esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach
### Architecture
#### 3.5.2 E-Volume Module  
Following classification, the system accurately estimates food volume using the E-Volume module, a learning-based multi-view stereo (MVS) approach. The process begins with the data pre-processing stage, leveraging the NutritionVerse3D dataset, which includes the following diverse 3D food models:  
* Data Generation. Food models are randomly sampled and rendered onto real-world dining table scenarios using Blender, generating a rich dataset of realistic food scene images from various perspectives. The E-Volume volume estimation comprises several sub-components:  
Figure 9: Overall architecture.  
MVS body predicts depth maps for each viewpoint through a learning-based MVS approach, using MVSNet as the backbone network to generate robust 3D reconstructions.
* Mask predictor conducts semantic segmentation on selected images to produce masks delineating food objects from their surroundings clearly.
* Point cloud generator integrates depth information from multiple viewpoints, synthesizing comprehensive scene point clouds.
* Object point cloud extraction employs generated segmentation masks to isolate food objects from the scene point cloud, preparing precise input for volume calculation.  
Finally, the isolated food object point clouds undergo convolutional neural network processing, extracting global geometric features and producing accurate food volume estimations.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 5 Numerical Results
### Results on Average BER
The following parameters are used for the simulation: \(L=8\), \(m=3\), \(\rho=0.2\), \((\Omega\cdot E_{b})/N_{0}=10\); and the distances of each hop is set as \(d_{\{1,\ldots,8\}}=\{8.2,6.1,11.3,7.2,8.5,6.8,6.9,9.8\}\) meters. The BER results under the equal power allocation scheme are calculated recursively using (A4). It can be seen that the performance under balanced power allocation scheme is generally better than that under the equal power allocation scheme. Also, the performance difference between the two schemes becomes larger when the path loss exponent is greater. This is because under the balanced power allocation scheme, the performances of all hops are always identical; while for the equal power allocation scheme, when the path loss exponent is larger, the variance of the performances of each hop also becomes larger. This eventually leads to poorer performance of the relaying system since the end-to-end performance of the DF relaying network is dominated by the performance of the worst hop. Figure 6: End-to-end average BER v.s. average SNR per channel in the scenarios of Rayleigh fading (\(m=1\)) and no fading (\(m=\infty\)). Figure 8: End-to-end average BER vs. total transmission power for different values of MCA impulsive index \(A\) and path-loss exponent \(\xi\) with (**a**) equal power allocation; (**b**) allocation scheme under (4). Figure 7: End-to-end average BER vs. MCA impulsive index \(A\) for various values of \(\rho\) and the Nakagami \(m\) parameter.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 3 Risk-Based Flood Management
### Assorting Suitable Measures
#### 3.6.3. Suitable Measures at Floodplain  
Densely populated and high economic growth areas have been protected by building dykes. The construction and maintenance cost of dykes can be justified for the level of protection they provide to the floodplain [72]. [PERSON] et al. [73] investigated the enhanced exposure and increased vulnerability of The Netherlands' defense system and found that the risk was increased because of increased vulnerability. However, floodproofing or relocation can be considered if it is a small town or if there are only a few houses in the region to be covered from potential flooding. Relocation is an option only when damage is high and there is no economic reason for any other choices. Medium-low economic growth limits the deployment of costly defense systems to high-risk areas [62]. In addition, in a region with a clay soil type, the construction of a dyke is not the preferred choice [58]. Because of environmental concerns, a dyke is not recommended where there are wetlands or marshes in the region, because they dry out in the absence of regular recharging by natural floods [58].  
Water storage, artificial groundwater recharging basins, and wetlands have multiple benefits in addition to flood attenuation. Artificial groundwater recharging is one of the effective options to handle flash flooding [66]. However, many considerations are required in order to implement theoption. For a single location to be protected from flooding, reservoirs are not an option, unless the project can be economically justified by considering additional uses, such as hydropower generation, water supply, recreation, etc. Most essential of all, a suitable place should be available for building a reservoir [58]. Farmers' willingness to allow flooding to their land is very necessary for the option of the retention basin. Water-logging (high groundwater levels) in the region should not be serious. There should be no historical site of significance, or it should be possible to protect the site from the damage caused by managed flooding by building a dyke around it or by any other steps [58].
