esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bespoke Network Emulator
### Emulator Testbed for QoE Experiment  
The modified emulator was then used to setup a testbed to be used to assess how the correlated QoS metric affected the QoE of cloud gaming applications. The cloud gaming testbed was created using a three-PC configuration. It was decided to employ a powerful PC with a highly capable i9 processor, RTX 2080 Ti GPU, and 16 GB RAM as a cloud server. The cloud game played was hosted on this computer. The user's client computer, which was a mid-range Dell laptop without a dedicated GPU, was used to play the game. This was the most typical case in an actual cloud gaming platform; the user takes advantage of the power of the cloud host computer because they do not have a powerful enough PC to run on their own system. A third PC running Linux-based NetEm that had been modified was used to connect the client and server computers.  
The NetEm box served as a bridge between the server and the client, as seen in Figure 4. However, neither a switch nor a hub could be used to create this connection. This was because, in order to change the QoS values, traffic between the client and server had to pass via the qdisc of the NetEm box. Additionally, using a hub or switch diverted the packet away from the Linux kernel's qdiscs. Therefore, we chose to connect the NetEm box to a device that had two separate Ethernet connections. As seen in Figure 4 the client PC was linked to the second Ethernet port (ETH2), while the server was connected to the first Ethernet port (ETH1). To enable packets to move between the server and the client, a bridged connection was established between these ports using the bridge utility tool. The network interface settings in the Linux distribution's /etc/network/interfaces file was changed to build a bridge between the Ethernet ports.  
Figure 4: QoE Experiment Testbed.  
Figure 3: Bespoke NetEm Process Flowchart.
# Probabilistic Model Checking GitHub Repositories for Software Project Analysis
## 4 Experimental Results  
### Repository Characteristics  
#### 4.1.1 Action Rate  
Understanding the rate of actions within a repository provides valuable insight into the overall trends and dynamics of that repository's development. (1) represents the property specification used for this query.  
\[S=?[actionLabel] \tag{1}\]  
This query uses the \(S\) operator to calculate the probability of each action being performed in the long term. The results of the model checking performed on five models are shown in Table 3. Looking at the results, we can see that the probabilities of the actions  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Repository** & **States** & **Transitions** \\ \hline DeSolver & 26 & 120 \\ kafkaVision & 30 & 126 \\ Redline & 31 & 90 \\ Sherlogs & 25 & 99 \\ Hearth & 29 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modeling for GitHub repository.  
Figure 3: DTMC model for Redline.  
\"accepted\" and \"merge\" are equal. This result follows from the modeling, which ensures that \"merge\" follows \"accepted\" in the state transitions. Furthermore, \"commit\" emerges as the most common action across all repositories, while \"rejected\" has relatively low probabilities. Further insight can be gained by comparing the probabilities of different actions. For example, the proportion of \"create\" actions is lower than that of \"pull request\". Developers typically create branches for different purposes, such as feature development, bug fixes or testing. When their work is complete, they initiate pull requests. If these processes were one-to-one, the results would be identical. However, this result shows that the pull request rate is higher, which may be related to the development workflow within each repository, or to the pull request approval rate. More specifically, by continuing to work on the same branch and making multiple pull requests, or by rejecting pull requests, there may be a process of modifying and re-requesting the code.  
Closer examination reveals that in the case of kafkaVision and Sherlogs, the sum of \"accepted\" and \"rejected\" actions is less than the number of \"pull request\" actions. This indicates the presence of pending or unprocessed pull requests. In contrast, the remaining repositories have the sum of \"accepted\" and \"rejected\" actions equal to that of \"pull request\" actions.
## 3 Results and Discussion  
### Untargeted Analysis  
In both monitoring campaigns, the first step was a general characterization of the most relevant contaminants present in aquarium waters. As already mentioned in the introduction, although POCISs were originally designed to sample polar analytes, the detection of lipophilic compounds in certain deployment conditions has been reported in the literature [9; 10].  
