esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation
Supported boundary conditions are constant velocity, constant pressure, and bounce back according to [27]. In addition to the optimisations mentioned above, we also applied a few low-level optimisations: we used the structure-of-arrays instead of the array-of-structures data layout, minimised the number of memory operations and placed them in a non-divergent code to allow coalescing, and used numeric constants reducing the number of floating-point arithmetic operations. These optimisations were applied after analysis of the generated GPU assembly. Moreover, we resigned from encapsulating functionalities inside Python classes due to a small drop in performance. Additionally, the whole code (except auxiliary functions) is placed in a single source code file which simplified the management of memory allocation and kernel generation. The complete code contains slightly more than 1000 source lines of code, including geometry generation and storage of results. For convenience, we also allocate memory for values of velocity \(\mathbf{v}\) and density \(\rho\), although these are not used during computations but only for data initialisation, storage of results, and visualisation. To allow run-time generation of images illustrating velocity fields, we also allocate an additional field for image memory, although it can be removed when not used. The source code structure contains a single, main library file _tilb.py_, which is then used in separate scripts responsible for creating and running simulations. The file _tilb.py_ provides a collection of functions that allow allocation of memory and perform computations according to Algorithm 1. These functions must be called in the correct order to properly initialise internal library data structures that are then needed to create Taichi fields (allocate memory) and generate computational kernels. The typical sequence is the following:  
1. Set the selected data type (single- or double-precision), data layout, and geometry resolution;
2. Create Taichi fields containing PDFs and auxiliary data--this requires information from the previous step;
3. Set required values of node types--solid nodes can be left in an uninitialised state which can reduce the memory usage when sparse data layouts are used;
4. Set initial \(\rho\), \(\mathbf{v}\), compute \(\omega\), and initialise PDFs to local equilibrium;
5. Generate a kernel for stream and collide--this step is performed automatically during the first kernel call and requires information from the first step and about geometry sparsity to allow optimisations;
6. Compute a required number of time steps with optional saves of simulation state.
## 1 Introduction
This is conducted in Section 2 of this paper, showing three examples of quantitative characterizations of active fires, comparing BIRD and FireBIRD data with the data of IR sensors on operational polar orbiting satellites acquired nearly simultaneously. Further objectives of the paper were as follows:  
1. To illustrate the growing role of new geostationary IR sensors in fire observation;
2. To simulate the fire recognition potential of the upcoming Meteosat Third Generation-Imager (MTG-I);
3. To predict the degree of synergy, i.e., the possible improved quality of the observation and quantitative evaluation of fires, especially in their diurnal cycle, if data from respective geostationary IR sensors, polar orbiting IR sensors and FireBIRD-type IR sensors are combined. These three issues, i.e., items (i)-(iii), are the main content of Sections 3-5 of this paper, respectively. Selected Application Examples--Using Data of BIRD, FireBIRD and Operational Polar Orbiting Satellite IR Sensors
# Dynamic Responses of Semi-Flexible Pavements Used for the Autonomous Rail Rapid Transit
###### Abstract  
The application of a semi-flexible pavement (SFP) is an effective solution to solve the rutting problems of the autonomous rail rapid transit (ART) system. The service environment of an ART pavement is significantly different from that of the conventional pavement due to the large axle load and high tire pressure of ART vehicles. A test section was constructed in the Zhuzhou ART system and a tire-pavement coupling FE model was built to explore the distribution features of the dynamic responses as well as to optimize the material and structural design. The tire-pavement coupling model was firstly verified by the field test data and then, utilizing the validated model, the parameter study was performed to analyze the influence of the vehicle operating state and pavement conditions. The simulation results show that the transverse tensile strain at the bottom of the SFP layer is dominant for the fatigue cracking of the pavement. Properly reducing the tire pressure can effectively improve the tensile environment at the bottom of the SFP layer. The action of the braking force may cause significant longitudinal tensile strains at the surface of the SFP layer and lead to transverse cracking of the semi-flexible ART pavement. The interlayer bonding between the SFP layer and the asphalt layer has significant influence on the amplitude and distribution of tensile stress at the bottom of the SFP layer. Moreover, to optimize the tensile environment of the semi-flexible ART pavement, the thickness of the SFP layer and the asphalt concrete layer cannot differ too much under the premise of meeting the requirements of rutting resistance performance.  
