esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 1 Introduction
There is still a lack of quantitative algorithms to generate image acquisition plans aiming at high-quality Earth surface 3D reconstruction, so as to decrease redundant remote sensing and data transmission which is usually costly in urgent cartography for emergency management. In this paper, we propose a novel stereo reconstruction planning method based on quality-driven satellite image pair searching. First, for each pair of stereo images, the quality of the DSM generated using them is calculated by building an estimation model and taking into account various factors. Second, the quality-driven stereo sensing planning method is modeled and solved, by predicting quality values using the quality estimation model while searching for optimal stereo image pairs with a metaheuristic algorithm. Finally, the proposed method is evaluated on extensive satellite orbital simulations aligned with a realistic multi-view stereo dataset. The experimental results show that the quality of the stereo surface can be significantly improved using the quality estimation and task planning method to search for optimal satellite image combinations. To the best of our knowledge, such remote sensing task planning to support high-quality DSM has not been conducted before. The contributions of this paper are as follows:  
* A regression method is customized to provide accurate estimation of DSM quality using real reconstructed point cloud geopositioning errors for training. Feature enhancement and selection are applied to retain key factors that contribute significantly to the quality prediction. * Multi-objective satellite image acquisition planning is modeled to maximize the 3D reconstruction quality while balancing with task quantity and timeliness, which is NP-hard and solved efficiently with constrained optimization algorithms. * The orbits of satellite WorldView-3 and its constellation are both simulated and aligned with the real stereo image dataset to form a comprehensive dataset for quality-driven stereo sensing task planning evaluation, which is to be released to the community.
## 3 Network Background
### Sixth-Generation Cellular Networks
#### 3.2.3 AI-Integrated Communication  
Sixth-generation cellular networks aim to achieve the Internet of Everything by connecting numerous heterogeneous devices while leveraging AI. However, AI applications require significant computational power, creating challenges in resource allocation. Traditional cloud-driven AI increases the network load and latency, while edge computing faces resource constraints [1]. Unlike previous networks that separated communication and computation, 6G introduces network-empowered AI, where AI and computational resources are fully integrated. The network manages and optimizes the connected devices,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **5G** & **6G** \\ \hline Mobility & 350 km/h & \textgreater{}1000 km/h \\ Delay & Tens ms & \textless{}0.1 ms \\ Reliability & 99.99\% & \textgreater{}99.99999\% \\ Peak data rate & 10 Gbps & \textgreater{}1000 Gbps \\ Connection density & \(1\) million/km\({}^{2}\) & \textgreater{}10 million/km\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of key performance metrics between 6G and 5G cellular networks.  
Figure 2: Potential applications and use cases of the 6G mobile technology.  
creating an intelligent infrastructure. Additionally, AI-empowered networks will enable 6G to dynamically adapt to changing environments, using AI-driven controllers to enhance both network efficiency and real-time performance.  
Sixth-generation cellular networks are envisioned as the successor to fifth-generation variants, being expected to revolutionize wireless communication by integrating AI, THz communication, and satellite-terrestrial networks. The key features of 6G include  
* Extreme data rates, potentially reaching terabits per second;
* AI-driven network automation for self-optimizing connectivity;
* Satellite integration for global coverage and seamless connectivity.  
Sixth-generation cellular networks will build upon SDN principles, further improving network flexibility and enabling adaptive control based on AI and real-time data analytics.
# Computational Study of Inertial Flows in Helical Microchannels
## 2 Theoretical Background
### Dean Force  
The Dean force is linked to Dean vortices in curved geometries. The origin of this secondary flow is a parabolic fluid flow within the cross-section. The fastest fluid, which is approximately in the centre of the cross-section, undergoes the highest centrifugal force. Therefore, it is pushed outward away from the centre of curvature. The rest of the fluid within the cross-section conforms to the movement of this fastest fluid, and this results in a pair of symmetric Dean vortices. The intensity of the secondary flow is dependent on the intensity of the primary flow but not linearly [1; 16].  
The Dean force acting on a particle in a flow can be evaluated as follows  
\[F_{D}=3\pi\mu aU_{D}, \tag{4}\]  
where \(\mu\) is the dynamic viscosity, \(a\) is the particle diameter, and \  
However, (5) and (7) should be used with caution. Both formulae were determined from an experiment with fixed geometry, where the curvature radius was significantly larger than in our study.  
