esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 3 Methods
### Modified Model Architecture
#### 3.2.1 Channel Attention Mechanism  
The Channel Attention mechanism enhances or suppresses specific channels within the feature maps. This mechanism is crucial for identifying which features are most important across the different channels of the input data. The process begins with applying average and max pooling operations, which generate channel-wise descriptors [37]. They are represented by:  
\[AvgPool(x)=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}x_{ij} \tag{1}\]  
\[MaxPool(x)=max\big{(}x_{ij}\big{)} \tag{2}\]  
Figure 3: Flowchart of the division of the dataset into training, validation, and testing sets.  
where \(H\) is the height, \(W\) is the width of the feature map, and \(x_{ij}\) is the individual elements of the feature map. These descriptors are then processed through a shared network, typically composed of fully connected layers implemented via convolution layers with a \(1\times 1\) kernel:  
\[f(x)=W*x+b \tag{3}\]  
where \(f(x)\) is the output of the fully connected layer, \(W\) is a weight matrix in the fully connected layer, \(b\) is a bias vector in the fully connected layer, and \(x\) is the input that represents the standard operation in neural networks.  
The _ReLU_ activation function is crucial for neural networks in deep learning when determining the accuracy of the model output where \(x\) is the input:  
\[ReLU(x)=max\ (0,x) \tag{4}\]  
Next, the output is passed through another fully connected layer to restore the channel dimensions, which is represented by:  
\[f_{2}(x)=W_{2}*x+b_{2} \tag{5}\]  
Finally, the output is passed through another fully connected layer to restore the channel dimensions:  
\[M_{c}=\sigma(f_{2}(ReLU(f_{1}(AvgPool(x)))))+f_{2}(ReLU(f_{1}(MaxPool(x)))) \tag{6}\]  
where \(M_{c}\) is the channel attention map, \(f_{1}\) and \(f_{2}\) are fully connected layers with learned weights and biases, and \(\sigma\) is the sigmoid activation function that limits the output between 0 and 1. \(AvgPool(x)\) and \(MaxPool(x)\) are operations applied to the feature map \(x\). This equation computes the channel attention map by passing the average and max pooled values through a series of fully connected layers, applying the _ReLU_ activation function, and then combining and calculating the result using the sigmoid function.  
This results in the creation of a channel attention map, which is used to multiply the original input feature maps to give a final output given by:  
\[x^{\prime}=x*\ M_{c} \tag{7}\]  
This final step adjusts the original feature map by multiplying it with the channel attention map, where channels with higher attention values are emphasized in the output. The most important channels with the most information are highlighted, improving the model's capture of critical features.
## 3 MIMO Detection
### Modified Multi-Branch LR-SIC (MMB-LR-SIC)  
When the system size increases, the performance of LR-SIC still exhibits a gap to the maximum likelihood (ML) detector, as has shown in [41; 43]. In [41], VLD (variable list detection) is proposed, which, in addition to the MMSE ordering-based LR-SIC as described above, also applies LR-SIC detectors with different randomly generated ordering to produce multiple estimates of the same transmitted symbol. The VLD allows the size of the candidate sets to be variable and hence the resulting complexity can vary over symbols, which can be high for certain channel uses. In this work, in order to keep the complexity tractable, we fix the number of detectors to be a finite number \(L\). As such, \(L-1\) random orderings are used. Denote these multiple estimates in the LR domain as \(\hat{\mathbf{z}}_{l},l=1,2,\cdots,L\), each corresponding to a specific ordering. One best estimate is selected according to the ML criterion in the LR domain [41] as  
\[\widehat{\mathbf{z}}_{*}=\arg\min_{\widehat{\mathbf{z}}_{l}}\left\|\mathbf{r} -\widetilde{\mathbf{G}}\widehat{\mathbf{z}}_{l}\right\|^{2} \tag{29}\]  
Finally, a single estimate of the transmitted symbol is generated as the output  
\[\widehat{\mathbf{x}}_{*}=\mathbf{T}\widehat{\mathbf{z}}_{*}. \tag{30}\]  
This multi-branch approach increases the detection complexity but can noticeably improve the error performance.
