esa-sceva/satcom-chunk-collection huggingface dataset:
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 3 The TYCOS Dataset
### Data Augmentation  
Data augmentation techniques offer a potent solution to the challenge of small original datasets. Through the augmentation of the data, these techniques effectively increase the number of samples in the dataset, thereby mitigating the risk of overfitting in models. Data augmentation plays a pivotal role in enhancing a model's generalization ability, improving robustness, and optimizing training outcomes, particularly in scenarios where data are limited. There are two primary approaches to data augmentation. The first approach is offline augmentation, where all augmentations are precomputed and subsequently provided to the network for learning. This method is well-suited for smaller datasets, enabling a substantial increase in the number of images through techniques such as rotation, translation, and flipping. The second approach is online augmentation, where the original data are directly transmitted to the network for batch-wise transformation. This method is more suitable for larger datasets.  
This work employed offline data augmentation techniques using the software tool PyCharm Community Edition 2023.2.3, The data augmentation techniques encompassing darkening, brightening, blurring, adding Gaussian noise, mirroring, rotating the original image by 90 degrees, and rotating by 180 degrees. Subsequently, the images with darkening, brightening, blurring, and noise were flipped by 90 and 180 degrees. Following this, brightness adjustment and noise addition were applied to the flipped images. As a result of these operations, the dataset was expanded 20 times compared to the original. Figure 7 illustrates some examples of the effects of data augmentation.  
Figure 5: A schematic diagram of spatial target circumnavigation.  
Figure 6: An example of satellite body occlusion of flyover windsurfing board in flyover state (local clipped effect).  
After augmentation, the dataset was split into training, testing, and validation sets at a ratio of 8:1:1. A total of 31,880 images were collected, with 25,504 used for training, 3188 for testing, and 3188 for validating. The number of dataset samples for each condition is shown in Table 5.  
Figure 7: Examples of data augmentation effects.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Algorithm Evaluation for LB = 106 dB and LB = 115 dB
#### 7.2.2 Algorithm Evaluation for \(LB=106\) dB, \(d=9.3362\) km  
In this subsection, another scenario was investigated for the technical solution in [57], when the distance between gateway and target WSN is 9.3362 km. It is different from the previous ones proposed for lower \(LB\), because the coverage range for the solution in [57] is higher and, thus, a lower number of UVs would cover the distance between the GW and the served WSN. Consequently, we propose an initial placement of seven UAVs, two USVs and five WSNs, as depicted in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(n_{UAV}\) & \(n_{USV}\) & \(n_{pu}\) & \(d\) (km) \\ \hline
19 & 2 & 7 & 2.393 \\
19 & 2 & 19 & 7.2209 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Parameters used in the simulations for \(LB=106\) dB and \(LB=115\) dB.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(n_{UAV}\) & \(n_{USV}\) & \(n_{vm}\) & \(d\) (km) & \(\rho\) & \(\delta d\) (m) & \(t_{2}\) (s) & \(\sigma\) \\ \hline \multirow{4}{*}{30} & \multirow{4}{*}{2} & \multirow{4}{*}{28} & 0.05 & 20 & 36.825 & 0.9821 \\  & & & 0.1 & 20 & 22.875 & **0.9887** \\  & & & 0.12 * & 20 & 20.203 & 0.9550 * \\  & & & 0.13 * & 20 & 19.875 & 0.9533 * \\  & & & 0.14 * & 20 & 18.383 & 0.9543 * \\  & & & 0.15 * & 20 & 18.465 & 0.9528 * \\  & & & 0.2 * & 20 & 14.774 & 0.9525 * \\ \hline \hline \end{tabular}
\end{table}
Table 12: Algorithm results: execution time (after time execution improvement) and performance indicator \(\sigma\), \(nt=3\), \(LB=92\) dB, \(d=9.3362\) km.