Another formula used in the literature [4,11] gives us an equation to calculate the maximal Dean velocity as  
\[U_{Dmax}\sim\frac{De^{2}\mu}{\rho D_{h}} \tag{8}\]  
and then the maximal Dean force becomes  
\[F_{Dmax}\sim\frac{\rho U_{max}^{2}aD_{h}^{2}}{2R_{c}} \tag{9}\]  
However, the authors in these works do not specify any parameter or a constant that would define more precisely the value of the \(U_{Dmax}\), additionally to a statement that it is proportional to a combination of the simulation parameters as we can see in (8). We can expect that this parameter would be not a simple constant but a variable depending on a position of the particle, or on the Reynolds or the Dean number.  
For a fixed simulation setup, the Dean drag force would be different across the cross-section of the channel, as the velocity in the Dean vortices is not the same within the cross-section. The previous formulae proposed only rough estimates of the values of the forces acting on a particle. We can see that the analytical calculation of the lift and Dean forces are not clear, and thus numerical simulations can help to better understand the trajectories of particles immersed in curved channels.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 5 Experiments
### Data Preparation  
We used the data collected at the University of California at Davis [5] ([[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) Pvev0 hJ82 usPh6 dWDiz7 Lv8L6h3 JpWhE]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) Pvev0 hJ82 usPh6 dWDiz7 Lv8L6h3 JpWhE) accessed on 16 April 2022) in [8], where five different types of QUIC traffic types were classified using machine learning techniques in addition to bandwidth and duration. The encrypted traffic flows were based on four Google services and one YouTube service, the details are shown in Table 2. The data for each service was organized in different folders in a text file format, where each text file data represents a single flow. In each text file again, there are multiple two-way communication data consisting of a time-stamp, relative time (from the first packet), and packet length. The data was preprocessed by applying the same method used in [8]. We used the inter-arrival time, packet length, and direction of each packet as features for the multi-class classification task. In more detail, for each packet, two features were used; inter-arrival time and packet length combined with direction. Both packet length and inter-arrival times were normalized using 1434 bytes and 1 s, respectively, as in [8]. Header information, statistical features such as bandwidth and duration of flow, and payload can be used as features. However, consideration of these as features required the observing of the whole duration of flow, which is undesirable for online encrypted traffic classification. Moreover, accuracy tends to be lower [11] when header information is used as features, and, therefore, we opted for using the said two features in this study as a further exploration.
## 3 ZigBee Protocol Stack Overview and Configuration
### ZigBee Overview
While the hierarchical topology is mainly designed for many-to-one traffic, the mesh topology facilitates the communication between any two nodes of the network, _i.e._, it supports point-to-point traffic. In mesh topologies, the basic procedure of finding a route consists in disseminating Route Request (RREQ) messages across the network, parting from the source node, until the destination node is reached. The destination node then responds with a Route Reply (RREP) message that is propagated back to the source node over the discovered route. The route selection is based on the path cost calculation metric used by ZigBee, which computes a link cost \(C\{l\}\) for every link on a route and calculates the cost of a path as the summation of the costs of all of the links along the path. When multiple RREQ messages reach the destination, the route corresponding to the one with the minimum total path cost is selected. \(C\{l\}\) is defined to range between one and seven and is calculated as:  
\[C\{l\}= \begin{cases}\textit{min}\left(7,\text{round}\left(\frac{1}{p_{l}^{4}} \right)\right)&\text{(default),}\\ 7&\text{(optional),}\end{cases}  \tag{1}\]  
where \(p_{l}\) is the one hop link delivery ratio (LDR) [13]. The LDR of a link can be estimated from the link quality indication (LQI) calculated by the receiving node on the reception of a packet. The LQI may be obtained from measurements of the signal-to-noise ratio (SNR), the received signal strength indicator (RSSI) or a combination of both. The conversion of LQI to LDR is not specified in the specification and is up to the implementation. Setting the link cost to a fixed value, as in Equation (2), is also an option provided by the ZigBee specification. This option is equivalent to applying the hop count (HC) metric that chooses the shortest available route. The APL layer is the top layer of the ZigBee protocol stack, which provides an optional end-to-end reliability mechanism to assure the successful delivery of data between two end nodes. End-to-end ACKs can be used to confirm the reception of the data at the destination node. The loss of a packet is assumed if after a certain amount of time, no application layer acknowledgment (APL ACK) has been received from the destination node. Then, a retransmission of the data packet is initiated. The duration of the time interval during which a response is expected is specified by the retransmission timeout (RTO) value. ZigBee uses blockwise acknowledgments, which are used to confirm the successful reception of several packets at once, similar to the cumulative acknowledgment method of Transmission Control Protocol (TCP) [26]. The recommended block size according to the ZigBee HA profile is one. With these settings, the end-to-end reliability mechanism in a ZigBee WHAN behaves like a simple stop-and-wait mechanism with positive ACKs.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 2 Experimental Analysis of the Key Performance of Hollow Self-Drilling Anchor Bolts
### Overview of the Experimental Site Conditions  
The experimental site is located in an open abandoned mine pit in the Jinpu New District of Dalian. Prolonged mining activities have resulted in multiple stages of artificial slopes with heights ranging from 30 to 50 m, presenting a stepped configuration. The slopes are primarily concave and straight, with some areas exhibiting irregular profiles. The slope angles vary between 35\({}^{\circ}\) and 60\({}^{\circ}\). Due to geological and weathering effects, the rock mass is fragmented, with loose rock blocks accumulated and signs of slumping observed. The stratigraphy, from top to bottom, can be classified as follows: a gravelly sandy soil layer, a strongly weathered dolomite layer, and a weakly weathered dolomite layer. The site is unaffected by groundwater.  