###### Abstract  
A circular sector-shaped dipole antenna with meandered arms based on an artificial magnetic conductor (AMC) is proposed in this study. A compact unit cell of the AMC operating within 19-485 MHz is designed using a ferrite material, which exhibits high-permeability characteristics in the high-frequency (HF), very-high-frequency (VHF), and ultra-high-frequency (UHF) bands. The configuration of the meandered arms is optimally designed using a binary genetic algorithm, so that the antenna combined with the AMC meets the voltage standing wave ratio (VSWR) specification of 3.5:1 or less within the HF, VHF, and UHF bands. The proposed antenna with the AMC is fabricated and measured. The measured VSWR frequency bandwidth (3.5:1 or less) is 25.8:1 (14-361 MHz), and the measured result matches with the simulated result well. Furthermore, to verify the performance of the proposed antenna with the AMC, the measured received power results are compared with a commercial reference antenna operating in HF, VHF, and UHF bands.  
artificial magnetic conductor (AMC); dipole antenna; electrically small antenna (ESA); HF (high-frequency); low-profile; UHF (ultra-high-frequency); VHF (very-high-frequency) +
Footnote †: journal: Applied Sciences  
AMC for application to an antenna. The operating frequency bandwidth of the artificial magnetic conductor is proportional to the equivalent inductance [22]. In order to increase the equivalent inductance, ferrite with a high permeability value was used as a substrate. Each element of the dipole antenna has a shape combined with the meandered arms symmetrically, which are located up and down around the circular sector-shaped antenna. The configuration of the meandered arm was optimally designed using a binary genetic algorithm, so that the antenna satisfies the VSWR characteristics of 3.5:1 or less in the HF, VHF, and UHF bands. Compared with the previous studies [19; 20], the size and operating bandwidth characteristics are improved from those of the study [19], and it operates at the lowest operating frequency, lower than the antenna of the study [20]. All the simulations in this study were conducted using the CST software. The AMC and antenna designs are described in Section 2. In Section 3, the simulation and measurement results of the designed antenna are presented. Section 4 compares the performance of the designed antenna and the commercial antenna. Finally, this paper ends in Section 5.
## 3 Design and Implementation of the Proposed CBC Method
### Feature Selection Using SelectKBest  
Feature selection is a crucial step in improving the performance of a system. Researchers previously devised a method for feature selection using machine learning for intrusion detection systems in IoT networks, emphasizing effective feature selection to enhance detection accuracy. The authors utilized feature ranking and dimensionality reduction techniques to improve model performance and reduce computational overhead [21]. For this purpose, the SelectKBest algorithm is employed, as it helps to select the optimal and robust features based on a scoring function. The top 10 selected features are UDP, psh_flag_number, rst_flag_number, TCP, Protocol Type, ack_flag_number, ack_count, ICMP, syn_flag_number, and fin_flag_number. After data loading, the necessary data pre-processing steps are performed, including the removal of null values and the handling of missing values. Afterwards, the SelectKBest algorithm is initialized with a scoring function, and it selects the top K features using Equation (2). Algorithm 2 provides the pseudo code of the SelectBest algorithm used to select optimal features, and a flow diagram is presented in Figure 5.  
\[X\_new=SelectKBest(X,y,k) \tag{2}\]  
In this study, the SelectKBest feature selection method with Mutual Information (MI) is applied as the scoring function. The fact that MI captures both linear and non-linear dependencies between features and the target variable makes it appropriate for both two-class and eight-class classification tasks. It helps retain the most informative features, thereby improving model performance.  
Figure 4: Cluster mapping for 2-class classification using 2 clusters.  
```
0:\(X\) (Feature matrix), \(y\) (Target vector), \(k\) (Number of features to select)
0:\(X_{\text{new}}\) (Feature matrix with top \(k\) features)
1:Step 1: Data Preparation
2: Import necessary libraries
3: Load dataset \(X\) and target \(y\)
4: Remove rows with null values from \(X\) and corresponding entries from \(y\)
5: Fill missing values in \(X\) using an appropriate strategy (e.g., mean imputation)
6:Step 2: Feature Selection
7: Initialize the SelectKBest feature selector with a scoring function (e.g., \(f_{classif}\) for classification tasks) and \(k\)
8: Fit the feature selector to \(X\) and \(y\)
9:\(X_{\text{new}}\leftarrow\) Transform \(X\) to select the top \(k\) features using the feature selector (Equation (2)): \[X_{\text{new}}=SelectKBest(X,y,k)\]
10:return\(X_{\text{new}}\)
```  
**Algorithm 2** SelectKBest Algorithm  
Figure 5: Flow diagram of the SelectKBest algorithm.