Therefore, an untargeted analysis by GC-MS was chosen by also considering the probable presence of volatile and semi-volatile contaminants coming from bunker fuel. GC-MS in EI mode allows the tentative identification of several compounds thanks to the match with a spectrum database. Non-targeted analysis highlighted the presence of hydrocarbons and two other chemicals as the most abundant: DPS and 2,4-DTBP. While hydrocarbons and DPS were already detected in Ligurian seawater [9], 2,4-DTBP was identified for the first time using POCIS.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Challenges in Industrial Environments
#### 3.3.3 IIoT Challenges  
While the opportunities presented by IIoT cannot be overemphasized, IIoT is still faced with challenges, some of which have received attention in recent years. Reference [21] highlight these challenges. Reference [56] discussed the challenges faced in the application of IoT in logistics industries. These challenges are based on common IoT structural design, which includes network topology, the latency of packets, throughput, power sustainability, the security of data, and scalability of the technology. To solve the latency issue as one of the challenges facing IIoT, [PERSON][103] proposed a low-latency distributed scheduling function (LDSF). Reliability and latency are important requirements in IIoT for industrial environments. The authors in [104] studied the impact of physical, media access control, and network layers on these two requirements. One of the impacting factors on IIoT in industrial environments is electromagnetic noise. To overcome this, [PERSON] [105] collected electromagnetic noise data from an automobile factory by measuring and characterizing the data for IIoT systems.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 5 Fake Image Detection Method--Using Neural Networks  
This section will discuss the techniques for spotting fraudulent images using neural networks. By giving neural networks data to train on, they can discover the hidden properties of a modified image. They can then predict and spot fake images based on their learning.
# Analyzing Benford's Law's Powerful Applications in Image Forensics
## 5 Conclusions  
Summarizing, the above-described algorithm uses the JPEG coefficients from the luminance channel of already compressed JPEG images. It, therefore, determines the quality factor with which the analyzed images were compressed. Without using any means of machine learning, this algorithm reached an accuracy of 89% for 500 random images. Moreover, the algorithm had an accuracy of 100% when detecting the images compressed with quality factors 80 and 90.  
As a final thought, we refer to the idea that \"the numbers but play the poor part of lifeless symbols for living things\" ([PERSON], 1938). In light of our research, we strongly believe that, when analyzed properly and meticulously, the numbers come alive, providing essential information. Such a case is the one of JPEG coefficients, which embed the traces of an image's compression history.  
Finally, it may be concluded that the methods of detecting the quality factor, such as the one presented above, along with the described properties of JPEG coefficients, are fundamental for developing deep learning algorithms used in image forensics. The results are presented in more details in the Discussion section of this article.  
Moreover, we strongly believe that not only is the detection of a compromised image of great importance, but also the detection of the quality factor with which it was compressed.  
Conceptualization, D.C. and D.G.; methodology, D.G. and A.I.; software, D.C.; validation, L.M., H.V. and A.P.; formal analysis, O.S.; investigation, D.C.; resources, D.G.; data curation, D.C.; writing--original draft preparation, D.C.; writing--review and editing, D.G.; visualization, D.G.; supervision, L.M.; project administration, D.G. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Fire Behavior of U-shaped Steel Beams Filled with Demolished Concrete Lumps and Fresh Concrete
###### Abstract  
To reuse waste concrete in a more straightforward and simplified way, a new kind of structural member containing fresh concrete (FC) and demolished concrete lumps (DCLs) distinctly larger than conventional recycled aggregates has been proposed. Previous research has shown that, at room temperature, the mechanical performance of the U-shaped steel beams filled with DCLs and FC is similar to that of the U-shaped steel beams filled with FC alone. This research explores the fire behavior of the U-shaped steel beams filled with DCLs and FC. Five specimens including three beams filled with DCLs and FC and two reference beams filled with FC alone were tested in fire. The experimental parameters included the replacement ratio of DCLs, the longitudinal reinforcement ratio, the load ratio, and the thickness of fire insulation. Based on the test results, numerical models in which the thermal resistance at the interface between the U-shaped steel and the in-filled concrete is considered are developed using SAFIR to determine the thermal and structural responses of the specimens. Lastly, parametric studies are carried out preliminarily to investigate the effects of some parameters on the fire resistance of such beams. It is found that the replacement ratio of DCLs within a range of 0% to 33% has a very limited effect on the temperature distribution, structural response, and fire resistance of the specimens, that embedding longitudinal reinforcements can significantly increase the fire resistance of such beams, that the interface thermal resistance can generate a temperature drop of up to 280 \({}^{\circ}\)C at the interface between the U-shaped steel and the in-filled concrete, and that the numerical models are capable of predicting the thermal and structural responses of such beams.  
