esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Heat Input on the Surface Quality of Wire and Arc Additive Manufacturing
## 2 Materials and Methods  
### Experiment  
The WAAM system used is composed of a WSE 200G welding machine, an automatic wire feeder, a gas conveying device, and a three-axis motion platform. The ER5356 aluminum alloy wire with a diameter of 2.0 mm is used as the material, and the 6061 aluminum alloy with a size of 150 \(\times\) 150 \(\times\) 10 mm is used as the substrate. The main chemical elements of the two materials are shown in Table 1. The protective gas in experiments is argon with a purity of 99.99%, and the gas flow rate is 5 L/min. The LEICA ABSOLUTE TRACKER AT960-MR reverse forming these WAAM specimens with an accuracy of 0.001 mm is used.  
In the non-melting electrode arc additive, the heat input can be calculated by the following formula [22]:  
\[HI=\frac{\eta UII}{v_{TS}} \tag{1}\]  
In the formula, HI--heat input, \(\eta\)--arc thermal conductivity coefficient, for TIG technology, taken as 0.7, \(I(A)\)--the welding current, \(U(V)\)--the welding voltage, \(U=10+0.04I\), \(v_{TS}(\text{m}/\text{min})\)--the torch traveling speed. It can be seen from Equation (1) that changing the current, voltage, and travel speed in the WAAM process can change the heat input of the specimen. The influence of heat input on the surface quality of arc additive manufacturing parts was analyzed by changing the current and travel speed. In order to eliminate the influence of unit consumables on the contour of parts, the ratio of wire feed speed to travel speed is kept at 14. Inputs, appropriate process parameters to conduct WAAM experiments  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{6}{c}{**Element**} \\ \cline{2-7}  & **Al** & **Mg** & **Cr** & **Ti** & **Mn** & **Other** \\ \hline Substrate & Bal. & 0.25\% & 0.6\% & 0.7\% & 0.15\% & 1.4\% \\ Wire & Bal. & 4.5–5.5\% & 0.05–0.2\% & 0.06–0.2\% & 0.05–0.2\% & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: The content of each element in the substrate and wire.  
to analyze its influence, were selected. The process parameters of each specimen during WAAM are shown in Table 2.  
In order to analyze the effect of the difference in heat input on the surface quality of WAAM, thermocouples were placed at three positions to monitor the heat changes at different positions under different heat inputs, as depicted in Figure 1.
## 5 Discussion  
### Impact Factor on Control of Landslide  
The study on the factors for the geological conditions is always the focus of landslide susceptibility evaluation. According to the spatial heterogeneity, the effect of controlling landslide is different due to factors such as the geology, soil, topography, climate, and land use. Highly similar factors can be screened out to avoid influence on the classification process by using correlation and collinearity and the IGR can evaluate the rank of input factors and understand the tendency of susceptibility.  
In this study, the NDVI was considered to be the most critical factor in determining landslide susceptibility. Landslides mostly occurred on exposed slopes, and dense woodlands were almost free from disasters. Land use and cover indicated that landslides occurred mostly on sloping farmland and around roads. On one hand, this implies that cultivated land and engineering activities have a certain catalytic effect on the occurrence of landslide. On the other hand, farmland and roads were the main threats of disaster, which was also consistent with the previous survey.
## 4 Evaluation
### Performance Evaluation  
To assess the effectiveness of MIRA, we used the common performance evaluation measure based on an independently distributed confusion matrix, as described in Table 4. The values were assigned based on the final recommendation label. The diagonal and off-diagonal values of the confusion matrix present correctly classified and incorrectly classified results, respectively. Similarly, the rows  
Figure 6: Country-based distribution of MIRA evaluation participants.  
and columns of the confusion matrix show actual values per label and predicted value per label, respectively. Furthermore, the characteristics of performance evaluation measurement are reflected in terms of accuracy, precision, sensitivity, specificity, and F-measure. The corresponding description along with formulas for these measures are described as follows. Each participant completed the interaction for three health conditions that included glaucoma, diabetes, and other. Figure 7 illustrates the value of each label. We recorded a total of 99 dialogue corpus based on the interactions of 33 participants.  