## 1 Introduction
### Related Works
Due to the difficulty of modeling nonlinear and flexible environments, some scholars propose the use of artificial neural networks to control the flexible joints [22; 23; 24; 25], and [PERSON] et al. propose a new neural network based on a disturbance observer-based integrated sliding mode controller that is only numerically simulated [26]. However, the state observer method can only realize the estimation of the internal state of the model, and must be combined with other closed-loop control methods to build a stable control system. Therefore, it is very important to choose a suitable observer and control strategy.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### The 3D Point Cloud Data Collection and Processing  
The use of 3D point cloud data is essential for spatial geometric modeling and deformation monitoring of bridge structures. By utilizing LiDAR technology, high-density point cloud data offer a precise and comprehensive representation of the bridge's three-dimensional geometry, enabling the accurate identification of localized structural damage and the assessment of overall deformation. This section outlines the methods for 3D point cloud data collection, data preprocessing, and the application of the PointNet model for processing the data, as shown in Figure 5.
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
###### Abstract  
Bearings are commonly used machine elements and an important part of mechanical transmission. They are widely used in automobiles, airplanes, and various instruments and equipment. Bearing rollers are the most important components in a bearing and determine the performance, life, and stability of the bearing. In order to control the surface quality of the rollers, a machine vision system for bearing roller surface inspection is proposed. We briefly introduced the design of the machine vision system and then focused on the surface inspection algorithm. We proposed a multi-task convolutional neural network to detect defects. We extracted the features of the defects through a shared convolutional neural network, then classified the defects and calculated the position of the defects simultaneously. Finally, we determined if the bearing roller was qualified according to the position, category, and area of the defect. In addition, we explored various factors affecting performance and conducted a large number of experiments. We compared our method with the traditional methods and proved that our method had good stability and robustness.  
bearing roller; surface inspection; convolutional neural networks; machine vision +
Footnote †: journal: Applied Sciences
## 3 Experiments on the Bearing Characteristics of Bag Grouting Piles
### Load-Bearing Characteristics Test Analysis  
#### 3.3.1 Pile Axial Force  
The values of the strain gauges were read during the graded loading. The strain at each pile section was measured with the strain gauge and the pile axial force was obtained using Equation (1) [15]. The distribution of pile axial force along the pile length during graded loading is shown in Figure 11.  
\[Q_{i}=EA\varepsilon_{i} \tag{1}\]  
where \(E\) is the pile modulus of elasticity, \(A\) is the pile cross-sectional area, and \(\varepsilon_{i}\) is the strain value at section \(i\).  
As can be seen from Figure 11, during the application of load at all levels, the maximum value of pile axial force was located at the top of the pile, and the pile axial force in the middle and upper part of the pile decreased rapidly, indicating that the pile side frictional resistance in the middle and upper part of the pile was more significant. Subsequently, the load was gradually transferred to the lower part of the pile. At the early stage of loading, the side friction resistance in the middle and upper part of the pile body was gradually stabilized. When the applied load was 180 kN, the pile axial force in the upper and middle section of the pile decreased significantly at a faster rate. When the applied load reached 288 kN, it was observed that in the middle section of the pile body, the pile axial force exhibited a notable increase compared to the previous load level, indicating that the pile side friction resistance was further exerted. At the bottom of the pile, the pile axial force approached 0, indicating that the soil was further compressed and the pile side friction resistance played a significant role, which reduced the pile axial force.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 4 Case Study  
### System Description  
The DC microgrid system considered in this study is a renewable energy-based microgrid that includes inverter-interfaced power sources and energy storage, inspired by the DC microgrid testbed developed at the City College of New York [55]. The microgrid considered has a radial topology (a common bus) to which a photovoltaic system, battery energy storage system (BESS) and a constant current passive load are connected, as shown in Figure 4.  
