esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 4 Results  
### Comparison of Three Radiometric Calibration Results from Different Targets  
The radiometric calibration coefficients of ZY3-02 MUX were determined through the least-squares method, linearly fitting band-specific TOA spectral radiance with DNs corresponding to the target regions by using Equation (4):  
\[L_{i}=Gain_{i}\times DN_{i}+Bias_{i} \tag{4}\]  
where \(L_{i}\) is the band-specific TOA spectral radiance with unit W\(\cdot\)m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)um\({}^{-1}\), \(DN_{i}\) is the digital number of the satellite imagery in band \(i\), and \(Gain_{i}\) and \(Bias_{i}\) are the radiometric calibration coefficients \(Gain\) and \(Bias\) in band \(i\).  
There would be three radiometric calibration results from different targets. The first radiometric calibration coefficient (coefficient A, as shown in Figure 9) was determined by permanent artificial targets, which just covered three gray-scale reflectances of 7%, 18%, and 56% in 400-1000 nm.  
The second coefficient (coefficient B, as shown in Figure 10) was determined by the four radiometric calibration tarps with four nominal reflectances of 5%, 20%, 40%, and 60%, and two color-scaled radiometric tarps of blue and red.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Date** & \begin{tabular}{c} **Overpass** \\ **Time (UTC)** \\ \end{tabular} & \begin{tabular}{c} **Solar** \\ **Zenith** \\ \end{tabular} & \begin{tabular}{c} **Solar** \\ **Azimuth** \\ \end{tabular} & \
\begin{tabular}{c} **Viewing** \\ **Zenith** \\ \end{tabular} &
\begin{tabular}{c} **Viewing** \\ **Azimuth** \\ \end{tabular} \\ \hline Baotou & 20 July 2016 & 03:45:08 & 34.687 & 140.411 & 1.71 & 47.459 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geometric conditions of ZY3-02 for radiometric calibration.  
The last coefficient (coefficient C, as shown in Figure 11) was determined by combining permanent artificial targets and radiometric calibration tarps, which means that there would be seven gray-scale targets and two color-scaled targets. All the calibration results show that linear correlations between DN and TOA radiances of ZY3-02 MUX are very high, with a correlation coefficient \(R^{2}\) value of up to 0.99 for each band.  
Figure 10: Calibration result of the radiometric calibration tarps (coefficient B).  
Figure 9: Calibration result of the gray-scale permanent artificial targets (coefficient A).
## 1 Introduction
(2016) developed a method similar to the concept of sidereal filtering to remove both the SICB and ground multipath effects, and the feasibility of this method was verified by the improved position solutions. However, the correction values can only be obtained station-by-station. In other words, this method is station dependent, which limits its application. Therefore, BDS-2 GEO satellites are excluded from our SICB modeling analysis. Since obvious SICB variations were identified for BDS-2 satellites, many studies have focused on the SICB for BDS-3S satellites since their real tracking data became available ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). It was concluded that the marginal SICB variations might be negligible for the two legacy signals, as well as the three new navigation signals of all available BDS-3S satellites. The reason for the remarkable SICB mitigation is that the voltage standing-wave ratios of power divider networks are cut down for the BDS-3S satellites ([PERSON] et al., 2016). All the above results were obtained based on all-in-view antennas, the code observations that suffered from the severe ground multipath effects. SICB variations with a small size could be easily concealed. To gain more insight into the SICB of BDS-3S satellites, a 40-m dish antenna was used for analysis by [PERSON] et al. (2016), so that the negative effects of the ground multipath errors can be removed. The results indicated that SICB variations with a level of about 0.1 m still existed for BDS-3S satellites. Most importantly, strong elevation-dependent SICB variations were still observed for the code observations from BDS-3S IGSO satellites on B1 and B3 frequencies. Therefore, an effective SICB correction model for the legacy signals of BDS-3S IGSO satellites should be established. In this study, an improved SICB piecewise linear correction model is proposed, taking into account the consistency of MP combinations. We use a denser elevation node separation of 1\({}^{\circ}\) to describe more precise SICB variations so that our correction model can also be applied to BDS-3S satellites with much smaller SICB variations. We start with the modeling method for the SICB (Section 2). Subsequently, we show and analyze our results (Section 3). Finally, the conclusions are provided (Section 4).