U-shaped steel beam; demolished concrete lumps; fire test; thermal response; structural response; interface thermal resistance; numerical models +
Footnote †: journal: Applied Sciences
## 4 Discussion  
Despite the applied simplifications in the numerical analysis and the lack of discharge modelling, the gas flow image preserved the features characteristic of the post-discharge fire-extinguishing gas movement in protected rooms. The modelled changes in the gas concentration in time do not deviate from the actual values by more than 20% (and in most cases not more than 10%), which can be considered a satisfactory value. Moreover, in specific cases, the numerical models rendered results closer to the experimental values than the retention time values identified with the standard model. Despite the discrepancies in the identified retention time values, the results of numerical calculations generally indicate that the model was close to the experiment, which can be interpreted as confiscation of the proper resolution of the phenomena that occur during the fire-extinguishing gas flow through the room. Based on these results, it may be considered reasonable to omit the discharge modelling in practical cases, as a method for optimising computational resources. However, one should be aware of the limitations of such an approach--uniform gas concentration within the domain is assumed, and not estimated as a result of the discharge simulation. If there is a risk of non-uniform gas distribution (e.g., non-uniform placement of nozzles, complex geometries), it may be necessary to resolve the discharge.  
The numerical model used herein could be further improved to reduce the error in the identified retention time value, however since the retention time is not the perfect measure of the real performance of the low \(\Delta\rho\) gas mixtures, these efforts seem futile. For mixtures with a large value of \(\Delta\rho\), both the retention time and the O\({}_{2}\) concentration were predicted with very good accuracy.  
This study shows a large potential in CFD modelling for assessment of the real fire-safety level in the protected space, not based on a time index, but rather on a realistic image of gas-mixing and resulting gas concentrations within the space. Use of the CFD method in place of standardised calculation methods gives a better overview of the fire safety of protected compartment, through a better presentation of the retention process instead of an arbitrary time value given by standardised methods. The retention time value does not give any information on what are the conditions within the compartment after this time passes. The universal application, regardless of the studied compartment, would be an important advantage of CFD models over standard models. Correct validation of the CFD model can help to obtain a model relevant for almost any scope of application.  
Figure 18: Changes in oxygen concentration (v/v) on the probe No. 1, indicating the end of the retention time during experimental tests and CFD simulations, fire-extinguishing gas Ar 7.5% v/v and N\({}_{2}\) 92.5% v/v.
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 2 Related Work
Additionally, the researchers [7] developed a system to improve the Quality of Experience (QoE) for live 4K video streaming using Hypertext Transfer Protocol (HTTP) Adaptive Streaming (HAS). They addressed the challenges of high latency and video freezing during bandwidth fluctuations using the HTTP/2 server push feature and an OpenFlow-based network controller. Their methods reduced live latency and mitigated video freezes, improving user experience in challenging network conditions. Their study concluded that these approaches offer a promising solution to enhance HAS performance in real-time 4K video applications. [PERSON] et al. [16] experimented with transmitting 4K video over a local 5G uplink. They intentionally introduced packet errors by adding load to the transmission path. This study compared the performance of Real-Time Transport Protocol (RTP) and Secure Reliable Transport (SRT) under different conditions, focusing on Round-Trip Time (RTT), video transmission speed, and subjective video quality. The results showed that RTP performed better in terms of RTT when there was no added load, while the use of SRT resulted in a higher average RTT, especially under heavy load. However, SRT demonstrated greater resilience in maintaining video playback continuity during network strain. This study emphasized the practical importance of Quality of Service (QoS) in prioritizing video transmission, providing valuable insights for network engineers and video transmission professionals. Ultimately, RTP was superior in low-load environments, while SRT excelled in challenging, overloaded conditions. Based on the previous literature, no prior studies have explored the integration of MPT-GRE with 4K video streaming, and this research is the first to investigate this area. Specifically, our study is new in focusing on MPT-GRE and examines the transmission of 4K video streaming over an MPT-GRE tunnel. This contribution is essential, as it offers new insights into the performance of MPT-GRE for delivering high-resolution video. Unlike previous research, which mainly explores general multipath transport solutions, our work delves into the challenges and benefits of using MPT-GRE to enhance video streaming performance by taking advantage of the tunnel throughput aggregation capability, equivalent to the sum throughput of the two physical paths. This paper provides an in-depth quantitative evaluation of video quality improvements facilitated by MPT-GRE, utilizing widely recognized metrics such as SSIM, MSE, and PSNR. These metrics enable a precise assessment of video quality enhancements achieved through MPT-GRE, setting this work apart from existing multipath solutions and evaluations presented in [6; 12; 13; 14; 15].