## 4 Analyses of Test Results
### Strain of Reinforcement  
Figure 16 shows the strain of the reinforcement at each layer with respect to the load. The maximum tensile strain was 0.14% at a height of \(-\)0.2 m in the 1L section, 0.11% at a height of \(-\)0.2 m in the 1R section, and 0.09% at a height of 4.6 m in the 2R section. It was determined that all reinforcements were stable, even at the final loading stage, which exhibited less than 5% of the design tensile strain of the general geogrid. The maximum compressive strain was measured at 0.29% in the 1L section, 0.12% in the 1R section, and 0.37% in the 2R section, all of which occurred at a height of 3.4 m.  
Unlike other heights, strain at the heights of 2.2 m and 3.4 m, with a 0.7 m distance from the wall, showed a large compressive strain as the load increased. This compressive strain may be owing to the plastic deformation of the soil and the resistance to the horizontal earth pressure of the rigid wall. [PERSON] proposed a spiral failure mechanism [31], in which the upper part of the wall is in a rigid state, and a plastic region occurs in a spiral form from the load point to the middle part of the wall, as shown in Figure 17. It was considered that compressive strain occurred in the reinforcement installed in the middle part (2.2 m, 3.4 m height) by applying force to the reinforcement while the soil moved along the plastic area. Here, the horizontal stress and displacement generated in the wall are the largest in the middle part of the wall, which is similar to the results of previous studies [8; 32]. In summary, it was confirmed that when short reinforcements are used, tensile force occurs in the lower part of the wall, and a reinforcement at the 2/3 H height is governed by the compression force. This may be owing to the short reinforcement located on the inner side of the failure surface in the soil.  
Figure 15: Horizontal displacement of wall with respect to height.  
Figure 16: Reinforcement strain with respect to load.  
Figure 17: Spiral failure mechanism ([PERSON], 2011).  
In the load-strain curves of most reinforcements, a strain pattern showing an inflection point at a load between 500 kPa and 583 kPa was observed. This is similar to the pattern of the settlement and horizontal displacement of the wall described above, and it shows that reinforcement has a greater influence on deformation control after the yield of soil occurs. This pattern is similar to that described above for the settlement and horizontal displacement of the wall, and it shows that the influence of reinforcement on deformation control increases as the load increases after the yield of the soil occurs.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work  
This section discusses the limitations and future directions for deep-learning-based methods for medical video analysis. The lack of available data and class imbalance in medical videos are some of the significant challenges. Another issue is the sparse labeling of videos, which results in biased and incomplete datasets. Furthermore, the inability to know the reason behind decisions made by the model and time complexity are major concerns. Medical data impose unique challenges that need to be handled during preprocessing, including video format, resolution, and computation time. To address these challenges, downsampling can be performed while attempting to avoid the loss of features useful in differentiating dissimilar classes. However, the ideal resolution size can vary depending on the complexity of visual structures and data type. This section also discusses different techniques and approaches to improve medical video analysis using deep learning. Transfer learning is explored as a solution to the limited availability of labeled data in medical cases. Multitask learning helps learn different parameters from limited available video data. Data augmentation techniques are widely used for imbalanced or small datasets. Incorporating expert knowledge and telemedicine can help improve the quality of medical videos. Semisupervised learning is helpful for medical videos because labeling a massive amount of videos for supervised learning is time-consuming and expensive. N-shot learning, including few-shot, zero-shot, and one-shot learning, is a data-efficient approach that can be used efficiently in the medical field. Further details about these limitations and future work are given below.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
###### Abstract  
Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites' redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS's performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS's positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites' signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC's performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.  
