esa-sceva/satcom-chunk-collection huggingface dataset:
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 2 Description of Periodic Defects
### Analysis of Features of Roll Marks  
Roll marks are a set of uneven defects with periodicity. They are generally due to roll fatigue, insufficient hardness, or foreign matter on the surface of the rolls during rolling operations. The morphological features of roll marks on the same batch are stable and similar; the morphological features of roll marks on different batches can vary due to the repeated rolling of the steel plates. Roll marks are present on both the upper and lower surfaces of plates, mainly at the operating side and the middle position of the plates. They are bright spots observable by the naked eye, and dark spots in the image captured by a camera.  
Figure 0(a) shows an original image of roll marks. Figure 0(b) shows the defect area and that the roll marks in the frame are arranged periodically. Figure 0(c) contains details of a single defect, which shows that the morphological features are similar.  
Roll marks are sequentially arranged with periodicity. According to the collected data statistics, the morphological features of roll marks on the same batch are similar, as shown in Figure 2. However, the morphological features of roll marks on different batches are very different, as shown in Figure 3.  
Figure 3: Roll marks on different batches.  
Figure 2: Roll marks on the same batch.  
Roll mark defects are not well-detected because of the greatly different morphological features of roll marks on different batches. The traditional CNN classifies defects by extracted morphological features [6]. Therefore, a CNN can easily misclassify roll marks due to their unfixed morphological features. Consequently, the classification accuracy is not high.  
However, as roll mark defects have strong periodicity, their time-sequenced characteristics are suitable for handling by LSTM.
## 4 Numerical Case Study--Assessment of SSI Effects on a Steel Building
### Braced Frame
#### 4.2.2 Braced Frame with SSI  
The braced frame with added foundation, vertical nonlinear links, and horizontal linear springs was studied. Single foundations under each column connected with foundation beams were added as it is presented in Section 4.2 and recommended according to EC7. Since the soil properties and geometry of the foundation did not change, the spring and link properties are the same as presented in Section 4.1.2.  
The braced frame was loaded with two different lateral load distributions as shown in Table 5. The results obtained within the numerical analyses are compared in Figure 11.  
The comparison of the capacity curves is presented in Figure 11 and it is possible to conclude the same tendencies as for the moment resisting frames are repeated. The coupled system with soil that is loaded with an uniform lateral load distribution is showing higher capacity and stiffness which is a result of the additional force at the foundation level that is significantly larger when compared to the foundation force for the modal lateral load case.
## 8 Emerging Applications  
The IoT can also contribute to the sustainable development of smart cities in terms of smart agriculture, smart education, smart tourism, and smart healthcare.  
In addition, [PERSON] et al. [108] and [PERSON] et al. [109] introduced IoT systems for smart agriculture. [PERSON] et al. [108] applied an IoT system to monitor and analyze the facility agricultural ecosystem in Shanghai. [PERSON] and [PERSON] [109] applied an IoT system for span greenhouse agricultural system in Tianjin. Their system included the automatic control function of the greenhouse and the agricultural intelligent frequency conversion irrigation function.  
For smart education, [PERSON] and [PERSON] [110] installed the ESPSAS IoT platform in an elementary school in Taipei. ESPSAS allowed students to create their own windflower things, watch their own windflower spinning on campus via mobile phones, and change its light color remotely via mobile phones.  
Regarding smart tourism, [PERSON] et al. [111] utilized an IoT system with ultrasonic sensors to help an XR tourism application sense the user's surroundings.  
For smart healthcare, [PERSON] et al. [112] introduced eBPlatform, an IoT system deployed in a patient's home that can measure the patient's blood glucose, blood pressure and ECG signals. In addition, [PERSON] et al. [113] used IoT to address the uncertain home healthcare supply chain network. Using data from a real-world case study in Montreal, they found that leveraging this IoT system could maximize the overall utilization of vehiclesand efficiently optimize the number of trips, thereby minimizing total costs and greenhouse gas emissions.
waiting for the battery to charge no energy can be spent collecting data, which may lead to days or hours of periods where data cannot be collected.  
