esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Measurement Setup
### Metrics
#### 3.2.2 Page Latency: Duration of a Complete Web Page Download  
Page latency is an estimated measure for the download time of the complete web page (including pictures, scripts, external content _etc._), an indicator for whole web page presentation in the browser window. There are many uncontrollable factors that have an influence on page latency, including current dynamic content, script execution, externally hosted content (smash-up), server reaction time, and the kind and version of web browser used.  
In order to provide a reproducible and browser independent estimate for extrapolating page latency from core latency, we utilized the GNU Wget tool for downloading multiple complete web pages of the Top 500 lists via the command line. Our comparison is based on the _wget_ and _wget_ -_p_ commands, where _wget_ -_p_ serves as an estimator for retrieving entire web pages including inline images, sounds, and referenced stylesheets.  
We calculated an extrapolation factor \(F=(\sum\limits_{i=1}^{n}lp_{i})/(\sum\limits_{i=1}^{n}l_{i})\), where \(n\) is the number of web pages (here 500), \(lp\) the latency with _wget_ -_p_, and \(l\) the latency with _wget_. Both latencies were measured 5 times in order to flatten one-time effects. Our results indicate an extrapolation factor \(F\) of 2.4, which means that on the average the following approximation holds: Page Latency \(\approx 2.4*\) Core Latency. Parallel control experiments using the _Yslow_ plugin for Firefox indicated that this approach provides a good estimation in the average case for our set of websites. However, current web browser download web pages via parallel connections which should speed up the download of a complete web page.  
To take this into account, we repeated our calculation of the extrapolation factor with the Parallel URL fetcher (_puf_) which is comparable in usage and functionality to _wget_ and is able to retrieve web pages with parallel connections. The results show that with _puf_ -_p_ the extrapolation factor is in average 20% lower due to the use of parallel connections. Although parallel connections simulate a more realistic browsing experience because all common web browsers support them, we also have to take into account this could underestimate the loading of a complete web page. The reason for this is the inability of both _wget_ -_p_ and _puf_ -_p_ to retrieve all page-requisites a browser would need, since for example they will not gather any content requested via Javascript. Therefore, we have a reason why _wget_ -_p_ would overestimate the extrapolation factor (no use of parallel connections), but also why it would underestimate this factor (content not retrieved). For the purpose of this paper and backed by initial tests, we suppose that these inaccurate estimations on the average balance each other, and therefore adhere to the extrapolation factor calculated by using _wget_ -_p_.
# Research on the Prediction of Insertion Resistance of Wheel Loader Based on PSO-LSTM
## 2 Experimental Data Acquisition and Analysis  
### Experimental Principle and Design  
To effectively collect the relevant parameters in the insertion process of the wheel loader, an automatic operating platform of the wheel loader is constructed in this paper. The experimental data required in this paper include cylinder pressure, insertion depth, and insertion resistance. The cylinder pressure can be measured by the sensors installed on the lift cylinder and the tilt cylinder. The insertion depth is approximately equal to the travel distance of the wheel loader and is measured by a fifth wheel gauge hinged to the rear of the wheel loader. The insertion resistance is measured by three sets of shaft-pin-type sensors and obtained by force synthesis. Meanwhile, the engine speed, the displacement of the lift cylinder, and the tilt cylinder during the insertion process are recorded. The recording frequency of the above data was 50 Hz, which means once every 0.002 s.  
The experimental site was selected on empty and flat ground, and two kinds of materials, gravel and sand, were selected as shown in Figure 1. Gravel is a typical non-uniform, high-density, and large-particle material, while sand is a uniform, low-density, and small-particle material, both of which have high representativeness. Three levels of insertion depth were chosen, 600 mm, 800 mm, and 1000 mm.
