esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 2 Theoretical Background
### Convolutional LSTM  
To consider spatial causality in learning, 2D data (image) should be used as input. As described previously, 2D data have a feature that even when the size is increased only slightly, the dimension of the data is significantly increased during training, such as calculating the weight of models. Therefore, a convolutional neural network (CNN) [11], one of the deep learning methods that is used to consider the characteristics of images, can be employed in the LSTM learning model. The most common technique involves a long-term recurrent convolutional (LRCN) network [10], where each 2D feature vector is first extracted by the CNN and used as an input of the LSTM. The difference between the LSTM and the LRCN networks is that the latter (LRCN) passes the input through a convolutional layer. Then, the input data can be an image, that is, 2D data. However, when using 2D data from multiple channels as input data, a 3D tensor is used as input, and feature vectors extracted through the CNN also need to undergo the same process inside as the LSTM. Therefore, it is not suitable for training long-range data, such as ocean weather. However, the ConvLSTM [12] uses an entirely different approach. By performing the LSTM internal operation itself as a convolutional operation, the amount of computation can be dramatically reduced. Figure 3 shows the internal cell state of the ConvLSTM.  
The modified key equations in the ConvLSTM can be expressed as Equations (7)-(12).  
\[f_{t}=\sigma(W_{Xf}*X_{t}+W_{Hf}*H_{t-1}+W_{cf}\odot C_{t-1}+b_{f}) \tag{7}\]  
\[i_{t}=\sigma(W_{Xi}*X_{t}+W_{Hi}*H_{t-1}+W_{Ci}\odot C_{t-1}+b_{Hi}) \tag{8}\]  
\[g_{t}=\tanh(W_{Xg}*X_{t}+W_{Hg}*H_{t-1}+b_{h,g}) \tag{9}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot g_{t} \tag{10}\]  
\[o_{t}=\sigma(W_{Xo}*X_{t}+W_{Ho}*H_{t-1}+W_{Co}\odot C_{t}+b_{h,o}) \tag{11}\]  
\[H_{t}=o_{t}\odot\tanh(C_{t}) \tag{12}\]  
Equations (1)-(6) and Equations (7)-(12) are quite similar when compared. However, there are two crucial differences. First, in the ConvLSTM, the input gate \(i\), forget gate \(f\), output gate \(o\), cell input \(X\), cell output \(C\), and cell state \(H\) are all 3D tensors. This is different from the original LSTM, where all elements were 1D vectors. Second, in the term where matrix multiplication was performed, all matrix multiplications are replaced by convolution operations. This indicates that the number of weights present in all \(W\) in each cell may be markedly less than in LSTM. This is the same as the effect when the  
Figure 3: Cell state configuration of the convolutional LSTM (ConvLSTM).  
fully connected layer is replaced by a convolutional layer; it can reduce the number of weights of the model significantly and is suitable for effectively training a large amount of data. Therefore, in this study, the ConvLSTM was used for the ocean weather, which is a very long-range and large scale.
# Virtual Simulation and Experiment of Quality Inspection Robot Workstation
## 4 Workflow Simulation Testing
### Quality Inspection Robot Workstation Test Process  
The simulation tests are carried out according to the operation process of quality inspection robot workstation in real work. The workflow can be broken down into the following steps: First, the Automated Guided Vehicle (AGV) picks up parts from the inspection area using an automated loading and unloading system. The AGV then transports these picked parts to the quality inspection robot. At the quality inspection robot workstation, the detection robot utilizes a high-resolution camera and sophisticated image processing algorithms to perform defect inspection on the parts. Once the inspection is complete, the AGV separately transports the qualified parts and the non-conforming parts to their respective locations. Qualified parts are sent to the finished product area, while non-conforming parts are routed to a specialized processing area for further handling. During the inspection process, data such as part inspection results and inspection time are automatically stored and uploaded to the Manufacturing Execution System (MES).
## 3 Experimental Results and Discussion
### Out-of-Plane Test
#### 3.1.2 Dynamic Compression
The influence of the area was not as obvious as the influence of the thickness. For the influence of shape, as shown in Figures 14c and 15c, the stress-strain curves for different shapes were similar, and the slope of the cylindrical specimen was a little bit higher than that of the cuboid specimen in the non-linear period. In general, the thickness had the greatest influence on the non-linear period stress-strain curve, while the shape of the cross-section had the least influence. As shown in Figure 16a, the modulus of these specimens could be fitted using points in the linear period of the stress-strain curves. As shown in Table 2 and Figure 16b, the modulus values did not show a strong relationship with the thickness, area, volume, or shape of the specimen. The maximum relative error when comparing the mean values was 7.5458%.
