esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Overall Architecture of PerMSCA-YOLO  
In this study, PerMSCA-YOLO is built upon the YOLOv8 framework [42] and designed to meet the requirements of multi-scale defect detection on track surfaces. It establishes a progressive three-tiered system that balances high accuracy with real-time performance: feature extraction, feature fusion, and object detection. The overall workflow, as shown in Figure 1, comprises the following key stages: First, the input image undergoes adaptive pyramid scaling for multi-resolution normalization. Then, the lightweight FasterNet backbone extracts multi-level features in a layer-wise manner, generating a hierarchical representation ranging from shallow textures to deep semantics. Following this, the Multi-Scale Convolutional Attention Module (MSCAM) integrates channel attention, spatial attention, and parallel convolutions to highlight defects of varying sizes, such as cracks and corrosion patches, while suppressing interference. Finally, the enhanced features are mapped to candidate bounding boxes and classification channels through the detection head, and perceptual loss is applied to align the texture of the target region, further enhancing the model's ability to differentiate small targets and defects in complex backgrounds.  
Unlike traditional YOLO-based models, PerMSCA-YOLO emphasizes lightweight design and multi-scale interaction at each stage. The FasterNet backbone optimizes operators to reduce redundant computations, ensuring real-time performance in resource-constrained environments. MSCAM employs attention reconstruction and parallel multi-core convolutions to effectively capture narrow cracks and diverse defects. Perceptual loss plays a crucial role in deep feature alignment, preventing performance degradation due to missed or false detections. Through the synergy of these three components, PerMSCA-YOLO significantly enhances both defect localization and classification on complex track surfaces, while maintaining high detection speed.
# Design and Construction of an LED-Based Excitation Source for Lock-In Thermography
## 2 Hardware Design  
### Housing and Specifications  
The excitation source described in this work is shown in Figure 1. On the left, the source is shown from the front, where the array of infrared LEDs is visible. The LEDs are mounted on an aluminium substrate PCB (printed circuit board), which acts as a heat spreader. For the housing, the hull of a commercially available infrared strobe lamp is used. It provides enough space for an LED board with the dimensions \(100\,\mathrm{mm}\times 100\,\mathrm{mm}\). The necessary control elements are mounted on the back side of the housing, see Figure 1b.  
Figure 1: Infrared LED excitation source: View on the LEDs (**a**) and the control circuit (**b**).  
The housing features a central cooling channel for heat dissipation (Figure 2). The channel splits the housing into two chambers and offers two surfaces, which are thermally well-connected to the ambient. The front chamber houses the LED boards, which are directly connected to the cooling channel. The back chamber contains the driver circuit and the output transistor bolted to the cooling channel. Thermal compound is pasted between the boards and the faces of the channel to increase the heat transfer coefficient. Running the excitation source for longer periods at full power reveals that passive cooling via the cooling channels in the middle of the housing does not provide sufficient heat dissipation. Therefore, two cooling fans (Sunon MF60252V2-1000U-A99) are installed to provide additional ventilation. The 24 V/60 mm fans are connected in series and powered by the same power supply as the LED driver. Parallel to each fan, a 220 \(\upmu\)F capacitor is added for current smoothing. A 3D-printed intermediate part builds the transition between the cross-sections of the fans and the cooling channel. With this addition, the temperature of the housing does not exceed 50 \({}^{\circ}\)C. To minimize the power dissipation inside the housing, an external switched-mode power supply module is used.  
To use the LED excitation source in a wide range of applications, including lock-in and transient measurements, different operating modes are implemented, to control the LED current and the duty cycle. As the control input, a BNC-connector is used, which comprises an analogue and a digital input. The analogue input is used for direct or pulse-width modulation dimming. The digital input allows for switching the output upon a trigger signal. A USB connection is added to allow a remote control via a computer. The excitation source is designed to have a usable frequency range from DC up to 1 kHz to fit a wide range of lock-in applications (Table 1). Rise and fall time are specified to allow the use of the excitation source in transient thermography applications.
## 3 Description of Research Methods
### Adversarial Examples
#### 3.2.2 Adversarial Attacks  
Gradient-Based Attacks  
These attacks leverage the gradient information of the model's loss function with respect to the input data to craft adversarial examples.  
