esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Precise Orbit Determination Methods
### Orbit Design for Space-Based Situational Awareness Systems  
The operational orbits of a space-based surveillance systems have a crucial impact on the observability of space targets relative to the surveillance platforms and the precision of target trajectory measurements. Designing adaptive operational orbits tailored to specific space surveillance platforms and monitoring tasks to achieve the continuous and accurate monitoring of targets is a key research focus of space situational awareness.
# A Novel Interactive Fusion Method with Images and Point Clouds for 3D Object Detection
## 3 Experiments
### Architecture Design Analysis  
Our fusion algorithms aim to reinforce interaction and independence of different types of feature maps and utilize linear models and nonlinear models to enhance expression. The single level fusion algorithm can approximate a general function:  
\[f_{im}^{[L+1]}\Big{(}f_{im}^{[L-1]}\otimes f_{im}^{[L]}\otimes \Big{(}f_{im}^{[L]}\otimes f_{pc}^{[L]}\Big{)}\Big{)}\otimes\] \[f_{pc}^{[L+1]}\Big{(}f_{pc}^{[L-1]}\otimes f_{pc}^{[L]}\otimes \Big{(}f_{pc}^{[L]}\otimes f_{im}^{[L]}\Big{)}\Big{)}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Easy**} & \multicolumn{2}{c}{**Moderate**} & \multicolumn{2}{c}{**Hard**} \\ \cline{2-7}  & **AP** & **AHS** & **AP** & **AHS** & **AP** & **AHS** \\ \hline baseline1 & 83.02 & 82.84 & 73.71 & 73.13 & 67.79 & 67.15 \\ baseline2 & 84.02 & 83.84 & 74.42 & 74.03 & 68.16 & 67.74 \\ ours & **84.62** & **84.41** & **74.88** & **74.45** & **68.30** & **67.79** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Our proposed multilevel fusion algorithm evaluated on the car class in the validation set compared with the baseline.  
where \(f_{im}^{[L+1]}\) is the image feature maps of \((L+1)\)th layer; \(f_{pc}^{[L+1]}\) denotes the point cloud feature maps of \((L+1)\)th layer; \(\otimes\) means concatenate or element-wise mean, and the multilevel fusion algorithm can also approximate a general function:  
\[f_{1}^{[L+1]}\Big{(}\Big{(}f_{im1}^{[L-K]}\otimes f_{pc1}^{[L-K]} \Big{)}\otimes f_{im2}^{[L]}\Big{)}\] \[\otimes f_{2}^{[L+1]}\Big{(}\Big{(}f_{im1}^{[L-K]}\otimes f_{pc1}^ {[L-K]}\Big{)}\otimes f_{pc2}^{[L]}\Big{)}\] \[\otimes f_{3}^{[L+1]}\Big{(}\Big{(}f_{im2}^{[L]}\otimes f_{pc2}^ {[L]}\Big{)}\otimes f_{im1}^{[L-K]}\Big{)}\] \[\otimes f_{4}^{[L+1]}\Big{(}\Big{(}f_{im2}^{[L]}\otimes f_{pc2}^ {[L]}\Big{)}\otimes f_{pc1}^{[L-K]}\Big{)}\]  
where \(f^{[L+1]}\) is the feature map of \((L+1)\)th layer and the subscript means a different source of feature maps; \(\otimes\) means concatenate or element-wise mean; K is an integer and less than \(L\).
# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 2 Materials and Methods
### Automated Retinal Vascular Leakage Segmentation
#### 2.3.2 Pre-Processing for U-Net Training  
Transfer learning, which has been called weakly supervised learning, was applied to U-Net, and it was found to improve retinal vessel segmentation [16]. Note that transfer learning needs a small number of supervised FA image data sets. Therefore, this paper proposes a learning method for U-Net that does not need supervised FA data sets by transforming RGB color images with supervised data sets in this section. In the current study, 60 color retinal images and 12 UWF images were used for training and testing of U-Net, respectively.  
