esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
## 2 Principle and Simulation Analysis
### Imaging Optical System Design  
The system adopts the ML101I24P red semiconductor laser diode produced by the Mitsubishi Electric Corporation of Japan as the illumination source. It has dimensions of \(\Phi\)5.6\(\times\)7 mm, with a typical emission wavelength of 658 nm, and a continuous output optical power of up to 100 mW. Through a collimation and beam expansion system, the beam diameter of the light generated by this source can be controlled to 0.0007 mm. To ensure the efficient utilization of energy in the illumination system, it is optically coupled with a Fourier transform imaging system, with the entrance pupil of the imaging system also set to 0.0007 mm. Additionally, based on the detection principle, the imaging optical system detects jitter errors through linear motion. Compared to selecting CCDs with two-dimensional structures for detection, a position-sensitive detector (PSD) with a one-dimensional structure is more suitable for measuring single-axis positions. PSDs typically have faster response times and can capture changes in light signals in real time, making them advantageous for real-time, non-contact measurement applications [12].  
Due to the system's diameter not exceeding 50 mm and the need for detecting jitter errors within a range of 3 mm for step shafts ranging from 500 mm to 700 mm in size, the PSD-1315 position-sensitive detector (OTRON, Shanghai, China) was chosen. This detector has a photosensitive surface diameter of no more than 30 mm, excellent resolution, a spectral response ranging from 320 nm to 1100 nm, and an effective photosensitive area of 1.3 \(\times\) 15 mm\({}^{2}\). It achieves a resolution of up to 0.1 \(\upmu\)m. These characteristics enable the detector to meet the requirements for detection range and precision in the design.
## 6 Results and Discussions
### Simulation of the Monitoring and Control of the Parks of a City
#### 6.1.2 Sensitivity of the Model Parameters of the Park Monitoring System  
First, we observed the effect of varying the number of sensors on the number of messages collected (nmsg) during a simulation time of 300 s, given that the sampling period sp = 0.94 s was a fixed value. At the same time, various scenarios related specifically to the total number of parks were used. Table 3 shows that the increase in ns in the monitoring processes increased the value of nmsg. Additionally, the increase in p produced an increase in the number of messages since this is related to the introduction of new sensors.  
The Effect of the Sampling Period Used by the Sensors  
Finally, we observe the effect of varying the sampling period on the number of messages collected (nmsg) during a simulation time of 300 s, given that the total number of sensors per park ns = 6 was a fixed value. At the same time, various scenarios related specifically to the total number of parks were used. In Table 4, we observe that nmsg decreased as the value of the sampling period increased, that is, the data was collected more slowly from the sensors. Additionally, the increase in p produced an increase in the number of messages since this was related to the introduction of new sensors.  
From the analysis, we could say that the change in the value of sp had an effect that was reversed to the change in ns and p. Furthermore, ns and p similarly affected the system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**nmsg**} \\ \cline{2-7}  & \(\mathbf{p=1}\) & \(\mathbf{p=2}\) & \(\mathbf{p=3}\) & \(\mathbf{p=4}\) & \(\mathbf{p=5}\) \\ \hline sp = 0.94 & 1812 & 3624 & 5436 & 7248 & 9060 \\ \hline sp = 1.88 & 936 & 1872 & 2808 & 3744 & 4680 \\ \hline sp = 3 & 594 & 1188 & 1782 & 2376 & 2970 \\ \hline sp = 5 & 366 & 732 & 1098 & 1464 & 1830 \\ \hline sp = 7 & 264 & 528 & 792 & 1056 & 1320 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of the sampling period sp and the number of parks \(p\) on the number of messages collected during 300 s, with the total number of sensors ns = 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**nmsg**} \\ \cline{2-7}  & \(\mathbf{p=1}\) & \(\mathbf{p=2}\) & \(\mathbf{p=3}\) & \(\mathbf{p=4}\) & \(\mathbf{p=5}\) \\ \hline ns = 2 & 604 & 1208 & 1812 & 2416 & 3020 \\ \hline ns = 4 & 1208 & 2416 & 3624 & 4832 & 6040 \\ \hline ns = 6 & 1812 & 3624 & 5436 & 7248 & 9060 \\ \hline ns =
# Study on Seismic Performance of a Mold Transformer through Shaking Table Tests
## 3 Test Results and Discussion
### Displacement Response of Test Specimen  
The data recorded from the TMDTs and static LVDTs were calibrated to determine the relative displacement response of the test specimen. The tri-axial relative displacement response at a specified location of the transformer could be derived from a system of quadratic equations:  
\[\left\{\begin{array}{l}(x-x_{o})^{2}+y^{2}+z^{2}=r_{x}^{2}\\ x^{2}+(y-y_{o})^{2}+z^{2}=r_{y}^{2}\\ x^{2}+y^{2}+(z-z_{o})^{2}=r_{z}^{2}\\ \end{array}\right., \tag{8}\]  
Figure 15: Amplification factor of test specimen at different locations of accelerometers. (**a**) At top beam; (**b**) At center coil; (**c**) At bottom beam; (**d**) At left coil.  
