esa-sceva/satcom-chunk-collection huggingface dataset:
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### YOLOv  
YOLOX is a high-performance object detection model proposed by [PERSON] et al. [20], which is an upgrade of the YOLO series.  
The YOLO series of models, starting from YOLOv3, has used the common three-part structure of an object detector: the backbone for extracting features, neck for integratingfeatures, and head for implementing detection. YOLOX continues to use this network structure (Figure 1).  
As shown in Figure 1, to increase the number of channels, YOLOX uses the Focus module from YOLOv5. In addition, it combines with the feature extraction network CSPDarkNet from YOLOv4 as the backbone network, followed by the SPP module [31] for multi-scale training. The neck part of YOLOX uses the PAFPN [32] structure to realize the integration of multi-scale features. After the input image is processed by the backbone network and the neck network, it is divided into three scales, 13 \(\times\) 13, 26 \(\times\) 26, and 52 \(\times\) 52, to predict large objects, medium objects, and small objects, respectively. Finally, the features of the three scales are, respectively, entered into the decoupled detection head to complete the detection.  
The biggest improvement is that YOLOX uses an anchor-free mode [33; 34; 35] and a decoupled head [36]. The anchor-free mode gets rid of the calculation required when using the preset anchors and greatly reduces the number of parameters. The coupled detection head couples the classification and regression of the prediction boxes, which will affect the detection performance. Therefore, YOLOX decouples the classification and regression (i.e., separates them into two branches), and adds a branch for IOU calculation.  
Although YOLOX is already an excellent detection model, there are some problems in using it to detect remote sensing images. We summarize these questions into the following three points:  
* Satellite remote sensing images contain rich, detailed information and many small targets, making it inconducive for the detection model to extract useful target information. If we only rely on ordinary convolutional structures to extract features, some key information may not be valued by model learning. Therefore, we need to introduce a new attention module and feature fusion module to enhance the feature processing capability of the convolutional network in YOLOX.
* In object detection, the target information is the positive sample and the background information is the negative sample. Due to the complex background of satellite remote sensing images, the numbers of positive and negative samples are unbalanced, which seriously affects the accuracy of model detection. The loss function used by YOLOX is the Binary Cross Entropy (BCE) function, which is a traditional loss function that cannot balance the numbers of positive and negative samples.  
Figure 1: Structure of YOLOX.  
Many satellite remote sensing images have a size of tens of millions of pixels, and target detection models, including YOLOX, have a poor detection performance where large-size images are concerned.
## 3 Encryption Based on the Integration of Chaos System and Linear Prediction
Among these \(n\) confused DEMs, the one with the maximum absolute \(S_{R}\) difference (\(|DS_{R}|\)) compared to the original is selected as the input for the diffusion phase in which the range of pixel values is changed. The absolute configurational entropy (\(S_{A}\)) is finally employed to determine which one is the most suitable for transmission. From a theoretical perspective of information, the higher \(S_{A}\) value, the higher the complexity (lower compressibility) of a DEM, indicating higher encryption performance. Two modes are provided for users: (i) complexity first and (ii) compressibility first. For the former, the one with the maximum \(S_{A}\) is finally selected. Regarding the latter, the one with the minimum absolute \(S_{A}\) difference (\(|DS_{A}|\)) compared to the original DEM is chosen. The encrypted image can be further processed by lossless compression techniques, such as Huffman encoding [31], free lossless image format (FLIF) [32], and multiscale compression [33], to reduce the burden on transmission and storage. To improve the encryption performance as much as possible, it is recommended that users encrypt a DEM for at least 4 times (i.e., \(m\geq 4\)) using the proposed algorithm.
## 3 Data Preparation
### Attention for Point Clouds  
In this section, we introduce how to calculate attention on point clouds. We will first demonstrate how we calculate the attention between two point clouds using two parts: calculating a similarity matrix between the point clouds and then converting the similarity matrix into an affinity matrix.  