* Accuracy identifies the effectiveness of an algorithm based on the probability of true values as stated in Equation (1). MIRA gets an overall accuracy of 89.8% because it correctly identified 90.9% glaucoma (30), 84.8% diabetes (28), and 93.9% other (31) labels among the recorded dialogue corpus (99). \[Accuracy=\frac{SumofCorrectlyClassified}{TotalNumborofClassification}\] (1)
* Precision or confidence presents the positive predictive value of a label that can be derived using Equation (2). We obtained the precision for each label including glaucoma (88.24%), diabetes (93.33%), and other (88.57%), with an average precision of 90%. \[Precision=\frac{TruePositive}{TruePositive+FalsePositive}\] (2)
* Sensitivity (also known as recall) corresponds to the true positive rate of a specific label and can be computed with Equation (3) for glaucoma (90.91%), diabetes (84.85%), and other (93.94%), with an average value of 89.8%. \[Sensitivity=\frac{TruePositive}{TruePositive+FalseNegative}\] (3)
* Specificity corresponds to the true negative rate and can be computed using Equation (4) for a specific label for glaucoma (93.94%), diabetes (96.97%), and other (93.94%), with an average value of 94.9%. \[Specificity=\frac{TrueNegative}{FalsePositive+TrueNegative}\] (4)
* The F-measure, also known as F-score or F1-score, is the weighted harmonic mean of precision and sensitivity (recall) as stated in Equation (5). The F-Measures for each label in MIRA were as follows: glaucoma (89.55%), diabetes (88.89%), and other (91.18%), with an average value of 89.8%.We used \(\beta=1\) that evenly balances the F-score based on precision and sensitivity. \[F-Measure=\frac{\left(\beta^{2}+1\right)*Precision*Sensitivity}{\beta^{2}*Precision+ Sensitivity}\] (5)
# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
###### Abstract
[33] introduced StrainNet, a lightweight CNN customized for sub-pixel displacement estimation from speckle images. StrainNet is inspired by FlowNet but is simplified through level reduction and filter minimization to enhance GPU efficiency. Evaluation using synthetic and real images demonstrates that the simplified StrainNet versions match the performance of the original CNN, suggesting their potential as an effective DIC alternative for displacement field measurements. A Convolutional Neural Network (CNN) is suitable for extracting a variety of features from images, and is therefore widely used in computer vision [34]. However, CNNs have a limitation in modeling long-range dependencies due to their nature of local connectivity [35]. The proposal of a Swin Transformer model [36] not only solves the modeling cost of long-distance dependence, but also achieves the feature interaction across windows to maintain the local characteristics while reducing the computational effort. The model is powerful in focusing on modeling global information and introduces different windows via Shift-Window to enable cross-window information interaction. In this paper, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed to compute sub-pixel displacement values through a three-level classification task. Firstly, the rotation-relative labeled value method is used to solve the problem of positive and negative displacements, and then a flat method of dataset expansion is proposed to expand the dataset quickly to meet the requirement that deep learning algorithms need a large amount of training data to show excellent performance. Finally, its accuracy is verified by simulation experiments, and its robustness is verified by real experiments. The contributions of this work are summarized as follows:  
* A Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, which avoids the traditional dependencies on interpolation methods, image gradient calculations, initial value estimations, and iterative calculations. The proposed method significantly enhances the efficiency and accuracy of sub-pixel displacement calculations. * A square dataset expansion method is introduced to rapidly expand the training dataset for the deep learning model. This method facilitates the rapid augmentation of training data, ensuring that the model is exposed to a diverse and comprehensive training dataset. * The accuracy and robustness of the ST-SDM model is validated through both simulation experiments and real rigid body experiments. These experiments demonstrate the proposed model's high accuracy, calculation precision, and efficiency, confirming its effectiveness in practical applications.
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 3 Microgrid Modeling and Design  
The proposed microgrid architecture includes distributed smart-meters, solid-state transfer switches, circuit breakers, three-phase inverters, PV panels, energy storage system (ESS), DC bus, AC feeders, critical and non-critical loads, and a centralized controller, as illustrated in Figure 4. PV systems are designed to supply all loads within the microgrid coverage area utilizing sizing methodologies in which batteries, PV panel rating, and panel numbers are predicted for the worst-case scenario. The effect on the weather variation on PV panels production was taken into account. The smart power meters, static transfer switches, and microgrid centralized controller are the main components of the proposed microgrid. The controller monitors the power available from the microgrid in addition to that required for the connected loads. If the overall load demand exceeds the microgrid capacity, the controller determines the number and location of loads that need to be transferred to the utility grid. When the microgrid conditions improve and its capacity and voltage attain their nominal values or higher, the transferred load can be restored to the microgrid again.  