where \(x\), \(y\), and \(z\) are the calculated relative displacements of a specified location of the transformer in X, Y, and Z-directions, respectively; \(x_{o}\), \(y_{o}\), and \(z_{o}\) are the absolute distances between the fixed locations of TMDTs to the measured locations of the transformer in X, Y and Z-directions, respectively; and \(r_{x}\), \(r_{y}\), and \(r_{z}\) are the absolute displacement values recorded from the TMDTs in X, Y and Z-directions, respectively. Figure 16 expresses the relative displacement response-time histories at the left coil of the test specimen subjected to 75% tri-axial AC156 input ground motion (AC156_75 test) and 300% tri-axial AC156 input ground motion (AC156_300 test). Figure 17 expresses the tri-axial maximum relative displacement response of the test specimen evaluated at the top beam, left coil, and bottom beam of the test specimen with respect to the PGA. In general, the maximum relative displacement of the test specimen increased along with the increase of PGA. The maximum relative displacement in the Z-direction was much smaller than those in the X and Y-directions during the shaking table tests.  
Moreover, according to the provisions and recommendations of the Korean National Radio Research Agency [25], the maximum displacement at the top of the equipment should not exceed 75 mm to ensure the safety and functional operation of non-structural components, as well as adjacent components. Figure 17 also shows the limit condition of 75 mm in comparison with the test results. The figure shows that the maximum relative displacements in the Z-direction do not exceed the boundary limit of 75 mm at the end of the tests. Meanwhile, the maximum relative displacements in the X and Y-directions exceed the boundary limit of 75 mm around the PGA of 0.70 and 0.5\(\times\)\(g\), respectively.
## 4 Results, Discussion, and Concluding Remarks
### Results of the Optimum GBDT Model
To further verify the model results, the cumulative frequency of the predicted values within a specific error range was repeatedly calculated (30 times) using the a20-index and P30, with the results shown in Figure 9. Specifically, for \(u_{\text{p}}\), the mean values of the a20-index and P30 were 0.724 and 0.819, respectively; the equivalent values for \(Q_{\text{u}}\) were 0.70 and 0.824. All these results are close to 1, indicating good generalization performance of the model. Figure 8: Performance of the optimal GBDT model on the experimental and predicted value: (**a**) evaluation of experimental and predicted \(u_{\text{p}}\); (**b**) evaluation of experimental and predicted \(Q_{\text{u}}\).