FGSM: The Fast Gradient Sign Method (FGSM) is a non-targeted attack method that generates adversarial samples by constraining the \(L_{\infty}\) norm of the original samples [11]. It operates by moving in the gradient direction of the adversarial loss function \(J(\theta,x,y)\) to maximize the loss. Specifically, the adversarial sample generation using FGSM can be expressed as:  
\[\left\{\begin{array}{l}\tilde{x}=x+\rho\\ \rho=\varepsilon\cdot\text{sign}(\
abla_{x}J_{\theta}(x,l))\end{array}\right. \tag{2}\]  
where \(x\) denotes the original sample, \(l\) the target label, \(\rho\) the perturbation, and \(\varepsilon\) the maximum allowed perturbation. The constraint ensures that the magnitude of \(\rho\) in the \(L_{\infty}\) norm remains within \(\varepsilon\).  
BIM: Building on FGSM, the Basic Iterative Method (BIM) [33] enhances adversarial example generation by repeatedly adjusting the gradient. This iterative process crafts more potent adversarial examples with less discernible perturbations compared to FGSM.  
PGD: Projected Gradient Descent (PGD) [12] is a more refined gradient-based method that aims to find the perturbations maximizing the model's loss function, given certain constraints. PGD optimizes the expected loss over potential perturbations in the input space, producing notably robust adversarial samples.
# Ballasted Track Status Evaluation Based on Apparent Track Stiffness Index
###### Abstract  
Currently, maintenance work for ballasted track is performed based on the Track Quality Index (TQI) without a fundamental cause investigation for track irregularity. In this study, a new evaluation standard for ballasted track status is proposed to overcome the limitations of current maintenance work. The new evaluation standard is based on the stiffness index. The stiffness index is defined as the difference between the average and standard deviation of the apparent track stiffness determined by a light falling weight deflectometer (LFWD). After some field tests and analysis, it is shown that the stiffness index has a strong correlation not only with TQI but also with maintenance history. In verification results, the new evaluation standard estimated the ballasted track status quite well.  
ballasted track; track status evaluation; light falling weight deflectometer; apparent track stiffness; stiffness index
## 3 Event Cameras  
Event cameras, unlike traditional cameras, generate data events described using the time (timestamps), pixel location (x, y), and 1-bit polarity, representing the brightness change (i.e., brightness increase ('ON') or decrease ('OFF')) [21; 22]. As shown in Figure 1, each DVS pixel continuously and asynchronously monitors the brightness level of the input. Whenever a pixel detects a significant step-change, such as a sudden increase or decrease in brightness compared to the last recorded value, this triggers a new event. As the DVS processes the visual input, events are generated in real time. Unlike traditional cameras that capture images at fixed intervals (frames per second), the DVS generates events asynchronously. Therefore, the output of a DVS camera has a variable data rate. The generated events represent non-redundant information about local brightness changes. Instead of transmitting full-frame images, the DVS selectively encodes only the essential changes. This approach leads to a more efficient representation of the visual input, as redundant information is omitted, and only the significant changes are captured in the event stream.  
The main characteristics of the events cameras are the following: the spatial resolution, determined by the number of pixels in the image sensor; the dynamic range (DR), range  
Figure 1: Principle of operation of events cameras [12; 22]. As brightness increases until reaching the “on threshold” value, positive events are generated. Conversely, as brightness decreases until reaching the “off threshold”, negative events are triggered. Due to the refractory period, events have a limitation on consecutive spiking, as indicated by an ‘x’.  
of brightness levels (difference between brightest and darkest detectable intensities) that the event camera can capture (expressed in dB); the temporal resolution, defined as the discrete measurement resolution of the event, characterized by the time unit of the event timestamp detection time [23]: the maximum throughput, referring to the highest data rate the camera can output to the shared bus, typically in million events per seconds (Meps); the latency or the time delay between the occurrence of an event and the transmission of the data to the output bus; and the contrast sensitivity, which is the sensitivity to changes in brightness that report events.  
The following subsections provide the background for the DVS pixel, and the potential impact of the key parameters on optical camera communication systems, bandwidth, the refractory period, and bias sensitivity.
