esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prediction Results of Mining Surface Subsidence of Northern Pei County for Reusing Land Resources
### The Influence of Geological and Mining Conditions on Prediction Parameters
#### 3.1.4 Unconsolidated Layer Thickness  
A clay layer may not be consolidated since its mechanical property is special and has no tensile strength. The unconsolidated layer only bends under the mining influence and will not crack, expand and separate. Meanwhile, the unconsolidated layer also produces water loss, consolidation and compression deformation that will increase the surface subsidence. In addition, the separation space is narrowed with the increase of the unconsolidated layer thickness. If the thickness of the unconsolidated layer thick is large, the subsidence coefficient may be greater than 1 in the first mining [9]. Under the condition of the thick alluvial, the relationship between the subsidence coefficient, the mining depth and the unconsolidated layer thickness can be expressed in Equation (8) [25]:  
\[q=0.68+0.036\frac{D_{\theta}}{H}+0.2\frac{h}{H}-0.0008H_{J} \tag{8}\]  
where: \(q\) is the surface subsidence coefficient; \(D_{\theta}\) is the projection surface length of the working face length according to the mining influence propagation angle, m; \(H_{J}\) is the bedrock thickness, m; \(H\) is the mining depth, m; \(h\) is the unconsolidated layer thickness, m.  
The unconsolidated layer thickness also has an influence on the horizontal movement coefficient. The unconsolidated layer will backfill the subsidence space in the movement process due to its rheological behavior. Meanwhile, the consolidated deformation and compression characteristics of the unconsolidated layer can lead to an increase of degree and area of surface horizontal movement.  
Consequently, the horizontal movement coefficient increases with the increase of the unconsolidated layer thickness. The relationship between the horizontal movement coefficient and the unconsolidated layer thickness in Hebi mine can be expressed by Equation (9) [27]:  
\[b=0.1379+0.12378\frac{h}{H}\pm 0.04221 \tag{9}\]  
where: \(H\) is the mining depth, m; \(h\) is the unconsolidated layer thickness, m; \(b\) is the horizontal movement coefficient.  
The tangent of the major influence angle decreases with the increase of the unconsolidated layer thickness. The reason is that the rheological behavior of the unconsolidated layer leads to the increase of the surface horizontal movement area. The bigger the unconsolidated layer thickness, the larger the area of surface movement basin. The relationship between the tangent of major influence angle and the unconsolidated layer thickness in the thick alluvium areas of Shan-dong province can be expressed by Equation (10) [25]:  
\[tan\beta=0.0169\left(\frac{H-h}{M}\right)+1.7729 \tag{10}\]  
where: \(tan\beta\) is the tangent of the major influence angle. \(H\) is the mining depth, m; \(h\) is the unconsolidated layer thickness, m. \(M\) is the mining thickness, m.  