## 8 Conclusions  
In this paper, a model based on the MCS was presented to estimate the load of the EVs charging station and was used in an optimal operation problem of a smart MG. This model covered the uncertainty parameters of EVs including type of EVs, type of chargers, SoC, battery capacity and price sensitivity. Ignoring any of these parameters changed the optimal solutions. The results of the sensitivity analysis on these parameters indicated that the uncertainty in price sensitivity, battery capacity and SoC of EVs have a higher influence on the operating cost. Therefore, ignoring them can lead to higher costs. The uncertainty of the types of chargers and types of EVs had not a significant impact on the cost because it did not increase the station's demand and the MG's manager prevents cost increases with a slight change in the dispatch of micro-turbine and buying from the upstream grid. The proposed MCS-based model takes into account the decisive factor on the EV's load profile concurrently. It has also an easy implementation process through the proposed MCS method and efficient simulation time. Therefore, the manager of MGs can estimate the load profile of EVs one day before of operation. Although the proposed model covers the uncertainty of EVs behaviors but there is a possibility of deviation of considered parameters for PDFs and therefore for estimated load profile.  
All the authors conceived the idea, developed the method, and conducted the experiment. [PERSON] contributed to the formulation of methodology and experiments. [PERSON] contributed to the data analysis and performance analysis. [PERSON] and [PERSON] contributed to the algorithm design and data sources. All authors read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China (61772454, 61811530332, 61811540410). It was also supported by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Net-work Technology (Nanjing University of Posts and Telecommunications), Ministry of Education (No. JZNY201905).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Removed Parameter** &
\begin{tabular}{c} **Micro-Turbines** \\ **Generation (kWh)** \\ \end{tabular} & **Main Grid (kWh)** & **Cost (S)** \\ \hline Type of EVs & 998 (\(-\)4\%) & 5177 (0.8\%) & 717.6 (0.08\%) \\ \hline Type of chargers & 1012 (\(-\)2.7\%) & 5182 (0.1\%) & 721 (0.55\%) \\ \hline SoC & 1028 (\(-\)1.15\%) & 5387 (4.97\%) & 752 (4.88\%) \\ \hline Batteries capacity & 1034 (\(-\)0.6\%) & 5533 (7.8\%) & 771 (7.5\%) \\ \hline Price sensitivity & 995 (\(-\)4.32\%) & 6230 (21\%) & 869 (21.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 10: The impact of ignoring the EVs uncertain parameters in the charging station load.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Evaluation Metrics  
The accuracy of pose estimation is generally determined by two metrics--the average distance (ADD) and average nearest point distance (ADD-S)--used to evaluate the accuracy of asymmetric and symmetric objects, respectively.  
The average distance (ADD) is defined as the mean distance between the sampled points on the 3D model transformed by the ground truth pose and those transformed by the predicted pose. It is calculated as follows:  
\[ADD=\frac{1}{m}\sum_{x\in M}\parallel(Rx+T)-(\hat{R}x+\hat{T})\parallel \tag{9}\]  
where \(m\) is the number of sampled points, \([R|T]\) and \([\hat{R}x|\hat{T}]\) represent the ground truth pose and the estimated pose, respectively, and \(x\) refers to the sampled points from the point cloud model.  
The average nearest point distance (ADD-S) is defined as the distance between each sampled point on the 3D model transformed by the ground truth pose and the nearest point in the predicted pose. The average of these nearest point distances is calculated across all sampled points as follows:  
\[ADD-S=\frac{1}{m}\sum_{x_{1}\in M}\min_{x_{2}\in M}\parallel(Rx_{1}+T)-\left( \hat{R}x_{2}+\hat{T}\right)\parallel \tag{10}\]
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 3 Experimental Results
### Placing Watermarks on Model Surfaces  
In the fourth experiment, we used the encoder to create embossed and engraved watermarks on the surfaces of the plate, the bowl, and a round cube. At first, a ROI was created in each of these test object. This ROI contains the surface layer and five consecutive distance levels adjacent to the surface of its host model. To create an embossed watermark, these adjacent levels were selected from the void space outside the model. On the other hand, the adjacent levels were extracted inside the model for generating an engraved watermark. Then, we invoked the SOM procedure to embed the watermark \"NTOU\" into the ROI. During the encoding process, the SOM procedure converted watermarked void voxels into model voxels (for embossed signatures) or replaced watermarked model voxels with void voxels (for engraved marks). Then, the watermarked models were manufactured by using the FDM printer. The printed parts of the plate were fabricated by using ABS while the bowl and the cube were printed with PLA. The heights and depths of the embossed and engraved patterns are about 1 mm.  