autonomous rail rapid transit; semi-flexible pavement; test section; FE model; dynamic responses +
Footnote †: journal: Applied Sciences
## 4 Results
### Markov Transition Field  
The MTF-encoded method utilizes the previous initial state to provide the probability of the next state. Figure 29 (left) illustrates an accuracy graph of the MTF CNN model. The MTF method does not perform well on our dataset; it solely achieved an accuracy of 49% in the training and 42% in the testing. It also illustrates a loss graph of the MTF-CNN mode. The error is very high in the model, as we can observe the loss is very high at the initial stage with a value of 2.8. It declined to 1.22 in the training and 1.28 in the testing, as shown in Figure 29 (middle). Although this decrease in error is smooth with no generalization gap, the final loss value is not significantly low. This high error value leads to a decrease in the accuracy of each class in the prediction.  
Figure 28: Accuracy (**left**), loss (**middle**) and confusion matrix (**right**) of the GADF CNN model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Accuracy Value of Classes**} \\ \hline
**Classes** & **GASF** & **GADF** \\ \hline
1 & 0.83 & 0.77 \\
2 & 0.58 & 0.67 \\
3 & 0.74 & 0.78 \\
4 & 0.61 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Class accuracy value of GAF CNN network.  
Figure 27: Model accuracy (**left**), loss (**middle**), and confusion matrix (**right**) of the GASF CNN model.  
One of the reasons the accuracy is the lowest in the encoding method is that the obtained encoding image has significantly fewer features. Most of the image area comprises white and black spaces, and the CNN is susceptible to this type of area in the input data, as there will be no learning. Increasing the size of the dataset can help overcome this challenge. The MTF is suitable for a highly dynamic system with more changes from one initial state to another. The MTF keeps the total value of a row equivalent to 1 only; therefore, in our case, we have all the features on the diagonal. Figure 29 (right) also illustrates the confusion matrix of the MTF-CNN model. The model did not achieve high accuracy in any class in the matrix.
## 1 Introduction  
Elevated temperatures and chloride ion erosion are affecting the durability of concrete structures [1]. Elevated temperatures cause the micro-structure of concrete to deteriorate and decrease impermeability. It is necessary to evaluate the chloride ion penetration resistance of structural concrete after a disaster.  
The permeability of chloride ions into concrete is mainly related to the pore structure and cracks [2]. Further, the chloride ion penetration of concrete increases with the increase in the water/cement ratio [3]. Adding fly ash and slag to concrete could improve pore structure and resistance to chloride ion penetration [4]. [PERSON] [5] studied the effect of crack width on the chloride ion diffusion coefficient. [PERSON] considered crack tortuosity and orientation and suggested that effective crack width and effective crack density were correlated with the chloride ion diffusivity of cracked concrete [6]. The chloride ion diffusion coefficient, under the action of elevated temperatures, gradually increased with the increase in heating temperature. Additionally, thermal damage reduced the penetration resistance of concrete [7, 8].  
The load changes the size of concrete pores, and the size of concrete pores, in turn, affects concrete permeability. The chloride ion content decreases as the compressive stress ratio increases and gradually increases after exceeding the critical stress level [9; 10]. Corrosion tests under compressive stress found that concrete's threshold stress ratio at room temperature is about 0.3 [11]. Concrete mixed with glass fiber has a higher resistance to chloride ion permeability than ordinary concrete [12]. Current studies have found that fibers' diameter and volume fraction influences the diffusion of chloride ions in concrete [13]. Incorporating basalt fiber could improve the toughness and impermeability of concrete to a certain extent. Basalt fiber has excellent resistance to alkaline erosion and is suitable for reinforcing the alkaline concrete matrix [14]. [PERSON] et al. [15] and [PERSON] et al. [16] conducted experimental studies on the high-temperature performance of BFRC and found that the basalt fiber can improve the performance of concrete exposed to elevated temperatures. However, the law of chloride ion penetration under the thermal-load coupling effect of BFRC has yet to be studied.  