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 2 Theory and Methods
### Experimental Setup: Long Pulse Thermography System  
The synchronized infrared image acquisition system that has been employed in this work is schematically illustrated in Figure 7. The infrared imager in the system is an uncooled InfraTec VarioCAM(r) HD980 (made in Germany) with a detector resolution of \(1024\times 768\) and a maximum acquisition frequency of 30 Hz. The excitation source is composed of four 400 \(W\) halogen lamps. The controller controls the synchronization of excitation and acquisition. During the process of the experiment, the halogen lamps are triggered by software to generate pulsed radiations toward the test object. The infrared camera is simultaneously triggered to start recording as soon as the pulsed radiation is initiated.
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 2 Updated Atomium PPP Algorithms
### Atomium Integer PPP
The code and carrier phase IF measurements are put in the least square to compute the real-valued \(N_{1}\) per track, and the integer \(N_{1}\) is fixed again by the GNSS bootstrapping method. The \(N_{1}\) ambiguities are also partially fixed as in step 1, the measurements with un-solved ambiguities are excluded from the next computation. 3. In the last step, since the integer ambiguities of the carrier phase IF combination have already been resolved, the carrier phase IF measurement is not ambiguous anymore and is rewritten again as  
\[L_{IF}-\lambda_{NL}\Big{(}N_{1}+\Big{(}\frac{\lambda_{WL}}{\lambda_{2}}\Big{)} N_{WL}\Big{)}-\mathrm{W}-\mathrm{e}=\rho+T^{R}+\mathrm{zpd}+\varepsilon_{L}, \tag{10}\]  
where the variables on the left side are already solved, the ones on the right side are to be solved, including receiver position (in \(\rho\)), receiver clock error, and troposphere error. The wind-up corrections are computed from step 2, together with the ambiguity fixing. At last, the carrier phase IF measurements are put in the least square without code IF measurements to solve the final solutions. If the WRB estimation is coherent during the whole period and the ambiguities are resolved correctly, the values of the discontinuities at the daily boundaries for the integer PPP clock solutions are the integer multiple of the GPS \(\lambda_{NL}\). Hence, the clock solutions for multiple days can be easily aligned with minimal error introduced. Attention also needs to be paid to the discontinuity inside the daily batch. If it is caused by any a hardware change in the receiver system, the integer feature of discontinuity is lost because the WRB may endure an integer cycle estimation error in Equation (7) after the discontinuity, and bias the solution in Equation (10) in terms of an integer multiple of \(\lambda_{NL}(\lambda_{WL}/\lambda_{2})N_{WL}\).
## 3 Results
### Comparative Assessment of Molecular Versus Phenotypic Diagnostic Methods  
Comparison with phenotypic DST revealed that molecular diagnosis relying solely on _pnCA_ sequencing exhibited a sensitivity of 48.3% with an accuracy of 69.8%. However, the detection of flanking region mutations in conjunction with _pnCA_ (_pnCA_ + FR) improved the diagnostic sensitivity to 83.8% with a concomitant increase in accuracy to 90.5%. Notably, with the identification of resistance-conferring novel mutations by WGS analysis, the sensitivity of molecular diagnosis was enhanced to 100%, and ultimately, 100% accuracy was achieved in the molecular diagnostic approach (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Genes**} & \multicolumn{2}{c}{**PZAR Isolates**} & \multicolumn{2}{c}{**PZAS Isolates**} & \multirow{2}{*}{**Sensitivity\%**} & \multirow{2}{*}{**Specificity\%**} & \multirow{2}{*}{**Accuracy\%**} \\  & \multicolumn{2}{c}{**n = 31**} & & & & & \\ \cline{2-2} \cline{5-8}  & **Non-** & **WT or** & **Non-** & **WT or** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** \\  & **Synonymous** & **Synonymous** & **Synonymous** & **Synonymous** & & & \\  & **Mutations (\%)** & **Mutations (\%)** & **Mutations (\%)** & **Mutations (\%)** & & & \\ \hline \hline \multirow{2}{*}{_pnCA_\({}^{\text{c}}\)} & 15 & 16 & 0 & 22 & 48.3 & 100 & 69.8 \\  & (48.3) & (51.6) & (0.0) & (100) & (30.1–66.9) & (84.5–100) & (55.6–81.6) \\ \hline \multirow{2}{*}{UFR} & 5 & 26 & 0 & 22 & 16.1 & 100 & 50.9 \\  & (16.1) & (83.8) & (0.0) & (100) & (5.45–33.7) & (84.5–100) & (36.8–64.9) \\ \hline \multirow{2}{*}{_pnCA_ + & 6 & 25 & 0 & 22 & 19.3 & 100 & 52.8 \\  & (19.3) & (80.6) & (0.0) & (100) & (7.45–37.4) & (84.5–100) & (38.6–66.7) \\ \hline \multirow{2}{*}{_pnCA_ + FR_\({}^{\text{f}}\)} & 5 & 5 & 0 & 22 & 83.8 & 100 & 90.5 \\  & (16.1) & (0.0) & (100) & (66.2–94.5) & (84.5–100) & (79.3–96.8) \\ \hline \multirow{2}{*}{_clpC1_} & 5 & 26 & 0 & 22 & 16.1 & 100 & 50.9 \\  & (16.1) & (83.8) & (0.0) & (100) & (5.45–33.7) & (84.5–100) & (36.8–64.9) \\ \hline \multirow{2}{*}{Total} & 31 & 0 & 0 & 22 & 100 & 100 & 100 \\  & (100) & (0.0) & (0.0) & (100) & (88.7–100) & (84.5–100) & (93.2–100) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative assessment of molecular versus phenotypic diagnostic susceptibility testing.  