GNSS; GPS; satellite selection; WGDOP; SNR-WGDOP; optimization algorithm; ABC; ACO; GA; PSO; SA
# Fronthaul Design for Wireless Networks
## 2 Background on C-RAN and Fronthaul Communication Technologies and Related Work
### Typical Fronthaul Communication Technologies
Other attenuation factors are atmospheric turbulence, i.e., changes in the atmosphere refractive index (e.g., due to high temperatures), which can cause deviations in the beam, and interference from background light sources (including the Sun). The deployment costs of FSO systems are independent of each hop length (if the same equipment is used). Moreover, these systems have the advantage of being license free, and no planning permission is required as long as they are eye safe. Nevertheless, the maximum achievable range per hop is lower than in MRT [17]. FO communication systems are light wave systems that employ optical fibers for information transmission, which means that these are not wireless systems (as opposed to MRT and FSO technologies) and, therefore, do not require line-of-sight. The capacity of current optical fibers can reach values up to several hundred Gbps (per wavelength channel) [19], being mainly conditioned by optical pulse widening due to fiber dispersion, which limits the maximum bit rate \(\times\) distance product (a metric that describes the fiber performance in terms of transmission capability). Since the optical fiber cable is usually installed underground, FO systems may be damaged by floods. Moreover, accessibility issues regarding the path of installation of optical fibers, such as those in mountainous regions, might lead to FO being regarded as an unsuitable option. The deployment costs of an FO system vary linearly (roughly) with the considered connection length [14]. Accordingly, the installation of FO systems is usually more expensive than MRT and FSO systems' installation. A summary of the main differences between MRT, FSO, and FO is presented in Table 1. As can be inferred from this table, the best choice regarding the fronthaul communication technology is not straightforward, as it depends on the required bit rate, terrain characteristics, and typical weather conditions of the installation sites, among others.
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 3 Materials and Methods
### Camera Calibration  
Camera calibration determines the intrinsic parameter matrix during data acquisition, establishing the transformation relationship between the 2D-pixel coordinates of feature points and their corresponding 3D real-world coordinates. The imaging process of an object involves mapping transformations from 3D real coordinates to 2D plane coordinates. To achieve inverse projection from a 2D image to a 3D model, the intrinsic parameter matrix must be calibrated by adjusting for the transformation of the imaging coordinate system corresponding to the feature point coordinates. This calibration allows for camera position calculation and object reconstruction.  
The camera imaging model illustrates how three-dimensional objects in the real world transform into two-dimensional images via the camera's imaging process. In the field of computer vision, this typically involves four coordinate systems: the pixel, image, camera, and world coordinate systems. A diagram depicting the three-dimensional coordinate transformation via these coordinate systems is shown in Figure 3.  
In 3D reconstruction, camera calibration involves determining internal parameters such as focal length and principal point. This study employs the [PERSON] calibration method [18], utilizing a checkerboard calibration pattern of 14 \(\times\) 10 squares. We collected approximately 15-20 images from various angles with the camera to be calibrated. Subsequently, we used the MATLAB 2024a camera calibration toolbox to output the internal parameters.  
Figure 2: The overall framework of the proposed method.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 5 Conclusions  
Due to the high precision requirements for satellite payload data transmission via a ground network, the TCP protocol can be considered competitive. However, the limitations of the standard TCP protocol on bandwidth utilization for networks with a long delay and large bandwidth reduce data transmission efficiency. This article uses TC to design a WAN simulation environment. Four congestion control algorithms--Reno, BBR, BoostTCP, and Cubic--are tested and compared in terms of throughput, fairness, and preemptibility. The results indicate that BoostTCP is more adaptable to network conditions and has a significantly higher throughput than the other three algorithms. In addition, it is fairly distributed across multiple data flows and has relatively strong preemption capability when multiple protocols are used. Finally, the throughput of BoostTCP is verified and tested in a real-world environment, and the results indicate that the real-world performance is identical to that in a simulated environment. Therefore, the proposed TCP acceleration algorithm can be used to improve the performance of ground networks when transmitting satellite payload data. In recent years, as a new field of quantum technology, the implementation of a quantum algorithm and quantum network has received increasing attention from scholars. In the next step, we will discuss the application of quantum algorithms and quantum networks in aerospace-ground service networks.  
Conceptualization, C.L.; methodology, C.L.; software, C.L. and J.Z.; validation, C.L., F.M. and S.C.; formal analysis, C.L. and J.Z.; investigation, N.F.; resources, X.W. and Y.C.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L. and S.C.; visualization, F.M.; supervision, N.F.; project administration, C.L.; funding acquisition, X.W. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Dataset Description
#### 4.1.2. Cicids2017  
The benign and most recent common attacks are included in the CICIDS2017 dataset, which accurately represents actual real-world data (PCAPs). It also contains CIC flow meter network traffic analysis results, attack types (CSV files), source and destination IP addresses, protocols, and labeling flows according to time stamps.