To clarify the permeability of BFRC under long-term axial load after elevated temperatures, a chloride ion penetration test of BFRC specimen under axial compression was conducted. [PERSON]'s second law is widely used to analyze the diffusion law of chloride ions in concrete [17]. A chloride ion diffusion model that considers the influences of different factors was developed by introducing parameters into the formula [18; 19]. Based on the results, a calculation model for the chloride ion diffusion coefficient of BFRC under the coupling effect of elevated temperatures and loads was proposed to provide a basis for evaluating the durability and service life of BFRC.
# Assessment of a Location-Based Mobile Augmented-Reality Game by Adult Users with the ARCS Model
## 2 Materials and Methods  
To achieve the aim and objectives of the project, quantitative research methods were applied for data collection in order to assess the educational activity that was carried out and was focused on the location-based MAR game Ingress. With the training session, participants familiarized themselves with the game menu, learned to follow the vocal instructions, to recognize symbols and icons of the game and learned the game's (most commonly used) procedures (selecting weapons, recognizing triangles, conquering portals, etc.). The educational research project involved 45 users in Greece, aged 20 to 60 years old. The participants were chosen on the basis of four characteristics: (a) they had been using an Android smartphone already, (b) they understood written and spoken English, (c) they could understand written or oral instructions the game provides them and (d) they were not familiar with Ingress at all. Of the participants, 12 were graduates of secondary education, 4 university students, 18 university graduates, 9 held a Master's degree and 2 held a doctoral degree. The participants were divided into three age groups: 20-35, 36-51 and >52 (Table 1) with 15 persons per age group, and they were trained to learn the basics of Ingress for two hours and then played Ingress for about one hour. Before and after playing the game, a questionnaire-based survey was conducted using the ARCS model to find out the level of learners' motivation in relation to the parameters measuring attention (A), relevance (R), confidence (C) and satisfaction (S) of the participants for playing Ingress. The survey questionnaire was composed of 31 closed questions, and was developed specifically for this research, allowing for answers based on a 5-point \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Age Group** & **20–35** & **36–51** & **52–60** & **20–60** \\ \hline n & 15 & 15 & 15 & 45 \\ \hline mean & 28.6 & 44.4 & 56 & 43 \\ \hline s.d. & 4.71 & 4.54 & 2.14 & 11.93 \\ \hline median & 30 & 44 & 56 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical description of participant age groups. Likert scale (with 1 being strong disagreement and 5 strong agreement). Each question corresponds to a certain category of [PERSON]'s ARCS model, as shown in Table 2.  
Data analysis employed quantitative approaches. The purpose of the quantitative data analysis was to measure how training of the game effects participants' learning motivation across the ARCS factors. To better visualize the relationships among statistical means and
# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 5 Use Case and Experimental Detail
### Use Case Detail and Semantic Data Applications Ontologies
#### 5.1.1 Affective Stress Care (ASC) Semantic Data Application  
The ASC is a WoO enabled application in which the various symptoms (i.e., sadness, grief, anger, high blood pressure) of youth have been monitored from physiological sensors and based on his/her current state the recommendation services are provided. This application uses various types of VOs, CVOs, and services. In order to provide quality services, we designed the contextual data quality analytics mechanism for the VOs and CVOs data respectively.  
The WoO application model for the ASC contains various VOs and CVOs. In the list of CVOs and VOs, Stress_monitoring_CVO data is used to monitor the stress symptoms such as sadness, grief, boring, scary. The Vital_health_CVO data is used to check vital health status individually. The vital health status includes body temperature, blood pressure, heart rate, respiratory rate, etc. ASC WoO model contains various VOs such as VO_PID receives the value for a person's information such as weight, location, age, etc.; VO_BVP receive the data values from the blood pressure sensors, VO_skin_temp receive the value from the skin temperature sensor and VO_heart_rate receive the data of person from the heart rate measurement sensor. The ontology model for the ASC applications VOs and CVOs is shown in Figure 17 and the excerpt view of the OWL/XML code of Stress_monitoring_CVO is shown in Figure 18.  