## 2 Methodology
### Thin-Airfoil Theoretical Analysis  
Allowing for a simple analysis of the flow mechanism, the airfoil is simplified as a flat plate with its camber and thickness neglected here, and then, a quantitative comparison can be made with the quasi-steady thin-airfoil theory. The governing equation for thin-airfoil theory is Laplace's equation. The idea is to place a vortex sheet singularity of unknown strength on the airfoil [32]. The strength of this vortex sheet is determined by satisfying the flow tangency on the camber line in conjunction with the Kutta condition at the trailing edge. Formulated in Fourier series, the general form of pressure distribution on the airfoil is as follows:  
\[\Delta C_{p}(\alpha,\theta)=4\Bigg{[}A_{0}\bigg{(}\frac{1+\cos\theta}{\sin \theta}\bigg{)}+\sum_{n=1}^{\infty}A_{n}\sin n\theta\Bigg{]} \tag{2}\]  
where \(\alpha\) is the AOA, and \(\theta=\cos^{-1}(1-2x/c)\) (\(x\) is the chordwise location with the leading edge denoted by \(x=0\)). The coefficients \(A_{0}\) and \(A_{n}\) are given by following equations:  
\[\begin{array}{c}A_{0}=\alpha-\frac{1}{\pi}\int_{0}^{\pi}\frac{\text{d}y}{ \text{d}x}\text{d}\theta\\ A_{n}=\frac{2}{\pi}\int_{0}^{\pi}\frac{\text{d}y}{\text{d}x}\cos n\theta\text{ d}\theta\end{array} \tag{3}\]  
where \(y(x)\) describes the camber line of the airfoil. Integrating Equation (2) along the chord, we can obtain the lift coefficient, \(C_{l}=2\pi(A_{0}+0.5A_{1})\), and the pitching moment coefficient about the quarter-chord axis, \(C_{m0.25}=-0.25\pi(A_{1}-A_{2})\). For PO, the pitch-rate term produces a linear variation in normal perturbation velocity [5], \(w(x)=-\dot{\alpha}(x-0.25c)\) (where \(\dot{\alpha}\) is the pitch rate and positive during upstroke) so that the induced camber is a parabolic arc (Figure 2). For OF, the normal perturbation velocity can be formulated as \(w(x)=V\alpha(\text{t})-V\alpha(t-x/V)\). Because the quasi-steady condition means a low frequency, this formula can be linearized as \(w(x)=\dot{\alpha}x\). Then, the term of \(\text{d}y/\text{d}x\) in Equation (3) can be replaced with \(w(x)/V\). Hence, the lift coefficients are given by \(C_{l}=2\pi(\alpha+\dot{\alpha}c/2V)\) under PO and \(C_{l}=2\pi(\alpha-3\dot{\alpha}c/4V)\) under OF, indicating an additional effective AOA, \(\alpha_{\text{eq}}=\dot{\alpha}c/2V\) and \(\alpha_{\text{eq}}=-3\dot{\alpha}c/4V\), respectively.  
