esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### Feature Extraction  
Different feature types were extracted from ZY-3 images, including spectral-based, texture-based, morphological-based, transform-based, edge-based, and vegetation indexes.  
1. Spectral-based features contained five spectral features (blue, green, red, near-infrared bands, and brightness).
2. Texture-based features were GLCM (gray-level co-occurrence matrix)-based features, including mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation. GLCM-based textures were influenced by the window size of images, so six different window sizes, \(3\times 3\), \(7\times 7\), \(11\times 11\), \(15\times 15\), \(19\times 19\), and \(23\times 23\), were chosen to calculate GLCM textures. For four spectral bands (blue, green, red, and near-infrared band), 192 GLCM-based textures were extracted.
3. Morphological profiles (MPs) and differential morphological profiles (DMPs) can express the morphological characteristics of land cover, so the opening and closing of both MPs and DMPs were used to extract the morphological-based features. Five different scales [1; 2; 3; 4; 5] were chosen to describe the morphological characteristics on a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Acquisition Date** & **Spatial Resolution** \\ \hline _ZY-3_ & 4 March 2013 & 5.8 \\ _ZY-3_ & 22 March 2018 & 5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-temporal optical data.  
Figure 1: Experimental data: (a) ZY-3 image in 2013; (b) ZY-3 image in 2018.  
fine to coarse scale. Eighty morphological-based features were extracted from four spectral bands. * Transform-based features were extracted using non-subsampling shearlet transform (NSST). NSST decomposition was used to obtain high-frequency sub-bands and low-frequency sub-bands, and NSST reconstruction was used to reconstruct image features. In order to describe the above features on a coarse to fine scale, NSST reconstruction was used on three different scales, to obtain twelve transform-based features for four spectral bands. * Sobel operator was used to obtain four edge-based features for four spectral bands. * Ten vegetation indexes (NDVI, NDWI, GR, DVI, RVI, SAVI, OSAVI, MSAVI, PVI, and EVI) were also extracted from four spectral bands, the formula of these vegetation indexes is listed in Table 2.
# Tantalum Alloy Welding: Does the Thermal Cycle Influence the Microstructure?
## 3 Results
### Hardness  
Hardness measurements, according to ASTM E92 (Hardness Vickers 0.05) (2017) [21], were taken from the samples. Standardized Vickers indenters (Class B) were used with a load of 50 g (HV 0.05) and optical indentation measurement. No less than three measurements were done in every zone of interest, and the average value was used as an estimator. Table 4 shows the result and scheme. This evolution in hardness values is partially explained by the effect of grain size, especially the increase in the recrystallized zone (the heat-affected zone). However, the increase in hardness in the large grain size weld metal zone must be explained by the change in the proportion of the two observed morphologies. One characterized by less stable structures and heterogeneity and the other recrystallized, ductile, and, presumably due to the presence of nitrogen and oxygen, with a strong effect on hardness [22].  
When correlating the oxygen content of the samples with the hardness, according to [22], it is found that the higher the oxygen content, the higher the resulting average hardness value; in particular, nitrogen and oxygen are strong hardeners of unalloyed tantalum, and the hardness of tantalum increased almost linearly with increasing oxygen content in the solid solution [23].
## 5 Numerical Results and Discussion  
In this section, we analyze the performance of the proposed scheme from multiple dimensions by simulating the bit error rate (BER), symbol error rate (SER) and packet error rate (PER) performance, variance, and phase robustness of different schemes, as well as the complexity of the numerical calculation process. Note that in our simulation, the MPSK modulation is used, the (1008, 504) LDPC code shown in Figure 4 is considered, and the code rate is 0.5. The sum-product algorithm (SPA) or MS algorithm is considered for decoding [34], and the maximum number of iterations is set to 10. The detailed simulation parameters are shown in Table 2. For all the simulation results, we simulated enough symbols for each \(E_{b}/N_{0}\) to collect at least 3000 symbol errors.
