esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In order to investigate the impact of macro-geometry parameter design of high-speed reducers for electric vehicle gearboxes on TSFT and scuffing load-carrying capacity, an improved semi-analytical TSFT calculation method was proposed and studied how gear design affects performance. Gear parameters such as tooth number and module were varied while the gear ratio was kept constant to analyze the effects on sliding velocity, load distribution, and flash temperature. The following conclusions were derived from the analysis.  
More teeth and smaller modules increase the contact ratio, improving gear motion smoothness. This design also reduces tooth surface friction and flash temperature, enhancing scuffing resistance.  
Increasing the pressure angle decreases the contact ratio, especially affecting the transverse contact ratio. It also reduces sliding velocity and tooth-bending safety factor, potentially impacting bearing life. However, a larger pressure angle can enhance scuffing load-carrying capacity.  
A higher helix angle significantly increases the total contact ratio, improving load-carrying capacity and contact fatigue life. It also reduces TSFT and improves the scuffing safety factor. However, higher helix angles increase axial force, which could reduce bearing life.  
In summary, gear design significantly impacts the performance of electric vehicle gearboxes. Increasing teeth number and using larger pressure and helix angles can improve certain performance metrics, but care must be taken to balance these improvements against potential reductions in bearing life. In the next step, we will carry out a large number of experimental tests on the specific effects of the macro-parameters of the gears on the scuffing load-carrying capacity, as well as on how to design better gear parameters, to verify the reasonableness of the results obtained in this paper from theoretical analyses and numerical simulations.  
Conceptualization, W.G. and S.M.; Methodology, B.B.; Software, W.G.; Validation, X.L. and S.M.; Investigation, B.B. and X.L.; Resources, W.G. and S.M.; Data curation, X.L.; Writing--original draft, B.B.; Writing--review & editing, B.B.; Visualization, W.G.; Supervision, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China (TC210H02Q).  
Not applicable.  
Figure 13: The tooth contact flash temperature and scuffing safety factors. (**a**) Contact flash temperature; (**b**) scuffing safety factors.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Chongqing Tsingshan Industrial Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Results and Discussion
As the SINR received by UEs increases, the system can consume less energy while providing the same data rate, yielding a higher energy efficiency. In order to demonstrate this analysis, we performed SINR comparisons, where the proposed algorithm was better than the other algorithms. In addition, we showed that the proposed algorithm performs well in environments with increased number of RRHs and UE speed. Figure 5: Comparison of the energy efficiencies. (**a**) 2x and (**c**) 4x speeds with Gaussian distributions; (**b**) 2x and (**d**) 4x speeds with uniform distributions.
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 2 Preliminary Knowledge
### Principle of Light Refraction  
The principle of light refraction says that when light is obliquely incident from one medium into another medium, the propagation direction changes so that the speed of light changes at the junction of different media, and the deflection of the route occurs [66].  
As shown in Figure 1, the light from the point \(P\) in medium 1 passes through the refraction point \(O\), is refracted, and finally reaches the point \(Q\) in medium 2. Suppose the refractive indexes of light in medium 1 and medium 2 are \(n_{1}\) and \(n_{2}\), respectively; the angle of incidence and refraction are \(\alpha\) and \(\beta\), respectively; and the velocities of light in medium 1 and medium 2 are \(v_{1}\) and \(v_{2}\). The ratio of the sine of the angle of incidence to the angle of refraction is equal to the ratio of the reciprocals of the refraction path or the ratio of the velocities of light in two media. That is, as shown in Formula (8):  
\[\delta=\frac{\sin\alpha}{\sin\beta}=\frac{n_{2}}{n_{1}}=\frac{v_{1}}{v_{2}} \tag{8}\]  
where \(\delta\) is called the refractive index of medium 2 relative to medium 1.  
```
Input: Population size \(N\), Maximum number of iteration \(MaxIt\). Output: The best location \(Xbest\), the best fitness value \(F(Xbest)\).