Conservative fixed data collection rates for animal behavior monitors best address the unpredictable nature of solar energy harvesting by attempting to operate with excess stored energy [1]. The opposite would be aggressive fix rates that would most certainly drain the battery and be unable to continuously collect data. Either remaining fully charged or running out of energy puts the monitoring device in situations where adjustments to the data collection rate might yield more consistent or high rates of data collection. Also, mostly charged Li-ion batteries (above 80%) have been found to last longer than batteries that are consistently fully charged and drained [6], which is the operating mode of a fixed collection interval scheme. In order to continually collect data, ENO controllers able to vary data collection rates proportional to the amount of energy harvested were sought for an IoT animal behavior monitoring device.
## 4 Simulation Setup
### Sensors Noise  
The kinematic control methods proposed in this paper need to have the position, attitude, and velocities of the UAV base as inputs. There are different combinations of sensors that can be used to obtain these data. Typically, sensor fusion techniques are used to obtain the best estimate of the pose of the vehicle [32].  
The base position can be measured by resorting to GPS (few centimeters of accuracy at a rate of up to 10 Hz) in outdoor environments or to external localization devices such as a VICON motion capture system (very high accuracy at about 200 Hz) or on-board camera-based systems (few centimeters of accuracy at about 20-50 Hz) [33]. As for attitude measurements, most of the UAVs today have an inbuilt IMU (Inertial Measurement Unit) equipped with three-axis gyroscopes, providing measurements of the attitude rates; three-axis accelerometers, providing measurements of the accelerations; and a magnetometer, providing the absolute heading.  
In real experiments, measurements from sensors are affected by noise. Therefore, noisy measures of the base linear and angular positions and their velocities, which are used in the inverse kinematics methods proposed, can affect the capability to accurately track a trajectory with the end-effector. In order to simulate the measured data in a realistic way in our simulator, normally distributed measurement noise with a null mean was added to the simulated measurements coming from the UAV. In particular, in the simulator, the noise is added to the measured variables (i.e., those estimated by the direct dynamics module), which are then used as inputs for the inverse kinematics module.  
The standard deviations of the noise corrupting each measurement used in this work are listed in Table 4.  
Figure 5: Examples of wind velocity profiles generated through the Dryden model (\(n=40\) and \(\Omega_{\rm i}\in[0.1,1.5]\) rad/s). In blue, the horizontal wind velocity component is shown, and in orange, the vertical wind velocity component is shown. (**left**) Example 1; (**middle**) Example 2; (**right**) Example 3.  
Figure 6: Horizontal (in blue) and vertical (in orange) force profiles associated with the wind velocity profiles of Figure 5. (**left**) Example 1 (force profiles acting in the simulations presented in the following sections); (**middle**) Example 2; (**right**) Example 3.
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 2 PEMFC System Model and Verification  
PEM fuel cells convert chemical energy into electrical energy through electrochemical reactions. Hydrogen is oxidized and split into hydrogen ions and electrons at the anode. Hydrogen ions travel across the electrolyte and electrons move through the electric circuit planted in the stack. On the cathode, they are combined with oxygen, and water is formed as a by-product. An electric potential is formed between the electrodes through the electrochemical reaction in the cell. Cells are stacked up to achieve a higher voltage and are used to construct a stack system. The PEM fuel cell system consists of a fuel cell stack and balance of plant (BoP). The BoP includes the air supply system, fuel supply system, and thermal management system. In this study, we address faults in the air supply system, fuel supply system, and fuel cell stack system. The Table 1 below shows sensors and actuators used in respective systems.
