esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Simplified CNN Model Suitable for Real-Time  
The authors simplified the CNN model by applying only two sequences of FEU instead of three. The authors used the same parameters and data of the CNN model for training, validating, and testing. Figures 17 and 18 show the training and validation accuracy graphs of the proposed model. The training and validation accuracy of the simplified model is better than the accuracy of the model proposed in [39]. It also shows that the training and validation curves of our two models are close to each other.  
Figure 17: Comparative accuracy graphs after applying our first model to our dataset which is drawn in red and applying the simplified models to the two datasets where our dataset is drawn in green and the other dataset is drawn in blue: (**a**) Results of the training accuracy of the two datasets; (**b**) results of the validation accuracy of the two datasets.  
The execution time for detecting markers in the simplified model is better than the complex one. Thus, this model is suitable for real-time identification of markers. The authors have simplified the convolutional layers of the CNN model and used the same parameters for training and validation. The simplified model can detect Aruco markers very well in challenging conditions as it gives approximately 95.5% accuracy for training and 99.82% accuracy for testing. Furthermore, the training  
Figure 18: Comparative loss graphs after applying our first model to our dataset which is drawn in red and applying the simplified models to the two datasets where our dataset is drawn in green and the other dataset is drawn in blue: (**a**) Results of the training loss of the two datasets; (**b**) results of the validation loss of the two datasets.  
and testing accuracy of the simplified model is better than the accuracy of the other dataset. The training and testing curves of our models are also close to each other. The execution time for detecting markers in the simplified model is better than the complex one. So, this simplified model minimized the response time and led to the verification H4. The authors formulate T4. T4: CNN models can be simplified by removing some convolutional layers to improve the response time. However, this model still failed to detect markers under occlusive conditions.  
As a summary, the authors found the following results based on the above experiments. Aruco markers are selected to be used based on a comparison with QR codes in different situations. A navigation system is proposed to help PVI in navigation indoors using Aruco markers. After testing the prototype by PVI and blindfolded individuals, feedback and comments were discussed. Based on the discussion, the prototype was updated and tested again by PVI. The authors solved the problem of detecting markers in challenging conditions by using CNN and compared the results to another model. The results showed that our CNN gives better accuracy. The CNN model was simplified to be suitable for real-time usage.
## 6 Conclusions  
The multi-layer interference cylinder matrix model and the finite element model under centrifugal force and temperature gradient are built, the influences of both factors on the contact pressure of the multi-layer interference cylinder are analyzed, and the algorithm is validated to be suitable for actual design. The specific conclusions are as follows:  
1. The matrix models of the relationship between contact pressure and interference magnitude of a multi-layer interference cylinder under centrifugal force and temperature gradient are deduced based on elastic mechanics and thick-wall cylinder theory.
2. The contact pressure of multi-layer interference cylinder under centrifugal effect decreases with the increase of rotational speed, and the curve slope increases with the increase of rotational speed. The maximum radial displacement of contact surface is at the outmost layer of interference cylinder. In the same model, the radial displacement of contact surface is gradually increased from inside to outside. The interference magnitude should be compensated according to the centrifugal force during the design of multi-layer interference fit.
3. Without considering the stress concentration, the numerical solution is in good agreement with the analytical solution. Even if the maximum relative error of midpoint is 11.62%, it is kept within the allowable range of design, which shows that the matrix model of multi-layer interference fit under centrifugal force can be used for design reference.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Contact Pressure (MPa)** & \(\mathbf{p_{1}}\) & \(\mathbf{p_{2}}\) & \(\mathbf{p_{3}}\) & \(\mathbf{p_{4}}\) \\ \hline Analytical solution & 169.374 & 147.059 & 88.408 & 40.801 \\ Numerical solution & 172.561 & 146.768 & 95.783 & 41.929 \\ Relative error & \(-1.85\%\) & 0.198\% & \(-7.69\%\) & \(-2.69\%\) \\ \hline \end{tabular}
\end{table}
Table 4: The result of analytic solution and numerical solution.  
