esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Supposing it is possible to grasp the change in local pixels between the original and varied images, it then becomes possible to capture the characteristics of the image changes caused by the variation. By analyzing these characteristics, the proposed method can maintain most of the characteristics of the neutral image while replacing only the changed areas with the pixel values of the variational one. For this, we first construct an auxiliary set consisting of neutral images and their variational images. Then, the normalized weighted interpolation map is extracted by using the log-scaled standard deviation of the absolute difference between the neutral images and the corresponding variational images in the auxiliary set. Each element of the weighted interpolation map reflects the degree of change caused by the variation in individual pixels, and a B-WIM is obtained via binarization. When generating a new image for a given query (neutral) image, the variational image corresponding to the neutral image having the highest correlation with the query image is selected from the auxiliary set. Then, a new image is generated by combining the query and selected variational images. The overall procedure of the proposed method is shown in Figure 1.  
The idea for the proposed method was motivated by the ICR concept and the weighted interpolation map method, which are face-generating frameworks. However, unlike ICR, by simply increasing the number of images by the weight combinations of two images, the proposed method has the advantage of generating a natural image with a specific variation. Additionally, the proposed method creates an image of higher quality than the weighted interpolation map (WIM) method by selecting the neutral image and the variational image corresponding to the query image. Face recognition experiments are evaluated using the following criteria. First, we measure the change in the face recognition rate according to the degree of variation of different databases. Second,the face recognition performance is analyzed using unsupervised and supervised learning methods. Finally, the overall face recognition rates of all methods are assessed. We compare the proposed method with other methods dealing with the SSPP problem: WIM, ICR, \(\text{E(PC}^{2})\)A+, SPCA+, \(\text{(2D)}^{2}\)PCA, SLC, MVI, and SRGES. The results of the experiment show that the proposed method exhibits high face recognition performance for all criteria. The remainder of this paper is organized as follows. Section 2 explains the proposed method for generating data and describes each procedure in detail. The experimental face recognition results are described in Section 3, and the discussion and conclusion follow.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 2 Research Foundation
### Fundamentals of OCDM  
OFDM is implemented using the Discrete Fourier Transform (DFT), where each subcarrier occupies only a portion of the total bandwidth. In contrast, OCDM is based on the Discrete Fresnel Transform (DFnT), where each subcarrier is a chirp signal that spans the entire bandwidth.  
Chirp signals are widely used in underwater acoustic spread-spectrum communication, as they enhance reliability at the expense of spectral efficiency. OCDM employs multiple sets of orthogonal chirp signals as subcarriers, with each subcarrier extending across the full bandwidth. Similar to OFDM, OCDM can modulate information onto the amplitude and phase of its subcarrier. The core DFnT matrix of OCDM is defined as follows:  
\[\mathbf{\Phi}_{N}(m,n)=\frac{1}{\sqrt{N}}e^{-j\frac{n}{4}}\times\begin{cases}e ^{j\frac{n}{N}(m-n)^{2}}&N\equiv 0\ (\bmod 2)\\ e^{j\frac{n}{N}(m+\frac{1}{2}-n)^{2}}&N\equiv 1\ \ (\bmod 2),\end{cases} \tag{1}\]  
where \(N\) is the size of the square matrix.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 4 Resolutions and Satellite Vibration
### Effect of Satellite Vibration  
During satellite orbit, due to the influence of gravity and other factors in the universe, a small-amplitude tremor effect will be produced, leading to satellite attitude jitter. The satellite attitude jitter will cause antenna pointing stability error, which will influence the location result.  
The satellite's attitude mainly changes in pitch, yaw and roll. Assuming that the attitude jitter error angles of pitch, yaw and roll directions are \(\xi_{\theta},\xi_{\psi},\xi_{\varphi}\), respectively, the jitter in the range and azimuth direction are  
\[\begin{array}{c}\Delta\theta_{a}=\xi_{\psi}\cos\theta_{0}\sin\varphi_{0}+ \xi_{\theta}\cos\theta_{0}\cos\varphi_{0}\\ \Delta\theta_{r}=\xi_{\varphi}-\xi_{\psi}\tan\theta_{0}/\cos\varphi_{0}\end{array} \tag{26}\]  
where \(\varphi_{0}\) is the range angle (angle of view) of the antenna and \(\theta_{0}\) is the azimuth angle (squint angle). The antenna azimuth jitter is mainly caused by the jitter of yaw and pitch angle, while the jitter of range direction is related to the roll and yaw angle.  