## 3 Experiment and Parameter Selection
### Influence of Network Parameter  
This part of the experiment uses the source domain data for experimental verification through a grid search for the number of hidden layers and the number of neurons per hidden layer. The influence of the depth of the neural network and number of neural nodes of DBN [32] on the research results is analyzed (see Figure 10). It can be seen from Figure 10 that when the other factors are unchanged, the highest accuracy is obtained when the number of deep neural networks is five. This means that the network structure contains five Restrict Boltzmann Machine (RBM)s. Thus, when the number of network layers is too small (especially when the number of layers is less than five), the complexity of the structure of the deep neural network is too low to extract the high-level features in the data, and the acquired features are poorly represented. This will result in a large deviation, because there has been a lack-of-fit phenomenon. When the number of layers is too large, the classification effect also deteriorates. This is because as the number of network structural layers increases, the structure becomes increasingly more complicated, and the over-fitting phenomenon occurs. Therefore, it is necessary to determine the number of layers in the neural network according to the specific size of the data in the process of constructing the structure of the deep neural network. If the number of layers is too high or too low, the classification result is unfavorable.  
The width of the deep neural network has an important influence on the experimental results. The main methods used in the current research are to determine the corresponding network width using a continuous test. The highest accuracy is a five-layer network structure and 50 neural nodes.  
Figure 10: Pavia center data classification accuracy grid search with the number of hidden layers and number of neural nodes.  
Figure 9: Pavia University false color image (left) and ground truth image (right).  
As seen from the figure, when the DBN network width is 50 neuron nodes, the optimal classification results occur at 98.3%. When the number of network neuron nodes is smaller than the optimal number of nodes, the complexity of the network is low, and the number of parameters is small. Although the training time and the accuracy of the network are low, the simple model means that the extracted features are simple and low, and the low accuracy of the network at this time is due to the network deviation. However, when the number of neuron nodes is higher than the optimal number of nodes, the model's language is complicated, and the data classification accuracy of the test set is not high enough for the training data to be well written. The model's generalization ability is poor, and the model suffers from the over-fitting phenomenon.
## 3 Results and Discussion
### Yeast and Mold Population Changes During Blackcurrant Postharvest Storage  
The results on yeast and mold populations of UVC pretreatments are shown in Figure 4. The results indicate that UVC pretreatments of varying lengths significantly differed from the control on most days (days 0 to 15) of postharvest storage. There were no significant differences on day 20, although UVC-pretreated blackcurrants had lower average populations (<24 Log CFU/g). Two hours of UVC-pretreated blackcurrants on day 0 effectively inhibited yeast and mold populations, with approximately 2 log CFU/g differences compared to the other pretreatments. Various lengths of UVC pretreatments showed effects on days 5 and 10 compared to the control (0 h); however, no significant differences were observed among the different lengths of UVC pretreatments.  
Compared to the mold and yeast population (average 17-18 log CFU/g) on day 0, the growth of yeast and mold was more dramatic on day 5 (average 22 log CFU/g), followed by less than 2 log CFU/g growth from day 10 to day 20. This was similar to the fungal development in barley ecosystems, although the fungal growth kinetics were not detailed before day 5 in this study [46]. In this blackcurrant storage, two hours of UVC pretreatment may not be sufficient to control mold growth in the long term, as evidenced by the lack of significant changes in yeast and mold populations on day 20. Therefore, repetitive UVC pretreatment or 360-degree radiation should be tested for blackcurrant postharvest storage.
## 3 Results and Discussion  
### Experimental Setup  
We built all models on the PyTorch [32] platform to compare their performance. Mean square error (MSE) was adopted as the loss function, which has excellent performance in regression tasks. Adam was used as the optimizer with a learning rate of 0.0001. We used the public dataset Ninapro for predicting the angles of 10 joints in 6 kinds of grasping motion. The first 60 percent and the last 40 percent of each movement were used for training and testing, respectively.  
Figure 4: The structure of dilated convolution and residual connections, where each parallelogram is a neuron, solid arrows are used to depict the data flow of the right-most neuron and dashed arrows represent other data flow. (**a**) Dilated convolution; (**b**) Residual connections.
