esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 6 Discussion and Findings
### Proportion Results of the UEs in the Cluster  
The unsupervised ML technique clustered the data into three groups. Thus, this study opted for a density-based clustering technique called Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Figures 6 and 7 reveal that the HDBSCAN clustering technique produces the DL and UL traffic proportions generated by the UEs. The data displayed the traffic size in different clusters, highlighting the significance of traffic size compared to the total number of UEs. In addition, the number of UEs had no impact on the UE distribution in the cluster. This result observed a random distribution pattern of the number of UEs in the clusters (blue line). Nonetheless, traffic size produced increasing patterns along the increase of the traffic size (red bar). Each cluster also comprised several traffic classifications that were determined by similar attributes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Simulation Parameters** & **Method** & **Remarks** \\ \hline
4 & Types of Transport Network & Defined & 1. & Optical Fiber Network \\  & & 2. & Satellite Network \\  & & 3. & DOCSIS Coaxial Cable TV \\ \hline
5 & Scenarios & Defined & 1. & A dedicated transport network deployed to a single node in a service area. \\  & & & 2. & A shared transport network deployed to an area with multiple service nodes. \\ \hline
6 & Transport Network: Link & Defined & 1. & Optical Fiber Network (300G for DL and UL) \\  & Capacity. & & 2. & Satellite Network (35G for DL and UL) \\  & & & 3. & DOCSIS Coaxial Cable TV(DL: 10G and UL: 6G) \\ \hline
7 & Transport Network: External & & \\  & Usages (for shared transport network scenario) & Random & Based on the randomly generated percentage applied to each instance’s total link capacity. \\ \hline
8 & Duration & Defined & The simulation is set to run, generate, and capture the relevant information for 60 instances. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial State** & **Clustering** & **Compositions** & **\%** \\ \hline  & & Normal\_Static UEs: 537 & 9.87 \\ \cline{3-4}  & & Normal\_Dynamic UEs: 597 & 10.97 \\ \cline{3-4}  & Cluster \#1: 29.67\% & \
\begin{tabular}{c} Use Case\_eMBB: 349 \\ \end{tabular} & 6.41 \\ \cline{3-4}  & & Use Case\_URLLC: 6 & 0.11 \\ \cline{3-4}  & & Use Case\_mMTC: 126 & 2.31 \\ \cline{3-4}  & & Normal\_Static UEs: 247 & 4.54 \\ \cline{3-4}  & & Normal\_Dynamic UEs: 100 & 1.84 \\ \cline{3-4} Total No. of UEs: 100\% & Cluster \#2: 29.89\% &
\begin{tabular}{c} Use Case\_eMBB: 209 \\ \end{tabular} & 3.84 \\ \cline{3-4}  & & Use Case\_URLLC: 59 & 1.08 \\ \cline{3-4}  & & Use Case\_mMTC: 1012 & 18.59 \\ \hline  & & Normal\_Static UEs: 609 & 11.19 \\ \cline{3-4}  & & Normal\_Dynamic UEs: 262 & 4.81 \\ \cline{3-4}  & & Use Case\_eMBB: 683 & 12.55 \\ \cline{3-4}  & & Use Case\_URLLC: 185 & 3.40 \\ \cline{3-4}  & & Use Case\_mMTC: 462 & 8.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The outcome of the clustering process.
## 3 Experimental Results and Discussion
### SEM Results and Analysis  
Images magnified from 100\(\times\) to 20,000\(\times\) were utilized to analyze the microstructure characteristics of concrete under different repair conditions, as shown in Figure 8. From Figure 8a, it can be observed that freeze-thaw cycles induced an increase in the number of pores and the roughness of the surface. However, the flatness of the specimens increased and the number of pores markedly decreased after the application of the surface reinforcer, as shown in Figure 8b. Figure 8c reveals that, for the standard concrete specimens, a large amount of C-S-H gel could be found on the surface of the matrix.  