Figure 3: (**a**) Conventional and (**b**) reverse frequency and voltage droop control characteristics.  
Detailed modeling and design parameters of the microgrid components were implemented including the PV array, battery array, converters, inverters, solid-state transfer swishes, and microgrid centralized controller. In the following subsections, each component is comprehensively described.
## 3 Dynamic Model and Equations of Motion
### Component Acceleration Analysis in HPGT  
To conduct the dynamic analysis, the acceleration analysis of each component for herringbone planetary gears should be first carried out. The generalized coordinates of herringbone planetary gears are established in the dynamic reference frames rotating along with the carrier. However, the accelerations of component centroids used in the dynamic analysis should be the absolute acceleration, so the absolute acceleration expressed in the dynamic coordinate systems should be derived. The relationship among the reference frames is displayed in Figure 3.  
Let \(\mathbf{G}\) be the assumed centroid. Since the origin of the static reference frame \(\{\mathbf{i}^{\prime},\mathbf{j}^{\prime},\mathbf{k}^{\prime}\}\) and the origin of the dynamic reference frame \(\{\mathbf{i},\mathbf{j},\mathbf{k}\}\) coincide at point O, the radius vectors of \(\mathbf{G}\) are both expressed by \(\mathbf{r}\) in these two reference frames and \(\mathbf{|r|}=r=\sqrt{\frac{2}{r_{x}}+r_{y}^{2}+r_{z}^{2}}\). The components in the dynamic reference frame of the vector \(\mathbf{r}\) are, respectively, \(x\mathbf{:i},y\mathbf{:j}\) and \(z\mathbf{:k}\), and those in the static reference frame are, respectively, \(X\mathbf{:i}^{\prime}\), \(Y\mathbf{:j}^{\prime}\) and \(Z\mathbf{:k}^{\prime}\). From Figure 3, the radius vector can be obtained as  
\[\begin{split}&\mathbf{r}=\{x\cdot\cos\omega_{c}t-y\cdot\sin \omega_{c}t,y\cdot\cos\omega_{c}t+x\cdot\sin\omega_{c}t,z\}^{T}\cdot\{\mathbf{i },\mathbf{j}^{\prime},\mathbf{k}\prime\}^{T}\\ &=\{r_{x},r_{y},r_{z}\}^{T}\cdot\{\mathbf{i},\mathbf{j}^{\prime},\mathbf{k}\prime\}^{T}\end{split} \tag{1}\]  
The absolute acceleration of \(\mathbf{G}\) can be written as  
\[\mathbf{a}\prime=\left\{a_{x},a_{y},a_{z}\right\}^{T}\cdot\{\mathbf{i}, \mathbf{j}^{\prime},\mathbf{k}\prime\}^{T}=\left\{\tilde{r}_{x},\tilde{r}_{y},\tilde{r}_{z}\right\}^{T}\cdot\{\mathbf{i},\mathbf{j}^{\prime},\mathbf{k} \prime\}^{T} \tag{2}\]  
The absolute acceleration of \(\mathbf{G}\) in the dynamic reference frame can be derived as  
\[\begin{split}&\mathbf{a}=\left\{a_{x}\cdot\cos\omega_{c}t+a_{y} \cdot\sin\omega_{c}t,a_{y}\cdot\cos\omega_{c}t-a_{x}\cdot\sin\omega_{c}t,a_{z} \right\}^{T}\cdot\{\mathbf{i},\mathbf{j},\mathbf{k}\}^{T}\\ &=\left\{\tilde{x}-2\omega_{c}\dot{y}-\omega_{c}^{2}x,\tilde{y}+ 2\omega_{c}\dot{x}-\omega_{c}^{2}y,\tilde{z}\right\}^{T}\cdot\{\mathbf{i}, \mathbf{j},\mathbf{k}\}^{T}\end{split} \tag{3}\]  
where \(\omega_{c}\) means the angular velocity of the carrier. \(2\omega_{c}\dot{y}\) and \(2\omega_{c}\dot{x}\) represent the Coriolis accelerations of \(\mathbf{G}\). \(\omega_{c}^{2}x\) and \(\omega_{c}^{2}y\) denote the centripetal accelerations.  
