esa-sceva/satcom-chunk-collection huggingface dataset:
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 3 Proposed Method
### Resize or Patches  
The limited amount of the memory of the GPU puts one in the impossibility of feeding an entire CT volume to a neural network. In 2D images, memory is not a problem, even when working with large batches. In 3D, the equivalent would be as if feeding more than 500 images at once in a network.  
Figure 1: The proposed 3D U-Net architecture.  
Our first approach was to resize the volumes (and the ground truth) to a volume that would be small enough to fit in the video RAM but large enough not to lose the details needed for segmentation. The first drawback was that the volumes from the benchmark did not come in a standard size. The X- and Y-axis were always of size 512, but the Z-axis ranged from 272 to 388. This meant that the aspect ratio after any resize to a fixed value would not be constant. Another important loss would be the impossibility to have augmentation under the given circumstances since even the 90-degree rotations would cause big ratio changes. The second drawback of this method was the loss of precision when upscaling. Extracting the centerline would imply getting as good a precision as possible since the vessel fitting algorithms that take as input a centerline depend on this accuracy. Upscaling precision loss can be observed in Figure 2, in which the ground truth centerline is discontinuous and inhomogeneous. Any algorithm without 100% precision would result in something even worse. Training the model using resizing posed problems with the dataset being too small.  
A second approach was proposed to divide the input into smaller patches, cutting only small parts of the volume (and the ground truth) and feeding them to the network. An example slice of a patch together with the associated ground truth is shown in Figure 3. This method does not have any of the downsides of the previous one but introduces a different one--the lack of context. Therefore, a tradeoff is reached between increasing the patch size so that the available contextual information for the model is enough to make a good prediction and the size of the model so that the model is large enough to capture the correlations about where the centerline is positioned and that it should be continuous and also cross the patches.  
Figure 2: Downscaled and upscaled centerline ground truth (80 \(\times\) 80 \(\times\) 64) plotted in 3D to highlight the difficulty of using it for training.
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data
### Emissivity
#### 2.1.3 University of Wisconsin Global Infrared Land Surface Emissivity Database  
The UW Base-line Fit infrared land surface emissivity dataset is made available by the University of Wisconsin-Madison at 10 wavelengths which were chosen at locations that provide as much characterization of the high resolution infrared spectra as possible. These band placements are at 3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 \(\upmu\)m. This product is based on the MODIS MOD11C3 emissivity product and is extended to the 10 channels using a conceptual model based on laboratory measurements ([PERSON] et al., 2018). [PERSON] et al. (2018) and [PERSON] (2018) further extended this emissivity dataset to cover 416 channels from 3.6-14.3 \(\upmu\)m using the UW HSR Emissivity Algorithm. This algorithm uses a Principal Component Analyses regression using high spectral resolution laboratory measurements from the ASTER spectral library ([PERSON] et al., 2018). Both the UW BF dataset and UW HSR Emissivity Algorithm are available at a monthly, 0.05\({}^{\circ}\) resolution from [[http://cimss.ssec.wisc.edu/iremis/](http://cimss.ssec.wisc.edu/iremis/)]([http://cimss.ssec.wisc.edu/iremis/](http://cimss.ssec.wisc.edu/iremis/)).
## 2 Methodology  
In order to improve the accuracy of tunnel collapse risk assessment, a multi-source information fusion assessment method based on the Improved Dempster-Shafer Theory is proposed by combining artificial intelligence models. Figure 1 shows the flow of the method proposed in this paper for assessing tunnel collapse risk. Cloud Models (CM), Gradient Boosting Decision Tree (GBDT) and Support Vector Machines (SVM) are used for the advance geological forecast information, site inspection information and instrument monitoring information, respectively, so as to obtain the tunnel collapse failure probability from a single information source. Then, the weight coefficient between each piece of evidence is calculated according to whether the conflict coefficient \(K\) is greater than the conflict threshold (\(\zeta=0.9\)), and the weight coefficient is adjusted and fused to obtain the overall collapse risk assessment result.  
