esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control of Thermal Deformation Optimization Strategy
### Thermal Deformation Analysis after Optimization  
#### 3.4.1 Support Temperature Control Design  
The temperature fluctuation of the reference support is controlled by precise temperature control.  
The design of the two heating circuits is shown in Figure 19a. The heating belts H1-H9 form a heating circuit in series, with a total resistance of 293.9 \(\Omega\), and the heating belts H10-H23 form another heating circuit in series, with a total resistance of 244.8 \(\Omega\). The heating belts are set on the web plate of the I-beam by means of adhesive. The size and resistance of the heating belt are shown in Table 8. The 28 V satellite bus voltage powers the two heating circuits. The closed-loop control of temperature is realized by setting up two temperature measuring circuits. The temperature sensor is pasted at T1 and T2 in Figure 19a. In order to maintain temperature stability, the reference bracket is coated with 20 units of multi-layer insulation components, the outermost layer being F46. The completion state of thermal control is shown in Figure 19b.  
The simulation of the temperature control effect is shown in Figure 20, and the temperature of the support can fluctuate between 19.86 \({}^{\circ}\)C and 22.18 \({}^{\circ}\)C. The temperature is used  
Figure 19: Reference support for thermal control design and implementation status: (**a**) active temperature control design; (**b**) completion state of thermal control.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Heating Belts ID** & **Size/mm** & **Electrical Resistance/\(\Omega\)** \\ \hline H1 & 70 \(\times\) 18 & 34 \\ H2 & 70 \(\times\) 18 & 34 \\ H3 & 70 \(\times\) 18 & 34 \\ H4 & 45 \(\times\) 18 & 21.9 \\ H5 & 70 \(\times\) 18 & 34 \\ H6 & 70  
as the input condition for the calculation of the pointing variation of both the star sensor and the laser communication load in the whole satellite model.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 3 Materials and Methods
### The Space Segment: The UVSQ-SAT CubeSat Platform  
UVSQ-SAT is a nanosatellite development project underway based on the CubeSat standard [39; 40]. UVSQ-SAT is a one Unit (1U) CubeSat designed to provide a 11.10 cm \(\times\)11.10 cm \(\times\)11.35 cm useful volume (stowed configuration). LATMOS is a prime contractor of the 1U CubeSat with the support of a manufacturer (Innovative Solutions In Space (ISIS)) to build a dedicated satellite platform. Although the CubeSat is small, it contains all the critical subsystems and functions present in larger satellites. A configuration of the UVSQ-SAT nano-satellite is shown in Figure 2, and an overview of the UVSQ-SAT CubeSat properties is given in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Properties** & **Value** & **Comments** \\ \hline Orbit & SSO & Maximum altitude of 600 km, LATAN of 10:30 \\ Design lifetime & 1 year for LEO & 3 years desired \\ Launch date & Q4 2020/Q1 2021 & Launch vehicle: Soyuz \\ \hline Size & 1U & 11.10 cm (X) \(\times\)11.10 cm (Y) \(\times\)11.35 cm (Z) \\ Mass & 1.6 kg & Maximum with margins \\ \hline \hline \end{tabular}
\end{table}
Table 2: UVSQ-SAT CubeSat properties.  
Figure 2: UVSQ-SAT computer aided design representation with an arrangement of all Printed Circuit Boards (PCBs). The UVSQ-SAT payload instruments are shown (Earth Radiative Sensors (ERS) and DEep uV INnovative detector technologies for Space observations (DEVINS)), except the three axis accelerometer/gyroscope/compass, which is located above the payload electronic board.