Figure 11: Relationship between contact pressure and the same temperature gradient.  
* The contact pressure of multi-layer interference cylinder decreases linearly with increase of temperature gradient. The maximum relative error of analytical solution and numerical solution under temperature gradient is \(-7.69\%\), which is in the allowable design range. It indicates that the matrix model of multi-layer interference fit under the same temperature gradient can be used for reference.  
[PERSON] and [PERSON] conceived the research direction and wrote the paper; [PERSON] designed finite element simulation; [PERSON] and [PERSON] analyzed the data; [PERSON] collected relevant information.  
This work was supported by the National Science Foundation of China (no. U1610109 and 51505475), the Shanxi Provincial Natural Science Foundation of China (no. 201601D011049), the Shanxi Provincial Key Research and Development Project (no. 201603D111017), and the Provincial Special Fund for Coordinative Innovation Center of Taiyuan Heavy Machinery Equipment (1331 Project).  
The authors declare no conflict of interest.
## 4 VRIS Multidimensional Assessment
### Visual Quality Assessment
#### 4.2.2 Results of the Experiment  
Robustness is a crucial metric for evaluating the resistance of image steganography models against various attacks and noises. In assessing the robustness of the VRIS model, we selected the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) as the two primary performance evaluation criteria.  
In this study, we evaluate the performance of the VRIS model by comparing the PSNR and SSIM of different datasets under different noise factors (as shown in Table 3). In Tiny-ImageNet, the PSNR fluctuates slightly with increasing noise factor, while the SSIM is relatively stable, which indicates that the VRIS model is more robust in maintaining the image structure by optimizing the image under a specific noise intensity than random noise on image impressions. In Mini-ImageNet and LFW, both PSNR and SSIM remain relatively stable with increasing noise factor, and this result proves that the VRIS model is robust enough to resist the interference of random Gaussian noise while maintaining the high quality of the encrypted image. In 256-ObjectCategories, PSNR fluctuates more under different noise factors, while SSIM is relatively stable, which implies that the VRIS model is more sensitive to the changes of parameters such as luminance and contrast on this dataset, and also indicates that the VRIS model shows strong stability in maintaining the image structure.  
We compared the quality of encrypted images generated by the VRIS model with the PSNR and SSIM of encrypted images generated by the state-of-the-art steganography model (shown in Table 4). The VRIS model is better on the ImageNet and LFW datasets, and the values of PSNR and SSIM are higher than or equal to those of ISGAN, SGAN, and the methods in the literature [38]. This indicates that VRIS outperforms or equals the other compared methods in terms of both image quality and similarity.  
To further validate the quality of images generated by VRIS, we compared the histograms of each channel for the cover and first-level encrypted image. The results are illustrated in Figure 6. The \(x\)-axis of the histogram represents pixel values, which correspond to the brightness or color values of each pixel in the image. The \(y\)-axis indicates frequency, showing the number of times pixels with the same value appear in the image. Thus, the histogram provides a clear understanding of the brightness or color distribution before and after steganography. The minor differences in channel histograms before and after steganography indicate that the proposed steganographic model possesses good embedding capacity and quality. It effectively maintains the visual quality of the images after embedding the secret image into the cover image, avoiding noticeable changes or distortions.
## 5 Conclusions  
This research was designed to address the cross-regulation challenge in a three-output forward converter using a weighted control method. The circuit description and mathematical modelling have been explained in detail. The proposed model reduces the change in voltages to a certain extent if appropriate weights are selected. In the experiment, cross-regulation improvement was observed under various loads.  
Also, in this research, notable points are that under different loads, efficiency reaches up to 86.8%, and cross-regulation falls within 3.33%. It is obvious that cross-regulation is 0.42% at the controlled output voltages. From the results, it can be observed that the winding ratios and diode selections are important parameters for cross-regulation. The cross-regulation of low-voltage windings, such as the +3.3 V output, changed relatively greatly compared to other windings, because no reference output was received. In summary, this experiment, which gave a result of only 0.09-3.33% cross-regulation under different loads, exhibited magnificent performance.  
