esa-sceva/satcom-chunk-collection huggingface dataset:
# Gamifying Massive Online Courses: Effects on the Social Networks and Course Completion Rates
## 3 Materials and Methods
### Analytical Tools  
The primary instrument used to analyze the instances of the course was the principal component analysis (PCA) [42]. PCA determines the orthogonal axis of maximum variance by finding the eigenvalues and the eigenvector of the correlation matrix. Variables are projected onto those axes, calling these \"factor loadings\". Observations, when projected onto the axis, are called \"factor scores\". Factor loadings show how variables group together. The dimension of a dataset can be reduced when factor loadings of different variables are somewhat close in their biplot, indicating that the variables are very closely correlated. Furthermore, if the factors resulting from PCA are adequate and interpretable, then the factor scores can also be used to analyze correlations and build predictive models of desired outcomes. Since groups can be different, independent PCAs were carried out for each dataset (group).  
The data gathered satisfy PCA assumptions. Firstly, all variables were measured at the continuous level. Second, we can assume a linear relation between variables since they all represent contributions to the learning platform, or they are measures of position in the social network. Third, [PERSON]'s test of sphericity returned that data for both groups were suitable for data reduction (\(p<0.001\) in both cases). Fourth, the Kaiser-Meyer-Olkin (KMO) test returned 0.880 for the experimental group and 0.788 for the control group suggesting that data had sampling adequacy. Finally, no significant outliers were found. A [PERSON]'s test returned two possible outliers with a significantly high number of messages and interactions. Subsequent analysis showed that this was due to a mistake in data collection that added messages from other courses. Records were fixed and included in the dataset. We also checked that this error did not occur in other records.  
The Gephi visualization-and-exploration software [43] was used to create the graphs that represent the social networks and to compute their global metrics. Gephi was also used to compute the individual metrics of each participant.
## 2 Materials and Methods
### Gravimetric Adsorption of Crushed Shale  
This study used a Rubotherm IsoSORP-HP Static II high-temperature and high-pressure adsorption instrument produced in Germany to conduct the isothermal methane adsorption experiments. The magnetic levitation balance in this equipment has an accuracy of 10 \(\upmu\)g. The maximum pressure and temperature for the equipment experiments were 50 MPa and 140 \({}^{\circ}\)C. Because the burial depths of the shale reservoirs in this study varied greatly, the temperature and pressure conditions set in the experiment were based on the formation conditions of the selected shale gas well. If the temperature and pressure of the shale gas well are higher than the maximum values of the equipment, it will be selected for conducting isothermal methane adsorption experiments. The experimental procedures and data processing of this study followed the Chinese Energy Industry standard NB/T 10117-2018 [61]. The adsorption capacity (m\({}_{\text{abs}}\)) of the shale sample was calculated as follows [33]:  
\[\text{m}_{\text{abs}}=\Delta\text{m}-\text{m}_{\text{sc}}-\text{m}_{\text{s}} +(\text{V}_{\text{sc}}+\text{V}_{\text{s}}+\text{V}_{\text{a}})\times\rho_{ \text{g}} \tag{2}\]  
where \(\Delta\text{m}\) is the balance reading, m\({}_{\text{sc}}\) is the mass of the sample container, m\({}_{\text{s}}\) is the mass of the sample, V\({}_{\text{sc}}\) is the volume of the sample container, V\({}_{\text{s}}\) is the sample volume, and V\({}_{\text{a}}\) is the volume of adsorbed phase. \(\rho_{\text{g}}\) is the density of free methane at a given temperature and pressure. Except for V\({}_{\text{a}}\), other parameters can be obtained through experiments.  