## 3 Results and Discussion
Future developments in SSIP technology should focus on several goals: improving SNR through hardware upgrades, multi-channel synchronous acquisition, and algorithmsfor correcting electrical heterogeneities; integrating AMT/CSAMT data for joint inversion to overcome current technical limitations; establishing three-dimensional observation systems and developing joint time-frequency inversion methods to enhance deep-resolution capabilities; deploying high-density electrode arrays in mineralized anomaly zones to better delineate boundary changes in electrical properties; conducting laboratory spectral IP experiments on core samples; and integrating gravity and magnetic data to perform joint multi-physics inversion. These enhancements will not only improve the spatial accuracy of orebody delineation but also allow for indirect estimation of enrichment levels of minerals such as tungsten and tin, providing a more reliable scientific basis for deep resource evaluation. Application examples from the Xitian tin deposit indicate that large-array, multi-channel SSIP technology has successfully detected deep tungsten-tin orebodies; however, it still faces limitations such as restricted resolution, strong inversion non-uniqueness, and insufficient spatial coverage. These findings highlight the need for stronger geological and drilling constraints in subsequent work, increased profile density to improve spatial coverage, and careful consideration of topographic and electromagnetic interference effects on data quality.
# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 3 Different Existing Machine Learning Based Techniques for Plant Disease Detection
### Deep-Learning-Based Identification of Diseases
Different CNN architectures, such as AlexNet, VGG16, VGG19 and ResNet, were used in [132] to identify the diseases in plant. In their approach, they used the camera-captured images of eight different diseases to train the model. For the feature extraction, they used these CNN models and for classification purposes, they used different classifiers such as KNN, SVM and extreme learning machine (ELM). They achieved a maximum accuracy rate of 97.86% using the ResNet architecture. A NASNet-based deep CNN architecture was used in [133] to identify leaf diseases in plants and they obtained an accuracy rate of 93.82%. A shallow CNN (SCNN) was used by [PERSON] et al. [134] for the identification of maize, apple and grape diseases. First, they extracted the features from the CNN and then classified the diseases using SVM and RF classifiers. In their approach, they claimed that the combination of a shallow CNN and classic machine learning classification had a good ability to identify plant diseases and the kernel SVM and random forest had the ability to overcome overfitting. A number of deep CNN architectures were used by [PERSON] et al. [29] for the identification of four different rice diseases. In their approach, they extracted the features from the deep learning model and classified the diseases using an SVM classifier; they showed that the SVM performed better compared with the deep learning classifier. We summarize the above papers as follows:  
* The extraction of features using a CNN model and the classification using different machine learning classifiers also give higher performance accuracies. * A CNN model extracts better features, which make a classifier such as an SVM or RF give better performance results. * An SVM and RF can tackle the overfitting issues. * A CNN model is used only for extracting the features and hence the training of the model is not required. Table 5 summarizes the DL models along with the dataset used to identify the diseases in plants with their class and labels. Table 6 shows the limitations of some of the implemented deep-learning-based techniques for the identification of plant diseases. From Table 6 it is seen that most of the researchers used the same dataset to train and test the DL models. It is also seen that the number of works on the identification of plant diseases having multiple diseases on a single leaf is also relatively small. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 2 Materials and Methods
### Closed Impeller Manufactured by Means of Classical Technologies  
Due to its relatively small dimensions, the closed impeller was manufactured in four steps: the disc part and the shroud part, followed by welding of the two parts and, finally, machining to remove the welding excess. Machining of the disc and shroud bodies was performed on a 5-axis CNC DMG MORI DMU-40 milling machine (DMG MORI, Seebach, Germany). The closed impeller disc body fabrication designed the blade tip to be up to 2 mm higher than the designed model, in order to allow the closed impeller shroud alignment. Also, this provided a surface on which the welding process would be performed. Before the welding process, a visual and dimensional inspection was conducted on both parts to verify their dimensional and geometrical tolerances (within \(\pm\)0.1 mm). The 3D scan was performed with the ATOS Compact Scan 5M measuring machine (Carl Zeiss, Oberkochen, Germany) (Figure 4). Although there was evidence of tolerance deviation at the closed impeller shroud upper and lower exterior diameters (+0.167 mm and +0.168 mm), this area was machined after the welding process. Roughness was measured using a Mars Surf apparatus, and was found between 0.8 Ra and 1.6 Ra, within the nominal values imposed by the pump assembly configuration (maximum 1.6 Ra).  