## 3 Results and Discussion  
### Model Fitting and Statistical Analysis  
A D-optimal design was used, which has an independent variable with four levels and three replications at the center point. The D-optimal design was used for the optimization of the formulation of spelt pasta, and different parameters of the optimized formulation were evaluated. The linear and quadratic models that were fitted on each response were evaluated. The chemical composition of spelt flour is described in Table 1, which we obtained from a previous study [39].  
The mathematical models were significant and presented the responses accurately; in all cases, the F-value was significant (\(p<0.01\)), and R\({}^{2}\) values were more than 0.87, according to the ANOVA results presented in Table 2.  
R\({}^{2}\) represents the coefficient of determination, defining the ratio of the variation of the answers that is explained by the model. The closer the R\({}^{2}\) is to 1, the better it fits the model. Adjusted R\({}^{2}\) is a correction of the value R\({}^{2}\) dependent on the number of degrees of freedom [40]. The color results, firmness, total dietary fiber, and total polyphenol content were fitted to the linear model with 75%, 95%, 98%, and98% of the data variation explained, respectively. The quadratic model explained 87%, 98%, 99%, and 99%, respectively, of the data variation for dough cohesivity, fracturability, crude proteins, and cooking loss.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 3 Method--SNR of ED and SD for Line Detection
### Measure of Noise Strengths  
To measure the SNR for line detection in a smoothed image, it is necessary to quantify the amount of noise after smoothing and the signal strengths. However, there have been few studies on the quantification of the amount of noise after smoothing. Thus, in the following section, the residual noise after the smoothing is first quantified, and the correlation between the pixels in an image of smoothed noises is derived using an error propagation scheme.
# Design Method Using Response Surface Model for CFRP Corrugated Structure under Quasistatic Crushing
###### Abstract  
The development of a carbon-fiber-reinforced plastic (CFRP) part is carried out by utilizing many experimental results in deciding the design. For this reason, the development period of a CFRP structure is long and an obstacle for commercialization. In this paper, multiple regression analysis is used to derive a response surface that estimates the generated load using the shape parameters of a corrugated collision energy absorbing structure to shorten the development period. To obtain the response surface, we conducted a quasistatic crushing experiment by using the length of linear portions (pitch) and the number of stacks (thickness) of a corrugated shape as parameters. When progressive crushing mode is observed, energy absorption efficiency decreases with the increase in pitch, and increases with the increase in the number of stacks. To discuss how energy absorption efficiency changes, a comparison examination is conducted using the derived response surfaces. Results indicate that specifications with high energy absorption efficiency can be accurately selected using the response surface of primary expression. In addition, differences in deformation mode were due to the influence of the stress at the corner portion of a part.
## 3 Proposed Optimization Algorithm  
As previously mentioned, it is the received constellation distribution that has significant impact on the BER performance, which rests with both the transmit constellation and the reflectance vector. This section proposes a two-step algorithm to optimize both the transmit constellation and the coherent reflectance vector:  
1. Solve for the optimal received signal constellation \(\mathbf{s}^{\prime}\).
2. Find the transmit constellation points \(\mathbf{s}\) and the coherent reflectance vector \(\bar{\mathbf{r}}\) corresponding to the optimal received constellation points.
## 4 Application and Results
### Diagnostic Analysis  
In this study, three diagnostic analysis methods (i.e., Taylor diagram and violin plot) were considered for visual evaluation of the model performance.
