esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Research on Aerodynamic Characteristics during Take-Off Phase in Ski Jumping
## 1 Introduction
(2022) found a correlation between the kinematic parameters of athletes during the take-off phase and the jumping distance, emphasizing the importance of powerful leg extension ability while utilizing speed and mastering the timing and direction of the take-off [22]. In summary, the research on the aerodynamic characteristics during the take-off phase of ski jumping mainly focuses on wind tunnel training and experimental testing, along with some analysis from a kinematic perspective. However, due to the rapid and significant changes in an athlete's postures during the take-off phase, and the limitations in the current level of experimental techniques in a wind tunnel, it is challenging to achieve consistent and stable reproductions of different take-off postures, and it is not possible to accurately test the aerodynamic forces and moments. Nevertheless, wind tunnel training can still be conducted to enhance athletes' subjective perception of the aerodynamic drag and their ability to adapt and adjust. To assist in improving the daily take-off and flight technique training for ski jumpers and accurately obtain the aerodynamic characteristics during the take-off phase, it is necessary to promptly initiate numerical simulation studies on the aerodynamic characteristics of the take-off phase in ski jumping. This will help in the investigation of the influence of athlete posture on the aerodynamic characteristics during the take-off phase, ultimately enabling athletes to achieve favorable flight postures after take-off. Considering the difficulties, complexity, and cost-effectiveness of conducting numerical simulations for the dynamic process during take-off, it is recommended that specific typical postures during take-off should be selected for static studies. This study aims to establish a detailed three-dimensional (3D) model and grid model of the athlete/ski system using the Partially Averaged Navier-Stokes (PANS) turbulence model for CFD numerical simulations. By doing so, the aerodynamic forces acting on the athletes and the flow field around them can be obtained for different take-off postures. This study will explore the impact of different take-off modes on the aerodynamic characteristics of ski jumpers.
# Blockchain Behavioral Traffic Model as a Tool to Influence Service IT Security
## 4 Network Attacks  
Real networks make it possible to carry out various network attacks due to the incomplete security of user actions. A network attack is an action which is aimed at seizing control (elevation of privileges) over a remote/local computer system, its destabilization, denial of service, or receiving user data from the computer system.  
Currently, the following attacks are distinguished: mailbombing, buffer overflow, the use of specialized programs, network intelligence, IP spoofing, man-in-the-middle, injection, denial of service attacks, and phishing attacks [15; 16; 17; 18].  
However, threats to distributed registries slightly differ from threats to typical computer networks. The main attacks, to which Blockchain systems are exposed, are as follows [15; 16; 17; 18]:  
\"Attack 51%\". Essentially, a hacker can type his/her own chain of blocks that will overtake the main one by controlling more than half of the confirming resources of the blockchain network. As a result, this chain becomes the main one. This allows including only one's own data in the chain. This attack can also be implemented at lower capacities, but, in this case, the potential success is sharply reduced. At the early stages of blockchain development, the network was vulnerable to this attack. Today, to attack the developed networks, the processing power would have to be many times higher than the power of all \"supercomputers\".  
* Sybil attack. A sibyl attack implies a situation when one network node acquires several entities. A hacker may refuse to transmit and receive blocks by \"disconnecting\" users from the network or see all transactions by means of special programs. Sybil attacks are usually excluded by using a set of heuristic rules, contacting a trusted certification server or by restricting the number of accounts able to be created by one user in a given period of time.
* DDoS. DDoS is another type of hacker attack that is aimed at resending a huge number of similar requests. To prevent this attack, many blockchain systems use techniques based on limiting the block size, limiting the number of signature verifications, and blocking suspicious transactions or behavior.
* Cryptography hack. Algorithms for calculating the hash functions of SHA-256 and ECDSA standard are considered to be impossible to hack at current computing powers. However, new high-performance quantum computers will increase the risk of hacking these functions. In this case, the system hash function should be replaced with a more complex one.  
Blockchain traffic is encrypted, but the analysis of the behavioral model allows the identification of data flows, with the possibility of further forecasting its impact on services.