## 2 Working Resistance Prediction Model for Supports in Ultra-Long Mining Faces
### Adaptive Graph Convolutional Recurrent Network
#### 2.1.2 DAGG Module  
In traditional GCN-based prediction models, a predefined adjacency matrix \(A\) is usually required for convolutional operations. However, the predefined matrix is subjective and cannot reflect the complete spatial information of the nodes in the graph. The DAGG module regards the normalized adjacency matrix as a learnable parameter, and the equation is as follows:  
\[D^{-\frac{1}{2}}AD^{-\frac{1}{2}}=softmax\left(ReLU\left(E_{A}\cdot E_{A}^{T} \right)\right) \tag{4}\]  
where \(E_{A}\in R^{N\times d_{e}}\) is a randomly initialized node-embedding matrix; \(d_{e}\) is the dimension of node embedding; and \(E_{A}E_{A}^{T}\) is the similarity between nodes.  
\(E_{A}\) is the inner product of the embedding vectors of node \(i\) and node \(j\) and it can learn the hidden dependencies between different sequences autonomously during the training process. The GCN improved by the DAGG module is as follows:  
\[Z=\left(I_{N}+softmax\left(ReLU\left(E_{A}\cdot E_{A}^{T}\right)\right) \right)X\Theta \tag{5}\]  
The gated recurrent unit (GRU) is used in the AGCRN to capture the time dependence, and the MLP layer of the GRU is replaced with the graph convolutional neural network learned by the NAPL module. The formula is as follows:  
\[\widetilde{A}=softmax\left(ReLU\left(EE^{T}\right)\right) \tag{6}\]  
The update gate \(z_{t}\) in the GRU model determines the extent to which the state information at time \(t\)-1 is passed to the current state at time \(t\); and the reset gate \(r_{t}\) is employed to control the extent to which the state at time \(t\) is passed to the current hidden state \(\widetilde{h}_{t}\). The GRU update formula optimized by the AGCRN is as follows:  
\[z_{t}=\sigma\left(\widetilde{A}[X_{:,t},h_{t-1}]EW_{Z}+Eb_{Z}\right) \tag{7}\]  
\[r_{t}=\sigma\left(\widetilde{A}[X_{:,t},h_{t-1}]EW_{r}+Eb_{r}\right) \tag{8}\]  
\[\hat{h}_{t}=tanh\left(\widetilde{A}[X_{:,t},r\odot h_{t-1}]EW_{h}+Eb_{t}\right) \tag{9}\]  
\[h_{t}=z_{t}\odot h_{t-1}+\left(1-z_{t}\right)\odot\hat{h}_{t} \tag{10}\]  
where \(\sigma\) is the sigmoid function; \(X_{:,t}\) and \(h_{t}\) are the input and output in time step \(t\); \(h_{t-1}\) is the output in time step \(t-1\); is the hidden state; \([.]\) is the connection operation; and \(\Theta\) is the Hadamard product. \(W_{Z}\), \(bz\), \(Wr\), \(br\), \(W_{\hat{b}}\), and \(b_{\hat{b}}\) are the learnable parameters in the AGCRN. The structure of the AGCRN is shown in Figure 2.
# Analysis of Spatiotemporal Gait Variables before and after Unilateral Total Knee Arthroplasty
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Pre** & **Post** & **Z-Value** & \(p\)**-Value** & \(\mathbf{d_{cohen}}\) \\ \hline Gait Speed (m/s) & 0.51 (0.16) & 0.54 (0.15) & \(-5.95\) & \(<\)0.001 * & 0.95 \\ Cycle Time (s) & 1.23 (0.22) & 1.17 (0.21) & \(-5.93\) & \(<\)0.001 * & 0.94 \\ Stance Time (\%GC) & 62.97 (4.16) & 63.45 (2.84) & \(-0.69\) & 0.49 & 0.11 \\ Swing Time (\%GC) & 37.02 (4.16) & 36.54 (2.84) & \(-0.69\) & 0.49 & 0.10 \\ Step Time (\%GC) & 50.74 (1.73) & 50.05 (1.44) & \(-6.08\) & \(<\)0.001 * & 0.97 \\ Double Limb Support Time (\%GC) & 14.93 (4.14) & 13.27 (3.45) & \(-6.82\) & \(<\)0.001 * & 1.13 \\ Step Length (\%S) & 31.51 (7.05) & 32.91 (5.37) & \(-5.56\) & \(<\)0.001 * & 0.87 \\ Stride Length (\%S) & 60.54 (12.34) & 64.37 (11.02) & \(-5.03\) & \(<\)0.001 * & 0.77 \\ Cadence (Steps/Minute) & 98.36 (17.07) & 103.35 (17.82) & \(-7.23\) & \(<\)0.001 * & 1.22 \\ Cadence (Strides/Minute) & 48.81 (9.15) & 51.23 (9.68) & \(-5.83\) & \(<\)0.001 * & 0.94 \\ \hline \hline \end{tabular} Note: An asterisk (*) indicates a significant difference (\(p<0.05\)). %GC: percentage of the participant’s gait cycle duration; %S: percentage of the participant’s stature.  
