esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 2 Fragility Analysis
### Seismic Wave  
Previous studies have indicated that 10-20 seismic waves should be selected for the fragility analysis of structures [25]. Thus, 15 real seismic data from the Pacific Earthquake Engineering Research Center (PEER) database are employed to calculate the seismic fragility of the TTLS. To ensure the rationality of the seismic data, the seismic wave should be selected according to the site category where the structure is located, the design of seismic grouping, and other information. In Chinese code [26], the site category is mainly determined by the equivalent shear wave velocity of the soil layer and the thickness of the site cover layer, which reflects the comprehensive amplification effect of the site soil on the ground motion and is generally determined by the geological survey report. For the convenience of engineering applications, sites are classified into three design earthquake groups based on the characteristic period, which is a parameter used to describe the influence of earthquake magnitude and epicentral distance. The seismic precautionary intensity refers to the earthquake intensity approved by the authorized agency as the basis for seismic fortification in a specific region, generally taken as the earthquake intensity with a 10% exceedance probability within 50 years. In this paper, the transmission line is located at a category II site. The design earthquake group and seismic precautionary intensity are 2 and 8, respectively. Therefore, based on the Chinese code [26], seismic data that meet the following criteria are selected: soil shear wave velocities between 260 m/s and 510 m/s within 30 m depth from the surface, fault distances within 15 km, and magnitudes ranging from 6.5 to 7.5 moment magnitudes. Table 2 lists the information of 15 seismic waves. Figure 4 presents a comparison between the selected seismic wave acceleration response spectrum and the seismic wave target spectrum. The mean spectrum of the 15 seismic wave acceleration spectra is basically consistent with the target spectrum specified in the Chinese code, which demonstrates that the selected seismic wave is reasonable.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case No.** & **Seismic Wave** & **Recording Station** & **Magnitude (Moment Magnitude)** \\ \hline SW1 & Kern County & LA-Hollywood Stor FF & 7.36 \\ SW2 & San Fernando & Anza Post Office & 6.61 \\ SW3 & Friuli\_Italy-01 & Barcis & 6.5 \\ SW4 & Tabas\_Iran & Bajestan & 7.35 \\ SW5 & Imperial Valley-06 & Cerro Prieto & 6.53 \\ SW6 & Trinidad & Rio Dell Overpass-FF & 7.2 \\ SW7 & Irpinia\_Italy-01 & Auletta & 6.9 \\ SW8 & Corinth\_Greece & Corinth & 6.6 \\ SW9 & Ierissos\_Greece & Ierissos & 6.7 \\ SW10 & Taiwan SMART1(25) & SMART1 C00 & 6.5 \\ \hline \end{tabular}
\end{table}
Table 2: Seismic wave information.  
Figure 3: The FEM of the tower-line system.
## 6 Experimental Results
### Message Brokers Comparison  
To evaluate the performance of message brokers, we performed an experiment to test the processing speed of the brokers, rather than their sending speed, i.e., we compared the speed at which they write and receive a message on and from a topic (in the case of Kafka) or a queue (for RabbitMQ); for ActiveMQ, we considered both its queue and topic modalities. For each message, size and timestamp have been saved upon sending and receiving.  
Figure 8: UML sequence diagram showing the interaction between objects during the processing of a message in the Samza version of a RAM3S application.  
Figure 9: The datalab computing environment.  
The experiment aims at comparing throughput of the three message brokers when feeding messages with constant size. Table 1 reports the processing speed of RabbitMQ, ActiveMQ (in both the topic and the queue modalities), and Kafka, when feeding messages of constant size to a RAM3S Receiver (in this case, the Analyzer is empty in order to avoid delaying the message reception). All message brokers exhibit similar performance, with ActiveMQ (in the topic modality) achieving the best result for small messages and RabbitMQ being the winner for larger message sizes. Kafka appears to be a valuable alternative, obtaining good performance in both considered scenarios. For this reason, in the following experiments we will only use Kafka as message broker.
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
###### Abstract  
The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people's attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.  
Keywords:connectors; solder joint defect; transfer learning; target detection +
Footnote †: journal: Medical and Control Engineering
## 3 Developed UWSN Ecosystem  
By using the ns-3 simulator and its energy and underwater frameworks, we have developed an UWSN ecosystem that enables us to accurately simulate underwater applications before their actual development. Moreover, we are able to use data (such as weather conditions, sound speed profile or bathymetry) from the location where the system will be deployed. This data is extracted from publicly available databases to tailor the simulations.  