## 4 Discussion
### Limitations Analysis  
The analysis was based on a typical drought year, 2009, in Xinjiang; thus, future studies should consider extending the analysis over a multi-year period to capture the interannual variability and trends in GPP and its relationship with climate indices. Furthermore, the number of available flux observation sites and valid samples from these sites may also have affected the accuracy of the GPP estimation. In the current study, only natural factors, specifically SIF, NIRv, LAI, FAPAR, TEM, PRE, NDVI, and PAR, were considered for GPP estimation. In future studies, the human activity factors that affect GPP should be explored to further enhance estimation accuracy.
## 3 Results
### Comparison of Models
#### 3.1.2 Computational Complexity  
Table 9 shows the time taken to train the model for each epoch, and the time taken for inference of an image with both Sentinel-1 and Sentinel-2 in addition to the number of trainable parameters of each model. The state-of-the-art models trained with ResNet50 backbones demonstrated longer training and inference times and a greater number of trainable parameters than their MobileNet_V2 counterparts, with the MA-Net models containing the greatest number of trainable parameters, and the U-Net++ models demonstrating a higher level of computational complexity than the DeepLabV3+ models. The proposed model fell between DeepLabV3+ and U-Net++ with ResNet50 backbones, similar to both MA-Net models in terms of both computation time and number of parameters, providing reduced training and inference time over the MA-Net models and the U-Net++ with ResNet50 backbone. When considered alongside its superior performance, this further emphasizes the suitability of the proposed model for practical use.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline U-Net++ ResNet50 & 0.9643 & 0.1123 & 0.7921 & 0.6827 \\ U-Net++ MobileNet\_V2 & 0.9756 & 0.0934 & 0.8237 & 0.7246 \\ DeepLabV3+ ResNet50 & 0.9689 & 0.0952 & 0.8109 & 0.6983 \\ DeepLabV3+ MobileNet\_V2 & 0.9462 & 0.1028 & 0.7827 & 0.6828 \\ MA-Net ResNet50 & 0.9622 & 0.1137 & 0.7202 & 0.6331 \\ MA-Net MobileNet\_V2 & 0.9635 & 0.1167 & 0.7536 & 0.6402 \\ Proposed & 0.9789 & 0.0883 & 0.8396 & 0.7307 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training scores with Sentinel-2 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline U-Net++ ResNet50 & 0.9638 & 0.1197 & 0.7892 & 0.6643 \\ U-Net++ MobileNet\_V2 & 0.9721 & 0.0968 & 0.8074 & 0.7121 \\ DeepLabV3+ ResNet50 & 0.9642 & 0.1007 & 0.7986 & 0.6912 \\ DeepLabV3+ MobileNet\_V2 & 0.9409 & 0.1095 & 0.7774 & 0.6784 \\ MA-Net ResNet50 & 0.9448 & 0.1557 & 0.7043 & 0.5901 \\ MA-Net MobileNet\_V2 & 0.9552 & 0.1242 & 0.7553 & 0.6273 \\ Proposed & 0.9763 & 0.0906 & 0.8238 & 0.7241 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation scores with Sentinel-2 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **F1 Score** & **IOU** \\ \hline U-Net++ ResNet50 & 0.9583 & 0.1346 & 0.7762 & 0.6529 \\ U-Net++ MobileNet\_V2 & 0.9702 & 0.1209 & 0.7980 & 0.6832 \\ DeepLabV3+ ResNet50 & 0.9573 & 0.1254 & 0.7923 & 0.6804 \\ DeepLabV3+ MobileNet\_V2 & 0.9364 & 0.1317 & 0.7705 & 0.6715 \\ MA-Net ResNet50 & 0.9218 & 0.2147 & 0.6889 & 0.5696 \\ MA-Net MobileNet\_V2 & 0.9307 & 0.2277 & 0.7048 & 0.5810 \\ Proposed & 0.9718 & 0.1143 & 0.8054 & 0.7031 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Testing scores with Sentinel-2 images.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Single-Node Spectrum Sensing
#### 2.2.1 Energy Detection  
Energy detection is a traditional and widely used method for spectrum sensing [26]. It calculates the energy of the received signal in the time or frequency domain to determine the presence of an authorized user. The core idea of energy detection is to use a band-pass filter to filter the signal or noise in the non-target band and then pass the filtered signal to a power detector for power measurement. Finally, the threshold set in advance is used to determine which state of \(H_{0}\) or \(H_{1}\) the PU signal is in. If the detected energy is greater than a set threshold, the primary user is in the busy state \(H_{1}\) and, vice versa, in the idle state \(H_{0}\). This approach is characterized by its simplicity and user friendliness, as illustrated in Figure 3.  