The PV system generates a maximum power of 6 kWp at a temperature of 25 \({}^{\circ}\)C. It is connected to the bus via a DC-DC boost converter with maximum power point tracking (MPPT) control, ensuring the maximum electricity output. The 12 kWh capacity BESS ensures that the excess power is stored and used when needed. The bidirectional DC-DC converter is in current control mode, enabling an effective bidirectional operation. The passive load is rated at 5 kW. The VSC connects the DC microgrid to the AC utility grid and regulates the voltage at the bus. All parameters of the microgrid are summarized in Table 1.  
The current measurement for fault detection is taken at the line's inception, denoted by the ammeter symbol, with the fault location represented by the red arrow sign. The fault location is varied along the line, ranging from 10% to 90% of the line length.  
Figure 4: Simulation model of a DC microgrid. Model includes battery energy storage system, PV module, passive load, and utility grid connection. The fault location is on the line that connects load to the bus.  
Figure 3: Overview of the proposed fault detection method, where, based on the FFT of the current, an intelligent classifier decides whether the event is known (and to which class it belongs) or not.
# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 2 Background
### UAVs-Based Data Aggregation
The sensor node is selected in waiting mode to sleep and not transmit the status of the node. The sensor node uploads data to the sink node in the second mode. In the third mode, the sensor node delivers data to the UAV based on the threshold value and distance condition during the UAV preplanned trajectory visit. The UAV of a fixed-wing aircraft is deployed at a constant velocity. This article uses a finite-horizon sequential Markov process and dynamic programming algorithm for the optimized transmission policy. Secondly, the proposed method optimizes the preplanned trajectory for UAVs using a recursive random search algorithm. The authors of [30] analyzed the performance of an energy-constrained Internet of Things (IoT) system that uses a power beacon and UAV for data collection. The study examined how different system and channel parameters affect outage probability, outage capacity, and ergodic capacity. In [31], the authors explored the challenges and possible solutions for implementing a fully immersive and interactive industrial metaverse, which is a virtual space that interacts with the physical world in real time. In the paper, the authors focused on improving key performance indicators, such as the Age of Information, latency,and reliability through optimizing short-packet structures in 6G URLLC communication. A cooperative strategy involving an unmanned ground vehicle and UAV is proposed in [32] to collect data from sensor nodes (SNs) in UAV-enabled data collection systems when SNs may not be able to upload their data because of factors such as insufficient energy and low flight altitude. A collaborative strategy selection algorithm that combines multistage-based SN association and UAV-UGV path optimization algorithms was used to determine trajectories for mobile data collection nodes on the ground and in the air to minimize mission completion time.
## 5 Conclusions  
Inverse synthetic aperture radar achieves the high-resolution imaging of non-cooperative targets, but maneuvering leads to the change in radar azimuth echo Doppler with time, which complicates azimuth imaging processing. In order to solve this problem, a modified chirp Fourier transform imaging method was proposed in this paper. By using the inherent relationship between the target rotation parameter and the radar echo parameter, meaning the ratio of the target rotational angular acceleration to the rotational angular velocity is fixed and the ratio is equal to the ratio of the chirp rate to the center frequency of the radar echo signal, fast focusing target imaging was realized. This method uses DCFT to quickly estimate the coarse rotation ratio; then, under the minimum image entropy principle, the accurate rotation ratio is obtained using the gradient descent method. The image entropy obtained by this method is the smallest, realizes the best evaluation results, avoids the estimation and separation of the sub-echo components, and considerably improves the imaging speed. The simulation and measured data also prove that the proposed method effectively performs two-dimensional, high-resolution, and fast imaging of maneuvering targets.  
[PERSON] and [PERSON] conceived and designed the method; [PERSON]. ([PERSON]) guided the students to complete the research; [PERSON] performed the simulation and experiment tests; [PERSON]. ([PERSON]), [PERSON], [PERSON], and [PERSON] helped in the simulation and experiment tests; and [PERSON] wrote the paper.  
The authors are grateful for the financial support received from the State 863 Project of China (No. 2014 AA7113016) and the Research Project of the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information Systems (No. 2017Z0203B).  
The authors declare no conflict of interest.