In Figure 1, a diagram of this ecosystem with energy-harvesting capabilities and a model of a real low-cost, low-power modem is shown. The energy-harvesting module is included in the physical layer, since it is a real energy-harvesting hardware model. The wake-up underwater modem implementation is divided between the physical and MAC layers. To test this wake-up modem, different MAC protocols where implemented and adapted to use the wake-up capabilities. The gray highlighted modules are left for future work and include energy-neutral operation algorithms and routing algorithms.  
This ecosystem is presented in detail below. The energy-harvesting model architecture is first introduced, followed by the wake-up underwater modem model.
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## Materials and Methods
### Characterization of Magnetic Nanoparticles (NPs)
Four groups of solutions were prepared, including PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in water, PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in physiological saline, urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in water, and urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in physiological saline, with two bottles of each sample and 5 mL of each bottle. One bottle of each sample was placed under the magnetic field generated by the NdFeB magnet on one side of the bottle while the other bottle was not affected by magnetic field. As shown in Figure 2f, the transmittance of these solutions increases from 0% to more than 80% from 0 to 100 min under the action of magnetic field, but remains below 1% without the action of magnetic field. This shows that the prepared NPs can be easily separated by an external magnetic field. Subsequently, the colloidal stability of NPs in water and physiological saline without magnetic field was investigated with a dynamic light scattering particle size distribution analyzer (DLS) (Horiba LB-550, Horiba, Kyoto, Japan). Figure 3 displays the DLS results of NPs at the beginning of dilution and 100 min later. As shown in the figure, PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs were dispersed in water or saline with a diameter 6-60 nm range and a mean value of 19.6 nm; urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs were dispersed with a diameter 8-70 nm range of and a mean value of 24.5 nm, even after 100 min. The results imply that the prepared NPs are water-soluble and stable in water and physiological saline. Figure 2: (**a**) Transmission Electron Microscope (TEM) image of PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) nanoparticles (NPs); (**b**) TEM image of urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs; (**c**) Field dependence of magnetization (M versus H) plot of OA-Fe\({}_{3}\)O\({}_{4}\) NPs, PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs, and urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs; (**d**) X-ray diffraction (XRD) patterns of OA-Fe\({}_{3}\)O\({}_{4}\) NPs, PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs, and urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs; (**e**) Fourier transform infrared spectroscopy (FTIR) spectra of OA, OA-Fe\({}_{3}\)O\({}_{4}\) NPs, PMAO, PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs, and urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs; (**f**) Magnetic separation property of PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs and urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in physiological saline and in water with and without an external magnetic field, as shown by the transmittance of solutions over time.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Adaptive Rotational Convolution Module
#### 3.2.3 Orientation Estimation  
To determine the appropriate rotation angle \(\theta(i,j)\) for each receptive field, we introduce an orientation estimation step before the convolution. This step involves calculating the dominant gradient direction within the receptive field, which serves as a proxy for the orientation of the object. The gradient of the feature map \(\mathrm{F}\) at position \((i,j)\) is given by:  
\[\mathrm{G}(i,j)=\left(\frac{\partial\mathrm{F}}{\partial x},\frac{\partial \mathrm{F}}{\partial y}\right), \tag{12}\]  
where \(\frac{\partial\mathrm{F}}{\partial x}\) and \(\frac{\partial\mathrm{F}}{\partial y}\) are the gradients along the \(x\)-axis and \(y\)-axis, respectively. The dominant orientation \(\theta(i,j)\) is then computed as the angle of the gradient vector:  
\[\theta(i,j)=\tan^{-1}\left(\frac{\frac{\partial\mathrm{F}}{\partial y}(i,j)}{ \frac{\partial\mathrm{F}}{\partial x}(i,j)}\right), \tag{13}\]  
This angle \(\theta(i,j)\) is used to rotate the convolutional kernel \(K\) before applying it to the receptive field centered at \((i,j)\).  