In short, PO and OF have an opposite effect on the effective airfoil camber. During upstroke, PO increases the effective camber, while OF decreases the effective camber. During downstroke, PO decreases the effective camber, while OF increases the effective camber. Therefore, the lift under PO is higher during upstroke and lower during downstroke compared with the lift under OF. Notice that this effect is directly related to the pitch rate \(\dot{\alpha}=2\pi\text{f}A\text{cos}(2\pi\text{f}\text{f})\). Consequently, the difference between PO and OF is negligible at low variation frequencies and substantial at high ones.
# Image Reconstruction Using Autofocus in Single-Lens System
###### Abstract  
To reconstruct the wavefront in a single-lens coherent diffraction imaging (CDI) system, we propose a closed-loop cascaded iterative engine (CIE) algorithm based on the known information of the imaging planes. The precision of diffraction distance is an important prerequisite for a perfect reconstruction of samples. For coherent diffraction imaging with a lens, autofocus is investigated to accurately determine the object distance and image distance. For the case of only the object distance being unknown, a diffuser is used to scatter the coherent beam for speckle illumination to improve the performance of autofocus. The optimal object distance is obtained stably and robustly by combing speckle imaging with clarity evaluation functions. SSIM and MSE, using the average pixel value of the reconstructed data set as a reference, are applied on two-unknown-distance autofocus. Simulation and experiment results are presented to prove the feasibility of the CIE and proposed auto-focusing method.  
auto-focusing; coherent diffraction imaging; single-lens system +
Footnote †: journal: Medical Imaging
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 4 Analysis of Numerical Simulation Results
### Analysis of the Effect of Extraction Time on Gas Pressure  
The simulated working conditions are as follows: initial gas pressure 1 MPa; extraction negative pressure is 25 kPa; borehole diameter is 203 mm; and extraction time is 30 d, 60 d, 90 d, and 120 d.  
Figure 6 shows the variation in gas pressure around the borehole at different extraction times (30 d, 60 d, 90 d, 120 d) when the diameter of the borehole is 203 mm and the negative extraction pressure is 25 kPa. Figure 7 shows the variation in gas pressure at different distances (0.5 m, 1.0 m, 1.5 m, 2.0 m, 2.5 m, 3.0 m, 3.5 m, 4.0 m, 4.5 m, 5.0 m) from the center of the borehole with different extraction times (30 d, 60 d, 90 d, 120 d). According to Figures 6 and 7, it can be concluded that the longer the extraction time, the larger the decrease in gas pressure, the faster the decrease rate, and the larger the effective extraction radius, which is basically a positive correlation, and the change in gas pressure is more obvious with the increase in extraction time. When the extraction time is 30 d, the change in gas pressure is small, and it only decreases in the range of 2.5 m in the borehole, and there is no significant change in the gas pressure in the rest of the range; when the extraction  
Figure 4: Gas pressure cloud at different borehole diameters: (**a**) 120 mm, (**b**) 153 mm, (**c**) 203 mm, (**d**) 253 mm.  
Figure 5: Variation in extraction radius with different borehole diameters.  
time is 60 d, the effect of gas extraction improves compared to 30 d, and it can satisfy the expected effect of extraction, but the scope of influence of extraction is limited; when the extraction time is 90 d, the effect of extraction improves significantly compared to 60 d, and the scope of influence of extraction increases, but the extraction time increases, the effective extraction radius increases, and with the increase in extraction time, the gas pressure changes more significantly. When the extraction time is 90 d, the extraction effect is obviously improved compared with 60 d, and the influence range of extraction is increased, but after the extraction time is more than 90 d, the gas pressure decreases, and the influence range is obviously reduced.  
Figure 6: Variation in gas pressure at different extraction times: (**a**) 30 d, (**b**) 60 d, (**c**) 90 d, (**d**) 120 d.  
Figure 7: Variation in gas pressure at different extraction times.  
Ideally, the longer the extraction time, the better the gas extraction effect, but in actual engineering practice, one needs to consider time, equipment operation, labor costs, and other factors. Increasing the extraction time will slightly improve the extraction effect but significantly increase the cost. Based on the principle of cost-effective optimization, the extraction time is 90 d.