The unconsolidated layer thickness also does not have a great effect on the inflection point offset and the mining influence propagation angle.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 5 Evaluation Results
### Space-Based Time System Stability Evaluation  
The frequency stability of the space-based time system is evaluated based on the satellite clock offset under two benchmarks: an anchor station reference and autonomous time scale reference. The influence of clock drift is deducted in the evaluation. The Allan variance of the satellite clock offset under the anchor station clock reference is shown in Figure 3a, and the Allan variance of the satellite clock offset under the autonomous time scale reference is shown in Figure 3b. Quantitatively, the statistics of satellite clock stability under the two benchmarks are shown in Table 3. Under the anchor station benchmark, the 10,000-s stability of hydrogen clocks and rubidium clocks is approximately \(2.6-2.8\times 10^{-14}\). The average daily stability of hydrogen clocks is \(8.67\times 10^{-15}\), and the average daily stability of rubidium clocks \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Short-Term Prediction**} & \multicolumn{2}{c}{**Long-Term Prediction**} \\ \cline{2-5}
**Satellite Number** & \begin{tabular}{c} **Anchor Station** \\ **Reference** \\ \end{tabular} & \begin{tabular}{c} **Autonomous** \\ **Time Scale** \\ **Reference** \\ \end{tabular} & \
\begin{tabular}{c} **Anchor Station** \\ **Reference** \\ \end{tabular} &
\begin{tabular}{c} **Autonomous** \\ **Time Scale** \\ **Reference** \\ \end{tabular} \\ \hline C25 & 0.17 & 0.11 & 1.52 & 0.96 \\ C26 & 0.21 & 0.13 & 0.89 & 0.24 \\ C27 & 0.20 & 0.12 & 0.93 & 0.25 \\ C28 & 0.23 & 0.17 & 0.87 & 0.25 \\ C29 & 0.19 & 0.12 & 0.93 & 0.19 \\ C30 & 0.23 & 0.17 & 0.89 & 0.56 \\ C59 & 0.19 & 0.14 & 1.49 & 0.71 \\ C20 & 0.21 & 0.16 & 2.31 & 1.79 \\ C21 & 0.22 & 0.18 & 2.44 & 1.89 \\ C22 & 0.20 & 0.16 & 2.18 & 2.25 \\ C23 & 0.21 & 0.17 & 2.87 & 1.67 \\ C24 & 0.24 & 0.18 & 2.14 & 1.10 \\ C32 & 0.20 & 0.15 & 1.28 & 0.68 \\ C33 & 0.20 & 0.16 & 1.78 & 1.60 \\ C34 & 0.17 & 0.11 & 2.18 & 1.47 \\ C35 & 0.21 & 0.15 & 1.94 & 0.86 \\ C36 & 0.22 & 0.18 & 1.18 & 0.89 \\ C37 & 0.19 & 0.15 & 1.45 & 1.16 \\ \hline \multicolumn{5}{l}{Average Value of Hydrogen Clock} & \multicolumn{1}{c}{0.20} & \multicolumn{1}{c}{**0.14**} & 1.07 & \multicolumn{1}{c}{**0.45**} \\ \hline \multicolumn{5}{l}{Average Value of} \\ \multicolumn{5}{l}{Rubidium Clock} & \multicolumn{1}{c}{0.21} & \multicolumn{1}{c}{**0.16**} & 1.98 & \multicolumn{1}{c}{**1.40**} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMS values of satellite clock offset prediction errors under different references. is \(1.18\times 10^{-14}\). Under the autonomous time scale benchmark, the 10,000-s stability of hydrogen clocks and rubidium clocks is about \(1.8\times 10^{-14}\). The average daily stability of hydrogen clocks is \(3.59\times 10^{-15}\), and the average daily stability of rubidium clocks is \(8.51\times 10^{-15}\).
## 3 Computational Methods  
### U-Net Architecture  
Our proposed solutions are based on the U-Net architecture [14; 15], modified for 3D volumetric data [17]. We chose U-Net as it has been proven as a baseline for the USOVA3D database for ovary detection [7]. It is also a popular architecture for the problem of follicle and ovary detection in ovarian ultrasound data [10; 11].  
The U-Net architecture in 3D space is identical to that in 2D, utilising 3D layers and/or 3D building blocks instead of their 2D counterparts. The basic U-Net architecture is constructed from a series of down-sampling encoders, followed by up sampling decodersorganised in layers representing the scale of the feature map. An example is shown in Figure 2.  
Each encoder contains a series of two convolution blocks and a max pooling layer. Each convolution block consists of a convolution, batch normalisation and ReLU activation. The output of the max pooling is used as the input of the next encoder, while the output of the second convolution block is passed to the decoder. As the size of the feature map decreases with pooling, the number of channels is doubled in the second convolution block. There is the encoder with only two convolution blocks and no pooling at the lowest level in Figure 2.  
The output is then passed to the first decoder's transposed convolution layer. Each decoder consists of a transposed convolution, concatenation and two convolution blocks. The transposed convolution up-samples the output of the previous decoder, while the concatenation combines this with the output of the second convolution block of the encoder on the same scale level. Lastly, the output of the final decoder's convolution block is processed by a classification block constructed of a convolution layer and sigmoid activation.  