Figure 3: proteolytic activity of the ClpC1P1P2 complex in response to POA treatment. ***** \(p\) \(<\) 0.00001; ns, no significant difference, \(p\) \(>\) 0.05. The D-value, representing the change in fluorescence intensity, was calculated by subtracting the initial fluorescence intensity from the fluorescence intensities of subsequent detections.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 4 Methods
### Overview of Uil-Hgan  
In UIL-HGAN, we first embed the nodes and different types of user-to-user edges to the latent space and generate the structural information nodes. Secondly, we use multiple attention layers to represent the user nodes by aggregating the feature nodes, the category nodes, the structural embedding nodes, the user profile nodes, the user-generated content nodes, and the structural information nodes. Finally, we perform a multi-layer perceptron to train the model and predict user identity linkage. The total loss function denotes as follow:  
\[L=L_{\text{SE}}+L_{\text{UIL}}. \tag{22}\]  
The model training process is shown in Algorithm 1, and the model parameters update by using Adam optimizer [29] to minimize the loss function. The model predicting process is shown in Algorithm 2.  
```
0: Two heterogeneous social network graph \(\mathcal{G}_{s},\mathcal{G}_{t}\), user pair \(v_{i},v_{j}\) where \(v_{i}\in\mathcal{G}_{s},v_{j}\in\mathcal{G}_{t}\)
0: The predicted value \(\hat{y}_{ij}\) of user identity linkage.
1: Calculate the aggregated vector \(h_{i}\) of \(v_{i}\) by multiple attention layers in \(\mathcal{G}_{s}\);
2: Calculate the aggregated vector \(h_{j}\) of \(v_{j}\) by multiple attention layers in \(\mathcal{G}_{t}\);
3: Calculate the predicted value \(\hat{y}_{ij}\) of user identity linkage by the multi-layer perceptron;
4:return\(\hat{y}_{ij}\)
```  
**Algorithm 1** Model training
## 3 Proposed Method
### Information Layer  
Data will be sorted, organized, and filtered accordingly to transform into meaningful information in an organized and retrievable form. This information will be stored as datasets on a distributed file system HBase to serve for distributed and parallel processing in a big data environment. Apache HBase is a distributed column-oriented NoSQL database built on top of HDFS. The system requires the ability to handle batch and real-time data. Consequently, we use Apache Spark for both the batch and real-time data processing. Spark has emerged as the next-generation big-data-processing engine because it works with data in memory that are faster and better able to support a variety of compute-intensive tasks. Spark Core processes the batch data from HDFS to organize content according to their semantics and to create and maintain the knowledge base (HBase) as an organizational memory. Spark Streaming involves mapping continual input of the data from Kafka into real-time knowledge views. Every single event is sent as a message from Kafka to the Spark Streaming. Spark Streaming produces a stream and executes window-based operations on them.  
The data collected from the hospital management system consist of many tables and many data fields. Depending on the goals of the medical diagnostic support systems, the appropriate data should be extracted. The historical datasets collected from hospitals will be used for the knowledge generation process, which is the input to the knowledge layer. These data are authentic, and the diagnostic results are given by the doctors with high professional confidence to help the labeling process in building knowledge models more effectively.  
Figure 6: Proposed architecture for healthcare knowledge management systems.