For U-Net training, 40 RGB color retinal images from color photographs of the Digital Retinal Images for Vessel Extraction (DRIVE) database [17] and 20 images from the Structured Analysis of the Retina (STARE) database [18] were used as training data for learning how to use the U-Net network. Each database includes manual vessel segmentation images by experts. The resolution of DRIVE images is 565 \(\times\) 584 pixels and STARE images are 700 \(\times\) 605 pixels. The resolution of FA images is 2500-4000 \(\times\) 2500-4000 pixels; thus, the input size of U-Net was unified at 1024 \(\times\) 1024 pixels. The training images were converted to 1024 \(\times\) 1024 pixels by zero-padding of peripheral pixels. The fundus region of the training images was not enlarged to prevent the vascular width increasing. On the other hand, test images were reduced to 1024 \(\times\) 1024 pixels, and their vascular width was also narrow. In order for the FA images to resemble RGB color images, as shown in Figure 1a, RGB images were pre-processed as follows. Grayscale images were generated after reading the RGB color fundus images. To improve limb darkening of the fundus image, contrast limited adaptive histogram equalization (CLAHE), as a histogram flattening method, was used for enhancement of the contrast in local areas of the fundus images. After that, gamma correction was used to adjust the intensity values, and negative-positive inversion was performed. U-Net was trained using these 60 pre-processed RGB color retinal images without FA images.  
For pre-processing of the test data images (retinal vasculitis consisted of three cases: early-phase images, three images; late-phase images, three images, for a total of six images. [PERSON]'s disease consisted of a total of three cases: early-phase images, three images; late-phase images, three images, for a total of six images; 12 UWF FA images underwent CLAHE and gamma correction. U-Net was tested using these 12 pre-processed UWF images.
## 3 Proposed CNN-Based Power Allocation Method  
This study proposes a CNN-based power allocation framework that jointly optimizes both CPU-to-AP and AP-to-UE power distribution in cell-free massive MIMO networks. Unlike conventional methods that allocate power in two disjoint stages, the proposed model performs end-to-end learning to dynamically assign power based on real-time network conditions and spatial context.
## 3 Other Applications
### Hydrological Application
#### 3.2.2 Hydrological Modeling  
Accurate hydrological models, which are generally supported by rain gauge and radar data, are essential for water resource management, disaster warning and hydrological research. However, considering the limited spatial representativeness of rain gauges and the coarse spatial resolution of radar, as well as their unavailability in urban and mountainous areas, there is an urgent need for additional near-surface precipitation information for small- and medium-scale hydrological modeling in these regions. As early as 2005, [PERSON] et al. [143] attempted to integrate rainfall measurements from microwave links into hydrological modeling. [PERSON] et al. [31] compared the urban drainage model derived from CMLs with that of rain gauges and found that the former can significantly improve pipe flow prediction. They also tried to input the rainfall measurements from CMLs and rain gauges into the runoff model at the same time [144]. The results showed that CMLs improve predictions of hydrograph dynamics compared to the model using only rain gauge data. Subsequently, in [145], they demonstrated that the performance of runoff prediction is related to the sensitivity of CMLs to rainfall and their location in the catchment. Moreover, as the bias in rainfall estimates from CMLs would propagate into hydrological models [146], they also made efforts to obtain unbiased rainfall estimates from CMLs to optimize hydrological modeling [147; 148]. In addition to modeling urban drainage, CMLs have also been used for river runoff simulation [149; 150; 151] and disaster warning [152; 153; 154; 155; 156].
## 3 Collaborative Knowledge-Based WSN Proposal
### Collaborative Scheme
#### 3.4.2 Integration of Data and Knowledge  
In the proposed collaborative knowledge-based WSN, each sensor can base its inferences on local or remote data and knowledge. This section proposes different ways to integrate local and remote information.  