For advanced geological forecast data, because it constructs a data set through geophysical prospecting results and expert scoring results, CM can map qualitative concepts to quantitative data, so it can be used to process geophysical survey data. For site inspection data, the Gradient Boosting Decision Tree is used to investigate causal relationships between tunnel collapse and its influential variables based upon the risk mechanism analysis and expert scores. For instrument monitoring data, since it has been classified, SVM with the advantage of small sample classification is used for risk assessment. By using improved D-S multi-source information fusion method and adjusting high conflict evidence, useful information of different evidence sources can be extracted, so as to improve the accuracy of collapse risk assessment. A typical hazard concerning the tunnel collapse in the construction of the Fujian Wenbishan Tunnel in China is presented as a case study. The results demonstrate the feasibility of the proposed approach and its application potential.
# Blockchain-Based Decentralised Authentication in Closed Environments
## 6 Discussion
In other words, our proposed protocol enhances the resilience of the architecture by allowing validator nodes to function autonomously without having to rely on a centralised cluster head. In scenarios where only one cluster exists, traditional cluster-based solutions may encounter scalability issues and potential bottlenecks due to their reliance on one single cluster head. This means that by avoiding clusters and cluster heads, our protocol simplifies the authentication process and reduces potential overhead caused by managing cluster dynamics. Therefore, our proposed decentralised authentication protocol could offer performance advantages over traditional cluster-based solutions, particularly where rapid authentication is required. While our proposed protocol offers a relatively efficient and secure solution to decentralised authentication in closed environments, it is important to discuss its scalability and adaptability in semi-open and fully open networks. Although our protocol provides a robust solution, we believe that it would be best suited for environments with fewer than 100 validator nodes due to the computational overhead that it would incur in the consensus mechanisms. This claim is supported by [PERSON] et al. [28], who showed that the communication overhead grew exponentially as the number of nodes increased for the practical Byzantine Fault Tolerance (pBFT) consensus algorithm. Moreover, [PERSON] et al. [28] also illustrated that when the number of nodes reached 100, the consensus latency would be over 50 milliseconds, which, according to [29], is when it would become noticeable. To extend the applicability of our protocol to semi-open or fully open environments, one potential future direction is to adopt a federated blockchain model [30], where multiple closed environments are connected to one another using a hierarchical model. This would allow for secure authentication across environments while still maintaining the benefits of decentralised authentication within each environment. However, we believe that this needs to be researched further to ensure the efficiency and security of the federated hierarchical blockchain model.
# Structural Crack Detection and Recognition Based on Deep Learning
## 4 Crack Detection Based on YOLOv3  
### Model Building  
In 2018, [PERSON] et al. [29] released a new algorithm, YOLOv3, which adopts some of today's better target detection ideas, into YOLO. Under the premise of ensuring a speed advantage, the detection accuracy is further improved, especially the detection ability for small objects. YOLOv3 mainly improves the network structure. The network structure is introduced below.  
The network structure of YOLOv3 is shown in Figure 9. The network input image size is 416 \(\times\) 416 \(\times\) 3. The meaning of each module in the figure is as follows:  
* DBL represents the combination of convolution, BN and leaky ReLU. In YOLOv3, the convolutional layer appears as such components and constitutes the basic unit of DarkNet. The number after DBL indicates that there are several DBL modules.
* Res: Res in the figure represents the residual module, which is essentially a residual network. The number after Res represents several residual modules connected in series.
* Up-sampling: up-sampling uses up-pooling, that is, the method of element replication and expansion makes the feature size expand, and without learning parameters.
* Concat: after up-sampling, the deep and shallow feature maps are subjected to the Concat operation, that is, channel-wise splicing.  
Figure 8: Image test.  
Figure 9: YOLOv3 model.  
YOLOv3 algorithm process: First input an image, and go through multiple Res modules; the first module is the deep feature output, which produces a smaller feature map and can detect larger cracks. The second is the deep feature and the penultimate layer output feature Concat produces a medium-sized feature map and can detect medium-sized cracks. The third is the Concat of deep features and the third-to-last layer feature to produce a relatively large feature map, which can detect small cracks. The advantage of this is that it can detect multi-scale targets in the images and avoid the problem of missed detection.  