Then, the excess adsorbed mass (m\({}_{\text{ex}}\)) can be calculated as follows:  
\[m_{ex}=m_{abs}-V_{a}\times\rho_{g}=V_{a}\times\left(\rho_{a}-\rho_{g}\right) \tag{3}\]  
The corresponding absolute adsorption capacity (m\({}_{\text{abs}}\)) can then be expressed as [58]:  
\[m_{abs}=m_{ex}+V_{a}\times\rho_{g} \tag{4}\]  
\[m_{abs}=m_{ex}/(1-\rho_{g}/\rho_{a}) \tag{5}\]  
The excess adsorption capacity (m\({}_{\text{ex}}\)) can be obtained through experiments. As the Langmuir model is the one most commonly used to fit the adsorption isotherms of methane adsorption in shale, m\({}_{\text{ex}}\) can be expressed as:  
\[m_{ex}=\frac{V_{L}+P}{P_{L}+P}\times\left(1-\frac{\rho_{g}}{\rho_{a}}\right) \tag{6}\]where \(\rm P_{L}\) is the Langmuir pressure, \(\rm V_{L}\) represents the maximum absolute adsorption capacity, and \(\rm P\) is the equilibrium pressure. We measured the excess adsorption capacity (\(\rm m_{ex}\)) via experiments, fitted it using Equation (6), and obtained the adsorption phase density (\(\rm\rho_{a}\)), after which, the adsorption gas capacity (\(\rm m_{abs}\)) can be calculated using Equation (5).
## 3 Dataset Construction of Landslides in Yiyang City
### Extraction of Landslide Predisposing Factors
#### 3.2.4 Hydrological and Meteorological Factors  
In terms of hydrological factors, rivers exert erosive effects on slopes, potentially compromising slope stability. Hence, distance to rivers is considered a crucial factor. As for meteorological factors, rainfall is typically the primary predisposing factor for landslide  
Figure 6: (**a–d**) Environmental factors.  
occurrences. According to the research conducted by [PERSON] et al., the effective rainfall duration before the landslide is ten days, and thus the cumulative volume of rainfall over the ten days preceding the landslide serves as the meteorological factor [27]. All the predisposing factors were analyzed with a spatial resolution of 30 m. Figure 7a,b display distance to rivers and the 10-day rainfall factor, respectively.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 4 Experiments and Results
### Training Setup  
In this study, the experimental setup utilized the Ubuntu 18.04 operating system (Lenovo, Hohhot, Inner Mongolia, China), with a GPU (RTX 3090, 24 G) and a CPU (Intel(R) Xeon(R) Platinum 8338C). The software environment was configured with Python 3.8, CUDA 11.1, and PyTorch 1.8. The learning rate for network training was set to 0.1, employing the SGD optimization algorithm with a weight decay of 0.9. The learning rate was adjusted using the CosineAnnealingLR strategy, and other parameters included a Dropout of 0.5, a batch size of 8, and training for 70 epochs. The trend of learning rate reduction is illustrated in Figure 14.  
During the training process, a warm-up method was employed, initiating training with a relatively small learning rate (\(lr\)) in the early stages and subsequently transitioning to the pre-defined learning rate for the remainder of the training.  
\[lr(t)=\frac{t}{T_{warmup}}lr_{\text{max}},t\leq T_{warmup} \tag{23}\]  
We employ the Stochastic Gradient Descent (SGD) algorithm for model optimization, and its specific procedure is as follows. Assuming a batch of training samples, denoted as n, from which a random sample \(i_{s}\) is selected, let \(W\) represent the model parameters, \(J(W)\) the cost function, \(E(g_{t})\) the gradient \(\Delta(W_{t})\), and \(\varphi_{t}\) the learning rate. The expression for updating model parameters using the Stochastic Gradient Descent method is given by:  
\[W_{t+1}=W_{t}-\varphi_{t}g_{t} \tag{24}\]  
Here, \(W_{t}\) and \(W_{t+1}\) represent the model parameters before and after the update, respectively. \(g_{t}\) denotes the gradient at time \(t\). This algorithm iteratively adjusts the model parameters based on the calculated gradient and the learning rate.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Improved Performance Optimization Model Based on HMM Algorithm
#### 4.3.2 Active Number Dynamic Estimation Based on the Hidden Markov Model  
The HMM produces relatively more accurate predictions than the other methods. The HMM estimates station channel decision results through the maximum a posterior (MAP). More accurate channel access information is obtained for the competitive stations when the HMM is combined with game theory mechanisms. Stations adopt appropriate access strategies, and the performance is optimized.  
The elements of an HMM are defined as \(\lambda=(\pi,A,B)\).  
* The initial probabilities that stations will judge the channel state \(\pi=[\pi_{0},\pi_{1}]\).
* The transition probabilities between hidden states \(A=\left\{a_{ij}\right\}\), with \(a_{ij}=p(S_{t}=s_{j}\big{|}S_{t-1}=s_{i})\), \((s_{i},s_{j}\in S)\). \(a_{ij}\) indicates the probability that a hidden state \(s_{i}\) will transition at time \(t-1\) to another hidden state \(s_{j}\) at time \(t\).