Moreover, this proposed circuit does not contain any additional components such as MOSFETs, and does not require an additional control circuit. To put it briefly, this circuit is simpler, cheaper, and has good cross-regulation.  
Conceptualization, S.D.; methodology, S.D.; software, S.D.; validation, F.H.; formal analysis, S.D.; investigation, S.D.; resources, S.D.; writing--original draft preparation, S.D.; writing--review and editing, F.H.; visualization, S.D.; supervision E.A. and F.H.; project adminis  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & [24] & [25] & [31] & **This Work** \\ \hline Converter type & Flyback converter & Flyback converter & Flyback converter & Forward converter \\ Total output winding & 2 & 3 & 2 & 3 \\ Output power & 40 W & 68 W & 8.5 W & 45.6 W \\ Cross-regulation & \%0.2 & \%2.26 & below 5\% & 3.33\% \\ Peak efficiency & 86.9\% & 87\% & none & 86.8\% \\ Switching frequency & 600 kHz & 30 kHz & 1 MHz & 66 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison table.  
Figure 13: (a) I\({}_{\text{DF1}}\) and I\({}_{\text{DR1}}\) current waveforms at 80 V under full load of proposed converter; (b) I\({}_{\text{L2}}\) current waveform at 80 V under full load of proposed converter; (c) I\({}_{\text{MI}}\) and I\({}_{\text{DM}}\) current waveforms at 80 V under full load of proposed converter; (d) V\({}_{\text{DS}}\) voltage and V\({}_{\text{Gate}}\) waveform at 80 V under full load of proposed converter.  
tration, E.A. and S.D.; funding acquisition, S.D. and K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank [PERSON] for his support in designing PCB for the proposed circuit. The authors also thank [PERSON] for his help in the measurements of the circuit.  
The authors declare no conflicts of interest.
## 3 Results  
### Case Study: Information Gap Analysis Results  
Our analysis of the CTM (Supplemental Material S1) showed that the most highly needed information across user personas was related to Public Perception and Dynamic Risk (Figure 5a). The most needed information across user stories, however, was Public Perception, as it relates to how the public perceives fire risk (Figure 5b) as fire hazard (i.e., the potential of fire to occur). Information on fire hazard was equally as important (Figure 5b) as information on exposure (Figure 5b), while information on how vulnerable a community is, was more needed across the decision space (Figure 5b). Our analysis showed that solely relying on HCD would have excluded the development of social media filtering, despite being the most needed piece of information across user personas and stories (Figure 5). While HCD identified some of the root problems, information needs for megafire mitigation and the creation of more effective solutions, it did not capture the unimagined solution of social media filtering. Specifically, because few technologies exist (if any at all) to filter social media data and there is often a widespread mistrust of information shared on social media, it was hard for interviewees to imagine  
Figure 5: Synthesis of the Change Traceability Matrix (CTM) focusing on (**a**) percent of user personas (out of 8) needing information directly related to or relying on filtered social media or evacuations from high fire risk areas defined as a function of hazard, exposure, and vulnerability; and (**b**) percent of user stories (out of 39) relying on this information to make a decision. a solution for using social media as a basis to inform public perception. By questioning interviewees about what drives decisions and the missing information, the systematic analysis from WKID Innovation highlighted the overarching need for information on public perception. Despite hazard information being most valuable across decisions, the most common information gaps across personas (Table 1) are in social media filtering and risk futures. Recognizing the need for not merely filtering by hashtag, it is critical to use of artificial intelligence for identifying deep fakes, misinformation, bots, verified accounts or unique contributions to the conversation during natural disasters. Risk futures include consideration for how we message risk and not just how we have historically calculated it as acres burned. The PTM showed that the most valuable requirement for any information technology across user personas (Table 2) was the need for interoperable \"plug-in\" technologies that work with decision makers' existing tools. Next was the need for information technologies to be accessible via limited connectivity and with limited compute resources. Finally, all information should be intuitive to interpret, and information should be consistent when scaled between federal reporting to local decision support for implementation.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 5 Real Scenarios Evaluation
### Analysis of Results
#### 5.2.2 Ideal Conditions with FEC Protection
A possible solution to the problem found at overlapping areas resides in using different service channels for each one of the overlapping RSUs and incorporating a seamless horizontal handover mechanism to guarantee a nearly uniform coverage area.