Local data are the values of variables that are locally obtained by the sensor, whereas remote data are the values of variables that are obtained or inferred by another sensor. Similarly, local knowledge refers to the KB (variables, fuzzy sets and rules) that is locally utilized by a sensor in order to infer the value of an output variable, while remote knowledge refers to knowledge that may utilize local and remote variables in order to promote a global objective.  
The sensor network and the application protocol allow the sharing of data and knowledge, so, if remote information is available, each sensor can base its inferences on local information (data and knowledge) and remote information (data and knowledge). However, if remote information is not available, each sensor can only base its inferences on its local information.  
The following methods are suggested to fuse local and remote information (data and knowledge):  
1. Utilize only a KB, which incorporates local and remote information. Remote variables and rules are added to the KB so that the rules are able to use external data in their inferences. The inference engine will then take into account those remote rules if remote information is available.
2. Incorporate a complete collaborative KB (Figure 3). In this method the sensor utilizes a KB with local information (data and knowledge) and another complete KB with collaborative information (remote data and knowledge). In this case, the local output and remote variables represent inputs to a second FRBS. After inferring the output based on the local knowledge base, the inference engine will take into account the collaborative KB, which makes available all the external information. This method presents additional advantages: the number of rules is smaller and the rules are more interpretable because of the two KBs.
3. Finally, the third option is to design a hierarchical system (using several KBs) in which local and remote data and KBs can be mixed.  
A common feature of all three methods is the use of a FRBS to fuse local and remote information; this feature is very effective in an environment with uncertainty and imprecision.
## 7 Conclusions  
A UUT is a new type of urban infrastructure. Existing operation and maintenance systems are highly dependent on patrol personnel and special gas leak detectors. To address these problems, this paper proposes a UUT gas-leakage emergency monitoring method based on VSR and a LSTM-based CRNN to identify natural gas leaks in a UUT and classify the leaks according to the stable gas column sprayed from the leak point. A model training dataset was produced using BasicVSR++, a sliding window, and data enhancement. After the CRNN experiments, it was shown that when the number of video frames for a single dataset was 10, the model performed best. On the validation set, its accuracy, mAP, and F1 score were 98.18%, 99.46%, and 0.9817, respectively; on the test set, the comprehensive performance of the dataset with 10 frames was also the best, with an accuracy, mAP, and F1 score of 98.52%, 99.34%, and 0.9849, respectively. In addition, the accuracy of the CRNN algorithm improved by two percentage points when compared with the traditional CNN algorithm, which verified the effectiveness of the time series analysis. The UUT gas leakage emergency monitoring method proposed in this paper can well meet the needs of industrial detection and can accurately detect the leakage of pipelines in UUTs.  
While this study achieved gas leak detection in a UUT, its scope remains confined to leakage identification rather than comprehensive defect monitoring. Future research should expand the detection range to cover more defect types. Apart from that, current localization capabilities are limited to camera positioning, requiring manual alignment of leak features with camera perspectives for approximate spatial estimation. In the future, the inspection module can be integrated with visual ranging technology to improve defect location accuracy. On the other hand, the complexity of the experimental environment and conditions can be increased to test the detection effect of the model under special situations, such as changes in environmental conditions caused by UUT structures, corners, and failures of operation and maintenance equipment. These works lay a solid foundation for UUT monitoring, which is expected to reduce reliance on manual inspection and improve safety management.  
Conceptualization, Z.J., C.Z., Z.X., and W.S.; Data curation, Z.J. and W.S.; Formal analysis, Z.J.; Funding acquisition, Z.X.; Investigation, Z.J., C.Z., and W.S.; Methodology, Z.J., C.Z., and Z.X.; Project administration, Z.X.; Resources, Z.J. and Z.X.; Software, Z.J., C.Z., and W.S.; Supervision, Z.X.; Validation, Z.J.; Visualization, Z.X.; Writing--original draft, Z.J.; Writing--review & editing, Z.X. 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 no. 2021 YFF0500900) and the National Natural Science Foundation of China (grant no. 72071043).  
Not applicable.  