* The emission probabilities of the symbols in each hidden state are \(B=\left\{b_{i}(k)\right\}\), (\(i\), \(k\) = 0, 1), where \(b_{i}(k)=P(O=d_{k}|S=s_{i})\). \(b_{i}\) (\(k\)) shows the probability of transition from a hidden state \(s_{i}\) to the observed state \(d_{k}\).  
The basic principle is that the station calculates the prior probability using the posterior probability for the last (\(t-1\)) slot and computes the posterior probability from the observation symbol in the \(t\) slot as follows:  
\[p(S_{t}=s_{i}|O_{t}=o_{t})=\frac{Q_{t}(s_{i},o_{t})}{\sum\limits_{s_{i}\in S}Q _{t}(s_{i},o_{t})} \tag{55}\]  
where:  
\[Q_{t}(s_{i},o_{t})=p(o_{t}|s_{j})\sum\limits_{j=0}^{1}Q_{t-1}(s_{j},o_{t-1})a _{ji} \tag{56}\]  
Expression \(Q_{2}(s_{j},o_{2}),Q_{3}(s_{j},o_{3}),\ldots,Q_{t}(s_{j},o_{t})\) is calculated from Equation (56) when \(Q_{1}(s_{1},o_{1})=\pi_{j}p(o_{1}|s_{j})\) is known. Channel state \(s_{i}\) is the current station that estimates competitive stations at time \(t\). It is computed by \(p(S_{t}=s_{i}|O_{t}=o_{t})=\underset{s_{i}\in S}{\max}p(S_{t}=s_{i}|O_{t}=o_{ t})\).  
At the start of the time slots, if station x plays an online game and estimates that the result of the game is one by adopting HMM, the current station saves the results in game set \(\Omega_{x}\) (\(\Omega_{x}\subset\{1,2,\ldots N\}\)). Probability \(\tau_{1}\) is obtained when the current station obtains the game results. Finally, the optimal saturation performance is calculated.
## 2 Materials and Methods
### Mobility Model
#### 2.1.1 Random Waypoint Mobility Model  
The RWP [7] model is one of the most widely used node mobility models. Due to its simplicity and practicality, it quickly became the benchmark for evaluating MANET routing protocols [24]. In RWP, nodes that follow a uniform or normal distribution randomly choose the speed \(v\in[V_{\text{min}},V_{\text{max}}]\) and direction \(\theta\in[0,2\pi]\) from their initial position. After running for a fixed time interval \(\Delta t\), they will select a new speed and direction to move until the simulation ends; the motion trajectory of the node is shown in Figure 1. When initializing the network, the state and position of nodes are randomly initialized, so at the beginning, the nodes are evenly distributed throughout the entire movement area [25]. The endpoint of each motion of a node is a random location in the entire region; the speed of node movement is also randomly selected within a certain range. The model is a simple random mobility model, but according to existing simulations, there are many sharp turns in the model, which is one of the shortcomings of the model. The model is also a memoryless mobility model. The selection of node speed and direction has no correlation with the previous speed and direction, and it will move along a new route. At the same time, if the time interval or moving distance is too small, the nodes will basically move in a small area, unable to traverse various states, resulting in the uneven spatial distribution of nodes. The following is the relationship between the coordinates \((x_{n},y_{n})\) of the node during the n-th movement and the coordinates \((x_{n-1},y_{n-1})\) during the n-1 st movement:  
\[x_{n}=x_{n-1}+v_{n-1}\cos\theta_{n-1} \tag{1}\]  
\[y_{n}=y_{n-1}+v_{n-1}\sin\theta_{n-1} \tag{2}\]
# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 2 Related Work  
### Object Detection  
As one of the fast-developing fields in recent years, deep learning-based object detection algorithms are becoming the leading methods to solve object detection tasks. Most successful methods can be divided into two main categories, two-stage detection and one-stage detection. The most representative methods following two-stage detection including the RCNN, Fast regions with convolutional neural networks (Fast-RCNN) and their variants [6,7]. The common idea of these methods is first to obtain region proposals, which might contain the objects, then change the task into a classification to attach each anchor box a label. It is almost the standard solution for long periods of time due to its relatively high accuracy. However, it has proven to be of less help in practical scenarios