Figure 3: Coordinate transformations.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation  
The performance of the scheme is evaluated using the NS3 network simulator (version 3.33) and measured using the following metrics: throughput, PDR and end-to-end delay. We compare the performance of the plain AODV with the modified version, TAODV, when both are under flooding attack. The parameters used to run this simulation are shown in Table 1.
# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 2 Clutch Temperature Prediction Model Construction Based on Bi-LSTM and Migration Learning  
### Bi-LSTM Neural Network  
Bi-LSTM (Bidirectional Long Short-Term Memory Network) is a deep learning model for processing sequential data. It is a variant of LSTM (Long Short-Term Memory Network) that introduces two LSTM layers, forward and backward, in the network.  
This bi-directional structure allows the model to capture both forward and backward contextual information, thus providing a more comprehensive sequence modeling capability, as shown in Figure 2. In terms of clutch temperature prediction, Bi-LSTM more accurately predicts future platen temperature scenarios by capturing past trends; in addition, long-term dependencies are effectively established through a gating mechanism; platen data intervals may be variable, and Bi-LSTM can be dynamically adapted to use different time scales for modeling.
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 4 Case Study
### Time Series Forecasting without Traffic Parameters
In case 2, the average MAE of SVR model and RR model is 0.7050 and 0.6259 respectively, while the figure for LSTM model is 0.3901. The advantage of LSTM model emerged in a significant way. In case 3, the average decrease in mean square error is 91.32% and the performance of LSTM model is still better than other models. Among these prediction models, LSTM model presents a robust performance in predicting flight conflicts with comparison to other models.
## 3 Novel Vehicle Terminal Based on Acoustic Communication Technology
### Overall Frame  
The whole frame of the novel time-sharing rental vehicle terminal for electric vehicle based on acoustic communication technology is shown in Figure 3, which includes power module, processor, GPS module, GPRS module, double microphone, power amplifier module, input/output (I/O) interface, controller area network (CAN) driver and on-board diagnostics (OBD) interface, and so on. When the instruction is sent by the platform or the mobile phone directly sends out the instruction, the processor analyzes the relevant instruction and transmits the signal to the related device in the vehicle through the I/O interface and wire to realize the remote control of vehicle equipment. The function of monitoring of vehicle status is achieved by obtaining vehicle operating status data provided by the OBD interface under the CAN driving.  
Communication channels include GPRS and acoustic waves. GPRS communication channel is composed of GPRS module, processor, and power module. The platform instruction is received by GPRS module. The acoustic communication channel composed of the microphone, power amplifier module, processor, and power module can send the control instruction directly through the mobile phone, which is received by microphone.
## 2 Materials and Methods  
Every lighting system that enters operation begins to degrade. This degradation is caused by various factors, such as the accumulation of dirt, the aging of light sources,or luminaires. Therefore, during the design phase of lighting systems, it is essential to determine the maintenance factor, which represents the maximum reduction in illuminance or brightness that the lighting system must compensate for to ensure that the design parameters are met at all times during the system's intended lifespan and under specified conditions [41].  
The maintenance factor accounts for the reduction in luminaire efficiency and luminous flux. This reduction is caused by dirt deposited on the luminaire's optics or inside the light source. The extent of degradation depends on the luminaire's surface finish, construction, degree of protection, type of light source, its placement, the environment in which it is used, and air circulation in the room. In humid and heavily contaminated areas, it is recommended to use enclosures with at least an IP54 rating. In very polluted environments, illuminance levels can drop by up to 50%, which also impacts economic efficiency. The surface finish and material of the luminaire are also important factors. The maintenance factor is expressed by a mathematical equation as outlined in the 97: 2005 Guide on the Maintenance of Indoor Electric Lighting [42]:  
\[\mathit{MF}=\mathit{LLMF}\times\mathit{LSF}\times\mathit{LMF}\times\mathit{ RSMF} \tag{1}\]  
where:  
* LSF--Lamp Survival Factor
* LLMF--Lumen Lamp Maintenance Factor
* RSMF--Room Surface Maintenance Factor
* LMF--Luminaire's Maintenance Factor  
Lamp Survival Factor--This factor identifies the probability of the luminaire or light source remaining operational. It also considers the replacement regime for the luminaire or light source. In practice, individual replacement of sources or luminaires is most commonly applied.  