The ARC module is integrated into the standard convolutional layers of the RASST model. For each convolutional layer, the orientation \(\theta(i,j)\) is estimated across the entire feature map, and the convolutional kernels are dynamically rotated to match these orientations. This process can be mathematically represented as:  
\[\mathrm{F}_{ARC}(i,j)=\mathrm{F}_{\mathrm{out}}(i,j,\theta(i,j)), \tag{14}\]  
where \(\mathrm{F}_{ARC}\) represents the feature map after the application of the ARC module. By applying the ARC module, the RASST model achieves rotation-invariant feature extraction, enabling it to more effectively detect objects with arbitrary orientations in aerial imagery. While the dynamic rotation of kernels introduces additional computational complexity, this is mitigated by the significant performance gains in oriented object detection. The rotation operation is efficiently implemented using interpolation methods that allow for fast computation, ensuring that the ARC module can be integrated into deep neural networks without incurring a prohibitive computational cost.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Continuous Flight Auger Piles (CFA)
#### 2.2.2 Pile Load Test Evaluation  
Determination of measured pile bearing capacity was based on analysis of load/pile cap settlement plot using the Mazurkiewicz method [40]. This method is further described in [41; 42; 43; 44; 45; 46; 47; 48; 49]. The method approximates the load/settlement curve to parabola and the bearing capacity is investigated graphically. Lines parallel to the loading force axis are plotted in random intervals \(a\) (Figure 3). While there is no exact guide to estimate this interval, several values were applied and evaluated. According to various authors, smaller intervals should lead to more accurate results. Perpendicular to the loading force axis is constructed the intersection of the parallel line and load/settlement curve. From the intersection axis, a 45\({}^{\circ}\) line is plotted to the intersection with next one perpendicular. These intersections create an  
Figure 2: Pile load test. (**a**) Loading and observation equipment during the pile test; (**b**) scheme of the loading equipment [39].  
approximation line that ends at the loading force axis where the bearing capacity is read. In some cases, the points do not create a clear line so certain adjustment of the line is allowed. A narrower interval \(a\) brings more accurate results of bearing capacity. The method is more reliable for curves close to the ultimate failure load. An example of pile bearing capacity evaluation for pile No. 5 is displayed in Figure 3.
## 3 Sparse Dynamic Modeling and Convex Regression for Inertia Response  
### Fundamentals of Sparse Dynamic Modeling  
Generally, the operational data for each module is dictated by its mathematical formulation, which can be represented as \(\dot{\mathbf{x}}=f_{i}\big{(}\mathbf{x},\dot{\mathbf{x}},\mathbf{u},\mathbf{p}\big{)}\), where the right-hand side includes inputs \(\mathbf{u}\), parameters \(\mathbf{p}\), and state variables \(\mathbf{x}\), along with their rates of change \(\dot{\mathbf{x}}\).  
The operational data for each module is gathered and depicted as a sequence in the time domain:  
\[\mathbf{X}=\{\mathbf{x}(1),\mathbf{x}(2),\mathbf{x}(3)\ldots\mathbf{x}(n)\}^{T} \tag{12}\]  
where \(\mathbf{X}\) is an \(n\times m\) data matrix, \(m\) is the number of state variables and \(n\) is the number of data points collected. The rate of change of the operating variables at \(i=t\) is further calculated using numerical methods, denoted as \(\dot{X}_{i}\).  
After obtaining the operating data and based on the dynamic equations of each module, a linear/nonlinear function library \(\mathbf{\Theta}\) is constructed to reflect \(f_{i}\) the dynamic characteristics, such that:  
\[\dot{\mathbf{x}}=f_{i}\big{(}\mathbf{x},\dot{\mathbf{x}},\mathbf{u},\mathbf{p}\big{)}=\mathbf{\Theta} _{i}\Big{(}\mathbf{x}^{T},\dot{\mathbf{x}}^{T},\mathbf{u}^{T}\Big{)}\mathbf{\xi} \tag{13}\]  
where \(\mathbf{x},\dot{\mathbf{x}}\) and \(\mathbf{u}\) are symbolic variables, and \(\mathbf{\xi}\) is the coefficient of the corresponding term in the function library, which corresponds to the original equation parameters. This can be further transformed into matrix form as follows:\[\dot{\mathbf{x}}=f_{i}(\mathbf{x},\dot{\mathbf{x}},u,\mathbf{p})=\mathbf{\Xi}^{T}\Big{(}\mathbf{\Theta}_{i }\Big{(}x^{T},\dot{\mathbf{x}}^{T},\mathbf{u}^{T}\Big{)}\Big{)}^{T} \tag{14}\]  
where \(\mathbf{\Xi}=[\mathbf{\xi}_{1}\mathbf{\xi}_{2},\ldots\mathbf{\xi}_{n}]\) and \(\mathbf{\Theta}_{i}\Big{(}x^{T},\dot{\mathbf{x}}^{T},\mathbf{u}^{T}\Big{)}\) are composed of combinations of \(\mathbf{x}\), \(\dot{\mathbf{x}}\), \(\mathbf{u}\). The combinations can take the form of first- or higher-order polynomials, trigonometric functions, or other types of nonlinear function representations.