Figure 7: Pore morphology and parameters for different depths of the repaired specimen. (**a**) Surface pore morphology. (**b**) 10 mm depth pore morphology. (**c**) 20 mm depth pore morphology. (**d**) Pore parameters under different depths.  
Figure 7: _Cont._  
When the surface reinforcer was applied, compared with the standard concrete, under the same magnification condition (10,000\(\times\)), the surface reinforcer had an obvious promoting effect on the numerous cementitious products generated by the secondary reaction between the surface reinforcer and the hydration product. Therefore, the surface reinforcer was able to fill the microvoids, as shown in Figure 8d. The above phenomenon indicated that the surface reinforcer could effectively help to increase the compactness and reduce the void ratio of the concrete specimen, resulting in an increase in the specimens' macroscopic mechanical properties. In addition, the microscopic morphology and elemental composition of specimens coated with different types of surface reinforcer were also obtained, as shown in Figure 8e-g. By comparing the above images, it could be observed that two microscopic morphology shapes (i.e., spherical and columnar) of surface reinforcer were observed for types A, B, and C in the present research. The primary constituents of surface reinforcer A are Si, O, and Ca. Regarding surface reinforcer B, the biggest percentage is accounted for by Ca, Si, and K. Ca and Si are the primary components of surface reinforcer C. In summary, the main components of the above three types of surface reinforcer were Si, Al, O, and K.
## 2 Basic Principles of RNS Arithmetic
### Computational Complexity and Optimization  
In the conventional non-redundant RNS, calculating both the rank \(\rho_{k}(X)\) and the digits \(\langle x_{k},x_{k-1},\ldots,x_{1}\rangle\) of the mixed-radix representation of the number \(X\in\mathbb{Z}_{M_{k}}\) from its residue code \((\chi_{1},\chi_{2},\ldots,\chi_{k})\) reduces to the parallel summation of sets of small-bitlength residues corresponding to the RNS moduli \(m_{1},m_{2},\ldots,m_{k}\), while accounting for the number of overflows that occur in the rings \(\mathbb{Z}_{m_{1}},\mathbb{Z}_{m_{2}},\ldots,\mathbb{Z}_{m_{k}}\) during the modular addition operations.  
At the same time, according to [25; 27], the computational complexity of calculating the rank \(\rho_{k}(X)\) is \(O(k^{2})\) in terms of modular addition operations and the number of required lookup tables. Consequently, this becomes computationally expensive as \(k\) increases, for instance, when implementing non-modular operations based on the rank form of integer representation (1), where operands and computational results belong to large numerical ranges.  
One possible way to improve modular computational structures is to employ more efficient and optimized variants of RNS arithmetic compared to the traditional approach. As is well known, the use of code redundancy can improve the arithmetic properties of an RNS [5; 6].  
As demonstrated in [25; 27], the use of a minimally redundant RNS enables the optimization and acceleration of the rank calculation. To this end, an additional redundant residue, \(\chi_{0}=\left|X\right|_{m_{0}}\), corresponding to an additional modulus \(m_{0}=2\), is added to the original residue code \((\chi_{1},\chi_{2},\ldots,\chi_{k})\) of the number \(X\in\mathbb{Z}_{M_{k}}\). Thus, the non-redundant residue code is extended by just one bit, representing the parity of the number \(X\).  
This modification reduces the computational complexity of rank calculation from \(O(k^{2})\) to \(O(k)\)[25; 27].  
Consequently, using minimally redundant RNS arithmetic not only optimizes but also improves the efficiency of non-modular operations that rely on the rank form of integers (1), such as the base extension operation.