The images of the physical objects are displayed in Figure 9. As the images show, the encoder successfully generates these fingerprints on the models' surfaces. They can be visually evaluated without using any photographic equipment. In some hobbyist and volunteer communities, people are willing to share their work. Therefore, copyright infringement is not a concern. Instead, they need an efficient way to quickly identify products [6; 7; 8]. This experiment ensures that our watermarking system can be employed to accomplish this goal.  
Figure 8: Visual verification for watermark signals hidden in physical models. Strong background light rays are used to uncover the watermarks.
## 2 Materials and Methods
### Validation of the Simulation Models  
The PCM-spackle used in this work was recently studied by [PERSON] et al. [43] in a climate chamber to measure its impact on the air and the surface temperatures. The chamber was designed to consider a typical office cubic for two occupants together with a realistic daily schedule of heat gains and suitable room ventilation setup (see Figure 2). The conditions were prescribed in such a way so that one can emulate the normal indoor conditions at summer time for an office environment in the region of Oslo, Norway. The experiments were carried out without and with the application of PCM spackling at the inner walls (1.7 mm average thickness) and ceiling surfaces (2.1 mm average thickness) for 5 days. Also, the working hours were considered between 08:00-18:00, and NV with enhanced air flow rates was set to ensure the charging of the PCM. Interested readers can find the detail of the experimental setup in [43]. We use the aforementioned temperature dependent specific heat capacity and the thermal conductivity as the input parameters to validate the \"PCMwallH\" module of IDA-ICE with the experimental results. This module uses the partial enthalpy method to account the variable heat capacity of PCM. The comparison between the average air temperature of the simulation and the last 3 days of the climate chamber experiments is illustrated in Figure 3. Fixed timesteps with a resolution of 1 min is used for the simulations to match the thermocouple storage points in the experiments.  
First, a very good agreement of thermal behavior between experiment and simulation is achieved for the case without PCM (see Figure 3a). Secondly, the accuracy of the prediction of the PCM model is studied and shown in Figure 3b.  
A very good agreement between the curves can be seen again, especially during the business hours, i.e., the melting phase of the PCM. The curves are marginally deviating during the solidifying phase of the PCM. Note that the PCM released more heat earlier in the simulations compared to the experiment. This is most likely due to the effect of the hysteresis or super-cooling, where the true heat capacity curve of the PCM would in reality not be identical when the material is solidifying as when it's melting. The peak of the curve would in fact be slightly shifted towards a lower temperature during cooling. This is often seen in DSC data for MPCMs when both phase transitions are measured, and typical for the paraffin based PCMs which are normally used for the microencapsulation method [48]. As only the heating curve of the PCM-spackle was known for this study, it was also used for the reverse phase transition during cooling in the IDA-ICE model. This has also been done in similar studies and argued to not be of significant impo  
Figure 2: Climate chamber of the experimental study [43].  
of the results [29]. Nevertheless, the results are found to be accurate with \(R_{2}=0.976\) and \(CV_{RMSE}=1.12\%\) which indicates a very satisfactory match between the curves (Figure 3b) and the model can be considered as reliable.
## 5 Conclusions  
We proposed a privacy-aware decentralized federated learning framework for effective heart disease prediction in healthcare in this paper. The proposed framework is a hybrid method of FedMA and M-ABC optimization techniques to improve heart disease prediction while addressing privacy concerns in a healthcare system. The primary goal of this paper is to improve heart disease prediction accuracy as well as training time and communication efficiency. To ensure that our proposed framework is correct and valid, we evaluated and compared the performance in terms of various model prediction-based parameters and communication efficiency with the baseline federated learning FedAvg, FedMA, and with FedMA using PSO optimizer algorithms. The proposed framework indicated improved performance in terms of accuracy, classification error, precision, sensitivity, and communication efficiency. It is observed that the proposed framework provides 2.6% higher accuracy, 7% less classification error, 1.8% more precision, 7.1% higher sensitivity, and 12% fewer rounds are required to achieve the highest level of accuracy.  