## 6 Applications  
The analysis of three real data sets is considered to show the usefulness of the proposed distribution in modeling different phenomena and the performances of the methods used to determine the unknown parameters. In addition to the traditional model selection techniques, we calculated the \(Y^{2}\) statistic to prove that the proposed model fits some data sets better than some commonly used alternative distributions, such as: \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\theta=5\) & \(\beta=0.5\) & \(\theta=5\) & \(\beta=0.5\) \\ \hline  & \multicolumn{2}{c}{\(n=10\)} & \multicolumn{2}{c}{\(n=25\)} \\ \hline MLE & 4.8841 (0.0122) & 0.5899 (0.0124) & 4.8993 (0.0105) & 0.5776 (0.0106) \\ WLS & 4.8973 (0.0120) & 0.5832 (0.0117) & 4.9176 (0.0103) & 0.5719 (0.0099) \\ MPS & 4.8812 (0.0136) & 0.6118 (0.0147) & 4.8935 (0.0119) & 0.5995 (0.0129) \\ CMEs & 5.0735 (0.0126) & 0.3099 (0.0131) & 5.0632 (0.0109) & 0.4023 (0.0113) \\ LSE & 5.0759 (0.0098) & 0.4244 (0.0093) & 5.0607 (0.0086) & 0.4355 (0.0085) \\ ADE & 5.1405 (0.0158) & 0.3768 (0.0159) & 5.1282 (0.0141) & 0.3928 (0.0141) \\ ADEL-R & 5.1477 (0.0167) & 0.3709 (0.0171) & 5.1354 (0.0150) & 0.3851 (0.0153) \\ ADEL-T & 5.1656 (0.0175) & 0.3653 (0.0177) & 5.1533 (0.0158) & 0.3782 (0.0159) \\ \hline  & \multicolumn{2}{c}{\(n=35\)} & \multicolumn{2}{c}{\(n=75\)} \\ \hline MLE & 4.9146 (0.0090) & 0.5611 (0.0089) & 4.9644 (0.0040) & 0.5348 (0.0037) \\ WLS & 4.9391 (0.0088) & 0.5567 (0.0082) & 4.9271 (0.0079) & 0.5445 (0.0070) \\ MPS & 4.9069 (0.0104) & 0.5832 (0.0112) & 4.9224 (0.0090) & 0.5567 (0.0077) \\ CMEs & 5.0517 (0.0094) & 0.4152 (0.0096) & 5.0364 (0.0072) & 0.4274 (0.0061) \\ LSE & 5.0454 (0.0071) & 0.4507 (0.0068) & 5.0329 (0.0055) & 0.4623 (0.0052) \\ ADE & 5.1068 (0.0127) & 0.4011 (0.0124) & 5.0852 (0.0111) & 0.4143 (0.0093) \\ ADEL-R & 5.1143 (0.0136) & 0.3980 (0.0136) & 5.0924 (0.0120) & 0.4112 (0.0099) \\ ADEL-T & 5.1319 (0.0144) & 0.3914 (0.0142) & 5.1103 (0.0125) & 0.4056 (0.0116) \\ \hline  & \multicolumn{2}{c}{\(n=200\)} & \multicolumn{2}{c}{\(n=400\)} \\ \hline MLE & 4.9869 (0.0027) & 0.5123 (0.0024) & 4.9989 (0.0012) & 0.5003 (0.0009) \\ WLS & 4.9586 (0.0061) & 0.5183 (0.0053) & 4.9813 (0.0043) & 0.5054 (0.0035) \\ MPS & 4.9437 (0.0072) & 0.5304 (0.0060) & 4.9763 (0.0056) & 0.5078 (0.0042) \\ CMEs & 5.0195 (0.0053) & 0.5412 (0.0044) & 5.0096 (0.0038) & 0.4834 (0.0026) \\ LSE & 5.0167 (0.0039) & 0.4785 (0.0035) & 5.0035 (0.0023) & 0.4908 (0.0015) \\ ADE & 5.0527 (0.0093) & 0.4408 (0.0076) & 5.0234 (0.0077) & 0.4733 (0.0058) \\ ADEL-R & 5.0633 (0.0102) & 0.4377 (0.0081) & 5.0302 (0.0086) & 0.4702 (0.0063) \\ ADEL-T & 5.0741 (0.0110) & 0.4321 (0.0094) & 5.0416 (0.0092) & 0.4646 (0.0076) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Estimators’ values of \(\theta=5\) and \(\beta=0.5\) with different estimation methods.