# Protection of Buried Pipelines from High-Speed Train Vibrations with Expanded Polystyrene Geofoam
## 3 Results
### Impact of Pipeline Material and Geometry
Nevertheless, the oscillation width remains high (equal to 5 mm and 5.5 mm for the vertical and horizontal deformations, respectively). The beneficial role of the EPS layer is also obvious in this case. Especially in the vertical dimension, where the peak oscillation width has been reduced to below 0.4 mm and the residual deformation is less than 1 mm. In the sequence, two smaller steel pipelines made also from X-65 material and with a diameter equal to 250 mm and thicknesses of 3 mm and 9 mm have also been studied. The diameter change in the smaller steel pipes is presented in Figure 17. The peak deformation of the unprotected steel pipe with a 3 mm thickness is equal to 3 mm and 2.8 mm at the vertical and horizontal directions, as shown in Figure 17a,b. The horizontal and vertical residual deformations are equal to 0.8 mm and 0.2 mm, while the oscillation width is equal to 3 mm. Obviously, the steel pipe is substantially more resistant to HST-induced vibrations compared to the polyethylene pipe with the same geometry. Nonetheless, it is evident  
Figure 16: (**a**) Vertical and (**b**) horizontal diameter change for a polyethylene pipe with \(D=250\) mm for thickness equal to 3 mm; (c) vertical and (**d**) horizontal diameter change for thickness equal to 9 mm. that the examined protection scheme significantly reduces HST-induced deformations. More specifically, the vertical residual deformation is reduced to almost zero, whereas the horizontal residual deformation is decreased from 0.8 mm to 0.2 mm, as illustrated in Figure 17b. On the other hand, the impact of HST passage on the thicker steel pipe is marginal. More specifically, the vertical peak deformation remains below 0.3 mm for a pipe with a 9 mm thickness (see Figure 17c). As illustrated in Figure 17d, the same trend occurs along the horizontal direction, in which the diameter change is marginal, and the residual deformation is almost zero after the implementation of the EPS layer.
## 3 Materials and Methods
### Characterization of Silica Particles
#### 3.5.4 Determination of the Surface Area and Pore Volume of Bare and mPEG-Grafted Silica Particles  
The surface area of mPEG-grafted silica particles was determined in order to compare the adsorption capacity of mPEG-grafted silica particles and commonly used polymeric adsorbents without incorporation of the surface area effect. Surface areas and pore volumes of bare and mPEG-grafted silica particles were estimated by nitrogen (N\({}_{2}\)) gas physisorption analysis with Micromeritis TriStar II PLUS Surface Area and Porosity Analyzer (Norcross, GA, USA). All samples were degassed at 40 \({}^{\circ}\)C under a vacuum of 0.05-0.1 mbar for 18 h. N\({}_{2}\) gas adsorption studies were performed at 77 K and in the partial pressure range of p/p\({}^{\rm o}\) = 0.0001 to 0.99. Surface area and pore volumes were estimated by N\({}_{2}\) adsorption using the Brunauer-Emmett-Teller (BET) method [57].
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 3 Results and Discussion
### Crack Segmentation  
Figure 5 shows the image processing steps that were applied to the images to create a crack mask through segmentation. The crack masks, seen after the morphological closing stage, were used for the maximum crack width measurement.  
Figure 5: Segmentation of cracks using image processing algorithms.  
The proposed implementation of the image processing algorithms achieved reasonably accurate crack segmentation. The initial parameters used in the image processing algorithms were manually specified and fine-tuned until satisfactory results were achieved. The reliance on the user to specify the initial parameters means the efficiency can vary depending on the experience of the user specifying the initial parameters. [PERSON] et al. [19] also highlighted how this could affect the overall accuracy of methods relying on manual parameter selection.  
Although this study does not focus on the measurement of crack lengths, it can be seen from Figure 5 that the lengths of cracks A, C, and D are shorter than before segmentation. Crack A and crack D were the most affected, followed by crack C. Crack B retained its length even after segmentation. This is a common problem. [PERSON] et al. [19] found there to be a conflict between obtaining high accuracy for both crack width and crack length and, as a result, proposed a hybrid method [19]. There are several factors that generally lead to this when using image processing algorithms for segmentation, such as image quality, lighting conditions, and distance from the surface [5; 35]. These factors also have a direct effect on the accuracy of crack width measurement.