The welding of the two impeller bodies was performed by means of gas tungsten arc welding (GTAW) using a Miller Dynasty 280 welding machine (Miller Welds, Appleton, WI, USA). After welding, the closed impeller was machined using a horizontal CNC lathe (EE 320 CNC) (Kaposvari Villamossagi Gyar, Kaposvar, Hungary), and a second visual and dimensional inspection was performed to verify its dimensional and geometrical tolerances. The manufacturing steps of the closed impeller are presented in Figure 5, while the dimensional analysis after welding and machining processes can be seen in Figure 6. The dimensional analysis after final machining showed tolerances in range of \(-\)0.074 mm to +0.066 mm, within the imposed tolerance of \(\pm\)0.1 mm. It should be noted that different impeller areas have different tolerances, the most critical ones being at the clearance area  
Figure 4: Dimensional control of the closed impeller body and shroud (units in mm).  
Figure 3: Closed impeller 3D model.  
(0.1-0.15 mm) and the blade area. The total mass of the closed impeller after machining was 33.25 g.  
The total manufacturing process for machining and welding (including creating the CNC program) was around 80 h. It should be noted that the type of welding process used affects the geometry of the impeller blades and, subsequently, its hydraulic performance, mainly due to the welding temperature reached and exposure time. There are also other more efficient welding processes (e.g., automated vacuum laser welding); however, this requires qualified operators, as well as specialized equipment, thus increasing the overall development costs of the impeller.
## 8 Conclusions  
Considering the rapid advancements in earthquake search and rescue robot development across various research institutions worldwide, this paper proposes a novel evaluation method for comprehensively analyzing the performance of earthquake search and rescue robots. By horizontally comparing and evaluating the overall performance of multiple earthquake search and rescue robots, the one with superior comprehensive capabilities can be identified.  
1. The evaluation criteria for the indexes of rescue robots are identified, resulting in the establishment of a comprehensive and hierarchical structure of earthquake search and rescue robots. This structure considers four aspects, namely survival ability, motion ability, detection perception ability, and communication control ability.
2. The weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. Qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. The performance of earthquake search and rescue robots is comprehensively evaluated and ranked using an improved radar chart method. Furthermore, sensitivity analysis is conducted to examine how changes in the weights of the main criteria affect the ranking results.
3. An in-depth case analysis using three types of earthquake search and rescue robots is presented as examples. The results demonstrate that the evaluation criteria are not only practical but also effectively measure and evaluate the performance and effectiveness of earthquake search and rescue robots. Furthermore, the proposed evaluation method has been validated using practical application, providing valuable insights for optimizing control strategies and structural designs of rescue robots.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 5 Results of the CIR-Based Methods
### Conventional Regression Method Results with CIR-Derived Features  
To provide a bridge from the previous work using traditionally available features [8], engineered features derived only from CIRs are considered using the conventional regression methods. The results are shown in Table 2. Compared to Table 1, it can be seen that traditionally available features perform better with these methods, as the features derived from the full OFDM waveform capture additional information not available in features derived solely from the CIR.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 3 Cascading Failure Model for DGCN
### Overload Failure Model
#### 3.3.2 Node Capacity  
Due to the cost constraints, there is an upper limit of node capacity in a load-induced network. The usual way to define the node capacity is to assume that it is proportional to its initial load. However, in most real networks, a node with a smaller capacity usually has a larger remaining capacity. The relation between node capacity and load is more likely to be a nonlinear model [34; 35]. In the DGCN of CSOS, if the initial load of the node is large, it indicates that the node is essential. The more important the node is, the more frequently it interacts with other nodes in information or service processing. Therefore, the corresponding residual capacity of the node is small. On the contrary, the load of the less important node is smaller, and there will be more free capacity [18]. Here, we adopt the nonlinear model to define the node capacity based on [PERSON]'s work [34].  