To calculate the similarity matrix, we first need to reduce the computation space using a shared MLP to operate a channel vector \(c_{i}\in R^{S}\) (\(S\) represents the original number of points) to reduce the point cloud tensors into tensors of smaller sizes. Specifically, we use two MLPs, \(N_{query}\) and \(N_{keys}\), to obtain the query matrix and key matrix to calculate attention. The query matrix and key matrix would look like the following:  
\[Q=[q_{1},q_{2},\ldots,q_{n}]\in R^{S^{\prime}\times n}K=[k_{1},k_{2},\ldots,k_ {n}]\in R^{S^{\prime}\times n} \tag{11}\]  
where \(S^{\prime}\) is the reduced dimension from \(S\), and \(n\) is the dimension of the feature map, as seen in Figure 3. Then, we can estimate the similarity matrix \(M_{s}\) (channel-wise) to be  
\[M_{s}=Q^{\intercal}K \tag{12}\]  
where the \(i\)th row and \(j\)th column matrix would be the similarity between the \(i\)th channel and \(j\)th channel of the given feature map.  
After deriving the similarity matrix, we use it to calculate the long-range dependencies following a self-attention structure, which requires taking inner products with the value matrix and our similarity matrix. We choose to process our channel-wise similarity matrix \(M_{s}\) into a matrix that describes non-similarity, according to the following equation:  
\[N_{s}=\text{expand dimension}(\max(M_{s}))-M_{s} \tag{13}\]  
In the equation above, we select the maximum similarities along each column of \(M_{s}\) and expand them so the channels in \(M_{s}\) that have higher similarities would have lower values. Then, we apply soft-max on matrix \(N_{s}\) for normalization. We take a weighted sum of all the channels by multiplying that with our value matrix to obtain a refined feature map.
## 4 Case Study
### Aircraft Failures Requiring Repair Development
#### 4.2.1 Corrosion in Aircraft Turbofan Intakes  
A corrosion defect that frequently occurs on the turbine intakes of the A320 is not mentioned in the original equipment manufacturer's (OEM) maintenance manual. This defect occurs in the contact area between the intake acoustic panels and the titanium mounting ring. Figure 3 shows the geometry of the Airbus A320 inlet and the corrosion defect.  
The inner inlet of the intake consists of three acoustic panels made of aluminum sandwich structures attached to a titanium ring with retaining ring screws (see Figure 3b).  
Figure 4 shows a typical corrosion pattern found in A320 inlets. This corrosion results from a galvanic corrosion process between a titanium ring and an aluminum doubler (aluminum sheet). The formation of the galvanic dielectric is caused by the formation of microcracks in the adhesive, due to aging of the adhesive and high stresses in the adhesive layer. These stresses are favored by thermal loads and by different coefficients of thermal expansion of the different materials in the adhesive joint, which can increase local stresses to values close to the shear strength of the adhesive joint.  
Figure 4: A320 intake (**a**) corrosion pattern, (**b**) aluminum corrosion detail.  
Figure 3: A320 intake (**a**) geometry and assembly (**b**) inner barrel panel.  
Titanium and aluminum have different coefficients of thermal expansion, so they are subjected to different contraction stresses at low temperatures. For this reason, relative contraction occurs at the interface between the different materials, resulting in different strain levels and creating interface stresses. These stresses are the same as those experienced by the adhesive, since its job is to bond the two surfaces without slippage.