## 1 Introduction
Figure 3 provides the effective communication range for VANET architecture. The architecture shows that during the V2V communication, two vehicles communicate with each other, i.e., vehicles A and B. During V2I communication, the architecture is communicated with the vehicle, i.e., RSU is communicating with vehicle G. The communication range is defined under every vehicle, based on the area surrounding the vehicle, in which it communicates with other vehicles or infrastructure [18]. Figure 3: Communication in VANET using V2V, V2I, and I2I [19]. Figure 2: Communication architecture under the VANET environment [16]. Machine learning has recently been a hot topic, and its application in vehicular ad hoc networks (VANETs) has gained much attention. VANETs are an emerging technology that enables vehicles to communicate with each other and the road infrastructure to improve road safety and traffic efficiency. However, the success of VANETs depends on the reliability and efficiency of communication between vehicles, which can be affected by various factors such as signal interference, dynamic network topology, and unpredictable mobility patterns [20]. Machine learning techniques can be applied to VANETs to overcome these challenges and enhance the network's performance. One of the main applications of machine learning in VANETs is predicting vehicles' behavior on the road. Machine learning algorithms can analyze the data collected from various sensors and communication devices installed in the vehicle to learn driving behavior patterns, such as speed, acceleration, and lane changes. This information can then be used to predict the future behavior of the vehicle and improve communication between vehicles [21]. The machine learning algorithm is very effective at using straight learning in the machine to provide effective and quality-based training. It supports algorithms, deep learning, and AI, and shows classification and detection accuracies in the network. We adopted ML in our model to train the network to detect DDoS-compromised vehicles [22].
## 1 Introduction
### Related Work  
Because of its high load capacity and stability, the overconstrained CDPM is an interesting topic for many researchers. As a consequence of the redundant actuators, formulating the model in order to directly obtain the feasible distribution of force in real-time is a challenge. The interval analysis and Levenberg-Marquardt algorithm for real-time forward kinematics are deployed by [PERSON] and [PERSON] in [7; 8]. The same problem in planar CDPM has been studied by [PERSON] et al. in [5] by using extra force sensors. [PERSON] et al. employed quadratic programming method in [9] to handle the tension distribution in the spatial CDPM. Linear programming method is used in [6] to distribute the tension by [PERSON] et al. A versatile tension distribution algorithm for two redundant actuators' CDPM was proposed by [PERSON], [PERSON] et al. in [10]. That algorithm is based on the properties of a feasible region related to the null space of wrench matrix and unidirectional searching technique. [PERSON], [PERSON] et al. employed a linear programming approach [11] to determine minimum L\({}^{1}\)-norm solution in feasible polyhedron (associated with the null space of wrench matrix). These numerical methods are confirmed as usable in real-time and their results are acceptable. However, not mentioning the elasticity and sagging can cause the degeneration in motion of the large-scale CDPMs. The sagging of the cable can be categorized by the source causing it: elasticity and adjusting length. Elastodynamic problem was analyzed by [PERSON] et al. in [12] by considering the dynamic stiffness of the sagging cable. [PERSON], [PERSON] et al. are honored to establish a simplified model for cable [13] in static analysis of large-dimension CDPM. The mass and elasticity of cable are taken into account in dual problems: static and kinematic of CDPMs mentioned in [14]. [PERSON], [PERSON] et al. also used numerical methods to obtain a feasible solution. The combination of interval analysis and the Newton-Raphson scheme, performed to solve the forward kinematics with the presence of sagging cable, is presented in [15]. Although the method proposed by [PERSON] et al. produces a good solution, it cannot be used in real-time. Another article that uses interval analysis to real-time solve forward kinematics for both elastic and non-elastic cables is shown in [16]. Meanwhile, the methods used in [16] do not deal with saggable cables and ignore the mass of them.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Theoretical Analysis of the Improved BPA
#### 3.3.3 Azimuth Compression  
The azimuth compression of a data segment is achieved by coherently accumulating \(S(\eta,x_{m},y_{n})\), the result obtained is defined as a subimage, which can be expressed as  
\[S_{sub}(x_{m},y_{n})=\int_{-\frac{\pi}{2}}^{\frac{\pi}{2}}S_{sub}(\eta,x_{m},y _{n})d\eta \tag{14}\]  
\(T\) is the time length of each data segment and the synthetic aperture time. The final SAR image is generated by accumulating each subimage, which can be expressed as  
\[S(x_{m},y_{n})=\sum_{i=1}^{N}S_{sub}(x_{m},y_{n}) \tag{15}\]  
\(N\) is the total number of data segments.  