## 2 Proposed Methodology
### 2.2. Fisture Layout Optimization Using Topology Optimization
#### 2.2.3 Stage 3--Optimization of Number and Position of Clamps  
All possible combinations are generated within the feasible area of clamps, as found in stage 2. Clamps are removed methodologically while deformation is kept below or up to 2 mm. The final layout obtained in each subcase is regarded as the optimal layout, with the optimized number and position of clamps. According to N-3-2-1, the fixturing principle minimum number of clamps in the primary plane is 4. When the number of clamps reaches 4, the allowable maximum deformation criteria is rechecked and the process to reduce the number of clamps is stopped. A total of 25% of the reduced population, after topology optimization, is tested by using GA. When 25% of the reduced population is tested GA is terminated.  
This stage comprises of following steps:  
1. Non-design region clamps are kept fixed because their position and number have very little effect on the reduction of deformation.
2. One clamp is removed from the design quadrant, which has the least effect on deformation.
3. All possible combinations of initial clamps in the design quadrant are generated. This process is just like population generation in the genetic algorithm. All clamps and locators are combined to generate the complete layout.
4. Each combination of clamps and locators on the workpiece are contained in a separate subcase. The generated subcases are analyzed using MSC Nastran. The subcase with least maximum deformation is selected for the next step.
5. Clamps are removed from the design quadrant until maximum deformation remains below 2 mm.
6. Once the optimal number of clamps is found, the selection process is executed and population is generated.
7. In the crossover process, the population selected from the selection process is split into two groups. The first half of the first group is paired with the second half of the second group, and vice versa.
8. In mutation one or more clamps' position is changed in some combinations to gain diversity in the population.
9. New population is generated which is different and more diverse than the initial population.
10. Population is analyzed in MSC Nastran.
11. The fitness is evaluated. When maximum deformation is below 2 mm the optimization process is terminated. If not, the procedure is recommended until convergence criterion is fulfilled. Proposed GA is used for static load. This proposed algorithm may be modified to accommodate the requirements of dynamic load. Flow chart of Stage 3 for fixture layout optimization using GA is shown in Figure 4.
# Sim-Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning
## 3 Sim-Real Model Performance
### Image Processing for Environment Matching  
The objective of the research was to obtain image-based robot control from the simulated environment. The challenge of such an approach is how the simulated environment mirrors the actual workspace. To address this, structural design-matching was considered as explained in Section 2.2, but this does not guarantee true uniformity. To this end, we adopted image preprocessing to eliminate chromatic information and sensor noise in a bid to increase mirroring as well as achieve dimension reduction in the inference process. Image processing has been used in machine learning and fields, including sim-real transfer, as a means to enhance training [24; 25; 26; 27].  
In the study, six types of image processing were adopted: grayscale, binarization, 4-level grayscale, 10-level grayscale, 4-level grayscale with a median filter, and 10-level grayscale with a median filter. The kernel size of the median filter was set to 3 \(\times\) 3. Sample scenes are shown in Figure 7a-e to showcase the processing strategies utilized. From the different operations, the background details and target object are impacted differently.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State** & **Reward** \\ \hline Successful grip & 1000 \\ The distance between the robot hand and the cylinder is within 1 mm & 1 \\ When not grasping & \(1/(1+distant)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reward setting.  
Figure 7: _Cont._
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 3 Algorithm Framework  
We propose a new method, the WCFC algorithm, for generating superpixels or supervoxels. It maintains a speed comparable to existing RGBD superpixel methods, is more memory efficient, and exhibits state-of-the-art boundary adherence. Weighted coplanar feature clustering uses a similar framework of the SLIC algorithm, which is an adaptation of k-means for superpixel generation.  
However, there are three important distinctions--it adds a fast preprocess to refine depth maps, an additional geometric part in the distance measure, and a dynamic content-adaptive weight according to the image content. Through these three modifications, WCFC makes full, robust use of the depth information and makes a trade-off between the color and depth information dynamically.
