi\), \(\psi\), and \(\alpha\) are per-point feature transformations, such as MLPs. \(\delta\) is a normalization function like softmax, and \(\gamma\) is an MLP layer. Positional encoding, crucial for attention generation and feature transformation, is calculated using the three-dimensional coordinates \(P_{i}\) and \(P_{j}\) of two points, with \(\theta\) being an MLP layer. The positional encoding function plays a key role in the attention mechanism, significantly impacting feature transformation.