## 5 Surface Subsidence Prediction Method for UCG
### Basic Roof Subsidence Space Calculate
#### 5.1.1 Combustion Cavity Surrounding Rock High-Temperature Burnt Characteristics  
Based on the above analysis, the main factor affecting the surface subsidence characteristics of UCG is the high-temperature combustion of surrounding rock in the combustion cavity. Therefore, to calculate the basic roof subsidence space, the temperature field of the surrounding rock should be calculated first, and the variation characteristics of surrounding rock mechanical parameters should be calculated according to the results in Section 2.  
Figure 6: Stress distribution of rock strata in various conditions (Pa). (a) norm mining. (b) thermal stress. (c) rock burnt. (d) synergistic effect.  
Assuming that a stable temperature can be reached in the combustion cavity after gasification, and the temperature propagation in the rock strata conforms to the Fourier heat conduction law, and the distribution of temperature in the rock strata during gasification is as follows:  
\[T(x,t)=(T_{s}-T_{0})erfc(\frac{x}{2\sqrt{at}})+T_{0}\quad(x>0) \tag{1}\]  
where \(x\) is the distance within the strata from the combustion cavity boundary, \(t\) is the gasification time, \(T_{s}\) is the gasification surface temperature, \(T_{0}\) is the original temperature in strata, and \(a\) is the thermal diffusion coefficient \(a=\lambda/\rho c\).  
According to Equation (1), to calculate the temperature field distribution in the roof and floor, the roof and floor are divided into \(n\) sections on average to calculate the average temperature \(T_{i}\). According to the properties with a temperature change of Young's modulus in Section 2, the average elastic modulus \(E_{i}\) of each section in the roof and floor is calculated, where the elastic modulus in the roof and floor is \(E_{ri}\) and \(E_{fi}\), respectively.  
According to the variation law of coal mechanical properties with temperature, the coal pillar loses its carrying capacity when the temperature exceeds 200 \({}^{\circ}\)C, and the width \(Fl\) of the failed coal pillar can be calculated from Equation (1). Assuming that the width of the original isolated coal pillar is \(cl\) and the width of the combustion cavity is \(gl\), the width of the effective coal pillar is \(cl^{\prime}=cl-2*Fl\) and the width of the effective combustion cavity is \(gl^{\prime}=gl+2*Fl\).
## 3 Methodology of FSACM  
In this study, the FSACM (Feature Selection and Attack Classification Method) is proposed in order to determine attacks dynamically in WLANs quickly and with high accuracy. In the first step of the proposed method, the feature selection process is performed. In this context, an approach called FSAP (Feature Selection Approach) has been proposed for feature selection. In the second stage, a technique called SABADT is proposed for the detection of attacks. The block diagram of this proposed method, consisting of 5 different blocks, is given in Figure 2.  
* Choosing and analyzing the dataset.
* Identifying important features and organizing them into groups.
* Detecting anomaly-based and signature-based attacks.
* Using the training and classification for identifying attacks.
* Classification of attacks according to their behavior.  
The basis of this architecture was proposed in our previous study ([PERSON] et al.). Block 5 has been added within the scope of this study, and the methods in other blocks have been updated according to our previous study. In the architecture given in Figure 2 and block numbers are given on the figure. Datasets and analysis methods from the literature are used in Block 1. In Block 2, a contribution is made within the scope of the study, and the FSAP approach is proposed for feature selection, which is the basis of the model. For detecting attacks in Block 3, a hybrid technique, which is a conjunction of signature-based and anomaly-based techniques, is presented for training and classification stages. Training as well as classification processes are performed in Block 4, using the proposed methodology and known machine learning algorithms from the literature. In Block 5, if an attack is detected in the network traffic, the behaviors of the attack are determined and the behaviors are grouped according to attack types.
# A Low-Autogenous-Shrinkage Alkali-Activated Slag and Fly Ash Concrete
###### Abstract  
Alkali-activated slag and fly ash (AASF) materials are emerging as promising alternatives to conventional Portland cement. Despite the superior mechanical properties of AASF materials, they are known to show large autogenous shrinkage, which hinders the wide application of these eco-friendly materials in infrastructure. To mitigate the autogenous shrinkage of AASF, two innovative autogenous-shrinkage-mitigating admixtures, superabsorbent polymers (SAPs) and metkaaolin (MK), are applied in this study. The results show that the incorporation of SAPs and MK significantly mitigates autogenous shrinkage and cracking potential of AASF paste and concrete. Moreover, the AASF concrete with SAPs and MK shows enhanced workability and tensile strength-to-compressive strength ratios. These results indicate that SAPs and MK are promising admixtures to make AASF concrete a high-performance alternative to Portland cement concrete in structural engineering.  