## 4 Case Study
### Analysis of the Prediction Results  
This study employed four parameter estimation methods: maximum likelihood estimation with a marine predators algorithm (MLE-MPA), maximum likelihood estimation with a genetic algorithm (MLE-GA), maximum likelihood estimation with a gradient descent algorithm (MLE-GD), and adaptive Markov chain Monte Carlo (AMCMC). The findings from these estimation methods were integrated into the tamping-cycle prediction model developed in this study to forecast the time intervals until the SDLL degrades to any given value \(X_{lim}\). The validity of the prediction model and the four parameter estimation methods was analyzed based on the statistical results for the prediction errors.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 4 Applications and Benefits of the IoV
### Traffic Flow Prediction  
One typical application of IoV is for traffic flow prediction. The theory of traffic flow is the basic theory of the intelligent ship transportation management. With the help of the IoV system, it can analyze the dynamic laws of the ships and the changes in the relationship between the traffic speed and the traffic density. Taking advantage of the prediction model and the information data obtained from the IoV system, the traffic flow prediction could predict the short-term, medium-term and long-term traffic. With the development of the IoV system, a number of advanced models are proposed to predict the traffic flow. [PERSON] (2017) used an improved model of robust support vector regression to predict the traffic flow of Tianjin Port. Based on the port's characteristics, ship behavior and historic data of the traffic flow, [PERSON] (2017) established a novel integrated traffic flow prediction model. Due to the problems of ship navigation safety and congestion caused by the increasing ship traffic and ship size in Pohang Port, [PERSON] simulates the ships' tracks in real time and the traffic flow to provide the best route guidance for ships. [PERSON] (2017) adopted the Monte-Carlo method to analyze the history data of traffic flow in the Wuhan bridge area of Yangtze River and to propose a generative approach to improve the safety of ship navigation. It can be seen from this, if these traffic flow prediction models are added to the IoV data resource layer and give the appropriate results in the application layer, they are quite useful for improving the safety of the ship navigation and the utilization of the ports, to promote the development of the intelligent management.
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
## 1 Introduction
The main feature of this method was that the interfacial velocity profiles between the bars and specimen were determined instead of the reflected and transmitted strain pulse in the middle of bars [8]. However, before the experiment, the shutters must be designed with reflector slices, and the reflector slices must be fixed to the front and back ends of the specimen. In this paper, we apply temporal speckle interferometry in-plane displacement measurement to a Kolsky bar. This method uses two sets of time-domain speckle interference in-plane displacement measurement devices to replace the strain gauges of a conventional Kolsky bar. Moreover, the wavelet transform, which is believed to be more suitable to process the non-stationary signal than the Fourier transform [9], is used to extract the dynamic mechanical properties of the measured specimen. This new method does not require any intermediate media, nor the pasting of devices, and has the advantages of being non-contact, real-time, and more reliable. Furthermore, the Kolsky bar can measure the dynamic mechanical properties of small specimens under higher strain rate conditions in this method. To the best of our knowledge, no such system has yet been presented.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 4 Offline Keypoint-Map Tracking  
The online rendering requirement of the previously described method can be computationally intensive, limiting its deployment on resource-constrained platforms. To address this, we propose an alternative approach specifically designed for scenarios where the camera's trajectory is known beforehand, such as in robotic applications involving repetitive tasks.  
This method fundamentally alters the computational paradigm by bifurcating the process into an intensive offline preparation phase and an efficient online tracking phase. The core idea is to shift the computational load away from runtime. During the offline phase, we pre-render all necessary views along the known trajectory and extract a sparse map of stable 3D keypoints. Consequently, the online tracking phase becomes significantly more lightweight, as its tasks are limited to retrieving candidate points from this pre-built map and matching them against the incoming camera frames. This strategy prioritizes runtime efficiency and predictability, making it the superior choice when the path is predefined.
## 4 Results  
### General Dynamic of The Rock Glacier  
Different publications have shown the behaviour and values of horizontal (distance) and vertical (sinking) displacements of the Corral del Veleta rock glacier, obtained using the total station throughout the period of measurement (27; 41; 46; 57).  