## 2 Methods  
Designing a drive system that ensures high work efficiency requires knowledge of the expected velocities of the manipulator effector movements and the occurring loads. This research aimed to determine the maximum movement velocities of the effector, limited only by the operator's perception, and the occurring accelerations, which determine the dynamic loads. Knowing them is necessary to properly estimate the power demand and energy consumption of the manipulator. Since the manipulator's movement velocities and power requirements may depend on the size, weight, and reach of the manipulator, it is necessary to conduct tests for different ranges and size ratios. The size ratio [10] should be understood as the ratio of the effector's displacement to the movement of the hand controlling the effector's operation in accordance with the relationship which results from Figure 1:  
\[K=\frac{|AB|}{|A^{\prime}B^{\prime}|} \tag{1}\]  
where \(|AB|\) is the effector displacement; and \(|A^{\prime}B^{\prime}|\) is the operator's hand displacement.  
Taking into account that dynamic loads (resulting from accelerations) and, consequently, the power demand and energy consumption of the manipulator's movement depend on the inertia of the manipulator together with the transferred load, the unit energy indicator was used to assess the energy consumption of the movement, expressed as follows:  
\[E_{k}^{*}=\frac{E_{k}}{m_{z}} \tag{2}\]  
where \(m_{z}\) is the equivalent mass reflecting the inertia of the manipulator and the transferred load, reduced to the local effector system; and \(E_{k}\) is the kinetic energy that is needed to move the manipulator effector that is determined from the following equation:  
\[E_{k}=\frac{1}{2}m_{z}v_{e}^{2} \tag{3}\]  
where \(v_{e}\) is the effector velocity resulting from the following equation:  
\[v_{e}=\omega L \tag{4}\]  
Figure 1: The test stands [10]: (**a**) a 3D model; (**b**) illustration of case study.  
where \(\omega\) is the angular velocity of the manipulator.  
The kinetic energy indicator \(E_{k}^{*}\) describes the amount of energy that is needed to move 1 kg of equivalent mass \(m_{z}\) and allows for estimating the energy consumption of the movement of a manipulator with any inertia. In a similar way, the power indicator \(N^{*}\) was defined, which determines the power demand resulting from dynamic loads.  
The power indicator describes the rate of change of kinetic energy that is necessary to move 1 kg of equivalent mass \(m_{z}\) and allows for estimating the expected drive power of a manipulator with a specific inertia. It is described by an equation:  
\[N^{*}=\frac{N}{m_{z}}=av_{e} \tag{5}\]  
where \(a\) is the effector acceleration. The power that is needed to move the effector results from the force \(F\) that is needed to move the effector and the velocity of the effector \(v_{e}\) according to the following equation:  
\[N=Fv_{e}=a\ m_{z}\ v_{e} \tag{6}\]  
Energy and power indicators defined in this way allow for the assessment of the power demand and energy consumption of moving the manipulator effector based on kinematic values that should be determined experimentally.
## 2 Methodology
### Research Method
#### 2.4.3 Decoupling Index  
The concept of decoupling originates from physics and is mainly used to indicate that the interaction between variables no longer exists. It was later introduced to economic growth, energy consumption, environment, and urbanization. The Organization for Economic Co-operation and Development (OECD) defines decoupling as the breaking of the link between environmental pressure and economic effectiveness [52]. Absolute decoupling means that with economic development, the ecological environment will be improved or kept stable; relative decoupling means that with economic development, the growth rate of the ecological environment is slower than the economic growth rate [52; 53]. In this study, a decoupling index based on the coupling coordination degree of the population, urbanization, and ecological environment was constructed, as shown in Equation (10).  
\[\mathrm{E_{t}}=\frac{\Delta f(y)}{\Delta s(x)}=\frac{(f(y)_{i}-f(y)_{j})/f(y)_{ j}}{(s(x)_{i}-s(x)_{j})/s(x)_{j}} \tag{10}\]  
where \(\mathrm{E_{t}}\) denotes the decoupling degree of the population, urbanization, and ecological environment from each other in time period t; \(\Delta f(y)\) and \(\Delta s(x)\) denote the development indices of the population, urbanization, and ecological environment in time period t, respectively; and \(f(y)_{i},f(y)_{j},s(x)_{i},\) and \(s(x)_{j}\) denote the values of the end year i and the beginning year s of each indicator, respectively. We classified decoupling into weak decoupling, moderate decoupling, and strong decoupling, and classified coupling into weak coupling, moderate coupling, and strong coupling using the four quadrants in Figure 5.