\end{table} 
Table 2: Median and (IQR) of the spatiotemporal gait variables for surgical knee participants pre- and post-surgery. Figure 1: Comparison between the distance covered during gait pre-TKA (red) and post-TKA (white), and the significant linear kinematic parameters. Despite the changes observed post-TKA surgery, Table 2 does not differentiate the surgical outcome considering if the operated limb was the dominant or the non-dominant. Table 3 presents the differences between the post-surgery and pre-surgery linear kinematics, differentiating the sample according to the dominance of the operated limb. Results are generally in agreement with those found in Table 2; however, it is highlighted that the magnitude of the outcome differs considering the dominance of the operated limb. Comparatively to the dominating limb, performing TKA to the non-dominant limb significantly improves gait speed (18.6%, \(p\) < 0.001) and reduces the double limb-support time (\(-\)12.7%, \(p\) = 0.041), while also increasing the stride length (10.5%, \(p\) < 0.001), allowing increased step cadence (7.5%, \(p\) = 0.019). Although there is also a statistical difference between the limbs in terms of stance, swing, and step time, these are very small changes, usually under 1% of the total gait cycle duration, and may lack clinical significance. Overall, participants who underwent surgery on the non-dominant limb demonstrated greater improvements in most parameters compared to the dominant (right) limb.
# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
## 2 Material
### Vegetation and Forest Type Maps  
The 14-class and 1 km spatial resolution land cover map generated by [25] was used to obtain a vegetation type map, which was used to simulate GPP (Section 2.3). Elevation extracted from the Shuttle Radar Topography Mission 3 arc second digital elevation model [26] and land cover data from the Spanish Land Use Information System (SIOSE) [27] were combined to obtain a forest type map [28], which was used to simulate forest respiration (Section 2.4).
# Evaluating the Drainage Density Characteristics on Climate and Drainage Area Using LiDAR Data
## 2 Data and Methodology  
### Study Sites and Data Sources  
The study sites were selected based on the availability of LiDAR data. The data were chosen from regions with minimal land use change and artificial structures including buildings, dams, and roads. The high-resolution LiDAR data were obtained from the Center for LiDAR Information, Coordination and Knowledge (CLICK, [[http://lidar.cr.usgs.gov](http://lidar.cr.usgs.gov)]([http://lidar.cr.usgs.gov](http://lidar.cr.usgs.gov)), accessed on 20 August 2015). The CLICK website offers data tiled by USGS Quarter Quadrangles in LAS and ASCII formats [48]. The spatial availability of LiDAR data from the CLICK website is presented in Figure 1a in blue. The red dots are 121 study sites that were selected for this study. The study sites are located in 17 states with various climate conditions. The ground returns of LiDAR data, in which vegetation and buildings are cleaned out by the data providers, are used in this study. The point cloud data are processed to derive 1 m DEMs and land surface topography using QCoherent software LP360 for ArcGIS ([[https://www.lp360.com/](https://www.lp360.com/)]([https://www.lp360.com/](https://www.lp360.com/)), accessed on 20 August 2015).  
The climate aridity index, defined as the ratio of potential evaporation to precipitation (\(E_{P}/P\)), is used as an indicator of the climate [49; 50]. This index provides a useful tool to differentiate energy-limited or humid regions, (\(E_{P}/P<1\)) and water-limited or arid regions (\(E_{P}/P>1\)). Monthly potential evaporation data at 8 km spatial resolution from [PERSON] et al. [51] is aggregated into mean annual values. The parameter-elevation regressions on independent slopes model (PRISM) provides the gridded annual, monthly, and event-based precipitation data [52]. Mean annual precipitation data from PRISM with a 4 km spatial resolution are used for the period of 1981-2010. Mean annual potential evaporation and precipitation data are averaged to the watershed scale values for computing \(E_{P}/P\). Figure 1b shows the distribution of \(E_{P}/P\) for the 121 study watersheds. The distribution is represented by normalized frequency, which is defined as the ratio of the number of watersheds in each bin to the total number of watersheds. The values of \(E_{P}/P\) for the selected study watersheds ranges from 0.3 to 10.4.  