In order to do so, annual checks of the 25 steel rods in the rock glacier were made during the observation period (the last week in August) (Figure 4). Information is not available for the years in which the rock glacier remained covered by seasonal snow in August (2010, 2011, 2013, and 2018), nor in 2015 or 2017 (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**Direction**} & \multicolumn{2}{c}{**Horizontal Displac.**} & \multicolumn{2}{c}{**Subsidence**} \\  & **Centesimal Graduation** & **Metres** & **Metres** \\ \hline
2001–2002 & 194.0490 & 0.064 & \(-\)0.246 \\
2002–2003 & 196.7580 & 0.157 & \(-\)0.384 \\
2003–2004 & 210.2730 & 0.044 & \(-\)0.115 \\
2004–2005 & 201.2640 & 0.302 & \(-\)0.654 \\
2005–2006 & 176.6910 & 0.207 & \(-\)0.468 \\
2006–2007 & 203.5660 & 0.214 & \(-\)0.482 \\
2007–2008 & 178.2790 & 0.146 & \(-\)0.454 \\
2008–2009 & 165.5000 & 0.114 & \(-\)0.266 \\
2009–2010 & SNOW (No data) & - & - \\
2010–2011 & SNOW (No data) & - & - \\
2009–2012 (3 years) & 165.9430 & 0.094 & \(-\)0.186 \\
2012–2013 & SNOW (No data) & - & - \\
2012–2014 (2 years) & 154.1460 & 0.065 & \(-\)0.317 \\
2014–2015 & NO (No data) & - & - \\
2014–2016 (2 years) & 153.9130 & 0.184 & \(-\)0.891 \\
2016–2017 & NO (No data) & - & - \\
2017–2018 & SNOW (No data) & - & - \\
2016–2019 (3 years) & 149.6480 & 0.178 & \(-\)0.845 \\ \hline
2001–2019 (18 years) & 180.3543 & 1.769 m & \(-\)5.308 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dynamic of steel rod 2 (located in frontal part of the rock glacier) in annual periods, and the total for the 18-year period of measurements (2001–2019).  
Figure 4: Displacement (horizontal and vertical) of the rock glacier (2001–2019).  
For three years (2006, 2007, and 2008), a further observation was made at the end of July in addition to the periodic measurements at the end of August. The importance of this measurement lies in the fact that the summer value does not include the influence of winter, since it covers the summer period from the end of July until the end of August. For the winter period, the opposite is true, since a dynamic value can be obtained from the end of August to the end of July the following year. This enables the determination with greater precision of the dynamic behaviour during the months when the rock glacier is covered with snow (normally from the middle of October to the middle of June) and the months that it is free of snow (normally from July to September) (Table 2). Only in August do the sun's rays shine directly on the surface of the rock glacier, since the walls surrounding it leave it in shade throughout the rest of the year.
## 4 Experiments and Analyses
### Datasets and Metrics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Language** & **Split** & **Header** & **Question** & **Answer** & **Other** & **Total** \\ \hline \multirow{2}{*}{ZH} & training set & 229 & 3692 & 4641 & 1666 & 10,228 \\  & test set & 58 & 1253 & 1732 & 586 & 3629 \\ \hline \multirow{2}{*}{JA} & training set & 150 & 2397 & 3836 & 2640 & 9005 \\  & test set & 58 & 726 & 1280 & 1332 & 3383 \\ \hline \multirow{2}{*}{ES} & training set & 253 & 3013 & 4254 & 3929 & 11,449 \\  & test set & 90 & 909 & 1218 & 1196 & 3413 \\ \hline \multirow{2}{*}{FR} & training set & 183 & 2497 & 3427 & 2709 & 8816 \\  & test set & 666 & 1023 & 1281 & 1131 & 3501 \\ \hline \multirow{2}{*}{IT} & training set & 166 & 3762 & 4932 & 3355 & 12,215 \\  & test set & 65 & 1230 & 1599 & 1135 & 4029 \\ \hline \multirow{2}{*}{DE} & training set & 155 & 2609 & 3992 & 1876 & 8632 \\  & test set & 59 & 858 & 1322 & 650 & 2889 \\ \hline \multirow{2}{*}{PT} & training set & 185 & 3510 & 5428 & 2531 & 11,654 \\  & test set & 59 & 1288 & 1940 & 882 & 4196 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The list of data from the XFUND dataset. Figure 11: An example of a MER document image. **Top**: class descriptions and text translation of some areas in the image. **Bottom**: color legend for the classes. Experiments were conducted to evaluate the model performance using precision (P), recall (R), and the F1 score (F1) as evaluation metrics. The associated entity detection experiments follow commonly used target detection assessment metrics of average precision (\(AP\)), including \(mAP_{50}\), \(mAP_{75}\), and \(mAP_{0.50.95}\).