## 3 Review of Methods
### Transformers  
In 2017, [PERSON] et al. introduced a new deep learning model, called a _Transformer_, whose purpose was to enhance encoder-decoder models [66]. Originally derived for sequence-to-sequence transductions, e.g., in _Natural Language Processing_ (NLP), Transformers have also gained importance in other fields, e.g., image processing, due to their ability to process patterns as sequences in parallel in capturing long-term relationships, thereby overcoming the difficulties with CNN- and RNN-based models. They consist of multiple encoder-decoder sets, where the encoder is a series containing a self-attention layer and a feedforward neural network, whereas the decoder has an additional layer, the _encoder-decoder attention layer_, which helps highlight different positions while generating the output.  
Self-attention mechanisms are the basis of Transformers. In the first step, they compute internal vectors (_query_, _key_, and _value_) based on the products of the input vectors and weighting matrices, which are then used to calculate scores after computing the dot products between the query vectors and the key vectors of all other input vectors. The scores can be interpreted as the focus intensity. Using the softmax function after normalization, the attention is calculated as the weighted sum of all value vectors. The weighting matrices are the entities that are tuned during training. Using multiple (_multi-head_) self-attention mechanisms (_MHSA_) in parallel, it is possible to build deep neural networks with complex dependencies.  
Transformers have also been applied in radar-based human activity. In [67], a Transformer was trained as an end-to-end model and used for the classification of seven coarse-scaled tasks, i.e., standing, jumping, sitting, falling, running, walking, and bending. In comparison with the two other benchmark networks, the accuracy of the Transformer was the highest at 90.45%. With a focus on making Transformers more lightweight, in [68], another novel Transformer was developed and evaluated based on two different datasets of participants performing five activities (boxing, waving, standing, walking, and squatting), achieving accuracies of 99.6% and 97.5%, respectively. [PERSON] et al. introduced another lightweight Transformer [69] that incorporated a feature pyramid structure based on convolution combined with self-attention mechanisms. The average accuracy achieved for the public dataset was 91.7%, whereas for their own dataset, it reached 99.5%.  
Figure 9: Example of a Convolutional Autoencoder.
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 5 Problems and Optimization
### Unmatched Nodes  
In the previous subsection, the issue of the mapping algorithm was addressed, but there is still a significant problem that has not been resolved prior to that. Specifically, the generation of virtual nodes depends on the node matching algorithm, which implicitly requires that the number of nodes in the graph be even. However, in practical applications, it is not uncommon to encounter cases where the total number of nodes is odd, and perfect matchings do not always exist in general graphs. These issues can lead to the presence of unmatched nodes in the graph \(G\). Therefore, effectively dealing with these unmatchable nodes has become an urgent problem to address. Below, a solution to this problem is presented.  
**Theorem 2**.: _After removing the unmatchable nodes from the virtual graph, the number of connected components is at most 4._  
Proof.: As shown in Figure 3, the furthest distance between the unpaired node \(v\) and the virtual nodes corresponding to its neighboring nodes in the subgraph \(G_{r}^{\prime}\) is \(3r/2\). That is, the subgraph formed by \(v\) and its neighboring virtual nodes is connected within a radius of \(3r/2\), while the connectivity radius when it acts as a virtual node is \(r^{*}\geq 2r\). Since \(3r/2\div r^{*}\leq 0.75<0.850\), by Theorem 1, we know that the number of connected components in the virtual graph after removing the unmatchable nodes is at most 4.  
Hence, unmatchable nodes only require up to four edges to maintain the connectivity of the graph, fulfilling the condition degree\({}_{+}\leq 4\). Due to the maximum degree of a node \(v\) in the virtual subgraph \(G^{*^{\prime}}\) being 6, no additional work is required when degree\((v)\leq 4\). When degree\((v)>4\), it implies that there are at least two neighboring nodes that have a path between them. When the degree of a node \(v\) is 5, select the pair of neighboring nodes that correspond to the smallest central angle of the circle. Then, determine if there are any nodes other than \(v\) within the neighborhood defined by the distance between these two neighboring nodes as the radius. If there are, remove the connection between \(v\) and one of these neighboring nodes. If not, connect the two neighboring nodes and remove the connection between \(v\) and one of them. If the degree is 6, the same method can be applied twice to address the situation. Let \(N_{G}(v)\) denote the set of neighbors of node \(v\) in graph \(G\) and \(UMN(V)\) denote the set of all unmatched nodes. The algorithm is described as Algorithm 3.  