1t = 0;
2 Randomly initialize the chimp population \(X_{i}(i=1,2,3\dots,N)\);
3 Initialize the parameters \(A\), \(C\), and \(f\);
4 foreach\(X_{i}\)do
5 Calculate fitness \(F(X_{i})\)
6 end fore
7\(X_{Attacker}\) = the best solution;
8\(X_{Barrier}\) = the second best solution;
9\(X_{Chaser}\) = the third best solution;
10\(X_{Driver}\) = the fourth best solution;
11 whil\(t<MaxIt\)do
12 foreachachchimpdo
13 update the position of the current chimp using Equation (7);
14
15 end fore
16 Update the parameter \(A\), \(C\), and \(f\);
17 Update \(D_{Attacker}\), \(D_{Barrier}\), \(D_{Chaser}\), and \(D_{Driver}\) using Equation (5);
18 Update \(X_{Attacker}\), \(X_{Barrier}\), \(X_{Chaser}\), and \(X_{Driver}\) using Equation (6);
19\(t=t+1\);
20
21 end fore
```  
**Algorithm 1**Pseudo-code of the Chimp Optimization Algorithm (ChOA).  
Figure 1: Principle of light refraction.
## 5 Experiment and Comparison
### Comparative Analysis
#### 5.2.1 Efficiency of Latency  
Latency is defined as the delay in processing the data after transmission. The increase in latency represents poor construction and performance of the system. The latency is measured and plotted with respect to the increase in transaction number and transaction rate.  
The proposed AAC-IoT incorporates authentication of the DO and the DU; then the ABAC policy scheme is used. These processes take place with the assistance of the HLF blockchain that enables the management of security credentials and is stronger against multiple attacks. The comparison of latency is demonstrated in Figures 9 and 10, which show the increase in transaction count and rate, respectively. From the results, the AAC-IoT achieves less latency than the previous LBAC method. The decrease in latency is due to the use of an ANN for authentication that is able to manage multiple requests based on parallel processing. This aspect is not present in the LBAC method. On average, the latency is 1.8 S and 2.0 S in the number of transactions; then it is 2.0 S and 2.5 S in the transaction rate for AAC-IoT and LBAC, respectively. However, the difference is minor. It is large when the transactions of users increases massively. The proposed work minimizes the latency compared to the previously implemented LBAC algorithm by approximately 10%. The reasons for the minimizing latency in the proposed AAC-IoT compared to the existing LBAC are given below:  
* Ability of the algorithm to validate the credentials in parallel that gives results for all the users arrived at the time t.
* The authentication of users also reflects on the minimization of latency, in the aspect of restricted unnecessary requests from malicious or illegitimate users.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System Parameter** & **Specification** \\ \hline Number of DOs & 3 \\ \hline Number of DUs & 3 \\ \hline Number of CA & 1 \\ \hline
**Hardware / Software Used** & **Version** \\ \hline Java Development Kit & 1.8 \\ \hline NetBeans IDE & 8.2 \\ \hline Windows & 10 (64-bit) \\ \hline RAM & 2 GB (Min) \\ \hline Processor & Dual core \& above \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tools and parameters.  