For the purpose of the experiment and to ensure the relative uniqueness of the variables, we selected a section of the slope with a thick exposure of the strongly weathered dolomite layer for the test. The slope inclination was artificially cut to 45\({}^{\circ}\) for the experiment.  
The equipment layout for the field experiment is shown in Figure 4. The anchor bolts are arranged on the same slope surface. To minimize the influence of anchor bolt spacing on the test results, the anchor bolts are uniformly spaced at 3 m apart. Vertically, only two rows of anchor bolts are arranged, and the experiment is conducted using a method where the bolts are installed perpendicular to the slope surface.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 2 Related Work
### Infrared and Visible Image Fusion
#### 2.1.2 DL-Based Fusion Methods  
DL learns with multilayered and deep neural networks. The networks can model the complex relationships between data and all trained parameters are preserved in the connective matrix. Compared with traditional feature extraction methods, DL techniques can extract features automatically without manual operations and have got state-of-the-art results in computer vision, pattern recognition, and related image processing areas. The DL-based infrared and visible image fusion method was first proposed by [PERSON] et al. [23] in 2016. In the article, the authors proposed a CNN based siamese network for multi-focus image fusion and extended it for infrared and visible image fusion. Later, considering the different imaging modalities, they combined the image pyramid decomposition and a local similarity strategy [24] with the former siamese network to fuse infrared and visible images. The siamese network consisted of two branches and the weights were constrained to the same. That was unsuitable for extracting the feature maps of infrared and visible images since they were in two modalities. In order to reduce the information loss and extract more salient features, [PERSON] et al. [2] decomposed the sources into base parts and detail content. Then they used the fixed VGG-19 [54] to extract deep features of detail content and reconstruct the detail parts of the final image with the multi-layers' fusion strategy while the base parts are dealt with using the average strategy. The decomposition process for the base part and detail content was complex. They also proposed a novel fusion framework with zero-phase component analysis (ZCA) and ResNet [25] to avoid performance degradation of fusion methods using deep features directly. Moreover, another fusion approach proposed by [PERSON] et al. [55] called the \"DenseFuse\" network, uses dense blocks to preserve more information from the middle layers. The above-mentioned DL-based methods all have made a breakthrough in infrared and visible image fusion research. Fusion techniques using the CNN model always have a critical prerequisite that the ground truth should be available in advance [26] which is unrealistic in the task of infrared and visible image fusion. Other fusion methods including trained networks, for example, VGG-19 or ResNet-50, and relevant decompositions were implemented intricately and cost a lot of computing resources. Therefore, the depth of networks and the computing requirements need to be taken into account when used in practical fusion applications. Last year, [PERSON] et al. [26] innovatively proposed the method with GAN which formulated the fusion as an adversarial game between keeping the infrared thermal radiation information and preserving the visible appearance texture information. There was no pre-training and the network was trained by cropped infrared and visible image pairs. This end-to-end model has got commendable results both in qualitative and quantitative comparisons.
## 3 Complex-Valued Conditional Generative Adversarial Network
### Discriminator Network  
The discriminator network evaluates the generated noise-free signals and the corresponding reference signals so that the reference signals score higher, and the output of the generator network will be closer and closer to the reference signals through the mutual game between the generator network and the discriminator network. In order to improve the identification accuracy of the CC-GAN by using as few labeled samples as possible and reducing the dependency on a large amount of labeled data for classification tasks, we integrated an auxiliary classifier into the discriminator network [30]. Compared with classical GANs, the discriminator of the CC-GAN can not only distinguish between real and fake samples but also assign the correct class labels to the samples.  