Figure 17: Affective Stress Care (ASC) application Virtual Objects (Vos) and Composite Virtual Objects (CVOs) ontology model.
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 4 Discussion
Fine tuning of the model should include environmental variables and agricultural management practices because they can potentially improve the accuracy of predictive models. Additionally, this study considered a two-year single site study, while the ideal condition to refine the modeling should involve multiple seasons and several commercial fields. Future research on RS tools for crop monitoring should also aim at near real-time diagnostics of crop status to support local interventions and optimize agricultural production. The advantages of RS applications for agricultural systems involve advancing on integrating machinery operation, according to the field conditions, which can be assessed with greater accuracy than traditional methods (low-scale and labor-intensive).
## 5 Comparison of the Pseudo-Dynamic Test and Non-Linear Dynamic Analysis Results
### Non-Linear Dynamic Analysis Overview
the existing frame (\(=\delta_{yst}/\delta_{udp}\)), \(\delta_{yst}\) is the yield displacement of the existing structure, \(\delta_{udp}\) is the yield displacement of the damper, \(C_{yst}\) is the yield strength of the existing structure expressed in the form of the shear force coefficient (\(=Q_{yst}/W\)), \(\beta\) is the energy dissipation ratio of the damper, \(\alpha_{c}\) is the ratio of the yield strength of the damper (\(C_{ydp}\)) to the strength of the existing structure (\(C_{yst}\)) or strength ratio of the damper (\(=C_{ydp}/C_{yst}\)), and \(\alpha_{k}\) is the ratio of the yield point stiffness of the damper (\(K_{dp}\)) to the yield point stiffness of the existing structure (\(K_{yst}\)) or stiffness ratio of the damper (\(=K_{dp}/K_{yst}\)). In this study, the target seismic performance after seismic retrofitting (\(E_{R_{o}}\)) was set based on Equation (21)--proposed by [PERSON] and [PERSON] [56] after researching the correlations between various seismic acceleration levels and the basic performance index for existing R/C buildings. The target performance (\(E_{R_{o}}=0.52\)) was the level of performance that led to less than moderate seismic damage (life safety) under the aforementioned seismic acceleration of \(\alpha=0.2\)g(\(200\) cm/\(s^{2}\)). \[E_{R_{o}}=2.7\alpha-0.02 \tag{21}\]  
Here, \(\alpha\) is the level of input ground motion divided by gravitational acceleration (g). Meanwhile, a non-linear static analysis was conducted to calculate the basic performance index of the target building before retrofitting (\(E_{o}\)), i.e., the yield strength (\(C_{g}\)), to determine the seismic reinforcement volume for the building. Figure 34 shows the load-displacement relationship of the existing target R/C building based on the non-linear static analysis. The yield strength and displacement in the figure were defined based on [PERSON]'s definition [57], which is the most realistic definition available for the yield displacement for RC structures. The yield displacement of the equivalent elastoplastic system with a reduced stiffness is the secant stiffness at 75% of the ultimate lateral load of the system. Table 16 shows the yield displacement, strength at the time of yielding (yield strength), and basic performance index of the target building calculated based on the yield strength. The structure and material properties of the LLFD were the same as the results shown in the specimen overview in Section 3 and those applied in the analysis model in Section 5. Based  
Figure 34: Load–displacement relations based on the non-linear static analysis of non-strengthened control buildings. on these considerations, the required strength and quantity of the LLFD V-bracing system for the target building were finally determined, as listed in Table 17.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 2 Background  
### Resnet Based ECG Classification  
With the development of global aging, cardiovascular disease, as one of the chronic diseases, has the characteristics of being sudden, hidden, and fatal [29]. Therefore, strengthening the early screening and long-term monitoring technology of cardiovascular disease has become an urgent problem. With the development of artificial intelligence and the popularization of ECG, there are more than 300 million ECG records every year in the world [30]. Therefore, the importance of automatic classification technology for ECG data is increasingly prominent. At the same time, the popularization of wearable health monitoring equipment brings huge data analysis tasks to medical staff. In this case, automated ECG signal classification technology provides great convenience for medical diagnosis. Although the development of early machine learning algorithms has improved the accuracy of automatic analysis of ECG signals, they require a large number of data preprocessing and manual feature engineering to make the processed data and feature sets suitable for interpreting ECG signals [31; 32; 33; 34]. It increases the complexity of the automatic data analysis process. In recent years, deep learning has been the preferred method for ECG classification [35; 36; 37; 38; 39].  