## 3 Discussion of Results  
Our results are presented in the histogram form. To construct each histogram we evaluated the number of fd values for each particular case: one fd value per one spectrum of the phase variations. Each spectra was obtained by applying the Fast Fourier Transformation (FFT) from the 1-h carrier phase record for the particular satellite and signal component with 50-Hz sampling. Thus, the general statistics of this study includes \(\sim\)2900 particular spectra of the phase variations (for instance, two days \(\times\) 24 h \(\times\) (6-8 GPS satellites in view \(\times\) 5 GPS signal components + (4-6) GLONASS satellites \(\times\) 4 GLONASS signal components + (2-4) GALILEO satellites \(\times\) 2 GALILEO signal components) + SBAS data).
## 2 Materials and Methods
### Datasets
#### 2.2.3 Hyperspectral Imagery Data  
The aerial hyperspectral imagery was acquired using HyMap Sensor (HyVista Corporation, New South Wales, Australia) in August 2010 in the WPA site with local time of 11:00 a.m. The HyMap sensor has four spectrometers (visible spectrum (450 nm-890 nm), near-infrared (890 nm-1350 nm), shortwave infrared 1 (1400 nm-1800 nm), and shortwave infrared 2 (1950 nm-2480 nm)), each sensor produced 32 spectral bands of imagery. We obtained the apparent surface reflectance from the raw radiance image using HyCorr software (HyVista Corporation, New South Wales, Australia), and we georeferenced the data using known field control points. During the flight time, we collected field-based spectral measurements with Analytical Spectral Device (ASD) FieldSpec(r)4 Hi-Res (Malvern Panalytical Inc. Westborough, MA, USA) of ground targets with 1 m above ground for calibrating aerial hyperspectral imagery. The spatial resolution of the aerial imagery was 3 m. More detailed information about the processing of hyperspectral imagery has been reported by [PERSON] et al. [38].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Plot\#** & \begin{tabular}{c} **Forest** \\ **Type** \\ \end{tabular} & **Density** & \begin{tabular}{c} **Overpass** \\ **Times** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Scan Angles** \\ \end{tabular} & **Scan Angle Range (\({}^{\circ}\))** &
\begin{tabular}{c} **Tree Height** \\ **(m)** \\ \end{tabular} \\ \hline WPA-P1 & Mixed & Low & 3 & 22 & -24 \(\sim\) -11, -8 \(\sim\) 0 & 6.0 -18.0 \\ \hline WPA-P2 & Conifer & Medium & 2 & 32 & -29 \(\sim\) -7, -3 \(\sim\) 5 & 8.7 - 31.0 \\ \hline WPA-P2E & Mixed & Medium & 5 & 57 & -29 \(\sim\) -7, -3 \(\sim\) 10, 18 \(\sim\) 28 & 3.7 - 25.4 \\ \hline WPA-P3 & Mixed & High & 4 & 43 & -26 \(\sim\) -16, -10 \(\sim\) 10, 18 \(\sim\) 28 & 12.0 - 34.0 \\ \hline WPA-P4 & Conifer & Medium & 1 & 13 & 7 \(\sim\) 19 & 13.0 - 27.0 \\ \hline PC-P1 & Conifer & Low & 2 & 22 & -10 \(\sim\) -1, 2 \(\sim\) 12 & 4.5 - 9.8 \\ \hline PC-P2 & Conifer & Medium & 2 & 24 & -8 \(\sim\) 5, -3 \(\sim\) 6 & 33.5 - 57.2 \\ \hline PC-P3 & Conifer & Medium & 2 & 23 & -12 \(\sim\) -1, 3 \(\sim\) 13 & 18.6 - 38.8 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of seven forest plots for forest plots in both Washington Park Arboretum (WPA) and Panther Creek (PC) sites.