## 1 Introduction  
Academic research trends often help companies formulate research and development (R&D) strategies. Specialized knowledge is becoming more individualistic and, in highly fragmented fields, the results may change depending on the selection of participants [1]. In many academic fields, the number of publications is growing exponentially and it is becoming increasingly difficult to obtain comprehensive perspectives of such fields [2]. As the amount of science and technology data are increasing, attempts are being made to contribute to innovation policies and R&D strategies by considering entire fields [3; 4; 5; 6; 7; 8; 9]. While endeavoring to obtain the overall perspectives of such fields, efforts have also been directed toward investigations of what kinds of research will attract attention in the future [10; 11; 12; 13; 14]. In fields such as the nanocarbon field where the linkage representing the distance between technology and science is high, the trends of academic papers are important for identifying future technological trends. There are many review papers on nanocarbons, including ones on exudation in carbon nanotube (CNT) polymer composites [15], chemical vapor deposition (CVD) of CNTs [16], and the application of CNTs as electrode materials in lithium-ion batteries [17]. It is possible to provide certain guidelines for overviewing work in this area from the past to the present. However, few studies have adequately discussed future projections.  
Extensive research has been conducted on the prediction or identification of emerging science and technology fields. The prediction of emerging research areas has traditionally been studied in bibliometrics or library and information science. It is known to be useful to focus on the citation relationships of papers as a method for extracting the essence of a field. The number of citations is a useful indicator for evaluating the quality of research. The use of regression of the number of citations allows us to downsize the number of papers and to avoid selection bias [18; 19; 20]. Due to the current advancement of prediction algorithms, such as machine learning algorithms and the improvement of computing capabilities, it has become possible to extract patterns that contribute to predicting future trends.  
This research-based forecasting of the future of science is needed when companies and governments discuss innovation policy, but a paper-by-paper perspective alone is insufficient information for actual decision-making. A semimacro perspective on what new areas are emerging is needed, not just a paper-by-paper view. This study focuses on the field of nanocarbons, not only in terms of papers but also in terms of emerging areas. A combination of micro and semimacro perspectives will enable us to understand the trends in the field in terms of both quality and quantity.
## 4 Experimental Results
### CAE-Based Generative Model  
As described in Section 2, there have been several studies that have applied CAE-based augmentation methods to resolve the data imbalance in WBMs. CAE is used as an effective unsupervised learning method that can be applied even when there is an imbalance between classes in the training data [43].  
Table 5 shows the structure of the CAE-based augmentation model used in the experiment to compare the performance of the DCGAN-based augmentation model. Data augmentation using a CAE is performed by adding noise to the latent vector generated as a result of passing the CAE encoder learned through the original data to generate data similar to the WBM used for learning. After the training of the CAE-based model is completed, the encoder and decoder generate a WBM that is similar to the input by passing it through the network. We used ReLU for the activation function of all layers except for the activation  
function of the output layer of the decoder; we used mean squared error (MSE) as the cost function, and Adam as the optimizer. Sigmoid was used for the activation function of the output layer of the decoder.  
\(\sigma=0.2\) was used for the noise added to the latent vector. Here, \(\sigma\) means a value to be multiplied by a random value that follows the normal distribution added to the latent vector generated through the encoder of the CAE so that the CAE-based model can form more diverse defect patterns. The shape of the WBM generated using a CAE depends on the value of \(\sigma\) to be multiplied by the Gaussian noise added to the latent vector. When a small value of \(\sigma\) is used, the WBM generated from the CAE simply forms a highly similar shape to that of the original WBM. In this case, there is a concern that the augmented dataset may be biased toward specific defect patterns. On the other hand, if \(\sigma\) is set too high, the generated WBM may show a defect pattern deviating from the pattern characteristics of the original WBM. In this case, the generated WBM may not satisfy the classification criteria of the dataset.