The discriminator network consists of ten network layers. The first to sixth layers are feature extraction layers with a consistent structure. Each feature extraction layer consists of complex-valued convolutional layers, complex-valued batch normalization layers, zReLU activation functions, and complex-valued global average pooling layers. The convolutional kernel size is set as \(3\times 1\), and the sizes of the pooling layers are 512, 256, 128, 64, 32, and 16 in sequence. The seventh layer is the flattening layer. The eighth and ninth layers are complex-valued dense layers with a unit number of 256 and 128, respectively. The last output layer of the discriminator network utilizes different activation functions based on the task requirements. A complex-valued sigmoid activation function is applied when distinguishing between real and fake samples, while a complex-valued softmax activation function is used for the classification task.
# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
## 5 Conclusions  
In this paper, we introduced a DH method that applies DBS and optimized HC(7,4) to AMBTC compressed grayscale images. The basic unit of AMBTC is the _trio_, which consists of two quantization levels and one bitmap and is represented by \(trio(a,b,bitmap)\). Therefore, AMBTC is a trioset, and the proposed DH method is applied to each block of an image. The proposed method may have different final performance results depending on the characteristics of each block. Therefore, we divided every block into two groups (smooth blocks and complex blocks) and applied the proposed method. The distinction of whether a block is a smooth block or a complex block is determined by the difference between the two quantization levels of the block. That is, if the difference \((|a-b|)\) between two quantization levels is smaller than or equal to the threshold \(T\), it is categorized as a smooth block. When hiding data in a complex block with a difference higher than threshold \(T\), the MSE errors increase compared to a smooth block. Therefore, it is important in terms of DH to distinguish the blocks. In other words, the smoother the blocks are, the more they help to maintain the image quality while concealing more data. In this paper, our proposed method achieved the optimized level by HC(7,4) based on the lookup table. As a result, it was shown through experiments that our proposed scheme surpasses the performance of [PERSON]'s method. Experimental results show that the proposed scheme provides a high EC while suppressing the loss of quality of the cover image. In the future, we will devise a method to calculate a more optimal distance when applying HC(7,4) to two quantization levels and conduct research to find a way to minimize data concealment errors.  
Conceptualization, C.-N.Y. and C.K.; methodology, D.S. and C.K.; validation, C.-N.Y. and C.K.; formal analysis, C.-N.Y.; writing, original draft preparation, C.K. and L.L.; funding acquisition, D.-K.S., C.-N.Y., C.K., and L.L. All authors read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (61866028), the Key Program Project of Research and Development (Jiangxi Provincial Department of Science and Technology) (20171 ACE50024), the Foundation of China Scholarship Council (CSC201908360075), and the Open Foundation of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition (ET201680245, TX201604002). This research was supported in part by the Ministry of Science and Technology (MOST), under grants MOST 108-2221-E-259-009-MY2 and 109-2221-E-259-010. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by 2015R1D1A1A01059253 and 2018R1D1A1B07047395 and was supported under the framework of the international cooperation program managed by NRF (2016K2A9A2A05005255). The authors declare no conflict of interest.
## 2 Materials and Methods
### Performance Metrics  
The success of the classification model is measured by the number of samples assigned to the correct class and the number of samples assigned to the wrong class. These numbers are specified in the confusion matrix and represent how much the model is correctly predicting on the entire set of data. In a two-class example, the confusion matrix [44] is shown in Table 2.  
Here, TP (True Positive) denotes the determination of the positive class as a positive class. FP (False Positive) means the determination of the negative class as a positive class. TN (True Negative) represents the determination of the negative class as a negative class. FN (False Negative) refers to the determination of the positive class as a negative class. In our study, the positive class represents failure and the negative class represents non-failure.  
The most popularly used performance metrics (recall, precision, specificity, accuracy, and F1-score) were used to evaluate fault classifiers in this study. The mathematical equations for each of these metrics are given in Equations (9) through (13):  
\[Recall\ (Rec)=\frac{TP}{TP+FN} \tag{9}\]  
\[Precision\ (Pre)=\frac{TP}{TP+FP} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\  & & Positive & Negative \\ \hline \multirow{2}{*}{**Actual Class**} & Positive & True Positive, TP & False Positive, FP \\ \cline{2-4}  & Negative & False Negative, FN & True Negative, TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: The representation of the confusion matrix.  
Figure 5: The structure of 1D-VGG16.  
\[Specificity\left(Spe\right)=\frac{TN}{TN+FP} \tag{11}\] \[Accuracy\left(Acc\right)=\frac{TN+TP}{TP+TN+FP+FN}\] (12) \[F1-score\left(F1\right)=\frac{2\times Precision\times Recall}{ Precision+Recall} \tag{13}\]  
In PdM fault classification applications, the performance of a classifier is calculated by its responses against unseen test input values before anything else. So, the dataset is separated into k-folds using the k-fold cross-validation (CV) method. When the k\(-\)1 portion of dataset is used for training, another is used for testing. This operation is repeated until all pieces are used for testing [45].