## 3 Pull Based Event-Triggered Strategy
Hence, for an arbitrary vector \(\delta(t)\), the following inequalities are applied:  
\[\gamma_{2}\delta^{T}(t)\delta(t)\leq\delta^{T}(t)\Lambda\delta(t), \tag{15}\]  
\[\delta^{T}(t)\Psi\Psi\delta(t)\leq\
u_{N}^{2}\delta^{T}(t)\delta(t). \tag{16}\]  
Combining (15) and (16), we have:  
\[\Lambda\geq\frac{\gamma_{2}}{\
u_{N}^{2}}\Psi\Psi, \tag{17}\]  
\[L_{d}L_{d}^{T}\geq\frac{\gamma_{2}}{\
u_{N}^{2}}\Psi\Psi \tag{18}\]  
and  
\[\frac{\gamma_{N}}{\omega_{2}^{2}}L_{d}L_{d}^{T}\geq\Lambda\geq\frac{\gamma_{ 2}}{\rho(L_{d}L_{d}^{T})}L_{d}L_{d}^{T} \tag{19}\]  
For the designed system (4) and control law (9), the event-triggered circle formation control for the distributed MASs can be solved according to Theorem 1. **Theorem 1**. : _Given any admissible circle formations characterized by d and considering system (4) and the designed control law (9) over a directed graph \(\mathcal{G}\) containing a spanning tree, the Circle Formation Problem can be solved when the event-trigger condition designs as:_  
\[t_{k+1}^{i}=\max_{\tau>t_{k}^{i}}\left\{\tau:\|\vec{e}(t)\|\geq\frac{\|\vec{ \delta}(t)\|}{\sqrt{2a\varphi(t)}}\right\}, \tag{20}\]  
_where \(0<a<\frac{2\gamma_{2}}{\
u_{N}^{2}}\), \(\vec{\delta}_{i}(t)=-\sum_{j=1}^{N}L_{d}^{T}(i,j)\delta_{j}(t)\), \(\vec{e}_{i}(t)=\vec{\delta}_{i}(t_{k}^{i})-\vec{\delta}_{i}(t)\), and the condition_  
\[ \begin{split}\varphi(t)&=(1-\frac{av_{N}^{2}}{2 \gamma_{2}})\frac{\gamma_{2}}{\rho(L_{d}L_{d}^{T})}-\frac{\omega_{2}\varphi(t )}{\
u_{N}}>0,\\ \varphi(t)&\geq\dot{s}(t)/s(t)\end{split}  \tag{21}\]  
_holds simultaneously, where \(s(t)\) is the weight function. That is, \(\lim_{t\to\infty}y(t)=d\)._  
Proof. : By Lemma 2, we get:  
\[\sum_{i=1}^{N}\xi_{i}\delta_{i}(t+1)=\sum_{i=1}^{N}\xi_{i}\delta_{i}(t)=...= \sum_{i=1}^{N}\xi_{i}\delta_{i}(0).\]  
Combining (8) and (13), all conditions lead to:  
\[\lim_{t\to\infty}\delta_{i}(t)=\lim_{t\to\infty}\delta_{j}(t)=\sum_{i=1}^{N} \xi_{i}\delta_{i}(0)=c, \tag{22}\]where \(c\in\mathbb{R}\) is a constant. Additionally, \(\sum_{i=1}^{N}y_{i}=2\pi\), \(\forall t\geq 0\) always satisfies, and \(\sum_{i=1}^{N}d_{i}=2\pi\). Together with \(y_{i}(t)=d_{i}\delta_{i}(t)\), we conclude that \(c=1\). More precisely, \(\lim_{t\to\infty}y(t)=d\). Therefore, the results indicate that the desired circle formation can be reached by all mobile agents. Consider a Lyapunov function as:  
\[V(t)=\frac{1}{2}s(t)\sum_{i}^{N}\xi_{i}(\delta_{i}(t)-\bar{\delta}(t))^{2}= \frac{1}{2}s(t)\delta^{T}(t)\Psi\delta(t), \tag{23}\]  
where \(\Psi\) is the same diagonal matrix as in (14), and \(\bar{\delta}(t)=\sum_{i=1}^{N}\bar{\xi}_{i}\delta_{i}(t)\) is the weighted average of \(\delta(t)\). As a result, \(V(t)\leq 0\) and \(V(t)=0\) if the Circle Formation Problem is solvable.