When the satellite works in the strip mode and is side-looking (\(\theta_{0}=0^{\circ}\)), the relationship between the antenna azimuth and range errors and the attitude error becomes  
\[\begin{array}{c}\Delta\theta_{a}=\xi_{\varphi}\sin\varphi_{0}+\xi_{\theta} \cos\varphi_{0}\\ \Delta\theta_{r}=\xi_{\varphi}\end{array} \tag{27}\]  
Assuming that the yaw, pitch and roll attitude jitters are of a single frequency and are not coupled to each other, the mathematical model of the jitter of the antenna in the azimuth and range directions is  
\[\begin{array}{c}\Delta\theta_{a}(t)=\theta_{am}\sin(\omega_{a}t+\varphi_{a }),\omega_{a}=2\pi f_{a}\\ \Delta\theta_{r}(t)=\theta_{rm}\sin(\omega_{r}t+\varphi_{r}),\omega_{r}=2\pi f _{r}\end{array} \tag{28}\]  
where \(\Delta\theta_{a}(t)\) is the antenna jitter in the azimuth direction, \(\theta_{am},\omega_{a},f_{a}\), and \(\varphi_{a}\) are amplitude, angular frequency, frequency and initial phase of azimuth jitter, while \(\Delta\theta_{r}(t)\) is the antenna jitter in the range direction, \(\theta_{rm},\omega_{r},f_{r}\), and \(\varphi_{r}\) are amplitude, angular frequency, frequency and initial phase of range jitter. Additionally, the jitter will influence the antenna gain in the azimuth and range direction.  
Thus, the effect of satellite vibration on location error can be analyzed by using the jitter model of the antenna in azimuth and range direction. Since the algorithm in this paper only needs to perform matched filtering in the azimuth direction, the influence of satellite vibration on location is mainly caused by the antenna azimuth jitter, so we can ignore the effect of the antenna range jitter and only consider the effect of the antenna azimuth jitter on the location error. The simulation results of the effect of satellite vibration are presented in Section 5.
## 2 OPF Problem Formulation
### Objective Functions
#### 2.1.10 Fuel Cost and Voltage Stability Enhancement  
Voltage stability is the ability of a power system to sustain stable voltages at each bus within acceptable level after being exposed to a disruption. It is represented by indices like the L index, which has been introduced to evaluate the stability limit [19]. The L index is a quantitative measure of how close a point is to the system stability limit. Reducing the value of the L index is very important in power system planning and operations.  
This function models the fuel cost and the L index maximum.  
\[F_{10}=F_{3}+\beta_{3}L_{max} \tag{22}\]  
where \(\beta_{3}\) is a weighting factor.  
The nodal admittance relates system voltages and currents as  
\[I_{bus}=Y_{bus}\times V_{bus} \tag{23}\]  
Equation (23) can be reformulated by separating the PQ bus--active and reactive power; and the PV bus--active power and voltage magnitude.  
\[\left[\begin{array}{c}I_{L}\\ I_{G}\end{array}\right]=\left[\begin{array}{cc}Y_{1}&Y_{2}\\ Y_{3}&Y_{4}\end{array}\right]\left[\begin{array}{c}V_{L}\\ V_{G}\end{array}\right] \tag{24}\]  
The \(L\) index is calculated by  
\[L_{j}=\left\lvert 1-\sum_{i=1}^{G_{N}}\gamma_{ji}\frac{V_{i}}{V_{j}}\right\rvert \quad j=1,2,\cdots,N_{L} \tag{25}\]  
\[\gamma_{ji}=-[Y_{1}]^{-1}\times[Y_{2}] \tag{26}\]  
where \(Y\_1\) and \(Y\_2\) are the system \(Y\) bus submatrices.  