The energy detection technique has the advantages of simplicity of implementation, low hardware cost, and the fact that it does not require a priori knowledge of the primary user's signal. However, it also has some drawbacks. The inability to distinguish between different types of signals, such as broadcast and noise, makes it susceptible to false alarms or false positives. In addition, energy detection is less sensitive in high-noise environments and may miss the detection of low-power signals [27]. Overall, the energy detection technique is one of the most fundamental techniques in spectroscopy, which is simple in principle and suitable for spectroscopic detection tasks in low-SNR environments. However, due to its susceptibility to noise and interference, its practical application needs to be combined with other signal identification and analysis techniques for comprehensive applications.  
Conventional energy-sensing methods typically use a single detection threshold, which can result in reduced detection performance. The authors of [28] used double-detection thresholds to improve detection performance and improved the K factor to change the confusion state between the two thresholds and obtained better performance than the single-threshold energy detection method at low signal-to-noise ratios. Although another researcher in [29] also uses double-detection thresholds but combines it with the Copula function to propose a new method and performs an experimental simulation under the orthogonal frequency division multiplexing (OFDM) system under the Rayleigh fading channel, which significantly improves performance over the traditional dual-threshold energy detection method. While the work of [PERSON], [PERSON] differs from the previous two, in [30], he proposed a method for detecting energy enhanced by intelligent reflecting surface (IRS) based on IRS enhancement; a closed-form expression for the mean detection probability is derived using the gamma distribution approximation and the central limit theorem, and simulation results show that the proposed method outperforms the benchmark scheme's energy detection method.
## 4 Discussion
The approach employed in this study allowed us to identify several previously reported IGF-IR promoter-binding proteins, including Sp1, KLF6, and WT1. Moreover, results of MS and Western blots confirmed bioinformatic predictions on transcription factor binding sites (e.g., WT1, ER, E2F, Sp1, _etc._), obtained using the TRANSFAC and AliBaba2 softwares ([[http://www.biobase-international.com](http://www.biobase-international.com)]([http://www.biobase-international.com](http://www.biobase-international.com)) and [[http://www.gene-regulation.com](http://www.gene-regulation.com)]([http://www.gene-regulation.com](http://www.gene-regulation.com))) (data not shown). Furthermore, the fact that we were able to detect differences in promoter binding between the two cell lines, despite the lack of differences in protein expression, indicates that the DNA affinity chromatography approach used was highly specific and sensitive. Given that differences in binding could be due to differences in the availability of these proteins, we confirmed that the proteins detected in the eluates were truly present in the nuclear fractions. Several proteins, including KLF6, Smad2/3, GSK3B, c-jun, and p21, were present in both cytosolic and nuclear fractions. A number of transcription factors, including BRCA1, p120, Fox01, p73, c-myc, p63, cyclin D and G, were undetectable in eluates of both cell lines. A potential explanation for these results is the fact that the expression of these transcription factors was very low in the nuclear fractions [59] or, alternatively, that they are engaged in multi-protein complexes and, therefore, cannot bind to IGF-IR promoter sequences. The capacity of a number of proteins identified by MS and/or Western blots (PARP, Sp1, c-jun, HMGA1) to directly bind to IGF-IR promoter DNA _in vivo_ was confirmed by ChIP assays. Furthermore, the functional relevance of binding data was addressed by coexpression experimentswhich revealed that c-jun, KLF6, and E2F1 significantly enhanced IGF-IR promoter activity. Differences in the capacity of these factors to stimulate IGF-IR promoter activity was seen between both cell lines. The luciferase assays corroborate the results obtained by Western blots and ChIP assays. We have previously shown using EMSA and ChIP that both Sp1 and ER\(\alpha\) bind to the IGF-IR promoter and stimulate gene transcription, whereas no ER\(\alpha\) binding was seen in C4 cells [60]. In addition, we have reported that Sp1 increased IGF-IR promoter activity with a 5.8-fold higher potency in MC7 than in C4 cells [60]. Taken together, there is a remarkable consistency between the various technological approaches used to identify IGF-IR promoter-binding transcription factors.