The outputs of the last encoder and each decoder are feature maps optimised for the targeted segmentation task. Feature maps from the final decoder are then passed through a classification layer, a convolution with a sigmoid transfer function, to produce the targeted segmentation map.
## 1 Introduction
### Effects on the Hematologic System  
During short-duration spaceflight, the plasma volume and total blood volume decrease within the first hours and remain reduced throughout the inflight period, a finding previously identified as space anemia [166]. Space anemia during spaceflight is perhaps due to a normal physiologic adaptation of newly released blood cells and iron metabolism to microgravity [167].  
During long-duration spaceflight, microgravity exposure could potentially induce hemoglobin degradation, leading to hemolytic anemia. In a recent study of 14 astronauts who were on 6-month missions onboard the ISS, a 54% increase in hemolysis was observed after landing one year later [50]. In another small study, nearly half of astronauts (48%) landing after long duration missions were anemic and hemoglobin levels were characterized as having a dose-response relationship with microgravity exposure [51].  
An additional study collected whole blood sample from astronauts during and after up to 6 months of orbital spaceflight [168]. Upon analysis, once the astronauts returned to Earth RBC and hemoglobin levels were significantly elevated. It is worth noting that these studies analyzed blood samples from astronauts collected after spaceflight, which may be influenced by various factors (e.g., the stress of landing and re-adaptation to conditions on Earth). In addition, these studies may be confounded by other extraterrestrial environmental factors such as fluid shifts, dehydration, and alteration of the circadian cycle.  
Further research is urgently needed to understand plasma volume physiology during spaceflight and delineate the etiology and degree of hemolysis with longer space exposure, such as 1-year ISS or Mars exploration missions.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 5 Resources, Sharing, Storage, and Energy in Crowd-IoT
To achieve this aim, the authors first formulated an energy-efficient D2D crowd task assignment problem such that a task can be executed either locally on its primary mobile device or offloaded to another nearby device. They further presented a graph-matching-based optimal task assignment policy to discover the minimal weight matching solution for the D2D crowd task assignment problem using Edmonds' Blossom algorithm. Performance evaluation of the proposed scheme via numerical analysis demonstrated an improved performance of over 50% energy consumption reduction in comparison to the scenario of local task execution. Figure 23: Crowd-MECS architecture for edge caching and sharing [61]. Figure 22: Crowdsourced energy services IoT architecture [60]. [PERSON] [63] designed a general 3C framework that allows mobile users to exchange all three categories of resources (communication, computation and caching) via D2D connection. Figure 25 illustrates the general 3C framework. The authors focused on an energy minimization problem to quantify the benefit of the designed framework and show that the 3C framework always achieve a lower overall energy usage when compared to previous 1C/2C models. In addition, they demonstrated that energy savings are greatest when the probability of user connection and content caching ratio are neither too high nor too small. Their numerical results revealed that overall 3C framework may cut total energy consumption by 82.98% when compared to the 1C/2C models. IoT networks are usually composed of heterogeneous and resource constrained devices. Numerous novel applications and services that require hybrid types of resources can be supported by pooling and sharing diverse processing and communication capabilities among mobile devices. Because of the heterogeneity of transmission technologies used in devices and the time-dependent feature of wireless transmission, the cellular connection's quality among devices might differ even when they are in the same location. Therefore, more effective data offloading techniques are required such that devices with weak cellular connection can offload their data to devices in close proximity with strong cellular connections to enhance energy efficiency. With modern mobile and IoT devices equipped with powerful sensors, capable of gathering and communicating large amounts of data, several applications can leverage on the crowd of collaborative devices' computation resources at the network edge for efficient in-situ data processing across diverse devices. Incentive mechanisms also need to be incorporated to encourage device collaboration. Figure 24: D2D crowd framework [62]. Figure 25: General 3C framework [63].