that ask for real-time monitoring because of their low detection speed. In contrast, one-stage detection methods try to solve the problem through regression. This first employs a feature extracting backbone, usually a convolutional neural network (CNN)-based one, to produce feature maps, then predicts the position, class and confidence of objects at the same time. Based on the improvement of the feature extractor backbone, YOLO evolves from YOLOv1 to YOLOv3 to achieve better accuracy and speed [8, 9, 10]. Adopting a cross stage partial network (CSP)-based darknet-53 as the backbone network and replacing feature pyramid networks (FPN) with a path aggregation network (PANNet), YOLOv4 improves the detection accuracy of the model in advance [11]. Recently, the YOLOv5 network model has added a focus structure to the backbone network on the base of YOLOv4, and balanced the detection speed and accuracy. Currently, one-stage detection methods are widely used in engineering practice due to the good time efficiency. Nevertheless, in most cases, the accuracy of YOLO and its variants is not as high as that of two-stage methods, especially for small targets and the low-resolution input.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 3 Materials and Methods
### Hydraulic Pumps and Control System  
Syntge pumps are used to control the volume of water within each actuator. These can be modelled as fixed, positive displacement hydrostatic pumps. In each pump a stepper motor (NEMA 23 2305 HS280 AW, Ooznest) coupled to a lead screw drives a gantry along a linear stage. 3D printed adapters are used to hold both the syringe piston to the gantry and the body of the syringe (50 mL, BD Plastipak) to the linear stage. The end of the syringe piston was modified to reduce deformation under pressure, which would impact  
Figure 2: Theoretical, measured, and calibrated actuator contraction per unit volume.  
the volume inside the syringe body. The outlet of the syringe is connected via PVC tubing to a double tee joint where a pressure sensor (MS5803-14 BA, TE Connectivity) is attached. A microcontroller (Arduino Uno, andduino.cc) controls the stepper motor using a stepper driver (DRV8825, Texas Instruments), reads the pressure sensor, and communicates with the control PC. Figure 3 shows the key components of the syringe pumps and part of the prismatic joint setup mentioned later.  
The volume delivered to the actuator, expressed as \(V(\theta)\) in Equation (3), is the current volume of the syringe minus the initial volume. A calibration procedure is therefore required, after which the initial volume in the actuator is assumed to be zero. The calibration step consists of retracting the syringe piston until a vacuum pressure is measured by the pressure sensor, in this case 85 kPa absolute. This negative pressure is maintained for 4 s to ensure that the actuator has indeed been emptied. The syringe piston must be stationary during this time because a transient vacuum gauge pressure could also be generated by resistance to flow.  
The resolution of the syringe pump, \(V_{res}\), is found by multiplying the cross sectional area of the syringe body with the resolution of the linear stage as in Equation (4). The resolution of the linear stage is determined by the lead, \(L_{screw}\), of the lead screw; the number of steps per revolution, \(S_{pr}\), of the stepper motor; and the number of microsteps, \(\mu_{steps}\), used by the stepper driver. The syringes used had an inner radius, \(r_{swing}\), of 13.25 mm from which their cross sectional area could be calculated.  
\[V_{res}=\frac{L_{screw}}{S_{pr}\cdot\mu_{steps}}\cdot\pi\cdot r_{swing}^{2} \tag{4}\]  
With \(L_{screw}\) equal to 4, \(S_{pr}\) equal to 200, \(\mu_{steps}\) equal to 4, and \(r_{swing}\) equal to 13.5 mm, the resulting volume resolution is 5.516 mm\({}^{3}\). The theoretical maximum volume is found by substituting \(\theta=\pi/2\) into Equation (3) along with the appropriate parameters representing the actuator geometry, resulting in a maximum volume of 15,066.332 mm\({}^{3}\). Therefore, 2731 steps would be required to fill the actuator to the theoretical maximum, rounded down to an integer number of steps.