## 3 Results and Discussion
### Relationship between Temperature Distribution and Skidding Damage  
Temperature is an important parameter in evaluating the running performance of the contact area between the roller and the inner ring [26]. During the test, the thermal infrared imager monitored the temperature of the contact area between the roller and the inner ring in real-time. The temperature curve of the contact area between the roller and the inner ring is shown in Figure 11. It can be displayed that the peak temperature appears at about 10.87 s, when the roller and the contact surface of the inner ring should have occurred skidded damage. The study and analysis of the rate of change of temperature showed that the temperature curve rose sharply around 8.4 s, which were preliminary inferred to be the moment when the rolling bearing began to occur skidding damage.  
Figure 10: Time-varying curve of radial load.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 3 Results
### BER Performance vs. Number of Network Layers  
Comparing the BER performance of new-SNNMS decoding networks with different numbers of layers, in Figure 7, we used 10, 12, 14, 16, 18, 20, 22, 32, and 42 layers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Encoding & LDPC code (576,432) \\ Coding Rate & 3/4 \\ SNR & 5,6,7,8,9,10 \\ Batch Size & 240 \\ Optimizer & Adam \\ Learning Rate & 0.001 \\ Channel Model & Rician \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental Setup.  
As shown in Figure 7, with the increase in the number of layers, the BER performance improves. The improvement in the decoding performance comes at the cost of increased computational complexity. Deeper network layers require more resources for additional multiplication and other operations. Therefore, while ensuring excellent BER performance, a reasonable number of network layers should be chosen to implement the decoding process.  
As shown in Figure 8, when the number of neural network layers reaches a certain depth, the BER performance of the decoder will not significantly improve and gradually converge. Therefore, considering the trade-off between performance and complexity, we suggest choosing a 22-layer neural network.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 2 The Real-Time Metaverse
### Dynamic Environments and Real-Time Interaction  
One of the key distinguishing features of the real-time metaverse is its dynamic nature. In a traditional metaverse, environments are typically static or periodically updated [21; 36], meaning that users interact with a world that may not reflect ongoing changes in the physical world. This makes the experience somewhat disconnected from reality. On the other hand, the real-time metaverse is designed to be constantly evolving [37]. As sensors capture real-world changes--such as the construction of a new building, the movement of vehicles, or even weather conditions--those changes are reflected instantly in the virtual environment [38]. This dynamic interaction opens up a wide range of applications. For example, a virtual replica of a city can be continuously updated to reflect real-time traffic patterns [39; 40], construction projects [41], or even local events. Users could navigate this city virtually and interact with it as they would in the physical world. The virtual ability to mirror real-world changes in real-time has far-reaching implications for industries such as urban planning [42], architecture [43], education [44], and entertainment [45].
## 4 Simulation of the Proposed Control
### Case A: Implemented DC Chopper  
The traditional FRT strategy was simulated in the simulation Case A. A 150-ms three-phase fault was simulated in the on-shore converter PCC side at \(t\) = 4 s (see Figure 6). The PCC voltage dropped to about 0 pu when the fault occurred. To ride through the fault, a DC chopper at the HVDC inverter side was operated by the DC threshold voltage. The DC voltage vibrated when the fault occurred, because the DC chopper did a chop-and-release operation on the DC side. When the DC chopper was not operating, the DC voltage went up by the power difference between the on-shore and off-shore sides. The chopper resistance (1k\(\Omega\)) was implemented between the positive and negative DC sides, so power went through the DC chopper, which caused the DC voltage to decrease. Therefore, during the operating period, the DC voltage went between chopper activation and deactivation voltage (1.06-1.10 pu). For the off-shore part, the wind turbine still generated power because the DC chopper handled the surplus power, which led to rotor speed not changing in this case. Hence, the DFIG rotor did not participate in reducing the surplus power to ride through the on-shore fault. The chopper had a 500-MW chopping rating, so if unbalanced power exceeded 500-MW, it could not operate properly. This means the chopper rating must be increased.