* The use of an HLF blockchain is decentralized which allows users to access from any location using the registered credentials.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 3 Experiment and Result Analysis  
### Experiments Setup  
The proposed method was developed with a 3.40 GHz, 16 GB RAM computer. Random subsets of images are used as the validation set during training. Segmentation results (region-based pixel-by-pixel) are compared with the ground truths. Metrics such as precision, recall, and F1 score are used to evaluate segmentation methods. In addition, five evaluation parameters are used to analyze the performance of the preprocessing stage: mean squared error (\(MSE\)), peak signal to noise ratio (\(PSNR\)), information entropy (\(IE\)), contrast (\(C\)), and combination of \(PSNR\), \(IE\), and \(C\) (denoted as \(S\)). The equations of the evaluation metrics are given below:  
A. Precision Rate (\(P\))  
\[P=\frac{TP}{TP+FP} \tag{15}\]  
B. Recall Rate (\(R\))  
\[R=\frac{TP}{TP+FN} \tag{16}\]C. \(F1\) Score \[F1=\frac{2 PR}{P+R}\] (17) D. Mean Square Error (\(MSE\)) \[MSE=\frac{1}{MN}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}[I(i,j)K(i,j)]^{2}\] (18) E. Peak signal to noise ratio (\(PSNR\)) \[PSNR=10\ log_{10}\Bigg{[}\frac{(MN.max(I)^{2}}{MSE}\Bigg{]}\] (19) F. Information Entropy (\(IE\)) \[IE=-\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}K(i,j)\ log_{2}K(i,j)\] (20) G. Contrast (C) \[C=\Bigg{[}\frac{1}{MN}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}(K(i,j)-\mu_{K})^{2} \Bigg{]}^{\frac{1}{2}}\] (21) H. Combination of PSNR, IE, and C (S) \[S=E.C\Big{[}sig(P)|P|^{\frac{1}{5}}\Big{]}\] (22)
## 2 Results
### DisA Does Not Interact with RecU
When HJ-J3 DNA was incubated with a fixed amount of RecU and increasing DisA concentrations, independently of the order of addition, protein-HJ DNA complexes became entrapped in the well (Figure 5B, lanes 9, 10, 12 and 13), so that the presence of a putative RecU-HJ-DisA complex remained poorly defined. To further analyze a potential interaction, we made DNAse I protection footprint assays. Since DNase I requires 10 mM MgCl\({}_{2}\) for its activity, but under this condition RecU binds and cleaves the HJ structure, the [\(\gamma\)\({}^{32}\)P]-HJ-J3 DNA-protein complexes were first pre-formed at 1 mM Mg\({}^{2+}\) (5 min at 37 \({}^{\circ}\)C). Then, fixed DNase I and MgCl\({}_{2}\) up to 10 mM were added (Figure 5C). RecU mainly interacts with the junction region, which is DNAse I resistant. Thus, a clear protected area was not observed. However, a defined band within the ssDNA junction was observed (Figure 5C, lanes 2 and 3, marked with an asterisk), whose position correlates with the expected cleavage of HJ DNA by RecU [46]. The footprint results are consistent with the observation that the RecU stalk region, by penetrating in the center of the HJ, distorts it so that the HJ adopts a square planar conformation with a central hole [36; 48]. In the presence of fixed RecU and increasing DisA concentrations, the band corresponding to RecU-mediated cleavage of HJ DNA significantly decreased, and the extended footprint of DisA was observed (Figure 5C, lanes 6 and 7). It is likely that both proteins co-exist on HJ DNA, and that DisA either alters the positioning of RecU, the conformation of the HJ DNA, or both, thus affecting RecU-mediated HJ cleavage.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Vectors
#### 3.2.4 Erax  
eFax is similar to a traditional fax although without the need for a fax machine. Sites such as efax.com utilize IP (internet protocol) to transmit faxes, in comparison with traditional methods that utilized phonelines. The advantage of this method is that faxes can be sent to a recipient's machine as emails, thus removing the need for a fax machine. However, due to the online nature of this method of communication it opens a new avenue by which phisher attacks can be made to attain victims' personal information [33].
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 3 Results
### 5 kW Case Study
#### 3.1.2 5 kW Case Study Sensibility Analysis  
For the 5 kW case study a sensibility analysis was performed considering a range of annual yields from 1100 to 1500 kWh/kWp with a step of 100 kWh/kWp in the range. Using the methodology applied in Section 3.1.1, it is possible to calculate the LCOE by considering the method described previously, fixing the regulated tariff at 17.4 c\(\ell\)/kWh including taxes and with a fixed interest rate of 10%.  
Table 6 shows the most relevant results for the five cases. Case 1 corresponds to 1100 kWh/kWp and Case 5 corresponds to 1500 kWh/kWp. The positions where the LCOE is lower than the retail cost of energy have been highlighted.  
Five case studies are considered in Table 3, but we consider the cost at 0.8 \(\ell\)/Wp as the current scenario. For that reason, the PDF for the panel cost is log-normal with a mean of 0.8 (\(\ell\)/Wp) and a standard deviation of 0.2 (\(\ell\)/Wp). The panel cost floor is set at 0.2 (\(\ell\)/Wp) so that 95% of the cases are represented in the PDF (Figure 6).  