The node capacity of the communication net is  
\[C_{\text{W}}^{i}=L_{\text{W}}^{i}(0)+\lambda_{\text{W}}\cdot L_{\text{W}}^{i}(0)^ {\gamma_{\text{W}}},i=1,2,\cdots N_{\text{W}}, \tag{6}\]  
where \(\lambda_{\text{W}}\) and \(\gamma_{\text{W}}\) are the adjustment parameters.  
The node capacity of the functional net is  
\[C_{\text{G}}^{i}=L_{\text{G}}^{i}(0)+\lambda_{\text{G}}\cdot L_{\text{G}}^{i}(0 )^{\gamma_{\text{G}}},i=1,2,\cdots N_{\text{G}}, \tag{7}\]  
where \(\lambda_{\text{G}}\) and \(\gamma_{\text{G}}\) are the adjustment parameters.  
Figure 5 presents the relation between the initial load and capacity in nonlinear and linear forms. It can be seen from the figure that the nonlinear model in this paper conforms to the actual situation analyzed above. Especially, the nonlinear model degenerates into a linear model when \(\gamma=1\), which shows that it is more general.
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 3 Transient Contents in VANETs: An Overview
### Main Insights
Moreover, NDN is the first popular networking architecture providing a reference in-network caching mechanism able to work in heterogeneous network environments, ranging from the wired Internet to vehicular and IoT networks.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### Track Anomaly Detection Based on MOD-Bi-LSTM
#### 2.1.2 Track Deviation Detection  
The MOD algorithm is used to detect the track deviation. It is necessary to determine the threshold of the MOD algorithm. Since the MOD algorithm is to detect static data, the nearest neighbor number k and the anomaly threshold T can be determined through experimental verification. In the same scene, once the values of k and T are determined, these values can be adopted in the subsequent track deviation detection. The MOD value of the tracks is calculated, and tracks with a MOD value higher than the threshold are detected as a deviational track, then the deviational tracks will be removed.
## 2 Background
### Wind Tunnel Tests
According to [PERSON] [9], a recommended blockage ratio of less than 5% should be achieved to acquire faithful results. Hence, it is difficult to obtain a full-scaled Reynolds number in wind tunnels. Secondly, the existence of sensors and other structures such as end plates might interfere with the flow field, and measurements can only be taken at sensor locations which must be chosen in advance. [PERSON] et al. [16] have proved that the size of endplates used in two-dimensional (2D) wind tunnel tests has a large impact on the results. In their experiment, when the blockage ratio was close to 0.15, a combined effect of the endplate and the high blockage ratio led to an overestimation of the drag force on the model. [PERSON] et al. [17] also showed that endplates might have a significant effect on flow separations. Another limitation is the approximation of the natural wind characteristics in the wind tunnel, especially the approximation of the inlet condition. To gain a similar velocity profile and turbulence intensity of the natural wind at the inlet, the wind tunnel should provide enough roughness at the wall. This can be achieved by installing fixed grids and spires at the inlet and other roughness elements along the testing section so that the turbulence can be generated passively. Due to the simplicity  
Figure 2: Layout of the closed-circuit wind tunnel. of the setup, the passive method is used in most bridge wind tunnel tests. However, this method is only suitable for long testing sections. [PERSON] and [PERSON] [18] suggest that the implementation of passive turbulence generation in short wind tunnels will lead to questionable results as the turbulence is not fully developed. There have been attempts to incorporate active controlling systems into the generation of inlet turbulence. For instance, [PERSON] [19] designed the first active grid turbulence generator which allows accurate control of the inlet flow by dynamically adjusting the opening of the grid. It is commonly accepted that the active controlling strategy yields better results than the passive method. However, due to the cost, there has not been any research highlighting the implementation of active turbulence control in bridge wind tunnel tests. Finally, the cost of wind tunnel tests can be high. In addition to the expensive maintenance cost of the wind tunnel facility, wind tunnel tests are often repeatedly conducted during bridge design. Additionally, a small change in the bridge structure could lead to a series of costly wind tunnel tests for validation.