# The Impact of Drilling Parameters on Drilling Temperature in High-Strength Steel Thin-Walled Parts
## 6 Limitations and Potential Biases  
The present study obtained temperature data by observing the temperature field on the underside (drill-exit side) of AF1410 high-strength steel thin-wall plates. The investigation focused on the influence of cutting speed (\(v_{\rm c}\) = 12.56-31.4 m min\({}^{-1}\)) and feed rate (f = 0.01-0.10 mm rev\({}^{-1}\)) on drilling temperature. The results indicate that significant stress concentration, vibration, and deformation occur during drilling of thin-wall plates, and that optimizing cutting parameters (especially cutting speed and feed) markedly mitigates these effects. However, due to limited experimental resources and time, the following limitations are acknowledged:  
The impacts of stress concentration, vibration, and deformation on drilling temperature were not explicitly considered in the experimental design or data analysis. Experiments were restricted to AF1410 steel and the specific parameter ranges tested; the findings may not fully represent other materials or cutting conditions. The influence of the fixture on heat transfer was not quantified. Measurement accuracy may be affected by the precision limits of the infrared thermal imager and the adopted measurement scheme. The limited number of experimental samples could compromise the statistical significance of the results.
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
[34] employed the Random Decrement Technique (RDT) to denoise raw data and demonstrated that the performance of this model was enhanced when exposed to low levels of noise. [PERSON] et al. [35] combined the Hilbert-Huang Transform (HHT) and a CNN to propose a novel structural damage-identification method. The research results demonstrate that this method possesses significant noise-immunity advantages. Although these works have shown that denoising preprocessing techniques can enhance the accuracy of damage detection, they require manual denoising. Nevertheless, this makes them difficult to apply in engineering practices. In summary, it is a challenging task to design an effective model to extract features from signals with noise for the accurate identification of structural damage. This paper presents a novel method that combines a dual-channel pseudo-supervised (DCPS) learning strategy and the Conformer model for structural damage detection. On the one hand, the existing methods rely on manual denoising to deal with the sensitivity of the model to noise in the signals, which limits their practical application in engineering. To address this issue, a DCPS learning strategy is proposed to improve the noise robustness of the model. The Cross Pseudo Supervision (CPS) method proposed in reference [36] uses two branches with the same structures but different initial weights in a dual-channel network to generate labels for the same input image separately. These generated labels are then used as supervision signals of the other branch. This method improves the noise robustness of the model. Based on this idea, the proposed DCPS introduces different levels of noise into each branch of the dual-channel network and uses the output of one branch as labels for the other branch to train the model. Through this learning process, the model adapts better to input signals with noise, consequently enhancing the stability and accuracy of structural damage detection. On the other hand, this paper introduces the Conformer model to address an issue of existing models, wherein it is difficult for the latter to robustly extract local and global features from response data. The Conformer model [37] exploits the advantages of CNNs and Transformers, and it has been widely used in speech signal processing to extract long-distance dependencies in speech signals. In contrast to LSTM, the Transformer model incorporates a self-attention mechanism and position encoding to effectively capture long-distance relationships within the input sequence. Thus, this paper explores the application of the Conformer model in the field of structural damage identification, combining the local feature extraction capability of CNNs with the long-term sequence modeling capability of Transformers to fully describe the information of the response signal. Notably, this is the first time that DCPS and Conformers have been integrated and applied to structural damage detection.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 4 Discussion  