Introducing all the variables in the probabilistic analysis with the Monte Carlo method delivers a wide range of results, giving the following representation (Figure 7), with the frequency of the LCOE above and below the retail electricity cost.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**LCOE (\(\ell\)/kWh)**} \\ \hline
**PV Module Cost** & **System Cost** & **Annual Yield** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** \\
**(\(\ell\)/Wp)** & **(\(\ell\))** & **(kWh/kWp):** & **1100** & **1200** & **1300** & **1400** & **1500** \\ \hline
1.0 & 15,464 & & 0.276 & 0.254 & 0.236 & 0.220 & 0.207 \\
0.8 & 14,012 & & 0.251 & 0.232 & 0.215 & 0.201 & 0.189 \\
0.6 & 12,560 & & 0.227 & 0.210 & 0.195 & 0.182 & **0.171** \\
0.4 & 11,108 & & 0.203 & 0.187 & **0.174** & **0.163** & **0.153** \\
0.2 & 9656 & & 0.179 & **0.165** & **0.154** & **0.144** & **0.136** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the LCOE based on the different annual yield for 5 kW (deterministic method).  
Figure 6: PDF of panel cost for a 5 kW system.  
Introducing the results of the iterations given by the Monte Carlo method for the business model, we obtain the IRR values. The IRR results for the 5 kW business case are shown graphically in Figure 8.  
A success rate parameter (%) is introduced by counting the number of iteration results below the retail cost of the electricity and dividing by the total number of iterations (10,000). By analysing the frequency of the results and the value itself, the success rate can be calculated for each case. In the case of a 5 kW installation, the success rate would be 82.26%. This means that in 82.26% of cases, grid parity is achieved, as the LCOE is lower than the retail cost. However, as a business case, the mean of the IRR is negative (\(-\)0.97%). Therefore, it can be concluded that the loan input in the model contributes negatively to the success of the investment.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Assumptions  
* Channel reciprocity  
The ray-tracing software is designed to have the BSs acting as signal sources and the MSs as receivers, so channel reciprocity is assumed for any purpose that require uplink transmission.  
* Network deployment and capability  
Network deployments can be massively different depending on usage. This study assumes a network with base stations that are capable of obtaining reliable AOA information, possibly through the use of antenna arrays, as is the case with massive MIMO Also, the network is assumed to be capable of resolving individual rays or multipath components. Next-generation wireless systems will utilize greater bandwidth than current generation systems, with GHz bandwidths possible at millimeter wave frequencies. Furthermore, bandwidth is not the limiting factor in time resolution as the Cramer-Rao bound on the maximum likelihood timing estimator [13] indicates that SNR is the limiting factor, and so super resolution algorithms can be used to improve timing resolution.  
* Noise  
Noise in the measured values as presented in the ray-tracer outputs, is neglected. Received power, AOA/AOD information and time delay estimation done in the ray tracing software is considered to be accurate enough for purposes of this study. No noise modelling is built into the algorithms used.
## 2 Description of the Datasets and Data Preprocessing
### Data Preprocessing  
Firstly, WTs are powered by wind, so the speed of the generator vibration signals collected by CMS at regular intervals is not constant. It is evident that fluctuations in speed will inevitably impact the amplitude, peak value, and other features of the vibration signal. This will cause great difficulties for the RUL predicting of generator bearings. To solve this problem, firstly, considering that WTs do not generate electricity when the speed falls below 1000 RPM or above 1800 RPM, data within this range should be filtered and deleted. Secondly, when the wind direction changes sharply in a short period of time, the yaw system of the WT automatically starts, which may lead to unstable operation of the generator, and the data under such speed are excluded. Finally, if the limited speed range is too small, the available data will be greatly reduced. Based on the above principles, this paper selected the vibration signal with the speed of 1100 RPM-1600 RPM to extract features.  