alkali-activated concrete; shrinkage; cracking; internal curing; metkaolin +
Footnote †: journal: Applied Sciences
## 7 Using Vehicles to Augment MEC Capabilities
The authors of [184] advocate leveraging resources of vehicles that are moving together at a low speed. They confer these idle resources to a resource pool to schedule task offloading to service vehicles in order to reduce task completion time. They utilize a min-max optimization model to solve it. Study [190] considers parked vehicles as edge computing nodes to perform different types of tasks in collaboration with cloud and MEC. To handle the PVs' erratic parking duration, a container orchestration framework based on the Kubernetes [140] platform is used. The authors formulate their model as a BIP problem to decrease the offloading cost and solve their problem, called EdgeGA, which is based on a genetic algorithm. The summary of the above-mentioned papers is presented in Table 7.
## 4 Results
### Qualitative Visualisation of the Detection Results  
This section presents a qualitative performance comparison between the proposed GLDD-YOLOv5 model and the baseline YOLOv5s model for defect detection. In the process environment, there are challenges such as the fact that the background (non-object parts) contains defect-like structures, there are hard-to-see defects on the lower parts of LPG cylinder surfaces, and the colours of the objects in the background and the object to be detected are very close to each other. In addition, there are likely to be changes in the ambient light level from time to time. The models were run for the defect detection task on test images simulating ambient conditions with different image processing techniques, and some of the results are shown in Figure 10. Figure 10a illustrates the defect detection results of the basic YOLOv5s model, while Figure 10b illustrates the defect detection results of the GLDD-YOLOv5 model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Input Size** **(pixel)** & **FPS** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **FLOPs (G)** & **File Size (MB)** \\ \hline RT-DETR & 640 \(\times\) 640 & 31.23 & 0.72 & 0.30 & 68.81 & 171.5 \\ \hline GLDD-YOLOv5 & 640 \(\times\) 640 & **163.9** & **0.806** & **0.34** & **8.4** & **10.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed model and the RT-DETR model.  
Figure 9: Normalised graphical representation of the impact of each module on the YOLO network.  
The proposed GLDD-YOLOv5 model showed relatively high confidence scores in the detection of small or less noticeable defects in low light, bright images, contrast, and complex background conditions. YOLOv5s failed to detect some defects (Figure 10, third and fifth columns) or gave a lower confidence score (Figure 10, second and fourth columns). Furthermore, the GLDD-YOLOv5 model has relatively high localisation accuracy in the bounding box placement around the detected defects (Figure 10, first, second, and fourth columns).  
This comparison reveals that the GLDD-YOLOv5 model outperforms the baseline YOLOv5s in defect detection under challenging conditions such as very small defect areas, different lighting conditions, similar backgrounds, and complex surfaces. This demonstrates the potential of GLDD-YOLOv5 to be applied when the accuracy and reliability of defect detection is essential.
## 5 Results and Discussion  
In this section, the performance of the proposed STAGCN model in predicting tool wear is evaluated on the PHM2010 dataset. To demonstrate the accuracy and reliability of the proposed model, eleven existing methods are selected as the main baselines, such as convolutional bidirectional LSTM (CBLSTM) [58], time-distributed ConvLSTM (TD-ConvLSTM) [59], bidirectional LSTM with a particle filter (BLSTM-PF) [60], integrated model based on a parallel convolutional neural network (PCNN), a CNN, deep residual networks (DRNs), LSTM (integrated model) [61], hybrid-information LSTM [15], a transformer-based neural network with self-attention (TBNN) [62], a CNN-transformer neural network (CTNN) [63], holistic-local LSTM (HLLSTM) [25], a hybrid model based on a deep convolutional neural network and stacked LSTM (DCNN-SLSTM) [64], a hybrid model based on a DCNN and multi-channel bidirectional LSTM (DCNN-McBiLSTM) [65], a hybrid model based on bidirectional LSTM, Performer, and residual-skip connections (BiLPReS) [66].
## 5 Experiment and Results
### Environment Details  
All models in this experiment were trained using the Ubuntu 18.04.6 LTS 64-bit operating system, which was configured with NVIDIA Tesla P100 PCIe 16 GB GPU, Intel(R) Xeon(R) Silver 4114 [EMAIL_ADDRESS] GHz and 126 GB memory, and trained using Python 3.8 and the Pytorch 1.12 framework. During the stage of testing the inference speed, we used the mobile graphics card MX350 and the Jetson TX1 developer kit to verify the efficiency of the models on devices with limited resources.