\[L_{max}=\max(L_{j})\quad j=1,2,\cdots,nb \tag{27}\]
## 6 Conclusions and Future Work
Concerning this challenge, the review presented in Section 5 in regard to sim-to-real transfer tools could help the reader-developers go one step further in their validation process and boost the transfer to a real-world scenario. Overall, to face the existing challenges and fully exploit DRL capacity, the next steps have been proposed. In the first instance, and this is the main point, validations need to be close to real environments: enhance the simulation of the real environment to ensure the fulfilment of the problem's specifications, improve the training phase and accelerate the deployment. In this sense, tools such as digital twins could help. Moreover, DRL algorithms have to include an evaluation of policy safety and robustness. In the second instance, standardise the implementation process. Defining common and easily understandable guides on how to implement and test DRL algorithms in a real environment will boost the validation and help the industries understand the DRL algorithms easing their deployment in testing in their facilities. In conclusion, the next steps should focus on accelerating and facilitating the validation of the algorithms in real environments to boost their deployment. In a nutshell, the review spotlights that DRL applicability is observed across all activities within the manufacturing industries, outperforming the conventional techniques and, most importantly, boosting the resilience and adaptability of the manufacturing process. However, there is still much work to be carried out, in both academics and industries, to fully exploit the potential of those disruptive tools, start the deployment in the industries, and take a further step towards the I5.0 transformation of industries. Conceptualisation, A.d.R.T. ; investigation, methodology and writing--original draft preparation, A.d.R.T., D.S.A. and A.O.R. ; software, visualisation and writing--original draft preparation, A.H.B. ; writing--review and editing and supervision, A.d.R.T. and L.E.A.G. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This publication has emanated as part of the research and activities done along MORSE project which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 768652. The authors declare no conflict of interest.
## 2 Introduction to Related Methods
### Multi-Dimensional Hybrid Gated Attention Mechanism  
The Squeeze-and-Excitation (SE) attention mechanism aims to assign a weight to each channel of the input sequence, reflecting the importance of that channel [16]. By learning the interactions between channels, it allocates varying weight ratios to different channels within the dimensional space. Spatial gated attention serves as a dynamic selection mechanism that generates a feature channel weight matrix, enhancing responses in critical regions. The multi-dimensional hybrid gated attention mechanism achieves feature fusion by constructing a cyclic structure of spatial-channel parallel networks and dynamically adjusting weights across dimensions. The model structure diagram is shown in Figure 2. The calculation process is as follows:  
Figure 1: Structural diagram of the improved U-Net model.  
Step 1: For the input feature map \(F\in R^{\text{C}\times H\times W}\) (where \(C\) is the number of channels, \(C=6\), and \(H\times W\) denotes the spatial dimensions, \(H\times W\) = 64 \(\times\) 1 = 64), first compress the input feature into a vector via global average pooling, as shown in Equation (1):  
\[Z=F_{\text{sq}}(U_{\text{c}})=\frac{1}{H\times W}\overset{H}{\underset{i=1}{ \sum}}\overset{W}{\underset{j=1}{\sum}}F_{\text{c}}(\text{i, j}) \tag{1}\]  
Step 2: Employ a spatial attention gating mechanism to dynamically generate a spatial attention weight matrix \(A\in R^{1\times H\times W}\), as shown in Equation (2):  
\[A=\sigma\big{(}f_{conv}\big{(}\big{[}F_{\text{max}};\,F_{avg}\big{]}\big{)} \big{)} \tag{2}\]  
where \(F_{\text{max}}\in R^{1\times H\times W}\) represents the result of max pooling along the channel dimension, \(F_{avg}\in R^{1\times H\times W}\) represents the result of average pooling along the channel dimension, \(f_{conv}\) denotes a convolutional layer, and \(\sigma\) denotes the Sigmoid activation function. Dynamic weight allocation involves assessing the stability of feature channels, such as by calculating the standard deviation, and subsequently assigning varying weights to different channels. This approach enhances the model's ability to focus on the most informative features while mitigating the influence of less stable channels.  
Step 3: Using two Sigmoid fully connected layers \(W_{1}\) and \(W_{2}\), map it to a smaller vector to generate channel dimension weights, and multiply it by the spatial attention weight matrix to generate multi-dimensional channel weight information \(s\):\(s=R^{1\times\text{C}\times C}\), as shown in Equation (3):  
\[s=\sigma(W_{2}\delta(W_{1}Z))\times A \tag{3}\]  
Step 4: Apply an activation function to compress each element of this vector into the range of 0 to 1 based on the weight information, and multiply it by the original input features to obtain the weighted output features \(F^{\prime}\), where the output range of \(F^{\prime}\) is [0, 1], as shown in Equation (4):  
\[F_{out}=F\times s \tag{4}\]
# DANet: Temporal Action Localization with Double Attention
## 4 Experiments and Results
### Ablation Study
#### 4.3.1 The Effectiveness of MLSA and MA  
In this subsection, we conduct the ablation study to investigate the impact of MLSA and MA. To establish a solid baseline, we initially employ a commonly used 1D convolutional network to model the temporal relationships of each backbone layer. Then we adopt the traditional Transformer Encoder with global self-attention to conduct modeling. Next, we use MLSA only for backbone without adopting MA. Finally, we adopt the final design, namely DABlock proposed in Section 3.2, which can extract both global and local features simultaneously.  