###### Abstract  
Natural disasters can cause severe damage to critical infrastructure such as road networks, significantly delaying rescue and recovery efforts. Conventional road damage assessments rely heavily on manual inspection, which is labor-intensive, time-consuming, and infeasible in large-scale disaster-affected areas. This study aims to propose a deep learning-based framework to automatically detect and quantitatively assess road damage using high-resolution pre- and post-disaster satellite imagery. To achieve this, the study systematically compares three distinct change detection approaches: single-timeframe overlay, difference-based segmentation, and Siamese feature fusion. Experimental results, validated over multiple runs, show the difference-based model achieved the highest overall F1-score (0.594 \(\pm\) 0.025), surpassing the overlay and Siamese models by approximately 127.6% and 27.5%, respectively. However, a key finding of this study is that even this best-performing model is constrained by a low detection recall (0.445 \(\pm\) 0.051) for the 'damaged road' class. This reveals that severe class imbalance is a fundamental hurdle in this domain for which standard training strategies are insufficient. This study establishes a crucial benchmark for the field, highlighting that future research must focus on methods that directly address class imbalance to improve detection recall. Despite its quantified limitations, the proposed framework enables the visualization of damage density maps, supporting emergency response strategies such as prioritizing road restoration and accessibility planning in disaster-stricken areas.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
Received: 9 June 2025  
Revised: 30 June 2025  
Accepted: 7 July 2025  
Published: 8 July 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON].  
Deep Learning-Based Detection and Assessment of Road Damage Caused by Disaster with Satellite Imagery.  
_Appl. Sci._**2025**, _15_, 7669. [[https://doi.org/10.3390/appr5147669](https://doi.org/10.3390/appr5147669)]([https://doi.org/10.3390/appr5147669](https://doi.org/10.3390/appr5147669))  
**Copyright:** 02025 by the authors.  
Unespace MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Results and Discussion
### Discussion on the Analytical Features of Thermodynamic Modeling and XRD  
As previously discussed, the saturation development of the investigated minerals that was revealed by the thermodynamic modeling showed good consistency with the XRD characterizations, thereby confirming the correctness of the model. Comparatively, the thermodynamic modeling provided direct information about the chemical states of the compositional minerals in the cement-stabilized soils, while the XRD provided indirect information about the changes in the diffraction intensities, which were related to the mineral mass fractions within the system. In addition, the thermodynamic modeling made up for the deficiencies in the XRD technique for mineral quantity characterization. For instance, this study was unable to characterize the development of the pozzolanic products from the PNS doses using XRD and the intensity changes that related to the effects of JA on AFt formation were not significant enough. Nevertheless, unexpected minerals (e.g., tilleyite) could be identified more easily using the XRD technique but could be neglected by the thermodynamic modeling due to the insufficiently comprehensive investigation into the aqueous species within the pore solution.
### Case Study No. 3: OpenPLC Linked with Node-RED Acting as a Software IIoT Gateway  
In the third case study (Figure 24), we will again use the free OpenPLC tool. However, let us imagine a situation where we want to use OpenPLC to control a system that can be remote (accessible via a local network or Internet). OpenPLC only supports the old protocol Modbus, but we want to access this remote system via the modern OPC UA standard for security reasons. Network elements are used for this - for example IIoT gateways, which, in simple terms, provide translation of Modbus messages into the OPC UA address space (and vice versa). In our case study, we will show a software implementation of an IIoT gateway using Node-RED, which can be implemented, for example, using a Raspberry Pi microcomputer. Thus, Node-RED will act as an OPC UA server and at the same time as a Modbus client. Factory I/O will be the OPC UA client and OpenPLC runtime will be the Modbus server.  
Of course, such communication cannot be considered as real-time, so such a solution is only suitable for non-time-critical systems (e.g., in a smart home). The control of systems over a network, where substantial delays can occur, is the subject of a special area of control theory focusing on networked control.  
For simplicity and greater clarity, we have chosen only a fragment of the production event system for demonstration (Figure 25). It is a simple system that contains parts from both case studies. In the case study, we will use OpenPLC, which will communicate using Modbus. Then the Modbus data will be converted using Node-RED to communicate via OPC UA to Factory I/O.  
Figure 23: Dashboard in Node-RED (cloud).  
Figure 24: OpenPLC linked with Node-RED acting as a software IIoT gateway.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
### Typical EMG Signals of the Right Abdominal Muscle  
To develop an efficient algorithm of EMG signal processing, the frequency characteristics of the registered signals were first calculated. Raw signals contain a high level of low-frequency disturbances due to the very high impedance of the front analog circuit. This part of the signal's spectrum has to be removed first using high or band-pass filtering. The typical signals and their spectral characteristics are demonstrated in Figures 1 and 2.