We can also see some advantages in the DarkNet-53 network structure in Figure 9:  
* Residual idea: DarkNet-53 draws on ResNet's residual idea and uses a large number of residual connections in the basic network, so the network structure can be designed very deep, and the gradient can be transmitted farther and relieved when the gradient is back propagated. The problem of gradient disappearance during training is solved, which makes the model easier to converge.
* Multi-layer feature maps: Through up-sampling and Concat operations, deep and shallow features are merged, and finally, feature maps of three sizes are output for subsequent prediction. Multi-layer feature maps have obvious advantages when detecting multi-scale objects or small objects.
* Without the pooling layer: The previous YOLO network has five maximum pooling layers to reduce the size of the feature map, but YOLO v3 down sampling achieves the same effect of reducing the size. The number of down-sampling is also five, and the overall sampling frequency is 32.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 6 Performance Analysis
### Energy Cost  
[PERSON] et al. [21] and [PERSON] et al. [22] have proposed energy cost evaluation, which considers that the maximum CPU power of devices (W) is approximately 10.88 Watts. The energy overhead was calculated as follows: ETotal = CCTotal \(\times\) W, where CCTotal is the computational cost calculated and presented in Section 5.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON] et al. [7]** & **[PERSON] et al. [6]** & **Proposed Protocol** \\ \hline HUP & 704n & 592n & 736n \\ PUP & 1600n & 1744n & 736n + 736n + 208(m-1) \\ TP & 2112n & 1792n & 864n \\ CP & 1504n & 1184n & 736n \\ TOTAL & 6920n bits & 4832n bits & 3072n bits \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of communication costs in bits.  
Figure 8: Communication cost comparison.  
Table 8 and Figure 9 show the comparison of energy costs among our protocol and other protocols from [7] and [6]. Figure 9 shows that our proposed scheme has the best performance regarding energy. The energy cost directly related to the computational cost and consequently the graphic results are for both costs and are very similar.  
Finally, the good results obtained in the security and performance evaluations prove that the proposed protocol can perform better than the works of [6] and [7]. Below are some aspects compared with the above-mentioned works:  
1. [label=()]
2. the protocols of [PERSON] et al. [7] and [PERSON] et al. [6] are based on asymmetric cryptography, while our approach is based on symmetric cryptography, which is able to produce lower computational and communication costs;
3. the security flaws of [PERSON] et al. [7] and [PERSON] et al. [6] are avoided in our protocol using access control to the patient's device, timestamps, temporary identities and freshly generated parameters in each authentication session;
4. by contrast with our proposed scheme, the protocols of [PERSON] et al. [7] and [PERSON] et al. [6] do not support D2D communication, which is a promising technology for the development of e-health systems due to the agility of data transmission it can provide. The proposed protocol was designed thinking about the criticality of the health systems that in some cases might depend on the agility of data transmission to save lives.
## 4 Elastic Net Regression Analysis
### Regression Analysis and Validation  
Through 10-fold cross-validation, the optimal L1_ratio was determined to be 0.1. Using this ratio, the parameters of the dimensionless rotation speed model were calculated as follows: k\({}_{1}\) = 6.25 \(\times\) 10\({}^{-2}\), k\({}_{2}\) = \(-2.71\times 10^{-3}\), k\({}_{3}\) = 1.91 \(\times\) 10\({}^{-1}\).  
The resulting dimensionless rotation speed model is expressed as:  
\[\frac{\omega D}{v}=6.25\times 10^{-2}\frac{V_{c}}{V}-2.71\times 10^{-3}\frac{F_{ s}T}{DF^{2}}+1.91\times 10^{-1}\frac{P}{D} \tag{8}\]  
To evaluate the predictive ability and effectiveness of the model, it was applied to the test set for cutterhead rotation speed prediction and compared to the actual values. The results demonstrated a coefficient of determination (R\({}^{2}\)) of 0.954, indicating a high goodness-of-fit and the model's ability to effectively capture the relationship between input and output variables.  