**Data Availability Statement:** Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
## 4 Analysis of Bilateral Signals
### Cosine Similarity  
We analyze the existence of numerical differences between the left and right data. We assumed that a high cosine similarity within the data of the same ear and a low cosine similarity between the left and right ears can imply a difference between the data of the two ears. We define the left and right datasets as \(x_{Li}\) and \(x_{Ri}\), respectively. \(i\) indicates the trial number, \(i=1,2,\cdots,30\). The cosine similarity of the data for each ear is calculated as follows:  
\[\text{Left}:\cos(x_{Li},x_{L(i+1)})=\frac{x_{Li}\cdot x_{L(i+1)}}{|x_{Li}||x_{ L(i+1)}|} \tag{2}\]  
\[\text{Right}:\cos(x_{Ri},x_{R(i+1)})=\frac{x_{Ri}\cdot x_{R(i+1)}}{|x_{Ri}||x_{ R(i+1)}|} \tag{3}\]  
The cosine similarity of the data between the left and right ears is calculated as follows:  
\[\text{Left-Right}:\cos(x_{Li},x_{Ri})=\frac{x_{Li}\cdot x_{Ri}}{|x_{Li}||x_{Ri}|} \tag{4}\]  
The cosine similarity of the 52 subjects was computed. The average results are listed in Table 1. The mean value of the cosine similarity for the left and right ears was 0.985 and 0.988, respectively. In contrast, the cosine similarity between the signals of the left and right ear was 0.915. The student's \(t\)-test was used to compare the cosine similarity of the left ear to the bilateral cosine similarity, and the \(p\)-value was less than 0.01. Similarly, the \(p\)-value for the right ear was less than 0.01. Therefore, we can conclude that there is a significant difference between the cosine similarity of one ear and that of the two ears. A high cosine similarity within the data of the same ear and a low cosine similarity between the left and right ears indicates a probable difference between the data of the left and right ears.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Cosine Similarity**} \\ \hline Left & 0.985 \\ Right & 0.988 \\ Left–Right & 0.915 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cosine similarity within one ear data and between two ears.  
Figure 4: Preprocessed ECIR data and their frequency characteristics of three subjects. **(a,d)** Subject No. 14. **(b,e)** Subject No. 26. **(c,f)** Subject No. 34.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 6 Simulation and Results Analysis
### Performance Evaluation Criteria  
(1) The normalized localization error for a single unknown node  
\[LocError_{u}=\frac{\sqrt{\left(x_{u}^{true}-x_{u}^{est}\right)^{2}+\left(y_{u}^{ true}-y_{u}^{est}\right)^{2}}}{R} \tag{23}\]  
where \((x_{u}^{true},y_{u}^{true})\) represents the actual coordinates of the unknown node \(u\), while \((x_{u}^{est},y_{u}^{est})\) denotes its estimated coordinates obtained through the localization algorithm. \(LocError_{u}\) refers to the normalized localization error of the unknown node \(u\).  
(2) Computation of the network-wide normalized average localization error  
\[ANLE=\frac{\sum\limits_{u=1}^{N_{u}}\sqrt{\left(x_{u}^{true}-x_{u}^{est} \right)^{2}+\left(y_{u}^{true}-y_{u}^{est}\right)^{2}}}{R\cdot N_{u}} \tag{24}\]  
where \(N_{u}\) denotes the number of unknown nodes, and \(ANLE\) refers to the normalized average localization error of the network. A higher value indicates poorer performance of the localization algorithm.
## 3 Problem Statement
### System Model  
We choose a topology with arbitrary graph structure as shown in Figure 2 to introduce our model. When modeling a traditional network, a tree-shaped structure is usually used to represent the network, which has clear hierarchical and upstream and downstream relationships. Generally, there is only one root node of this structure to facilitate network analysis. In the actual ICN scenario, root nodes are not unique and connected to each other, and the ubiquity of the cache obscures the upstream and downstream relationships between nodes, so an arbitrary graph topology should be used to represent the ICN structure more appropriately.  