# Design and Optimization of Cooling Plate for Battery Module of an Electric Vehicle
## 5 Conclusions  
The temperature results predicted by the single battery thermal model showed good agreement with experiments by a difference less than 5%, implying that the heat generation model and the assumptions were reasonable.  
A methodology for the design and optimization of the cooling plate for the battery module was proposed. A complex heat transfer model for the whole module was created, including batteries, two cooling plates, silicone gel pads, and coolant. Orthogonal experimental design was implemented by the numerical analysis to optimize the main parameters of the module. The cooling plate geometry was further optimized by the surrogate model method. With the optimized geometry, the cooling plate was rebuilt in the module thermal model for the analysis. The comparison showed that the maximum and minimum temperature difference in the cooling plate was reduced by 5.24% and the pressure drop was reduced by 16.88%.  
It was concluded from the orthogonal design analysis that the battery temperature difference and the pressure drop decreased with the increase of the cross-section and number of the coolant channel when the coolant flow rate was constant at the inlet. From the sensitivity analysis of the plate, the maximum temperature and pressure drop in response to the plate geometric parameters in the surrogate models, it was found that the center channel distance, L1, and the size of the inlet plenum exhibited the greatest influence on the pressure drop.  
Conceptualization, H.L. and B.Y.; methodology, H.L.; software, B.Y.; validation, B.Y.; formal analysis, B.Y.; investigation, B.Y. and M.R.H.R.; resources, H.L.; data curation, B.Y.; writing--original draft  
Figure 26: Pressure distribution of the cooling plate with optimized geometry.  
Figure 25: Temperature distribution on cooling plate with optimized geometry.  
preparation, B.Y. and M.R.H.R.; writing--review and editing, H.L. and M.R.H.R.; visualization, B.Y.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L.  
This research was supported by the National Natural Science Foundation of China under Grant No. 51605444.  
The authors
## 3 Algorithms for Calculation
### Partial Tensioning of Strands over Multiple Cycles
#### 3.2.1 First Cycle (c = 1)  
At the start of the first cycle, the strands are not anchored at the active anchor. The calculation procedure involves tensioning the strands successively, starting from i = 1 to i = n, after which the strand to which the force \(Z_{u}\) is applied is anchored. For the tensioned i-th strand with force \(Z_{u}\), calculations are conducted for the forces in all previously tensioned and anchored strands, as well as for the i-th strand. The stress and deformation state of the cable, after the tensioned and anchored number of \"j\" strands, is expressed through the forces in the strands \(Z_{ij}{}^{(1)}\), and in the cable \(Z_{k}{}^{(1)}=\Sigma\ Z_{ij}{}^{(1)}\), and the shortening of the cable axis, or strands, where \(\dot{\xi}_{k}{}_{k}{}_{j}{}^{(1)}=\xi_{ij}{}^{(1)}\), and \"i\" refers to the strands for which the tension force and cable deformation are determined, and it can have a value less than or equal to the number of tensioned and anchored strands \"j\" (j = 1:n). The outlined calculation procedure is depicted by the algorithm (Figure 7).  
Figure 7: Flowchart of the algorithm for the first cycle of multi-cycle strand tensioning.
# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 3 Problem Formulation  
To improve the accuracy of the trained model, it is desirable to bring as much data as possible into the training process [19]. Meanwhile, it is also beneficial to reduce the consumption of wireless resources in parameter transmission. Hence, the utility function of the FL-aided wireless IoT network is formulated as  
\[U_{\text{FL}}=\sum_{i\in M}x_{i}^{t}b_{i}^{t}|S_{i}|-\tau\sum_{i\in M}x_{i}^{t}b_ {i}^{t}B_{i}^{t}P_{i}^{t}, \tag{8}\]  
where  
```
1:Input:\(\mathcal{M},B_{T},|S_{i}|,B_{i}^{*},P_{i}^{*}\)
2:Initialize:\(W_{T}=0,x_{i}=0,\Phi=\mathcal{M}\)
3:for\(i=1:M\)do
4:if\(W_{T}\leq B_{T}\)then
5:\(j=\arg\max_{i\in\Phi}\frac{(|S_{i}|-R_{i}^{*}P_{i}^{*})}{B_{i}^{*}}\)
6:\(W_{T}=W_{T}+B_{j}^{*}\)
7:\(x_{j}=1\)
8:\(\Phi=\Phi-\{j\}\)
9:endif
10:endfor
11:Output:\(\mathbf{x}\)
```  
**Algorithm 1** Greedy Algorithm for **P4**.  
With Algorithm 1, the proposed client and resource optimization can be summarized as Algorithm 2.  