```
Input:\(UMN(V)\),\(G^{*^{\prime}}\) Output:\(G^{*^{\prime}}\) for\(v\) in \(UMN(V)\)do \(n=degree(v)\) while\(n>4\)do \(n=n-1\) \(edges=N_{G^{\prime}}(v)\times N_{G^{\prime}}(v)\) \((u_{1},u_{2})\)=min distance in edges \(r=\|u_{1},u_{2}\|_{2}\) if\(N_{G^{\prime}}(u_{1})\cap N_{G^{\prime}}(u_{2})-\{v\}=\emptyset\)then  add \((u_{1},u_{2})\) to \(G^{*^{\prime}}\) endif  remove \((u_{1},v)\) from \(G^{*^{\prime}}\) endwhile endfor
```  
**Algorithm 3** Unmatched Node Handling Algorithm
## 1 Introduction
The accuracy of the one-stage target detection algorithm is relatively low, but it has a high detection speed, making it well suited to the task of detecting steel surface defects in industry. [PERSON] et al. [16] proposed an improved SSD model to enhance the performance of steel surface defect detection and a deep residual network (ResNet) for defect classification. [PERSON] et al. [17] proposed an improved YOLO-V3 defect detection model which utilizes the anchor-free mechanism to optimize the detection performance at multiple scales and shorten the detection time. [PERSON] et al. [18] proposed an improved YOLOv5 method for real-time detection of surface defects on strip steel which uses an attention mechanism and a multi-scale detection block to enhance the detection capability of the model. The detection speed of the model was improved to a certain extent, and it had an mAP value of 72% in the steel surface defect detection task. [PERSON] et al. [19] introduced the reparameterized C3 module into the C3 module of YOLOv5s and constructed a feature fusion structure using the multipath spatial pyramid pooling module, which resulted in improved model detection accuracy. The model had an mAP of 76.8% on the NEU-DET dataset, and the detection speed was relatively fast. [PERSON] et al. [20] proposed an improved PP-YOLOE model for small targets. The model introduces coordinate attention into the backbone structure and simplifies the traditional PAN + FPN component into an optimized FPN feature pyramid structure, which improved the performance and overall accuracy of the model for small target detection. [PERSON] et al. [21] proposed the EW-YOLOv7 defect detection model. The model combines the GhostNetV2 module and the Acmix attention machine to effectively optimize the performance of the model's defect detection. Although these detection methods, compared with traditional defect detection and machine vision defect detection, were improved in terms of detection speed, the detection accuracy still could not meet practical work requirements. Aiming at the existing algorithms that are difficult to practically apply in industrial environments due to factors such as low accuracy and slow speed in steel defect detection, this paper proposes a new algorithm for detecting surface defects on strip steel, DEW-YOLO, based on the YOLOv8n algorithm. In summary, the main contributions of this paper are as follows:  
* The DCN is introduced and combined with the C2F module to form the C2F_DCN module, which enables the model to be more flexible in extracting the features of complex textures and irregularly shaped defects. * We embed the EVC structure into the backbone network of YOLOv8 to achieve adjustment and optimization for different feature scales. This enables the model to better capture global information and local details at various scales, thereby enhancing the accuracy and robustness of object detection. * The WIoU loss function is used to replace the original YOLOv8 bounding box loss function, providing more stable and reliable detection results, especially for real industrial inspection scenarios.
## 2 Materials and Methods
### Determination of Caffeine and Chlorogenic Acid Content  
The quantification of caffeine and chlorogenic acid in brews was performed by ultra-high-performance liquid chromatography (UHPLC) using an ECOM spol. s r.o., Czech Republic system (model ECS05). The system is comprised of a quaternary gradient pump (ECP2010H), a gradient box with degasser (ECB2004), a column heating/cooling oven (ECO 2080), an autosampler (ECOM Alias) and diode array detector (ECDA2800 UV-VIS PDA Detector). A Phenomenex(r) reversed-phase column (Synergi\({}^{\rm TM}\) Max-RP 80 A; 4 \(\upmu\)m particle size, 150 \(\times\) 4.6 mm) was used at 25 \({}^{\circ}\)C. The sample injection volume was 10 \(\upmu\)L. Chromatographic analysis was performed using a gradient of Milli-Q water with 0.1% formic acid (solvent A) and methanol with 0.1% formic acid (solvent B), at constant flow rate of 0.5 mL/min. The gradient program was as follows: solvent A was decreased from 70% to 55% after 5 min; followed by another decrease to 35% until 15 min; while it was finally reduced to 10% at 18 min. Then, solvent A was maintained at 10% for 2 min and returned to initial conditions (70% solvent A). Detection was accomplished with the diode array detector and chromatograms were recorded at 276 nm for caffeine and 330 nm for chlorogenic acid. Identification of caffeine and chlorogenic acid was performed by comparing the retention time and the UV-Vis spectra with those of reference standards, while quantification was established with the aid of calibration curves (Equations (2) and (3), respectively). All chromatographic data were analyzed using Clarity Chromatography Software v8.2 (DataApex Ltd.).  