As mentioned earlier, the Bernoulli sampling probability \(p\) was set to be 0.3, and the dropout-based ensemble was used to boost the performance in our proposed SSD-SAR-BS. To confirm the effectiveness of these settings, we provide a set of comparative experiments. For the Bernoulli sampling probabilistic \(p\), only 0.1, 0.3, and 0.5 were compared because when the \(p\) was too large, most pixels of the input speckled images were lost and the despeckling performance rapidly fell. For the objective evaluation, we present their despeckling performance on 100 synthetic speckled images from the AID dataset in terms of the average PSNR and SSIM values, under different numbers of the average times \(K\) in the dropout-based ensemble. As shown in Figure 15, as \(K\) improves, the average PSNR and SSIM values increased significantly, especially the average PSNR values where K was set from zero to twenty and the average SSIM values where K was set from zero to forty. This was similar for the different Bernoulli sampling probability \(p\), in other words, \(p\) = 0.1, \(p\) = 0.3, and \(p\) = 0.5. Moreover, from the magnified curves as shown in the right column of Figure 15, the average PSNR and SSIM values of \(p\) = 0.3 were larger than those of \(p\) = 0.1 and \(p\) = 0.5. This was because when \(p\) = 0.1, the randomness of Bernoulli sampling was not strong enough; when \(p\) = 0.5, the preserved pixels were not enough to construct the despeckled images. Hence, 0.3 was the superior value of Bernoulli sampling probability \(p\), and the dropout-based ensemble could effectively boost the despeckling performance of our proposed SSD-SAR-BS. We also list the inference runtimes of the compared and our proposed methods in Table 4. All methods were implemented on the same system environment described in Section 3.1.2. We employed the dropout-based ensemble in our proposed SSD-SAR-BS, enabling the model to operate as two: (1) Accurate model (e.g., \(K\) = 100): This model was more expensive due to it having more average times, providing superior speckle noise suppression and detail preservation. (2) Fast model (e.g., \(K\) = 40): Facing more real-time SAR image despeckling tasks, this model can improve the inference efficiency (reduce the test runtime) by reducing the average times \(K\). From Table 4, we can see that as the
# Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool
## 4 Conclusions  
This study aimed to develop a robust and effective spindle thermal displacement modeling method to establish the relationship between the spindle thermal errors and the temperature changes. The selection of KTs from the machine tool is important information that affects the performance of the prediction model and the cost of system implementation. The KTP selection method proposed to solve this problem removes the ITPs of invalid or highly identical features. The number of KTs was reduced from 47 possible points to 5. Based on the proposed KTP selection scheme, three type of modeling methods, LSTM, MLR and BPNN, were discussed and compared. The results demonstrate that the performance of ANN-based modeling schemes (LSTM and BPNN) significantly outperformed the MLR modeling method, especially under a high rotation spindle speed. Different to BPNN, the proposed LSTM modeling scheme has a memory characteristic that can keep track of long-term dependencies in the input sequences. The experimental results demonstrate that the RMSE of the proposed LSTM is better than that of a BPNN at all spindle operating conditions. It proves the proposed LSTM is an ideal means of modeling temperature changes and spindle displacement.  
The proposed spindle thermal error prediction scheme is verified at 3000, 6000, and 9000 rpm spindle rotation speeds. For the actual cutting state, more training data for modeling on different processing conditions, such as random spindle rotation speeds, need to be further considered and collected to simulate the actual cutting state, changes in temperature increases, and displacement of the spindle under different ambient temperatures. At the same time, the predicted effect of the machine tool in a real cutting stat also needs to be further studied.  
Conceptualization, Y.-C.L.; data curation, K.-Y.L.; investigation, K.-Y.L.; methodology, Y.-C.L.; project administration, Y.-C.L.; resources, Y.-C.L.; software, Y.-C.T.; validation, K.-Y.L.; visualization, Y.-C.T.; writing--original draft, Y.-C.T.; writing--review & editing, Y.-C.L.; All authors have read and agreed to the published version of the manuscript.  
Please add: This research received no external funding.  
This study was supported in part by grants from the Ministry of Science and Technology of the Republic of China (Taiwan) (Grant No. MOST 109-2221-E-167-014).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spindle Speed** & **MLR** & **BPNN** & **LSTM** \\ \hline
3000 rpm & 2.716 \(\upmu\)m & 0.690 \(\upmu\)m & 0.529 \(\upmu\)m \\
6000 rpm & 3.792 \(\upmu\)m & 0.828 \(\upmu\)m & 0.554 \(\upmu\)m \\
9000 rpm & 4.966 \(\upmu\)m & 0.958 \(\upmu\)m & 0.625 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 8: RMSEs of the MLR, BPNN, and the proposed LSTM models at different spindle rotation speeds.