The mean squared error (MSE), a key metric for assessing model error, was calculated as 0.00286. This low MSE value confirms the minimal discrepancy between the predicted and actual values, further validating the model's accuracy.  
Figure 6 shows the residual plot of the model, which is based on the results of the test set. The residuals predominantly range between \(-0.1\) and \(0.1\), indicating small prediction errors and excellent model fit. The residuals exhibit uniform fluctuations around zero, suggesting no significant systematic bias in the model. Furthermore, the concentration of residuals within a narrow range reflects a low noise level in the data, validating the effectiveness of the preprocessing steps.  
Figure 6: Residual plot of the elastic net regression model.
## 2 Inconsistent Load-Deformation Problem of FLSPs
### The Load-Deformation Inconsistency Problem of the FLSP  
Taking the Yulinzi Tunnel as an example, we illustrate the load-deformation incoordination problem of the FLSP. According to the deformation characteristics of the primary lining in Section 2.2, it was concluded that when the vault settlement is controlled at 16.5 cm, the bearing capacity provided by the arch foot is 475 kN, and the arch foot subsidence of the primary lining is 6.2 cm. Because the numerical analysis adopts a plane model and the distance between the steel arches is 0.75 m, the bearing capacity required from the FLSP is 356 kN.  
In the original design, four \(\phi 42\times 4\) mm FLSPs 3.5 m in length are installed on the upper bench, as shown in Figure 9. The required bearing capacity of a single \(\phi 42\times 4\)-millimeter FLSP is approximately 90 kN. When the deformation value of a single \(\phi 42\times 4\)-millimeter FLSP has an arch foot subsidence value of 6.2 cm, it can provide a bearing capacity of 63 kN, but cannot reach the 90 kN required to control the settlement. The original FLSP cannot control the vault settlement within the range of 16.5 cm. Combined with the load-deformation characteristics of the FLSP in Section 2.1.2, it can be concluded that the FLSP provides a bearing capacity of 90 kN, and that the FLSP end needs to be deformed by 8.8 cm. At this point, the deformation value of the FLSP is greater than the primary lining arch foot subsidence value of 6.2 cm. There is a problem of deformation inconsistency between the FLSP and the primary lining arch foot.  
Figure 8: Vault and arch foot settlement-arch foot bearing capacity curve.  
Figure 9: Design drawing of FLSP.
# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 6 Discussions  
This study investigates the user acceptance of in-vehicle applications where recent vehicle models are equipped with the application and automotive aftermarket service vendors offer the application as a vehicle accessory that can provide advanced driver assistance to users. Due to the variety of vehicle owners and users, this paper aims to answer several research questions by modeling and quantifying the user acceptance of in-vehicle applications based on survey responses. A research model based on the integration of the TAM, trust, system characteristics, social influence and personal characteristics is presented. In addition, the empirical results on the user intention to use in-vehicle applications in a driving experience are provided. Trust and system characteristics are important to Malaysian users. Users will simply turn-off or ignore the warnings from the application which annoy them. They are keener on trusting their instincts then letting technology influence their driving decisions. Their biggest worry is that the application may be faulty and tempered by unauthorized personnel. However, if the users are getting enough information and have seen or observed their close contact using the application before, they have a more positive attitude toward the technology.  
The results of this study are limited to users in Asian countries that are in the early phase of implementing autonomous vehicles, specifically Malaysia. Because previous studies focused mostly on European countries, which have a different culture, there may be a variation of the results when it comes to investigating the user acceptance of the technology. We assumed that in-vehicle applications would be made available to users in recent vehicle models and be available as vehicle accessories to be installed in earlier vehicle models. We did not consider the variety of in-vehicle applications and providers. Hence, the respondents' opinions are based on the general usage of in-vehicle applications.  