As shown in Table 3, DABlock consisting of MLSA and MA outperforms the other structures, achieving an overall average mAP of 66.5%. Figure 3 is the visualization of the effectiveness of MLSA and MA. In addition, convolution has the poorest performance, which demonstrates that Transformer is more suitable for TAL tasks, compared with convolution. In addition, compared with DANet, the global self-attention-based Transformer performs poorer. The plausible reason for this is that global self-attention makes the boundary blurred. When using MLSA only, the feature in time step, \(t\), may discard the connection with the global background. The proposed structure simultaneously focuses on the local changes between adjacent frames and models the global temporal relationship. In addition, DABlock preserves the most informative feature in local clip embedding, which helps to solve the TAL task better.  
Figure 3: Visualization of the effectiveness of MLSA and MA.
# Tumor-Induced Osteomalacia as a Result of a Phosphaturic Mesenchymal Tumor: Detecting Rare Origin
## 3 Discussion
Lastly, routine laboratory examinations do not include serum phosphate, vitamin D, serum alkaline phosphatase, and plasma FGF23. As a result, important laboratory results, including the presence of hypophosphatemia, a critical clue to the diagnosis of TIO, could be neglected. Investigations for diagnosis include physical examination, laboratory investigations, pathologic investigations, and venous sampling. Physical examination is usually not helpful for diagnosis unless profound symptoms of osteomalacia are present. If a patient presents with clinical symptoms such as bone pain, muscle weakness, gait disturbance, skeletal deformities, and multiple bone fractures, TIO should be suspected. In the present case, the patient presented with bony deformities, such as scoliosis, which helped clinicians suspect TIO. As hypophosphatemia, elevated alkaline phosphatase, and elevated FGF23 levels can be critical diagnostic clues for the diagnosis of TIO [2], laboratory investigations including these biochemical profiles should be performed in cases of suspected TIO. Measurementof FGF as a tumor marker is useful. Because of the short half-life of FGF23, between 20-50 min, it can be used for intraoperative monitoring and evaluation of postoperative outcomes [29; 30]. For successful removal of tumors, it is important to accurately locate the tumors. To locate the tumors, functioning imaging techniques, including octreotide scintigraphy, anatomical imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), and the combination of functioning and anatomical techniques, including positron emission tomography along with computed tomography (PET/CT), have been used [6; 23; 31; 32]. Indium-111 octreotide scintigraphy (octreotide scanning) can be used as a method of detection for tumors because PMTs may express somatostatin receptors (SSRTs) [33]. Recently, venous sampling of FGF23 has been proposed as a method for definitive confirmation of the tumor secreting FGF23 [30]. However, in the absence of suspicious tumors on radiological imaging, random venous sampling cannot be successful in localizing the tumors [34]. Complete surgical resection with an adequately wide margin is the most appropriate treatment. There is a risk of recurrence of these tumors if they do not have a wide margin [35]. Postoperative normalization of laboratory values is typically observed following successful resection of the tumor. However, considering anatomical and functional factors, it could be difficult to perform complete surgical resection in all cases due to concerns about surgical morbidity with complete resection. If resection is not complete, the patients with a persistent syndrome can be partially managed with oral phosphate supplements and 1,25-dihydroxyvitamin D (calcitriol) administration for symptomatic improvement [36]. Furthermore, periodic postoperative assessment of laboratory examinations has been recommended as a means to monitor for recurrences of tumors.