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 6 Measurement Results and Analysis  
### Received Power  
Figure 7a-c show the measured received power (the antenna gains are included) with Tx-Rx separation distance. As expected, it is shown that the received power decreases as the Tx-Rx separation distance increases. The received power decays follow the power-law distribution with the law exponents of 0.21, 0.18, and 0.26 for the Manufacturing hall at Dokka Fastners AS, the Test room at Dokka Fastners AS, and NTNU MANULAB industrial environments, respectively. The received power degradation values are around 17 dB and 20 dB at a 5 m Tx-Rx separation distance compared to the received power at 0.5 m for the Manufacturing hall at Dokka Fastners AS and NTNU MANULAB industrial environments, as can be seen in Figure 7a,c, respectively. For the Test room at Dokka Fastners AS industrial environment, the received power degradation is around 7 dB at a 5 m Tx-Rx separation distance compared to the received power at 2 m, as depicted in Figure 7b. However, this is approximately the same power degradation as seen for the two former locations when calculated from 2 to 5 m instead of from a 0.5 to 5 m distance.
# Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
## 3 Results
### CNN Model Performance with Reduced Dataset with Augmentation  
The CNN model performance with a reduced dataset with augmentation shows strong performance with accuracy (0.77), precision (0.74), recall (0.71), F1 score (0.72), and AUC (0.80). These results show that the model is effective, and it maintains a good balance between these three measures even with the use of limited data.  
Regarding the quantification of the results, the accuracy achieved was 0.77, showing that the model can classify 77% of pneumonia cases correctly, an okay result considering  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Type** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline Original Dataset (No Augmentation) & 70.6\% & 84.4\% \\ \hline Original Dataset (With Augmentation) & 71\% & 81.7\% \\ \hline Reduced Dataset (No Augmentation) & 67.6\% & 80.1\% \\ \hline Reduced Dataset (With Augmentation) & 67.2\% & 77.2\% \\ \hline ResNet-50 Model (With Augmentation) & 71.7\% & 74.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result for CNN models and ResNet50 model for sensitivity and specificity.  
the reduced data, and a precision of 0.74 shows the ability to correctly predict 74% of the time. A recall at 0.71 shows that the model identifies 71% of true cases of pneumonia, and is correct 74% of the time. The F1 score shows that the model has a balanced performance. The AUC of 0.80 highlights an ability to distinguish pneumonia cases.  
The result signifies that, despite the reduction dataset, the model performed well, although with lower metrics than achieved by using the original datasets. The AUC of 0.80 indicates an ability to distinguish between classes.  
The results were evaluated from the test set and are trustworthy, though associated with the challenges of using a reduced dataset; on the other hand, the AUC of 0.80 supports its ability to make correct predictions.  
While the results are contained, there is room for improvement. Improving augmentation, increasing datasets, or applying transfer learning could boost performance.
# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 3 Methods  
This paper proposes DAMNet, enhancing the DBNet text detection model. The network consists of the following three main components: a dual-branch feature extraction network based on ResNet18, a multi-scale adaptive feature fusion module, and a binarization post-processing module. The overall structure of this approach is illustrated in Figure 2.  
Figure 2: The architecture of the DAMNet.  
In the feature extraction phase, ResNet18 serves as the backbone network. To effectively address the complexities of container numbers, we incorporate a dual-branch structure into the feature extraction module, an innovative backbone comprising a main branch and an auxiliary information branch. The main branch is dedicated to capturing the global features of the image and providing contextual information to the network. The auxiliary information branch uses preprocessed images as input, focusing on capturing local detailed features.  
Additionally, EMA is integrated into the residual network within the main branch. EMA effectively mitigates the interference from complex background noise and enhances the network's feature extraction capabilities. This paper combines the Feature Pyramid Network (FPN) [29] with ASFF in the feature fusion phase. Through the FPN architecture, the network generates a feature pyramid with four different resolutions, each capturing distinct scale information from the image. Subsequently, the ASFF module selectively and adaptively integrates features with higher discriminability and semantic information. To address the issue of imbalanced positive and negative samples in the dataset, we propose a dual-loss strategy. This strategy combines the binary cross entropy loss(BCE loss) [30] for addressing classification issues with the Dice loss, with both being integrated through weighting.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
###### Abstract  
This paper proposes a dual-band antenna to support 5G communication with linear polarization and the global navigation satellite system (GNSS) band with circular polarization. A single inverted T-shaped patch antenna with a defective ground was designed on the Schott Foturan II (Ceramized 560 degrees) substrate. Then, an L-shaped stub and slot were inserted into the ground to achieve the 5G and GNSS bands. The antenna was then designed as a \(1\times 2\) multiple-input and multiple-output (MIMO) antenna to increase the directivity. A square ring-shaped frequency selective surface (FSS) was intended on the FR-4 substrate to improve the gain of the MIMO antenna. The FSS MIMO antenna increased the 3D gain from 2.8 to 5.4 dBi for the GNSS band and from 4.9 to 6.43 dBi for the 5G n79 band. The proposed antenna can receive and transmit the frequency bands covering sub-6 GHz 5G band n79 (4400-5000 MHz) and GNSS band E6 (1260-1300 MHz), respectively. A multi-port unmanned aerial vehicle antenna was fabricated, and its performance was characterized in terms of bandwidth, axial ratio, and gain.  