For comparison, the PE index, precipitation effectiveness index, for the watersheds from Melton [33] and Madduma Bandara [39] are converted to \(E_{P}/P\). The relationship between PE index and \(E_{P}/P\) is derived after removing the outliers from the data by [PERSON] et al. [51]. The estimated PE index is \(E_{P}/P\) for the 121 study watersheds.  
and Wu [53] as shown in Figure 2a. Lower \(E_{P}/P\) corresponds to higher PE index. The U-shaped trend for data points from Melton [33] and Madduma Bandara [39] is still visible after converting PE to \(E_{P}/P\), and the transition occurs at \(E_{P}/P\approx 2\) (Figure 2b).
# Research Status and Progress of All-Position Narrow-Gap GMAW for Pipelines
## 3 Methods to Improve Weld Bead Formation in Narrow-Gap GMAW
### Changing Process Parameters to Control Melt Pool Flow to Improve Weld Forming
Reducing the wire feeding speed leads to a decrease in welding current, thereby reducing heat input [47], which effectively controls the flow of the molten pool. However, in all-position welding, this would result in a thinner weld layer at that position, thus requiring an appropriate reduction in welding speed to ensure the height of the weld layer. [PERSON] et al. [48] analyzed the changes in weld width and residual height at different wire feeding speeds during overhead welding. As the wire feeding speed gradually increases within the range of 3-5.5 m/min, both the weld width and residual height show an increasing trend. However, when the wire feeding speed exceeds 6.0 m/min, hump defects begin to appear. The study does not fully explore the impact of different welding positions on this conclusion. While optimizing process parameters can improve the weld bead quality in narrow-gap GMAW welding of pipelines to a certain extent, existing research mostly focuses on localized optimization under specific conditions, lacking systematic comparisons and validations across various conditions.
# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 6 Experimental Verification
### Fault Simulation  
Since the fault cannot be specifically set in actual production, it is necessary to put up a test rig in the laboratory to perform a fault simulation test. The monitoring data is collected in the fault state. In this test, the parameters of the brake system are adjusted to simulate the faults and mixed faults of each brake system. Also, the correctness of diagnosis is verified. These parameters include brake disc swing offset, brake shoe clearance, and hydraulic station residual pressure. The specific fault simulation method is as follows [27]:  
(1) Change brake shoe clearance test  
Step 1: Start the hydraulic pump and turn the switch to the rope adjustment indicator so that the hand brake in the released state. At this moment, the brake oil pressure of the brake system is 4.8 Mpa;  
Step 2: Take out the hexagon socket head bolts in the center of the brake disc and loosen the larger bolts in the center of the brake disc. Carry out the same operation on other brake discs, and keep the number of turns of the bolt on the same side to loosen the consistent bolt;  
Step 3: Turn the switch to the normal indication and run the hoist, then collect the data.  
(2) Change brake disc swing offset test  
Step 1: Start the hydraulic pump when the hoist is stopped. After this, turn the switch to the rope adjustment indicator to ensure that the hand brake is in the fully released state. Thereafter, turn off the hand brake switch of one side of the oil way. At this time, the oil circuit brake is in the fully released state and does not participate in the brake work of the hoist;  
Step 2: Adjust the brake shoe clearance of the oil circuit brake on the other side;  
Step 3: Turn the switch to the normal indication and run the elevator. Then, brake and repeat the brake operation. After this process, collect the data.  
(3) Chang hydraulic station residual pressure  
Step 1: Start the hydraulic pump in the state of parking and turn the switch to the indication of the rope adjustment so that the hand brake is in a tight state. The oil pressure at this stage is 0.2 MPa;  
Step 2: Adjust the screw on the far-right side of the oil pressure gauge to make the residual pressure of the system reach 0.3-1 MPa. Finally, collect the data to observe the brake effect.  
Combined with the test conditions, the fault types simulated by adjusting the above parameters include: brake shoe clearance is too small, brake disc overheating, idle motion time is too long, emergency brake fault, residual pressure is too large, disc spring fault and normal, etc. The fault types are numbered as shown in Table 5.