Lumen Maintenance Factor--This factor expresses the gradual reduction in the light source's luminous flux during normal operation due to the aging of the luminaire or light source. It is defined by the ratio of luminous flux at the current observed period to the initial flux of the luminaire or light source. This factor is determined for luminaires with integrated light sources.  
Room Surface Maintenance Factor--This factor considers the degradation of surface reflectivity. When determining the maintenance factor in indoor environments, it must account for all light-reflecting surfaces.  
Luminaire Maintenance Factor--This factor expresses the relative luminous flux emitted by the luminaire due to dirt accumulating on its optical parts or components affecting the luminous flux.  
Two software tools were selected for the study, which provide the capability to calculate the maintenance factor. These tools, Dialux Evo and Relux Desktop, are most commonly used in lighting system design due to their availability, free access versions, processing and interpretation capabilities, and support for current legislation and standards for lighting assessment.
## 4 Results and Discussion  
### Deterministic Model Validation  
To validate the accuracy of the deterministic, coupled thermal-structural model, the simulation results of this paper were compared with other findings reported in the literature. For the thermal analysis model validation, the length of the receiver tube was 3 m with an inner/outer radius of 10.5/12.5 mm. The inlet fluid parameters and the heat flux distribution are in accord with that of [PERSON] et al. [2]. The temperature analysis results of this paper and  
Figure 4: Error analysis results of the maximum temperature inside the tube wall as the model output.  
Figure 5: Error analysis results of the maximum stress inside the tube wall as the model output.  
those reported by [PERSON] et al. [2] are shown in Figure 6. The comparison demonstrates that the simulation results of this paper and those of [PERSON] et al. [2] are in good agreement, and the maximum relative error of the temperature in this study was only 1.12%.  
For the thermal stress analysis, the geometry of the receiver tube was the same as the above. The inner and outer tube wall temperatures were, respectively, 700 K and 750 K, which indicates that only the radial temperature gradient was considered. The simulation results of this paper were compared with the theoretical equation results of [PERSON] et al. [12] and are shown in Figure 7. The comparison of the results proves that the thermal stress analysis model also has good reliability.  
Figure 6: Temperature analysis results of this paper and those indicated by [PERSON] et al. [2].  
Figure 7: Thermal stress analysis results of this paper and those of [PERSON] et al. [12].
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 2 Theoretical Background and Research Hypotheses
### Interactions Between Two Users and an IoT-CA  
IoT-CAs support multiuser interaction in a collocated space, which makes the exploration of self-disclosure to IoT-CAs in a context of more than one user a timely topic. Through the combination of multiple far-field microphones and speakers and a tubular design, IoT-CAs support interactions with multiple users from different angles. Furthermore, an update to Google Home in April 2017 provided more multiuser support, allowing Google Home to distinguish up to six people by voice [41]. Recent work has begun to examine the interactions of multiple users with IoT-CAs. [PERSON] et al. [5] studied audio data from month-long deployments of Amazon Echo in five households and investigated how their use in the complex social lives of multiple users. [PERSON] and [PERSON] [4] investigated how an IoT-CA might be used in collaborative work meetings and how multiple users interacted with it. Previous studies have shown that people might have conflicts regarding a routine or methods of using an agent. [PERSON] et al. [42] pointed out that household members have conflicting priorities when using intelligent agents for household energy conservation--namely, comfort versus financial goals. [PERSON] et al. [43] interviewed smart home households, and one inhabitant complained that he had to change his control rules when his girlfriend moved in because they have different opinions about whether the lights should be on or off when watching television. The two-user context is the simplest multiuser context and can aid in the understanding of multiple users' use of an IoT-CA. Thus, we have the following research question:  
**Research question:** Are the above hypotheses supported when two users are interacting with an IoT-CA?