## 2 Introduction to Bandpass Sampling Theory and BPS-FTS
### Bandpass Sampling Theory
This analysis demonstrates that, according to the bandpass sampling theory, if the broadband spectrum detected by the Fourier transform spectrometer is converted into a series of continuous narrowband spectra, the interferogram sampling step can be increased, while the interferogram sampling frequency and difficulty of instrument development can be reduced. Therefore, BPS-FTS has been proposed internationally. Its basic principles involve using spectral splitting techniques to subdivide the broadband spectrum into several consecutive narrowband spectra, utilizing bandpass sampling theory to obtain the interferogram for each narrowband spectrum, and subsequently recovering all of the narrowband spectra using Fourier transformation to finally synthesize the broadband spectrum.
## 4 Simulation Conditions
### Combined Wind, Snow, and Seismic Conditions
#### 4.2.2 Analysis and Optimization Suggestions
Although the deformation in a single direction is well controlled with a 2.0 mm wall thickness, the deformation is the largest in a combination of multiple directions, which increases the risk of structural failure in practical applications. The 1.5 mm wall thickness provides the best balance overall. Although the stress is slightly higher in individual directions, it maintains low deformation in all directions, especially under multi-directional combination effects. This illustrates the optimization of the design, making its structural design more reasonable. In structural design, control can avoid structural functional failure or premature fatigue. Therefore, the 1.5 mm wall thickness design demonstrated in simulation its advantages in maintaining structural integrity under the influence of comprehensive seismic waves, as well as its ability to maintain consistency and stability in all directions. This performance, taking into account structural safety and reliability, makes the 1.5 mm wall thickness a strong candidate. In summary, the combined simulation analysis of wind, snow, and seismic conditions not only enhances the understanding of the structural behavior of the box-type substation in this study but also highlights avenues for performance improvement through structural optimization.
# Multi-Step Design Optimization for the Improvement of an Outer-Rotor Brushless Direct Current Motor
## 3 Taguchi Method and Response Surface Methodology (RSM)
### Respond Surface Methodology (RSM)
Consequently, the design variables were confined to three key factors, with their ranges being meticulously defined to ensure the structural feasibility of the BLDC motor's configuration. When adjusting the number of turns and width of the stator slot opening, the coil diameter is modified to achieve a slot fill factor that is suitable for production. The objective function is formulated to ensure a motor efficiency of 82% or higher at a torque of 0.1 Nm or greater. Minimizing power losses leads to increased efficiency and output power, and hence, the inclusion of the output power in the objective function. The efficiency, torque, and maximum operating speed of all the design models generated by the RSM are assessed using FEM results, as presented in Table 7. Figure 9 illustrates a graph depicting the RSM optimizer, which determines the optimal values of design variables to meet the specified objective functions using Minitab v 22.0. The motor's efficiency is affected by the thickness of the magnet, the width of the stator slot opening, and the number of turns; the output power and torque of the target level can be achieved at the maximum efficiency point, as shown in Figure 9. The thickness of the magnet is 3.5 mm, the width of the stator slot opening is 1 mm, and the number of turns is 79, which can cause maximum efficiency of 83.6519%, torque of 0.1039 Nm, and output power of 29.8839 W. Based on the results and reviewing the RSM, as shown in Figure 9, we can find the optimal design parameter value satisfying the design target. The final result from the simulations is shown in Table 8 and demonstrates that the reference motor, compared with the Taguchi method and Taguchi method-based RSM, is under investigation using FEM analysis. The proposed (optimized) multi-step optimization can be more efficient than the Taguchi method optimization and reference motor design. Figure 9: RSM analysis of proposed model optimization.