## 3 Results and Analysis
### Geophysical Characteristics of Shangtang Filed
#### 3.2.2 Geological Characteristics of Zhongtang and Wentang Geothermal Field  
1. General Situation of Zhongtang and Wentang Hotsprings  
Zhongtang hotspring is located 10.5 km east of Shangtang geothermal field, with an altitude of 221 m, 8.3 m higher than Sha River. Zhongtang hotspring appears in the form of spring group, and the spring water is led into the pool for utilization. The long-term observation data (continuous observation for one year, observation interval ten days, measured by thermistor thermometer) showed that the water temperature was 61-63 \({}^{\circ}\)C and the average flow was 10.0 m\({}^{3}\)/h. The long-term observation data (same measuring method as above) of the geothermal well at about 20 m south of the hot spring were 63 \({}^{\circ}\)C and 3.94 m\({}^{3}\)/h.  
Wentang hotspring is located at the back edge of the first terrace on the south bank of Sha River, with an altitude of 220 m, 820 m away from Zhongtang hotspring, and the two springs are facing each other across the river. The long-term observation data (same measuring method as above) showed that the flow was 2.2 m\({}^{3}\)/h and the water temperature was 49 \({}^{\circ}\)C. After the spring water was also led into the pool for utilization, the measured temperature reduced to 39-40 \({}^{\circ}\)C.
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 1 Introduction
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Topic** & **Publication Year** \\ \hline
[16] & Surface defect detection using deep learning techniques and classify them into three categories: detection context, learning, and localization and classification. & 2021 \\
[17] & Discuss surface defect detection based on deep learning from three aspects: supervised method, unsupervised method, and weakly-supervised method. & 2021 \\
[22] & Steel surface defect recognition. & 2022 \\
[19] & A systematic review for vision-based defect recognition from a feature perspective. & 2022 \\
[18] & Deep learning defect detection methods for X-ray images. & 2023 \\
[21] & Deep CNN-based visual defect inspection. & 2023 \\
[20] & Summarize studies published between 2020 and 2023 in the field of deep learning-based surface defect detection applications in industrial products. & 2024 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of reviewed studies based on deep learning for surface defect inspection. The structure of this study is as follows: Section 2 outlines the popular generative models that can be used for inspection tasks. Section 3 presents a detailed overview of the generative model-based methods for surface defect inspection, where the papers are selected since 2022. Section 4 provides an introduction to the public datasets used in surface defect detection and the popular metrics used to evaluate surface defect detection models. The performance of the selected methods is also presented in this section. Section 5 concludes this study and offers a thorough discussion of the main challenges and future research directions for surface defect detection.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 7 Conclusions  
In this paper, a segmentation threshold-based channel estimation algorithm (ST-DFT) was proposed to address the energy spread of the estimated channel impulse response due to the presence of virtual subcarriers. The algorithm sets the decision threshold of the CIR effective path based on the distribution law of the channel energy dispersion, thereby obtaining a performance improvement compared with the traditional single threshold-based algorithm (T-DFT). Moreover, a joint channel estimation algorithm based on ST-DFT and DWT was also proposed to tackle the estimation performance floor problem in the DFT-based channel estimation algorithm under high SNR conditions. The simulation  
Figure 12: BER performance of different algorithms with 64 QAM and 2/3 coding rate in channel D.  
Figure 13: BER performance of different algorithms with 64 QAM and 2/3 coding rate in channel UMi.  
results show that, compared with similar DFT channel estimation algorithms, the proposed ST-DFT channel estimation algorithm has a better estimation performance under low SNR and can alleviate the problem of an estimation performance floor under high SNR. Secondly, the theoretical estimate of MSE is very close to the simulated value. Finally, based on the estimated value of MSE, the proposed joint channel estimation algorithm can achieve optimal dynamic switching of channel estimation algorithms, and its channel estimation performance is better than using ST-DFT or DWT alone in the simulated SNR range.  
Conceptualization, Z.Z., M.L. and X.B.; methodology, Z.Z. and M.L.; software, Z.Z.; validation, Z.Z.; formal analysis, Z.Z. and M.L.; investigation, Z.Z.; resources, M.L. and X.B.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, M.L. and X.B.; visualization, Z.Z.; supervision, M.L. and X.B.; project administration, Z.Z.; funding acquisition, X.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (grant number 2018 YFB1802201) and the Shanghai Super Postdoctoral Incentive Program (grant number 2020000125).  
Not applicable.  
The authors would like to thank the Shanghai Advanced Research Institute, Chinese Academy of Sciences, for supporting this investigative project.  
The authors declare no conflict of interest.