Secondly, when RUL is predicting, the acquired signals need to meet an equal interval. Considering the actual operating conditions, the degradation process is slow, so it is assumed that the degradation state of the bearing is the same in one day so that a vibration signal meeting the speed range can be selected in the 24 data a day to represent the running state of the day. The predicted time interval is one day.  
Finally, in the actual operation of WTs, there are inevitable shutdowns for maintenance, which will lead to the loss of vibration signals from generator bearings. Additionally, due to the uncertainty of wind speed, it is challenging to ensure that the speed of all 24 vibration signals collected in a day falls within the previously mentioned speed range. Therefore, it is necessary to complete the missing data. This paper utilizes the interpolation method for completion operations. Figure 4 shows the CTF image of RUL features after the original 12 TFs and complete time domain features (CTF) of the WT No. 10#.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 4 Evaluation
### Evaluation of Tracking Module  
The tracker with VGG-16 deep feature is called TVGG, and tracker with Darknet-53 deep feature is called \"Tdarknet\". Two trackers have same parameters except for the depth feature of the target area. Both trackers use FHog feature, IC feature and deep feature. Weight coefficient of FHog feature \(\beta_{\text{FHog}}\) is 1.0. Weight coefficients of IC feature \(\beta_{IC}\) and deep feature \(\beta_{feature1}\)\(\beta_{feature2}\) is 0.75.  
We use OTB50 dataset to test target tracking modules using different deep features. OTB50 has 50 fully annotated datasets to evaluate algorithm performance. It integrates most of the publicly available trackers and sets a unified input and output format for large-scale performance evaluation.  
Figure 5 showed the top ten trackers. For a given overlap threshold (image \(x\)-axis), the success rate (image \(y\)-axis) represents the proportion of the number of frames in which the tracking frame overlaps with the real frame greater than the overlap threshold in the  
Figure 4: Ideal response distribution.  
total number of frames. The Tdarknet tracker using the darknet deep feature achieves an AUC score of 64.6% with 12 FPS; the TVGG tracker using the VGG deep feature achieve an AUC score of 60.9% with 13 FPS.  
We can see that Tdarknet surpasses TVGG in almost all the cases except the \"fast motion\" case. There are 24 out of 26 videos from the OTB50 dataset \"fast motion\" case, and the target is very small-scale. We think that the VGG-16 network is sufficient for this scale. On the contrary, the layer structure of the Darknet-53 network is too complicated that too much irrelevant content has been extracted, which interferes with the characteristics of the target itself.  
Figure 5: Tracking evaluation on OTB50 benchmark. OTB50 has different type of video, include illumination variation, scale variation, occlusion, deformation, motion blur, fast motion, in-plane rotation, out-of-plane rotation, out-of-view, background clutters and low resolution.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 6 Conclusions  
This article aimed to promote the automation of bridge traffic impact management, effectively evaluate the impact of bridges on traffic operation status, maintain bridges in a timely manner, and avoid traffic accidents by modeling the bridge traffic impact assessment system, screening evaluation model factors, and processing parameters. On this basis, a bridge traffic operation status evaluation model is proposed based on existing bridge data, with the Analytic Hierarchy Process (AHP) as the main model structure, combined with the Delphi and Singular Value Decomposition (SVD) methods. However, the current research on the operation status of bridge traffic is still not sufficient, and improvements can be made to the following aspects in the future: Firstly, there is still significant room for optimization in terms of the accuracy and applicability of the model, and parameter refinement can be carried out according to different regions and types of roads and bridges. Secondly, reference can also be made to the actual road conditions and relevant standards of different countries and industries to further consider more traffic influencing factors and achieve more accurate assessments.  
Conceptualization, J.G. and Y.L.; methodology, Y.Z.; software, G.Y.; validation, L.W. and Z.D. All authors have read and agreed to the published version of the manuscript.  
Shanaxi Natural Science Basic Research Program (No. 2022)Q-735.  
Not applicable.  
Figure 11: The average vehicle speed recorded by sensor 379 when the traffic status is excellent or poor.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Author [PERSON] was employed by the company Huawei Technologies Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.