```
1:Input:\(\mathcal{M},h_{i}^{t},B_{T},P_{M},\theta\)
2:Initialize:\(\mathbf{x}=\mathbf{1}\)
3:for\(i=1:M\)do
4: compute \(P_{i}^{t}\) by iteration (17)
5: compute \(B_{i}^{t}\) by (18)
6:endfor
7:run Algorithm 1 to obtain \(x_{i}^{t}\)
8:Output:\(\mathbf{x},\mathbf{B},\mathbf{P}\)
```  
**Algorithm 2** Client and Resource Optimization for FL-aided IoT.  
The block diagram of the proposed algorithm is presented in Figure 2. We propose an alternative direction-based algorithm to handle the high complexity of **P1**. First, \(\mathbf{x}\) is fixed to **1**, and **P1** can be simplified to **P2**. Then by combining and substituting C4, (5) and (6) into **P2**, it can be further simplified to **P3**. Letting the derivation \(\frac{df_{P3}}{dP_{i}}=0\) in **P3**, the iteration Equation (17) is derived. Through the iteration of (17), \(P_{i}^{*}\) can be obtained. Then, by substituting \(P_{i}^{*}\) into (14), \(B_{i}^{*}\) is also solved. Next, by substituting and into **P1**, it can be transformed to **P4**, which belongs to the knapsack problem. Hence, we adopt Algorithm 1 to solve \(\mathbf{x}\) from **P4**. After that, \(\mathbf{x}\), \(P_{i}^{*}\) and \(B_{i}^{*}\) are obtained and used for the client and resource optimization.  
Figure 2: Block diagram of the proposed algorithm.
## 4 Simulation and Model Experiment Validation  
In this section, the small-signal model was evaluated and validated in both simulation and hardware experiments. The simulation model was built in MATLAB(r)/SIMULINK software (2018a, MatchWorks, Natick, Massachusetts, USA) using the derived transfer functions in Sections 2 and 3. The hardware control compensator was implemented with Texas Instrumental microcontroller TMS320S28335. The design parameters are shown in Table 1. In the hardware experiment, three WEDES battery modules were implemented and utilized.  
For the verification of derived small-signal models of the WEDES system, the dynamic response of both simulation and hardware experiments under different operation conditions was compared. If the dynamic performance from simulation results and experimental results are in good agreement, it can be implied that the developed model is valid.  
As discussed in previous sections, there are three different control loops (battery module voltage control loop, bus voltage control loop, and SOC balancing control loop) in the WEDES system. During the test for a specific control loop, one of the operation parameters was changed while the rest of the operation parameters were set constant.  
Figure 8: The bode plot of the uncompensated (red-dashed curve) and compensated (blue-solid curve) bus voltage control loop gain.
# Sample-Based Gradient Edge and Angular Prediction for VVC Lossless Intra-Coding
## 3 Proposed Method
### Intra-Prediction Flowchart  
In the SGAP coding, the PLANAR mode and DC mode are not retained. The GED mode is set to mode 0. The average prediction mode is set to mode 1. The angle mode in SGAP is set to modes 2-34. Figure 6 shows the intra-prediction flowchart of SGAP.  
Figure 6: The flowchart of the proposed SGAP in VVC lossless intra-coding: (**a**) Luma component. (**b**) Chroma component.  