multi-band; 5G; GNSS; industry; innovation and infrastructure; FSS; MIMO 2025
# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 5 Discussion  
In order to test the fault classification performance of the proposed algorithm, it was compared with several classic algorithms in machine learning, such as KNN, ANN, SVM, and RF. In the algorithm test, the selected training set is shown in the table. However, no matter what the training set is, the test set is the dataset generated by the physical production line. The results are shown in Table 3. Three common single classifier parameters were set as follows: the number of k-nearest neighbors of the KNN model was set to 5, and the Euclidean distance calculation method was adopted. The number of ANN network layers was set to 3. The accuracy of using ANN alone was only 74.6%. Using the transfer learning method to transfer ANN parameters, combined with DT, the final test accuracy rate can reach 92.7%. The number of LSTM network layers was set to 4. The number of LSTM neurons in the first and second layers was 128, and that in the third layer was 64. The number of softmax classifiers in the last layer was 6. We used dropout to prevent the overfitting of models and selected an Adam optimizer. For SVM, we chose a radial basis function (RBF) as the kernel function. The number of decision trees in a random forest was 100. In DT + IRF, the initial number of decision trees was 300. After hierarchical clustering screening, it had 100 decision trees. The results are shown in Table 3. In KNN, ANN, LSTM, SVM, and RF, using only the physical production line training set gives an average accuracy of 79.98%. Just using the data simulated by the digital twin workshop as the training set gives an average accuracy of 72.4%, using both leads to an average accuracy of 83.98%. These results indicate that different data distributions and the amount of data will affect  
Figure 10: Confusion matrix for fault classification of automobile rear-axle assembly line: (a) IRF + DT algorithm; (b) RF algorithm.  
the results of the algorithm. It can be seen that the accuracy of the integrated method has been greatly improved compared to the single model, and the proposed DT + IRF method further improves the accuracy of the integrated method, proving its effectiveness.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Deployment Approaches
#### 4.1.3 Mesh Approach  
In this approach, any single network deploys a mesh connectivity with existing networks (see Figure 6). This enables any single network to forward the received attack definitions directly to all the connected networks, instead of pushing the attack definitions linearly one by one to its neighboring network. This way the mitigation process is very fast.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 3 ISLs Design
### Global Connectivity  
Mega-constellations need to meet global connectivity requirements to communicate between any two locations via satellite nodes. Applying adjacency matrix in graph theory to describe and analyze the connectivity of satellite nodes is a valuable method [42]. The adjacency matrix [43] of ISLs can be expressed as:  
\[A=\left[\begin{array}{cccc}a_{11}&a_{11}&\dots&a_{1N_{s}}\\ a_{21}&a_{22}&\dots&a_{2N_{s}}\\ \vdots&\vdots&\vdots&\vdots\\ a_{N_{s}1}&a_{N_{s}2}&\dots&a_{N_{s}N_{s}}\end{array}\right]\]  
where \(N_{s}\) is the total number of satellites contained in the mega-constellation, and \(a_{ij}\) indicates whether there is an ISL between the \(i\)-th satellite and the \(j\)-th satellite. If there is  
Figure 4: The visibility geometry of any two satellites in space.  
an ISL between two satellites, then the element in the corresponding position of matrix A is set to 1. Otherwise, it is set to 0.  