## 3 Experiments and Results
### Case 2: Various Walking Stride Experiment
#### 3.3.2 Results  
The control performance of the proposed method is displayed. Figure 4a,b shows the tracking trajectories of leg joints. In the first 20 s, the subject walked on level ground with a small walking stride, and the walking speed was approximately 0.5 m/s. Then, the walking stride gradually changed from small to normal and then to large, each lasting for 20 s. Figure 4c shows the tracking errors of the leg joints. Despite the change in walking stride, the tracking errors of the joints did not seem to change significantly. Figure 4d shows the change in the control torque of the leg joints. Figure 4e shows the change in \(\hat{Y}\). Figure 4f shows the change in \(\hat{\tau}_{e}\).  
In Table 3, the tracking errors are statistically calculated for the two subjects. The mean errors (MEANs) of the tracking performance are 2.7\({}^{\circ}\) and 3.1\({}^{\circ}\), respectively. And the minimized mean square errors (MSEs) of the tracking performance are 1.8\({}^{\circ}\) and 2.4\({}^{\circ}\), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Walking Stride** & **MEAN (Degree)** & **MSE (Degree)** \\ \hline \multirow{3}{*}{1} & small & 2.7 & 1.8 \\  & normal & 2.9 & 2.3 \\  & large & 3.4 & 2.6 \\ \hline \multirow{3}{*}{2} & small & 3.1 & 2.4 \\  & normal & 2.8 & 1.7 \\ \cline{1-1}  & large & 3.1 & 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Trajectory tracking performance in Case 2.  
Figure 4: Tracking performance of various walking strides. (**a**) Tracking performance of the knee joint. (**b**) Tracking performance of the ankle joint. (**c**) Tracking errors of the joints. (**d**) Control torque of joints. (**e**) Estimation of the Y of joints. (**f**) estimation of \(\tau_{e}\) of joints.
## 1 Introduction
Limited by the upper temperature and pressure limits of experimental equipment, the experimental pressure of high-temperature air injection for light oil reservoirs is basically in the range of 5-15 MPa [5; 6]. For deep, light oil reservoirs with higher pressure, the research on high-temperature oxidation characteristics and the displacement law of air injection is still lacking. At the same time, there is a lack of research on the feasibility and oxidation characteristics of air injection to enhance oil recovery in old oilfields with a high water cut and high recovery degree. In this paper, the Hudson reservoir after water injection in Tarim Oilfield serves as the research object. It is a sandstone reservoir, with the sand body's primary sedimentary microfacies being waterfront, backshore, and foreshore. The Hudson reservoir is char acterized by a large depth, high permeability, high temperature, and high salinity. The reservoir is situated at a depth of 5074 m, with a formation pressure of 53.3 MPa and a formation temperature of 105 \({}^{\circ}\)C. The average porosity is 15.6%, the average permeability is 234 mD, and the edge and bottom water are well developed. At present, the reservoir pressure after water injection is 40 MPa, the recovery degree of water injection is 39.2%, the recovery degree of the main part of the reservoir exceeds 50%, and the comprehensive water cut is 91.9%, which is in the stage of an ultra-high water cut and ultra-high recovery degree. Under formation pressure conditions, the viscosity of Hudson crude oil is 3-5 cp, the density of oil is 0.84 g/cm\({}^{3}\), and the dissolved gas-oil ratio is 18 m\({}^{3}\)/m\({}^{3}\). The viscosity of degassed crude oil at 108 \({}^{\circ}\)C is 15-20 cp, and the average content of resin and asphaltene is 10%. In summary, the Hudson reservoir is a typical intermediate reservoir. Based on the 20 MPa one-dimensional displacement experimental device [6], the authors' team further upgraded the experimental device to achieve a maximum pressure of 40 MPa and a maximum temperature of 800 \({}^{\circ}\)C, enabling the simulation of the one-dimensional oxidation displacement process of air injection in deep high-pressure medium oil reservoirs under conditions closely resembling actual reservoir environments. The air injection one-dimensional oxidation displacement experiments of the original reservoir of Hudson crude oil from low pressure (5 MPa) to high pressure (30 MPa) and 30 MPa water injection reservoir reveal the unique oxidation displacement characteristics of light oil air injection under high-pressure conditions and light oil air injection after water injection.