The flowchart of the luma component is shown in Figure 5(a). The optimal luma mode of the Current Coding Unit (CU) is determined through two steps: coarse screening and fine screening. In the first round of coarse screening, the Sum of Absolute Transformed Difference (SATD) of the prediction results of the GED mode, average prediction mode and type 0 of 17 even angular modes is calculated. A certain number of modes with the smallest SATD are selected to enter the mode candidate list. In the second round of coarse screening, the SATD of the prediction results of type 0 of the angular modes adjacent to the angular modes in the mode candidate list are calculated. Then, the candidate list is updated according to SATD. In the subsequent coarse screening steps, the SATD of the prediction results of type 1 and 2 of angular modes in the Most Probable Modes (MPM) list and modes in the Matrix-based Intra-Prediction (MIP) list and MPM list are calculated. Then, the candidate list is updated according to SATD. The fine screening is implemented by Rate-Distortion Optimization (RDO). In the RDO step, the Rate-Distortion Cost (RD cost) of all modes in the mode candidate list is calculated. The mode with the smallest RD cost is selected as the optimal luma mode.  
The flowchart of the chroma component is shown in Figure 5(b). The obtainment of the optimal chroma mode also goes through coarse screening and fine screening. In the coarse screening, the SATD of the prediction results of type 0 of the angular modes and other modes in the chroma mode list are calculated. Since the GED mode, Linear Model (LM) and Derived Mode (DM) must be tested, the SATD of the prediction results of GED mode, LM and DM are set to 0. A certain number of modes with the smallest SATD are selected to enter the mode candidate list. Then, the SATD of the prediction results of type 1 and 2 of angular modes in the mode candidate list are calculated. After completing the SATD calculation, the candidate list is updated. Finally, in the RDO step, the RD cost of all modes in the mode candidate list is calculated, and the mode with the smallest RD cost is selected as the optimal chroma mode.
## 2 Materials and Methods
### Simulations  
We used a commercial CFD package to solve the governing equations, and ANSYS Fluent v19.2 (ANSYS, Canonsburg, PA, USA) was used for the computations. A laminar model using a second-order implicit method for transient formulations was evaluated. The pressure-velocity coupling was set as a SIMPLE (semi-implicit method for pressure-linked equation) to solve the continuity and momentum equations under a second-order upwind scheme for spatial discretization. The convergence value for the residual velocity and continuity was set to \(1\times 10^{-5}\). The solutions were computed in 282-step cycles with a timestep of 0.01 s.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Application to a High-Level Vision Task

Table 3: The quantitative dehazing evaluation results for the non-uniform RS hazy dataset (SateHaze1k [39]). The best value for the specific metric in each category is marked in bold. accounts for both false positives and false negatives. To evaluate the road extraction accuracy, we used a synthetic uniform hazy dataset consisting of 90 images as the input for different dehazing methods. The dehazed images were processed using D-LinkNet to extract roads, and the road extraction accuracy was calculated using the extracted results and the ground truth road masks. Figure 6: The D-LinkNet [47] road extraction results using the dehazed images by different dehazing methods. (**a**) Hazy image; (**b**) _DCP_[8]; (**c**) CEEF [6]; (**d**) FADE [41]; (**e**) DehazeFlow [42]; (**f**) SRKTDN [43]; (**g**) DWGAN [44]; (**h**) RefineDNet [17]; (**i**) USIDNet [45]; (**j**) ZID [14]; (**k**) DDIP [35]; (**l**) YOLY [13]; (**m**) ZIR [16]; (**n**) Ours; (**o**) ground truth. Table 4 shows that the road extraction accuracy of D-LinkNet using clear images as the input is significantly better than that using hazy images, indicating that haze has a severe negative impact on D-LinkNet's performance. Table 5 presents the road extraction accuracy comparison for different dehazing methods. It is evident that the _DCP_, SRKTDN, and our proposed method achieved the best results for the four metrics in the traditional, learning-based, and zero-shot dehazing categories, respectively. Moreover, our proposed method outperforms the other methods in traditional and learning-based categories for the four metrics, demonstrating its superior performance.