As a kind of deep learning network model, the residual network is mainly proposed to solve the problem of network degradation caused by the increase in network depth; that is to say, the residual network can ensure: first, that the network performance can be continuously improved; second, that when the optimal performance is achieved, the redundant network layer does identity mapping. Based on the above properties, the structure has been applied to various fields, including the field of ECG signal processing. For example, [PERSON] et al. proposed a new model framework by combining the residual network with the extrusion and excitation block and the bidirectional short and long memory network [40]. The proposed framework can enable the direct diagnosis of arrhythmia types in clinical trials based on the accurate detection of the minority class. [PERSON] et al. proposed a new heartbeat classification method based on ResNet-101 hybrid time-frequency analysis and migration learning [41]. However, there are few applications in the ECG signal quality assessment task at present. In this paper, we apply the residual network to the ECG signal quality assessment task and optimize the network structure to make it more suitable for one-dimensional ECG signal processing tasks.
## 4 Methodology
### Analysis Strategies
#### 4.6.3 Scenario #3  
This scenario involves resetting the TT&C bias, its covariance, and solar radiation pressure coefficient, Cr, to their nominal values along with perturbed initial states (see Table 1). This disentangles the scenario from the operation solution in terms of a priori initial conditions.  
Since TT&C measurements are single-site measurements, the accuracy of the TDRS orbit is heavily dependent on the accuracy of these measurements. If any bias in these measurements is not accounted for, it can significantly affect the accuracy of the TDRS orbit. The scenario assumed that the TT&C bias was unknown and estimated its value along with other parameters. However, to estimate TT&C bias, it was necessary to have knowledge of the a priori covariance of the TT&C bias.  
To generate a cluster of a priori covariances for the TT&C basis, Monte-Carlo analyses were conducted. The covariances were systematically increased from zero meters to infinite values. For each specific a priori covariance, the TT&C bias was estimated while reconstructing the TDRS orbit solely using TT&C measurements.  
When the covariance is set to zero, the TT&C bias estimation becomes essentially ineffective, resulting in the estimated value being equal to the initial value of TT&C bias. On the other hand, an infinite covariance causes the TT&C bias estimation to be unbounded and potentially leads to an overestimation of the TT&C bias. From here onward in this manuscript, solutions with zero meters covariance are referred to as \"constrained\" solutions,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **A Priori States** & **A Priori TT\&C Bias** & **A Priori TT\&C Bias** & **A Priori Cr** \\ \hline \#1 & Operational Values & Operational Values & Operational Values & Operational Values & Operational Values \\ \#2 & Operational Values & Operational Values & Operational Values & Operational Values \\  & \(\pm\)Offset & & & \\ \#3 & Operational Values & 0 m & Varying & 1.0 \\  & \(\pm\)Offset & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approach for a priori initial conditions to reconstruct TDRS orbit in various scenarios.  
while solutions with infinite covariance are referred to as \"unconstrained\" solutions. The objective of this investigation was to analyze the TDRS orbit error's evolution based on the selection of initial conditions and to identify the best and worst-case scenarios in situations where initial conditions are poorly known (see Section 5.1.3).