As we all know, with the increase of scene area and synthetic aperture time, the computational cost of BPA will increase sharply. This makes BPA difficult to adopt in SAR technology. Fortunately, the two main steps (the range compression and the back-projection) of BPA can be highly parallelized. A software program taking advantage of a GPU can benefit from speed improvements thanks to parallel computing. In the next section, we will analyze these two steps.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 7 Suppression Method of Photodarkening
### Add Deexcitation Channel  
The results show that co-doping a Yb-doped fiber with Er\({}^{3+}\) can significantly reduce the additional loss caused by PD [6; 105]. [PERSON] et al. [106] tested the additional loss of Yb/Al and Yb/Al/Er fiber respectively. It was found that the additional loss of Yb/Al/Er fiber was significantly reduced, and its fluorescence intensity in the visible band was relatively weak. They thought that this was because a small amount of Tm\({}^{3+}\) ion in Yb\({}^{3+}\) cluster or Yb\({}^{3+}\) ion carried out energy transfer with Er\({}^{3+}\) in Yb/Er fiber, while Er\({}^{3+}\) did not have effective up conversion and excited-state absorption. Electrons in the high-energy state jump to the ground state with non-radiation, reducing the ultraviolet radiation, thus inhibiting the PD effect. Other Co-doped ions such as Tb\({}^{3+}\), Eu\({}^{3+}\), and Nd\({}^{3+}\) can also increase the de-excitation channel, but the additional doping of other ions requires careful analysis of the laser characteristics of the fiber to ensure that other properties of Yb-doped fiber laser will not be lost.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Architecture
#### 2.1.4 Discriminator Network Design  
The discriminator, as shown in Figure 6, plays the role of steganalysis, which accepts both cover frames and steganographic frames as the inputs. It is made up of three conv-modules and one final convolutional layer to get a score for cover frames and stego frames, respectively. When the encoder is fixed and the discriminator is trained, the discriminator can distinguish between the cover frames and stego frames. On the contrary, the discriminator cannot distinguish them. After the alternating iterative training, the performance of the encoder continues to improve, and stego frames can confuse the discriminator.
## 3 Results and Discussion
### Thermal Performance Factor  
The _PEC_ values of the enhanced tube with BTs are illustrated in Figure 8. In all cases, the thermal performance factors are below unity, but the _PEC_ values increase with an increase in the Reynolds number. In general, both the Nusselt number ratio and friction factor ratio increase with the increase of the Reynolds number, as respectively shown in Figures 5b and 6b. However, the effect of the Nusselt number ratio is dominant over the friction factor ratio when a high Reynolds number is used, resulting in the increase of thermal performance factor. Moreover, it is noted that when a smaller spacer length (\(S\) = 40 and 51.77 mm) is used, the _PEC_ values for BTs with smaller diameter ratios (\(BDR\) = 0.5 and 0.75) is higher than the diameter ratio of \(BDR\) = 1. The reason can be explained by the fact that using a smaller spacer length between the BTs is more effective to frequently disturb the boundary layer, which leads to stronger disturbance upon the fluid. This result suggests that it is preferable to use a smaller ball diameter ratio and a smaller spacer length for the design of BTs.  
Figure 7: Velocity contours at Re = 20,000.
# vIS: An Immersive Virtual Storytelling System for Vocational Training
## 3 Materials and Methods
### Study Design  
In order to empirically evaluate the above research questions, we developed a between-subject experiment in which participants were allocated, in a round-robin format, to one of three training methods:  
1. vIS: In this method, the participants were given HTC Vive headsets and a headphone to view the immersive story. The immersive story was 2 min and 50 s long.