Our proposed model has some limitations, including the possibility of extending it for scalability in terms of the number of IoMT client sites with the effect of the learning rate on the overall model. In the future, we aim to further improve the privacy-aware healthcare predictive system by using other feature selection algorithms and optimization methods. The diagnosis, treatment, and control of health-related diseases is a major issue due to privacy concerns, hence, in the future, we will work on recovery and treatment of many other critical diseases such as breast cancer, diabetes, skin cancer, and Parkinson's Disease.  
Conceptualization, M.M.Y., M.N. and A.Y.; methodology, M.M.Y., A.D.A. and H.E.; software, M.M.Y. and A.A.S.; validation, M.M.Y., M.N., A.A.S. and M.A.K.; formal analysis, M.M.Y., M.N. and M.A.K.; investigation, M.M.Y.; resources, M.M.Y. and A.D.A.; data curation, M.M.Y. and A.Y.; writing--original draft preparation, M.M.Y., M.N. and A.A.S.; writing--review and editing, M.M.Y., M.N., A.Y., A.D.A. and M.A.K.; visualization, M.M.Y. and M.A.K. supervision, M.N. and A.Y.; project administration, M.N., H.E. and A.D.A. and M.A.K.; funding acquisition, A.D.A. and H.E. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R51), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
We ran simulations to see how well the proposed approach performed. Any questions concerning the study in this publication are welcome and can be directed to the lead author ([PERSON]).  
The authors sincerely appreciate the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R51), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 3 Methodology  
To mitigate the shortcomings of traditional network traffic classification models--particularly their excessive dependence on port numbers and protocols, user privacy concerns, challenges in accurately classifying encrypted traffic, and their limited adaptability to emerging traffic types--this article introduces an efficient and high-precision encrypted network traffic classification model termed TransECA-Net. The model comprises two principal components: the first component is the CNN module integrated with the ECANet framework, while the second component is the Transformer module. (As shown in Figure 1).  
The operational flow of the TransECA-Net model entails the sequential processing of input data through several components to achieve enhanced classification of encrypted network traffic. The process begins with feature extraction via a convolutional neural network layer. This is followed by channel attention adjustment through the ECANet module, which focuses on optimizing feature representation. The extracted features are then directed to the Transformer module, where dependencies between sequences are further captured. Finally, a fully connected layer is utilized to execute the classification task.  
This model seamlessly integrates CNN, ECANet, and Transformer, capitalizing on the strengths of each component: the robust feature extraction capabilities of CNN, the advantages of the Transformer in managing sequential data, and the enhanced focus on significant features offered by ECANet. Collectively, these elements contribute to improved classification performance. Below is a detailed explanation of each component.
## 2 Materials and Methods
### Finite Element Modeling  
#### 2.2.1 Model Description  
In this research, a numerical model for analyzing the RC beams strengthened via the NSM method with FRP bars and the innovative double-strap joint has been proposed  
Figure 3: Experimental test: (**a**) front view of test specimen; (**b**) back view of test specimen with instruments.  
Figure 2: Preparation of the test specimens: (**a**) cutting of the groove for GFRP strengthening; (**b**) FRP installation and detail of the double-strap joint; and (**c**) epoxy installation.  
through the use of the FEM and Abaqus/Standard 6.13 software. The model validation has been conducted by comparing the load-deflection diagrams for all of the experimentally tested beam cases, as well as load-strain diagrams for GFRP bars in model B-G4. The proposed numerical models have also been used for a parametric analysis of the strengthened RC beams.  