Additionally, because attitude and behavior are important in marketing in-vehicle applications, the TAM has been adopted to investigate the underlying relationship between attitudes and the behavior or intention to use in-vehicle applications. The attitude-behavior relation is not always straightforward and linear but may display non-linearities. Weak attitude evaluations might not have much of an effect on the user's intention to use the in-vehicle applications. Additionally, an attitude change will not necessarily be followed by an equal change in the user intention or actual application use. Thus, segmenting users based on their attitude extremity before designing a marketing strategy can be valuable.
## 5 Results and Discussions
### Angle-Averaged Processing Method  
Instantaneous lift coefficient calculated by Equation (6) and angle of attack calculated by Equation (7) at each sampling time were plotted as scattered points in Figure 15. It is shown that the distributions of the scattered point are almost overlapped at all the pitching angles.  
However, it is difficult to determine relationship between lift coefficient and angle of attack, because each angle of attack corresponds to a number of lift coefficient values. An angle-averaged method was employed to process the multi-value lift coefficient at one angle of attack to single-value. In the angle-averaged method, all the angles of attack were divided into several data sets with the length of \(1^{\circ}\). Each angle of attack set is \(\alpha_{k}=[\alpha_{k1},\alpha_{k2},\ldots\alpha_{kn}]\), and the corresponding lift coefficient dataset is \(Cl_{k}=[Cl_{k1},Cl_{k2},\ldots Cl_{kn}]\). Then the lift coefficients and angles of attack in each dataset were averaged to obtain the angle-averaged lift coefficient and angle of attack, as shown in Equation (10), where \(i=1,2,\ldots\ n\).  
\[\left\{\begin{array}{c}\alpha_{k}=\frac{\sum\limits_{i=1}^{n}\alpha_{ki}}{n} \\ \frac{\sum\limits_{i=1}^{n}Cl_{ki}}{n}\end{array}\right. \tag{10}\]  
Figure 14: Time history of azimuth angle (**a**) and rotor speed (**b**) in the testing cases.  
Figure 15: Instantaneous lift coefficients at r/R = 0.64 with different pitching angles.  
The relationship curves of angle-averaged lift coefficient and angle of attack are shown in Figure 16a. All the curves at different pitching angles are almost overlapped also. Thus, the cases at the different pitching angle can be regarded as one case to simplify the data analysis. Finally, the instantaneous data of all the cases at different pitching angles was angle-averaged to plot in one curve, as shown in Figure 16b.
###### Abstract  
With the rapid proliferation of wireless traffic and the surge of various data-intensive applications, the energy consumption of wireless networks has tremendously increased in the last decade, which not only leads to more CO\({}_{2}\) emission, but also results in higher operating expenditure. Consequently, energy efficiency (EE) has been regarded as an essential design criterion for future wireless networks. This paper investigates the problem of EE maximisation for a cooperative heterogeneous network (HetNet) powered by hybrid energy sources via joint base station (BS) switching (BS-Sw) and power allocation using combinatorial optimisation. The cooperation among the BSs is achieved through a coordinated multi-point (CoMP) technique. Next, to overcome the complexity of combinatorial optimisation, Lagrange dual decomposition is applied to solve the power allocation problem and a sub-optimal distance-based BS-Sw scheme is proposed. The main advantage of the distance-based BS-Sw is that the algorithm is tuning-free as it exploits two dynamic thresholds, which can automatically adapt to various user distributions and network deployment scenarios. The optimal binomial and random BS-Sw schemes are also studied to serve as benchmarks. Further, to solve the non-fractional programming component of the EE maximisation problem, a low-complexity and fast converging Dinkelbach's method is proposed. Extensive simulations under various scenarios reveal that in terms of EE, the proposed joint distance-based BS-Sw and power allocation technique applied to the cooperative and harvesting BSs performs around 15-20% better than the non-cooperative and non-harvesting BSs and can achieve near-optimal performance compared to the optimal binomial method.  
combinatorial optimisation; CoMP-JP; energy sharing; energy efficiency; hybrid power; smart grid; heterogeneous networks +
Footnote †: journal: Computer Science and Technology Letters  
_Article_  
Energy-Efficient Joint Base Station Switching and Power Allocation for Smart Grid Based Hybrid-Powered CoMP-Enabled HetNet