# ICT Skills in the Deployment of 21 st Century Skills:
## 5 Implementing ICT Skills into ECE  
Efforts to introduce ICT skills in ECEC have been made, and there is some research on ECEC teachers' perceptions of ICT and ICT skills and their implementation [20]. Often education entails the simple use of digital tools instead of an analogue equivalent, e.g., [28]. While this simple use of digital tools might be exciting and, in the short term, motivating for the children, it remains questionable how digital tools can be used in a meaningful way to support children's 21 st century skills or even their learning of traditional skills, such as language and STEAM (science, technology, engineering, arts, and mathematics).  
Suggestions for K-12 education include the implementation of computer science as a mandatory school subject. However, this approach is not feasible for ECEC due to administrative and educational issues, such as the already limited and much-needed play time in early childhood classrooms and lacking knowledge of ECEC teachers [25]. Indeed, ECEC is characterized by child-centeredness. However, the use of ICT can undermine this child-directedness if the teachers fail to implement them adequately. For example, whiteboards are seldom used interactively in ECEC. Instead, teachers use them for instruction [106], and in effect, ECEC becomes less child-centered. On the other hand, [PERSON] and [PERSON] [107] found that the promotion of ICT skills in ECEC mainly contains leaving the children free to play with digital technologies without any guidance from teachers. However, leaving children to play with ICT devices without any guidance can lead to demotivation on the part of the children and hamper their learning [25]. Without guidance or high amounts of guidance bordering on direct instruction, the simple use of ICT devices is unlikely to support children's acquisition of ICT 21 st century skills. Consequently, [PERSON] and colleagues [25] found that ICT can be used as a means, e.g., to foster 21 st century skills as well as children's development, or a subject to foster ICT skills.  
Drawing on studies from children's developmental antecedents and the literature on ICT 21 st century skills, we suggest possible learning opportunities on the basis of constructivist approaches that can be implemented into ECEC, and that can be administered in the context of playful learning or digital play [26; 108]. Fostering these 21 st century skills in ECEC on the backdrop of children's development is not only desirable for children's further learning and development but also gives early ICT literacy education a purpose apart from only being an end in itself. The seven skills described by [PERSON] and colleagues [4] can be implemented into ECEC by employing simple, non-complex tasks and measures fit for young children.
# Heat-Flow Coupling Law for Freezing a Pipe Reinforcement with Varying Curvatures
###### Abstract  
Using the temperature and seepage field-coupling module within COMSOL Multiphysics software, we examined freezing behavior and its evolving patterns in curved underground freezing pipes. This study employed transient states, with the Darcy's law and porous-media heat-transfer options activated in the Physical Field Interface of the Physical Field and Variable Selection column. The models were created to establish numerical models of freezing reinforcement for both single and multiple pipes with various curvatures. These models were designed to simulate the evolving temperature and seepage fields of soil under diverse freezing conditions. Subsequently, this research utilized the models to simulate the freezing and consolidation conditions of a shallowly buried tunnel within the context of shallow tunnel conditions. The study reveals that after freezing a single pipe using water flow, the change in thickness of the frozen wall in curved pipes is notably smaller than that in straight pipes. This difference is particularly pronounced in the upstream section. Specifically, at a distance of \(-2000\) mm from the main surface, the change in thickness of the frozen wall in straight pipes exceeds that in \(s=7\) curved pipes by approximately \(350\) mm. The smaller the long arc ratio \(s\), the greater the arc of the freezing tube and the better the water-blocking effect. In the multi-pipe freezing model, the \(s=7\) curved pipes exhibit a frozen-wall thickness approximately \(120\) mm greater than that of straight pipes at a distance of \(-2000\) mm from the main surface. Under the condition of a shallow buried concealed excavation with surging water, a pipe with a long arc ratio \(s=7\) arc freezing at \(46\) d attains a permafrost curtain thickness that is equivalent to that achieved by the straight pipe freezing at \(58\) d. This reduction in thickness shortens the working period by \(12\) days, resulting in a more efficient process. The successful application of the freezing method in the water-rich aquifer is expected to be a valuable reference for similar projects in the future.  
arc freeze tube; seepage field; shallow buried cathodic excavation; long arc ratio; temperature field; freezing method +
Footnote †: journal: Applied Sciences