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.8 Selectivity Studies for Removal of CR  
It is important to perform the selective removal of CR over other toxic dyes in aqueous solution. Thus, the adsorbent DE-LDH was studied for the selective removal of CR in the mixture with various organic dyes such as Eriochrome Black T (EBT) and Rhodamine B (RB), shown in Figure 10. The mixture of dyes was soaked with DE-LDH and the extracted supernatant solutions were recorded using a UV-visible spectrometer. From the UV-visible spectrum, the DE-LDH was able to selectively remove the CR in a mixture of various dye solutions. It is worth noting here that the absorbance spectrum of CR entirely disappeared, whereas the spectra of EBT and RB dyes were observable. This study shows the selective removal of CR dye is based on its chemical structure and interaction. The positively charged DE-LDH surfaces adsorbed the CR molecules by electrostatic interactions and hydrogen bonding due to the functional SO\({}_{3}\)\({}^{-}\) and -NH\({}_{2}\) groups, respectively. Though the other dyes, EBT and RB, have nitro and hydroxyl (NO\({}_{2}\) and -OH) groups and tertiary amine and carboxyl (=N(C\({}_{2}\)H\({}_{5}\))\({}_{2}\) and -COOH) groups, respectively, the adsorption is more prominent in the case of the CR dye due to the stronger interaction between the readily available functional groups of CR and the adsorbent surface. Thus, these dyes were removed to a lesser extent compared to CR by DE-LDH.
## 3 Numerical Results
### Quasi-Static Rolling  
The quasi-static rolling results are now presented. In order to study the deformation of the structure, the displacement field is extracted for different points as shown in Figure 3.  
The first selection provides insight into the displacement variation in the radial direction while the second one provides an insight into the displacement variation in the lateral direction.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 3 Simulation and Experimental Results
### Simulation
Since the carrier frequencies of the BDS B1 and B3 signals are different, the Doppler slopes and the initial phase of the two frequency echoes are different. In order to realize the phase difference correction of the dual-frequency signal, it is very necessary to correct the slope and initial phase of the two frequency echo signals to be consistent. For the correction of the signal phase slope, the correction coefficient \(k\) is calculated according to Equation (12). Then, the phase of the B3 signal is multiplied by the correction coefficient \(k\) for correction. The corrected signal phases of the two frequencies are shown in Figure 12b, and it can be seen that the slopes of the two signals have become consistent. Then, the corrected phases of the B1 and B3 signals are differentiated, and then the initial phase deviation is subtracted to obtain echo signals with consistent phases. The phase of the corrected differential signal is shown in Figure 13, and the variation range of the signal phase (the blue lines) is basically maintained within the range of \(\pm 45^{\circ}\) (the red lines), which ensures the coherence of the signal phase. After the target echo signals are demodulated and correlation compressed, the signals are in the form of complex numbers. Before phase correction, residual Doppler will cause an irregular rotation of the signal phase in the slow-time dimension, as shown in Figure 14a. Because the GNSS echo signal is very weak, it is necessary to perform coherent accumulation on the signal to enhance SNR. As shown in Figure 14b, in order to achieve gain improvement after signal accumulation, it is necessary to adjust the phase difference in the adjacent signals to within \(120^{\circ}\). From Figure 13, it can be seen that the phase difference between adjacent signals is less than \(90^{\circ}\), which ensures an increase in amplitude after signal addition. The phase-corrected signal is coherently accumulated for \(200\) ms along the slow time \(t_{m}\), and the trajectory of the target echo signal is obtained as shown in Figure 15. The SNR of the signal in Figure 15 is calculated to form the curve shown in Figure 16. Compared with Figure 9, the SNR of the signal is greatly improved, and it no longer attenuates with increasing velocity. Figure 12: Variation in signal phase after unwrapping: (**a**) The phase of the B1 and B3 signal before correction. (**b**) The corrected signal phases of the two signals. Figure 13: Signal phase after dual-frequency correction. Figure 16: SNR of the echo signals in Figure 15.  
Figure 8:0 Schematic diagram of Signal phase: (**a**) Phase rotation of complex signals affected by residual Doppler; (**b**) Only when the angle between two adjacent signals is less than \(120^{\circ}\) can the amplitude after addition be increased. Figure 8:1 The trajectory of the target after differential correction.