## 1 Introduction
In the proposed scheme, the total space consumed by the payload and the disambiguation information introduces an overhead of \(2(d-1)/d\cdot 2^{d}\approx\frac{1}{2^{d-1}}\) bits per each original bit, which becomes negligible as \(d\) increases, i.e., it is less than 7 bits per a thousand bit when \(d=8\). We prove that the _payload_ occupies \(\approx\frac{(d-2)}{d}\), and the _disambiguation information_ takes \(\approx\frac{2}{d}\) of the final representation, and the two partitions consume in total almost the same space with the input data. Following the space consumption analysis, we investigate how much information can be inferred from disambiguation data for the payload and vice versa, which leads us to the conclusion that encrypting one of the partitions and leaving the other one in plain can be considered for the privacy of the data. Since disambiguation information consumes less space, it would be ideal to encrypt it while leaving the payload in plain. However, the other way, encrypting the payload and leaving the the disambiguation information plain can still reduce the to-be-encrypted amount. Yet, nested application of the coding schemes on either partitions can also be considered a good strategy for privacy. It might have captured the attention of the reader that the analysis assumes the input bit stream is i.i.d., which seems a bit restrictive at a first glance. However, the target data types of the introduced method are mainly the sources that have been previously entropy encoded with some data compression scheme, where each symbol is represented by minimum number of bits close to its entropy according to [PERSON]'s theorem [18]. Such typical sources are video files in mpeg4 format, sound files in mp3 format, images in JPEG format and similar others. The output of the compression tools squeezing data down to its entropy is actually a quite nice input for our proposal. We support this observation by the experiments performed on various compressed file types on which the results are observed to be very close to the theoretical bounds computed by the uniformly i.i.d. assumption. In that sense, the proposed scheme can also be seen as a method of providing privacy of the compressed data as well. The outline of the paper is as follows. In Section 2 we introduce the proposed non-UD coding method based on the non-prefix-free codes, and analyze its basic properties mostly focusing on the space consumption of the partitions. We provide verification of the theoretical claims based on uniformly i.i.d. assumption on some files that are already entropy-encoded and investigate the hardness of decoding in absence of the partitions in this section for privacy issues. The implementation of the proposed technique is introduced here and currently available speed is presented as well. Section 3 summarizes the results obtained. We discuss possible practical usage scenarios in Section 4 and then conclude with addressing further research avenues.
# Analytical Algorithm for Oxygen Concentration of Aircraft Fuel Tank in Various Inerting Stages
## 4 Validations and Discussions
### Validation of Ullage Washing Dynamic Model in Flight Phases  
The flight test data and usage washing model in [7] are used to verify the proposed method in the pre-takeoff, climb, and cruise phase. The working conditions are shown in Table 2.  
Experimental and calculated results of usage oxygen concentration are shown in Figure 6. The average error of the usage oxygen concentration between the proposed unlage washing model and the flight test is 0.0024 and that of the model in [7] is 0.0054. The average relative errors of the proposed model and the model in [7] are 5.781% and 13.137%, respectively. The results calculated by the proposed unlage washing model show a better agreement with the flight test data in understudied flight phases.