2. Two-dimensional video: In this method, participants were presented with a 2D tutorial video, without any storytelling components, which explained all the steps of using a micrometer. The video was 3 min long.
3. Text manual: In this method--the traditional approach to learning a new skill--participants were given an instructional manual which outlined all the steps of using a micrometer. The manual contained pictures of the components of a micrometer along with the usage steps. The length of the manual was 2 pages. No storytelling elements were included in this manual.  
In all of the above methods, the participants were given the freedom to repeat the training again until they felt confident in the use of a micrometer. To measure the learning and recall of these methods, our user study was divided into two phases with a gap of seven days between both phases.  
1. Day 1: Training session. Participants were assigned to one of the above-mentioned training methods, and they interacted with the assigned system until they felt confident in the use of a micrometer. Once the training was complete, they were given a cylindrical object and a micrometer and were asked to measure the diameter of the object using the same steps taught in the training material. Finally, their answers along with the SUS scale responses were then collected.
2. Day 7: Recall session (seven days after the training session). Participants were asked to calculate the diameter of the same cylindrical object, and their answers were collected.  
The study was conducted on a university campus, in a controlled lab environment, and was approved by the University of Texas Arlington Institutional Review Board (UTA IRB, 2020-0055). We recruited participants via word of mouth. During the study, only one participant indicated having some knowledge on the use of a micrometer.
## 7 Discussion
For example, the X2 and X5 combination in Elastic Net results in an RMSE of 190.44 for DC power and 0.645 for DC current, both of which are competitive with Lasso and Ridge. The coefficients for each model highlight the diverse effects of the predictor variables on DC power and current. For example, temperature (X2) often has a positive effect on DC current, while Relative Humidity (X5) shows varying effects across models, sometimes shrinking to zero in Lasso and Elastic Net. Interestingly, Wind Speed (X7) and Wind Direction (X8) show mixed influences depending on the model and the combination of predictors, which suggests that their role may be context-specific or influenced by interactions with other variables. Across all models, the combination of temperature (X2) and Relative Humidity (X5) consistently outperforms other variable combinations, both in terms of RMSE and R-squared. Lasso and Elastic Net offer slight improvements over LR by reducing RMSE and performing feature selection, making them better suited for practical applications where model simplicity and interpretability are crucial. RR performs well in controllingmulticollinearity but may not provide the same degree of variable selection as Lasso or Elastic Net. The best-performing models, particularly LAR and Elastic Net, demonstrate strong predictive performance and are well suited for practical applications in SPVS monitoring and optimization. These models effectively handle key predictors such as temperature (X2) and Relative Humidity (X5), making them reliable for estimating DC current and power. Given their ability to perform feature selection and reduce model complexity, they are ready for practical use, though additional validation with real-time data and site-specific adjustments would enhance their robustness. While they are currently applicable for monitoring and predictive tasks, their effectiveness in diverse, operational environments would benefit from further testing.
###### Abstract
These are usually low-order features, even if their number can reach thousands. Most of the studies in the literature utilized different machine learning algorithms using these extracted and selected radiomic features for model construction [21]. Deep learning offers better-performing models and a simplified pipeline by automatic feature learning. Convolutional neural networks (CNNs) are one form of deep learning that consists of a series of chained convolutional layers followed by an output vector of class probabilities [22]. Multiple convolutional layers with non-linear activation functions allow the learning of complex features. CNNs have shown promising capabilities in automatic segmentation [23, 24], classification of molecular subtypes in gliomas [25, 26], and survival outcomes [27, 28]. To our knowledge, no previous study has applied a deep learning-based radiomic approach for novel biomarkers, such as the aforementioned multiple-gene genetic signatures. Our study utilized the 3-gene signature that stratifies LGG patients into high- and low-risk groups, according to [PERSON] et al. [14]. We aimed to determine whether neuroimaging features on preoperative MRI studies entered into a CNN pipeline could facilitate the proposed LGG stratification.