\[\rm Caffeine\ Concentration\ (\frac{mg}{mL})=0.000051*x*area-0.000893;\ (R^{2}=0.99952) \tag{2}\]  
Chlorogenic acid concentration \(\rm\frac{mg}{mL})\ =\ 0.000041*x*area+0.000691;\ (R^{2}=0.99999)\) (3)
## 2 Materials and Methods
### Statistical Analysis
#### 2.2.1 Relative Standard Deviation (RSD)  
Figure 2a shows the entire LIBS spectrum of mixed coal samples 4 and 34 as shown in Table 1. The peaks for carbon (247.9 nm), sulfur (416.3 nm), hydrogen (656.3 nm), and oxygen (777.4 nm) are clearly distinguished because they are abundantly present in coal samples. However, as shown in Figure 2b, the peak (416.267 nm) for sulfur is indistinguishable due to its small presence in the coal samples. The composition of S is 1.0632 and 0.7837 wt% in samples 4 and 34, respectively. The multivariable analysis can be performed based on the maximal intensity of each element.  
It is important to reduce the peak noise of the sulfur emission line to construct a robust PLSR line. When the reproducibility is assessed, the relative standard deviation (RSD) is generally used as an appropriate measure. The RSD can be calculated by using the following equation [21]:  
\[\text{RSD}=100\%\times\left[\sum{(x_{i}-M)^{2}/(n-1)}\right]^{1/2}/M, \tag{1}\]  
where \(n\), \(x_{i}\), and \(M\) are the number of a set for the measurement, the result of each measurement, and the arithmetic mean value of the set of repeated measurements, respectively. As the RSD is closer to zero, this means that the reproducibility is better.
## 3 Experiment Results and Analysis
According to Figure 3, the exponential function can reflect well the variation regulation of c with FTC. The overall trend of c decreased with FTC number, and c decreased considerably when FTCs varied from 0 to 10, especially when samples underwent the first cycles. After that, decline was slow and c was basically stable at around 30 kPa between 10 and 60 cycles; \(\varphi\) increased when below 30 cycles, and then decreased rapidly after 60 cycles. The Fourier function reflects the law of \(\varphi\) with FTC very well. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Shear Strength** & **S (\%)** & **FTC = 0** & **FTC = 1** & **FTC = 5** & **FTC = 10** & **FTC = 30** & **FTC = 60** & **FTC = 120** \\ \hline \multirow{4}{*}{c (kPa)} & 0.0 & 101.19 & 56.09 & 50.69 & 40.09 & 36.78 & 36.62 & 27.09 \\  & 0.5 & 96.73 & 55.67 & 49.40 & 38.61 & 34.43 & 32.33 & 25.01 \\  & 1.0 & 96.04 & 54.96 & 47.45 & 36.97 & 31.96 & 31.18 & 18.12 \\  & 2.0 & 95.66 & 52.87 & 45.27 & 36.62 & 31.22 & 30.78 & 16.34 \\  & 3.0 & 94.98 & 49.42 & 43.90 & 36.53 & 30.18 & 29.80 & 12.93 \\ \hline \multirow{4}{*}{\(\varphi\) (\({}^{\circ}\))} & 0.0 & 15.17 & 15.41 & 15.52 & 15.62 & 15.71 & 15.41 & 13.03 \\  & 0.5 & 13.72 & 14.28 & 14.51 & 14.74 & 14.91 & 14.73 & 11.86 \\ \cline{1-1}  & 1.0 & 12.92 & 13.81 & 14.11 & 14.43 & 14.69 & 14.07 & 10.11 \\ \cline{1-1}  & 2.0 & 12.70 & 12.87 & 13.47 & 13.97 & 14.11 & 13.74 & 8.81 \\ \cline{1-1}  & 3.0 & 13.13 & 13.87 & 14.35 & 14.63 & 14.80 & 13.88 & 7.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample cohesion c and friction angle \(\varphi\). Figure 3: Variation of shear-strength parameters with FTC: (**a**) cohesion c; (**b**) internal friction angle \(\varphi\).