The network topology shown in Figure 2 consists of three source nodes represented as SNa, SNb and SNc connected to the content servers, several edge nodes represented as ENa, ENb, ENc, etc. connected to users, and intermediate nodes represented as INa, INb, INc, etc. connected only to other nodes but not directly to users or servers. It is important to note that an arbitrary graph topological network of any size still applies to this model. In this model, factors such as the number and location of content servers or  
Figure 2: The Arbitrary Graph Structure Network Model.  
users and network structure only affect the allocation results as input parameters, but not the allocation scheme itself. Therefore, this model can easily be extended to a larger arbitrary graph structure network. Table 1 represents the main notations used in this paper.  
In this paper, to facilitate analysis, we make the following assumptions:  
The three content servers store all the content for retrieval.  
Each node in the network is able to be equipped with cache storage to cache content. When a node stores the requested content, it responds to the request and no longer forwards it to the server; otherwise, the node forwards the request to the next node along the shortest path.  
This network model uses an on-path caching strategy, requests are forwarded along the shortest path, and content is forwarded along the same path as requests.
# Mechanical Strength Study of a Cranial Implant Using Computational Tools
## 2 Materials and Methods
### Numerical Modeling Strategies
#### 2.6.3 Case Study I  
A parametric study was performed to determine the number of screws that optimize the model. The presence of screws leads to stress concentration in nearby regions. An increase in the number of screws along the interface between the implant and the skull bone can reduce those high-stress concentrations, preventing the failure of both implant and skull bone. However, reducing the number of screws to the lowest possible is convenient to lower the costs, the number of external bodies, and the procedure's complexity. Also, the displacement of the implant and the relative displacement between skull and implant are essential parameters to take into account, because the higher the displacement, the higher the risk of inducing brain injuries by direct trauma. For this purpose, an impact study was selected involving a fall from a bed scenario. As [PERSON] et al. [29] reported, for an impact load of 4.2 m/s, the normal component to the ground of the head velocity stands between 3.44 m/s and 3.86 m/s. Therefore, a velocity of 3.5 m/s was defined for an impact occurring on the central zone of the implant. Then three criteria were compared: von Mises stress field on the model, displacement field on the model, and maximum values of displacement and von Mises stress on both implant and skull bone interfaces.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 4 Results
### Improvement of the Detection Stage  
To assess the practical applicability of our model for future satellite data detection, we performed QP event detection on data collected in June 2023 using our proposed QP-DETR automatic detection model. From the 1918 intercepted EFD power spectra, we detected 164 QP events, with 87 correctly identified and 32 QP events missed in the remaining images. The results from June 2023 show precision of 53.0% and recall of 81.5%. These results are consistent with the results from the 1918 intercepted EFD power spectra.  
Figure 9: Comparison of detection results between QP-DETR and DETR. Left column: results from QP-DETR; right column: results from DETR. (**a**–**h**) show cases with higher confidence levels in QP-DETR. (**i**–**l**) show cases where DETR failed to detect QP events but QP-DETR successfully detected them.  
results indicate that our model accurately identifies the majority of QP events, as shown in Figure 10.  
At the same time, we have identified some new problems, as shown in Figure 11. It was observed that certain low-frequency noise, including signals from lightning events, had similar texture characteristics and occurred below \(f_{\mathrm{cp}}\). Since the cut-off frequency of the QP emission exceeded \(f_{\mathrm{cp}}\), a proton cyclotron frequency limit was introduced during the prediction phase. Figure 2b shows the image and Equation (1) shows the calculation of \(f_{\mathrm{cp}}\).  
In identifying the pixel coordinates corresponding to \(f_{\mathrm{cp}}\) within the image, interpolation was performed to align the latitude and frequency values with the pixel count. At the center of the prediction box, represented by the coordinates (x,y), a validation process is performed to ensure that the y value at the x position is greater than the \(f_{\mathrm{cp}}\) corresponding to that pixel. If the y value is lower, the prediction box is rejected. This method significantly reduces the occurrence of false alarms during detection.