# Optical Pupil Shift Correction Method for Large Ground-Based Optical Telescopes
## 4 Simulation Analysis and Experimental Verification
### Compensation of Optical-Axis Deviation under the Influence of Gravity
After the Hexapod platform compensation, the maximum angular deviation decreased to 7.1\({}^{\prime\prime}\), and the maximum position deviation to 0.0175 mm. Compared with a decrease in angular deviation, the compensated position deviation decreased by a smaller margin, i.e., from 0.5 to 0.0175. This is because the secondary mirror's secondary surface fitting error was omitted in the nonlinear least-squares fitting process for simplicity. A nonlinear spherical fit was performed on the current position of the hyper-mirror node. The geometric line passing through the fitted central position and node of the secondary surface was used as the optical axis. This axis deviated from the actual one by a certain margin, which led to an initial deviation in the primary and secondary mirror positions. Based on the coma-free theory of the Cassegrain system, we found that the optical-axis compensation of the secondary mirror rotating about the coma-free system did not change the image quality of the main system. It only changed the optical axis of the main system, which is essentially the process of aligning the optical axis of the secondary mirror with the main mirror. The maximum deviation of the optical axis before compensation was 123\({}^{\prime\prime}\). After compensation, the maximum deviation decreased to 3.5\({}^{\prime\prime}\). As indicated in the Hartmann detector imaging diagram, as the target azimuth and elevation angle are varied, the uncompensated beam impinges on the pupil surface shifted by 18 mm after stabilization, and the compensated pupil shift plummeted to 1 mm (Figure 16). Thus, the optical system performance is significantly improved.
## 5 Experimental Results
### Experimental Platform  
A 35 kW Experimental Substation is applied to validate the performances of the proposed fault diagnosis model. As Figure 6 shows, we take a photovoltaic string as experimental subject, which consists of sixteen modules in series. We took fifteen of them into a 5 \(\times\) 3 photovoltaic array, which consists of three photovoltaic strings in parallel, and each string has five modules in series. A reference photovoltaic string behind experimental array is used for comparison, which consists of five modules. The solar irradiance (G) is collected by the illumination intensity detector TBQ-2 (Beijing Huatron Technology Co., Ltd., Beijing, China), and the ambient temperature (T) is collected by the temperature sensor PT100 (Haodu Sensors Technology Co., Ltd., Shenzhen, China). The current is provided by the DC (Direct Current) resource DH1718-A (Dahua Technology Co., Ltd., Beijing, China).  
In the fault diagnosis model, the fault of abnormal aging is simulated by connecting a series resistor, while the fault of short circuit is simulated by paralleling a photovoltaic module with a constructor, disconnecting the constructor between two photovoltaic modules that represent the fault of open circuit. Four instances are implemented and studied, including in the states of normal, short circuit, open circuit, and abnormal aging. 80 sample groups in each state (320 sample groups altogether) were obtained, of which 60 sample groups were used as the training set, and the remaining 20 groups were used as the testing set.
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.5 High-Speed Internet Access in the Air  
The digitization of visual and aural data was the main focus of communication services in previous generations. But with the advent of the 6G era, users' tactile information may now be gathered, digitized, and transmitted across the network, resulting in the creation of the tactile Internet.  
With 5G, achieving a high-quality aerial network infrastructure is difficult. There are two main ways that airborne network services can be provided: satellite transmission and ground-based stations. Due to the aircraft's quick mobility, long cross-border range, and other issues including high maneuverability, Doppler frequency shift, frequent handovers, and restricted base station coverage, choosing the ground base station mode is challenging. On the other hand, satellite broadcasting comes at a prohibitive cost but guarantees a reasonably assured quality of the air network. In the context of 6G, cutting-edge network designs and communication technologies outside of cellular networks will be utilizedto provide consumers with high-quality, high-speed Internet access services while also lowering network usage expenses.