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 2 Sound Source Localization Methods for One-Dimensional (1D) Structures  
The one-dimensional positioning method mainly utilizes time difference to locate the sound source (Figure 1).  
\[\mathbf{t}_{1}-\mathbf{t}_{0}=\frac{\mathbf{x}}{\mathbf{c}}\] \[\mathbf{t}_{2}-\mathbf{t}_{0}=\frac{\mathbf{L}-\mathbf{x}}{\mathbf{c}}\] \[\mathbf{x}=\frac{\mathbf{L}-(\mathbf{t}_{2}-\mathbf{t}_{1})\mathbf{c}}{2}\]  
x is the distance from the crack to sensor 1, where x represents the position of the acoustic emission source, L is the length of the detection section, c is the wave velocity, \(t_{0}\) represents the time when the crack begins to form, \(t_{1}\) represents the time when sensor 1 receives the signal and \(t_{2}\) represents the time when sensor 2 receives the signal.
## 5 Experimental Evaluation
### FPGA Implementation Results  
FPGA implementation on PolarFire SoC demonstrates the practical feasibility of our design with excellent resource utilization and timing margins, as summarized in Table 4.  
The implementation achieves significant timing margin (3.1 \(\times\) required frequency) while maintaining low resource utilization (<2.5% LUTs), confirming practical deployability on PolarFire SoC. Critical path analysis reveals well-balanced design with the NN multiplier cascade representing the longest path at 6.4 ns, providing 156% timing margin above required 50 MHz operation. Power analysis shows efficient distribution with the DShot core consuming 42.1 mW (31.7%), ML engine requiring 23.4 mW (17.6%), and neural network using 18.9 mW (14.2%), resulting in 16.6 mW per channel on PolarFire SoC.
###### Abstract  
Geopolymer-based grouting materials often have a higher early strength, better durability, and lower environmental impact than those of traditional cement-based grouts. However, existing geopolymer grouts face common challenges such as rapid setting and low compatibility with treated substrates. This study develops a new grouting material using industrial byproducts to overcome these limitations while optimizing performance for reinforcing silty mudstone slopes. The base materials used were ground granulated blast furnace slag (GGBFS) and zeolite powder, with calcium lignocellulosic (CL) serving as the retarding agent and NaOH as the alkali activator. The investigation focused on the effects of the mix ratio and water-binder ratio on the setting time, flowability, bleeding rate, concretion rate, and compressive strength of the new grouting material. Scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses were employed to examine the action mechanism of the material components in the slurry. The one-factor standard deviation method and Grey Relational Analysis (GRA) were used to assess the influence of each material component on the slurry performance indices and the correlation between each performance index and its optimal mix ratio. Subsequently, the optimal mix ratio of the new grouting material was ascertained. The results indicate that the setting time is positively correlated with the zeolite powder and CL dosages and the water-binder ratio, while it is inversely related to the NaOH dosage. The flowability is significantly enhanced with increasing zeolite powder and NaOH dosages, but decreases at a higher CL dosage and water-binder ratio. This insight is crucial for optimizing the workability of the grouting material under various conditions. The optimal ratio of the grout is zeolite powder:GGBFS:CL:NaOH = 30:70:5:7, with a water-binder ratio of 0.6. Compared to existing commercial grouting materials, the compressive strength of this new grout is comparable to that of silty mudstone. This significantly reduces the problem of stress concentration at the grout-rock interface due to strength differences, thus effectively reducing the risk of secondary cracking at the interface. These findings provide a new material solution for grouting and repairing fractured silty mudstone slopes.  
alknali activation; GGBFS-zeolite powder; grouting materials; mechanical properties; mechanisms Article