## 4 Numerical Simulation Analysis
### Numerical Simulation Calculation Results  
The numerical simulation results of the non-persistent joints are compared with those of the indoor tests, as shown in Figure 11. The failure modes and shear curves of the numerical models resemble the outcomes of the laboratory testing. Taking Figure 11a as an example, both the numerical model and the direct shear test are tensile failure patterns. The failure morphology and crack propagation are pretty comparable. The peak strength, residual strength, and shape of the shear stress versus shear displacement curve are also quite similar. This shows that the selected numerical model parameters are relatively accurate and also reflects that the FEM-CZM method used can effectively simulate the shear failure process of non-persistent joints. It provides a guarantee for the subsequent stress evolution analysis.  
Figure 11: _Cont._
## 4 Case Study of a Real-World Power Grid
### Small-World Characteristics and Robustness of the Indian Power Grid  
As in (9), the parameters of the Indian power grid were calculated and are listed in Table 1. It is revealed that \(C_{\text{rand}}\ll C_{\text{Indian}}<C_{\text{regular}}\) and \(L_{\text{rand}}\leq L_{\text{Indian}}<L_{\text{regular}}\), which satisfies (9), implying the Indian power grid has the characteristics of small-world networks.  
The avalanche index of the Indian power grid was simulated with real data. Figure 4 depicts the probabilities of cascading failures occurring. It can be seen that the larger the parameter \(\alpha\) (here, shown for the four values 0.5, 1.0, 1.5 and 2.0) is, the smaller is the avalanche index, thus the lower probability of a cascading failure. This leads to the following conclusions:  
* The probability of medium and small cascading failures (\(p(a_{r}\leq 0.3)\)) grows as \(\alpha\) increases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & \(L\) & \(C\) \\ \hline Random & 5.7011 & 0.003576 \\ Indian & 11.4473 & 0.1121 \\ Regular & 94.2454 & 0.3844 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different networks.  
Figure 3: Probabilities of the extents of cascading failures for three network types under lowest loads attacks.  
* With the increase of \(\alpha\), the probability of a definite cascading failure (\(a_{r}=1\)) becomes smaller and is just 7% for \(\alpha=2.0\). This implies that the probability of a definite cascading failure is very small under natural failure conditions, i.e., the grid is robust under these conditions.
* The probability of a large cascading failure (\(a_{r}>0.9\)), which means nearly 515 out of the total 572 nodes fail, is also very small. The probability of a cascading failure is nearly zero under natural failure conditions when the tolerance parameters \(\beta\) and \(\gamma\) are both larger than 0.3 in real power systems. Hence, the Indian power grid is robust under natural failure condition, which is reasonable for its small-world features.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 3 Adaptive GNSS Uncertainty Estimation by CNN
### Positioning Error Prediction by CNN  
Given the proposed eight features, the CNN model further outputs prediction (\(P_{CNN}\)) of the GNSS solution uncertainty. Figure 3 exhibits the overview of the whole structure. Specifically, the first convolutional layer receives the input feature in dimension of \(8\times 1\times 1\) and filters it with 64 kernels in the size of \(2\times 1\) with unit stride. After a MaxPooling layer is applied, the second convolutional layer repeats the settings of the first one, and the data are further max pooled by 64 (size \(2\times 1\)) kernels. This network utilizes the activation function rectified linear unit (ReLU) to guarantee non-negative feature values, and batch normalization and regularization are also applied to speed up the convergence [44]. Finally, followed by the \(64\times 1\) fully connected neural network, the model outputs the predicted uncertainty through a regression layer. The rationale of using a convolutional layer lies in its ability to extract local features, which can further constitute more complex patterns, and the MaxPooling layer is adopted to reduce dimensions of the feature map and improve the network's robustness to variations and noise of input data while keeping the most essential information inside. The regression layer enables the CNN to generate continuous value prediction, which coincides with the expectation on continuous outputs of 2D positioning errors.  