The geometry of the FE models of the RC beams has been created according to the geometry of the test beams (Figure 1) [16]. The geometry of beam B-G3 has been presented in Figure 4a, and the geometry of the reinforcement and GFRP bars has been presented in Figure 4b. The geometry of all other beams is analogous, except for some small changes in the epoxy and GFRP geometry due to the (non)existence and length of the double-strap joint.  
In the experimental setup, one end of the RC beams had pinned support, and the other end had roller support. These supports were not modeled in detail, but appropriate boundary conditions have been defined. Namely, in the support axes, at a distance of 150 mm from the beam ends, the displacements of the nodes at the bottom side of the beam have been restrained. For the pinned support, all three translations have been restrained, and for the roller support, only vertical and lateral translations have been restrained (Figure 4a). In the experimental tests, load has been applied via steel plates set at thirds of the span. In the numerical models, this was simplified, such that steel plates were omitted, but the load has been applied as surface load across the top surface of the concrete beams (Figure 4a).  
The mechanical properties of concrete (compression strength and modulus of elasticity), as well as the tensile strength and modulus of elasticity of GFRP and steel reinforcement bars have been experimentally determined [16], and they are presented in the following sections of the article.
## 4 Validation of the Availability of Proposed Model
### Analysis of Dynamic Variable Characteristics  
We selected Monitoring points (0, 0, 0) and 2 (4.0, 0, 0) to analyze the characteristics of dynamic variables in the single-column explosives blasting test.  
From Figures 3 and 4, we note that the overall variation trend of effective stress obtained from the proposed damage model (tension-compression damage model) is consistent with that from the PLASTIC-KINEMATIC model. The peak of effective stress obtained using the proposed damage model is 1 GPa at Monitoring point 1, which is 0.2 times the peak obtained using the PLSTIC-KINEMATIC model. The peak of effective stress obtained using the proposed damage model is 1.7 MPa at Monitoring point 2, which is 0.8 times the peak obtained using the PLSTIC-KINEMATIC model. Meanwhile, we noted that the effective stress decreases as the distance from the center of the explosion increases. The blasting dynamic effect can be neglected over 4 m away from the center of the explosion.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Density (g/cm\({}^{3}\))** & **Blasting Speed (cm/\(\up\)s)** & **A (GPa)** & **B (GPa)** & **R\({}_{1}\)** & **R\({}_{2}\)** & **\(\up\) (GPa)** & **\(E_{0}\) (GPa)** & **\(V_{0}\)** \\ \hline
1.64 & 0.693 & 374 & 3.23 & 4.15 & 0.95 & 0.3 & 7 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of the explosives in the explosive blasting test.  
Figure 3: Effective stress-time curves for monitoring point 1.  
From Figures 5 and 6, we note that the overall variation trend of acceleration obtained using the proposed damage model is basically consistent with that obtained using the PLASTIC-KINEMATIC model, but the acceleration values obtained using the proposed damage model during about 150 us after the peak of acceleration is some different from that obtained using the PLASTIC-KINEMATIC model (Figure 5).  
From Figures 7 and 8, we note that the overall variation trend of velocity obtained using the proposed damage model is basically consistent with that obtained using the PLASTIC-KINEMATIC model, but after the peak of velocity, the velocity obtained using the proposed damage model is some different from that obtained using the PLASTIC-KINEMATIC model (Figure 7).  
Figure 8: Velocity–time curves for monitoring point 2.  
Figure 6: Acceleration–time curves for monitoring point 2.  
Figure 7: Velocity–time curves for monitoring point 1.  
From Figure 9, we note that since monitoring point 1 is located near the blasting zone, the damage degree at monitoring point 1 reaches 1.0 at about 300 \(\upmu\)s after the detonation at the bottom of the explosive. From Figure 10, we note that since monitoring point 2 is away from the blasting area, after the detonation, the rock goes into the damage state at about 850 \(\upmu\)s and then reaches the maximum damage value of 0.001 at about 950 \(\upmu\)s; the blasting damage of rock at above four meters away from the blasting center is rare.