The judgment matrix can be constructed by utilizing the adjacency matrix, which can be employed to analyze the connectivity of ISLs. The judgment matrix is given by  
\[R=A+A^{2}+\ldots+A^{N_{s}-1}.\]  
A sufficient condition for global connectivity of a mega-constellation is that all elements of the matrix \(R\) are not zero. In addition, the element \(R_{ij}\) indicates the number of different paths from the \(i\)-th satellite to the \(j\)-th satellite.
## 3 Results and Discussion
### Effect of Open-Cell Structure on the Microwave Absorption Performance
#### 3.4.2 Effect of Cell Size on Microwave Absorption Performance
As shown in Figure 7b, the deformation of the normal GA is large, and its shape is difficult to recover after removing the weight. By contrast, the deformation of the OCGA-20K is smaller after adding same weight, and it almost fully recover after removing the weight (Figure 7c). It should be noted that these two aerogels contain the same content of graphene. We consider that the increase of compressive properties of OCGAs is mainly due to the spherical cell structure that is supposed to bear much pressure.
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 4 Prototype Research  
### Prototype Manufacturing  
A prototype of the linear generator designed in this paper was manufactured. Figure 9 shows the prototype and its main components. A displacement sensor is mounted inside of the shaft of the linear generator. It is a resistive position sensor with a repeatability accuracy of 0.01 mm and a max movement velocity of 10 m/s. A current sensor and a voltage sensor are mounted in the power convertor of the linear generator. The real-time mover position, coil current, and terminal voltage of the linear generator can be tested synchronously at the sampling frequency of 10 kHz.
## 5 Proposed Approach and System
### Local Computation
Furthermore, as our actively secure evaluation protocol utilizes \((T,N)\)-threshold MPC to construct our secure framework, our PPBFL can resist collusion between at most \(T-1\) edge nodes, as well as supporting edge node dropout up to a level of \(N-T\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Function** & **Gas** \\ \hline construct() & 3602173 \\ \hline setEligible() & 209906 \\ \hline register() & 157970 \\ \hline submit() & 17224780 \\ \hline similarity() & 20643343 \\ \hline aggregate() & 26843542 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gas cost of each function in TrainFL smart contract. ### Performance

As discussed in Section 8, we conducted a series of experiments to evaluate the efficacy of our PPBFL. Based on the empirical results, the following conclusions can be drawn. * Privacy of Device Data: The PPBFL allows each IoMT device to collaboratively train the FL model locally with their local data. The PPBFL method is unlike the centralized machine learning approach, where the IoMT devices need to send their local data to the cloud for the learning process. Therefore, the proposed FL approach can ensure the privacy of these devices' sensitive data. * Robustness of Local and Global Model: In our scenario, we consider adversaries that perform poisoning attacks on the IoMT device's datasets. Such a poisoning attack will lead to a faulty local model and a poisoned global model. Our PPBFL utilizes the cosine similarity to detect and eliminate poisoned local model updates during the aggregation process. Judging from the experimental results, our PPBFL can effectively mitigate the influence of poisoned local model updates. From this result, our PPBFL can guarantee the robustness of both the local and global models. * Privacy of Local Model: In a traditional FL framework, attackers can perform a membership inference attack on the local device models, thus leaking sensitive data from the model. Therefore, we leverage the MPC-based secret sharing protocol, which maintains data confidentiality throughout FL processes, effectively addressing the inherent tension between data privacy preservation and robust defense against poisoning attacks. As the local gradient is protected, model inversion attacks and parameter stealing cannot be performed on the local model by an attacker. * MPC-based Secure Aggregation: In traditional FL approaches, the local models of devices are collected and aggregated into a global model. The aggregation process is the core step of FL to achieve a learning model with higher accuracy. As the aggregation is performed in the blockchain node using MPC-based actively secure evaluation protocol, adversaries cannot tamper with the aggregation process, thus maintaining the model's accuracy.