As for the training data preparation, different datasets holding the extracted features are combined together sequentially epoch-by-epoch for shuffling after normalization on the feature values. Such normalization is also applied on the testing dataset when making predictions to guarantee that training and testing datasets are processed in the same way. It is worth mentioning that, strictly speaking, the inputs of this model are not images but a 1D array with GNSS feature values. The main idea of using CNN falls on its advantages, including exploring the correlation across different features and the higher-level efficiency of parameter usage through weight sharing to prevent overfitting, as mentioned in the Section 1. The training samples are the eight features extracted from the GNSS dataset and the label is the 2D error of the receiver positioning results with respect to the ground truth. The loss using the mean square error (MSE) method gives:  
\[Loss=\frac{1}{R}\Sigma_{i=1}^{R}(y_{i}-P_{CNN,i})^{2} \tag{27}\]  
where \(R\) is the number of samples, \(y_{i}\) indicates target output, and \(P_{CNN,i}\) is the predicted output at \(i\)th sample. The learning objective is to determine the coefficients and biases in the neural network that can minimize the loss. The above CNN model will generate regression results that represent the estimated GNSS 2D positioning error, which also indicates the solution uncertainty in this study.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 3 Proposed Approach
### Overall Pipeline  
Our overall approach to image-based Arabian camel breed classification is best illustrated as a pipeline framework as shown in Figure 2. The pipeline consists of several stages as follows: (1) Arabian camel dataset creation: we collect and annotate a diverse dataset of Arabian camel images with their corresponding breeds; (2) data pre-processing: we apply various techniques to enhance the quality and diversity of the image dataset, such as cropping, resizing, and normalization; (3) data partitioning: we split the dataset into three subsets for training, validation, and testing, ensuring that each subset has a balanced distribution of camel breeds; (4) CNN model training with transfer learning: we experiment with well-known CNN models pre-trained on the ImageNet dataset and fine-tuned on our Arabian camel dataset to learn camel-specific features and we classify camel images into six breeds; (5) CNN model evaluation: we evaluate the performance of the fine-tuned CNN models on the test subset using various metrics, such as accuracy, precision, recall, F1-score, and more; (6) CNN model integration: we integrate the best-performing, pre-trained, and fine-tuned CNN model into a web service that takes camel images from users using our mobile app and predicts the camels' breeds in real time.
## 7 Conclusions and Discussion  
### Research Conclusion  
This study combines metaverse technology with education to explore users' willingness to engage with a metaverse education platform. By integrating the UTAUT, the TTF model, and Flow Theory, the research constructs an indicator system to identify factors influencing user willingness. Through GT coding, pre-testing, and formal survey data collection, the study validates the indicator system's scientific rigor and the hypotheses' feasibility using SEM. The specific analysis results are as follows:The empirical results of SEM indicate that interactivity, narrative, and presence significantly positively impact the flow experience, with presence having the most significant influence coefficient. Factors such as PE, EE, SI, FC, TTF, and flow positively affect users' willingness to use the platform. Among these, TTF has the most decisive influence, while EE positively affects PE, and SI positively influences FC. Additionally, within TTF, both TEC and TAS have a significant positive impact on TTF.  
Second, the research model confirms that PE and EE significantly positively affect users' intention to use the metaverse education platform, aligning with previous studies [21; 41; 42; 43]. Notably, PE has a higher influence coefficient. Additionally, SI and FC also significantly impact users' intention to use. This finding contrasts with [PERSON] et al. [24], who concluded that FC and SI do not significantly affect people's intention and behavior in using digital museums.  
Finally, the study developed a more detailed research framework and hierarchical integration model by incorporating the UTAUT, TTF model, and three external factors (interactivity, narrative, and presence). This integrated model is beneficial for identifying the key factors influencing users' intention to adopt the cultural-heritage-based metaverse education system. It also helps deduce the primary antecedent configurations that drive user intention. Furthermore, the model offers valuable insights and guidance for policymakers, businesses, and designers in developing strategies and practices related to metaverse educational systems.