esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 4 Exploring Future Directions
### Training a Sign Language Recognition Model with AI-Generated Images  
One potential area to explore is the use of AI-generated images to train SLR models. Training these models requires large datasets of sign language videos or images (since these models use supervised learning), which can be hard and time-consuming to obtain. By using images generated with artificial intelligence, scientists can create datasets that mimic real sign language movements. This approach has the potential to expand existing training data and allow models to learn a wider range of gestures and types of sign language.  
In addition, this method can solve the problems related to the lack of sign language datasets for less commonly used sign languages. AI-generated data can provide a scalable solution for creating models that are more inclusive and adaptive. Future research should focus on evaluating the effectiveness of AI-generated images to improve model performance and optimizing the token engineering used to generate these images to ensure they accurately reflect real gestures.  
Figure 6: The trending of the keywords “sign language recognition” over the last 15 years (November 2009–November 2024) from Google Trends.
Therefore, most schemes are still prone to manipulation by an adversary with even partial access to the entropy source, and their statistical properties can deviate if the expected environmental conditions change [33]. Given this, surprisingly, few assessments of the applicability of these generation schemes for random prime number generation have been made [34]. Random prime numbers are typically distilled from the output of a deterministic RNG using provable prime number generation algorithms such as the Shawe-Taylor random prime routine described in the Digital Signature Standard (FIPS 186-5) [3]. Alternatively, one can sequentially test different RNs with either primality-proving algorithms, such as the Pocklington and AKS tests, or probabilistic approaches such as the Miller-Rabin primality test [4; 35]. In either case, the statistical quality of the retrieved prime number sequence is non-trivially impacted by the chosen RNG. Despite these routines acting as an additional postprocessing layer and thus masking some input biases, a highly correlated output can still be present when a compromised randomness source is used [34]. In this work, we extend the preliminary analysis made in [34] and proceed with the statistical validation of a probable quantum prime number generator based on raw homodyne measurements of vacuum quadrature fluctuations. To evaluate the extent to which the additional classical entropy impacts the statistical quality of the gathered prime sequences, we additionally perform a comparative analysis with a purely classical entropy source derived from the proposed vacuum-based QRNG and present the key merit figures such as the variation in the diversity of prime numbers obtained and the search times for different prime lengths. Moreover, a length-agnostic statistical validation approach based on measuring the frequency of observations in an equiprobable binning of the prime output distribution is proposed, and the resulting sequences are submitted to the NIST statistical test suite. This paper is organized into 4 sections. In Section 2, we describe the experimental QRNG implementation and the additional probabilistic prime-searching algorithm implemented. In Section 3, the output noise from the quantum source is characterized in comparison with measurements from the classical scheme, and the prime sequences are statistically validated. Moreover, the key figures of merit for prime number generation are compared. Finally, in Section 4, a brief conclusion is presented.
## 5 Conclusions  
We studied the effect of new fault gouge produced by the wear of the fault surface and revealed that fault stability over slip is influenced by asperity interlocking and the fault gouge.  
The experimental measurements showed that the shear stiffness is mainly four phases, including \"rapid increase, keep unchanged, slow increase, and rapid decrease\" over the whole slip process. An unstable slip is more likely to occur in the last two stages. The small fault surface roughness and the large normal stress favor fault unstable slip. When the slip displacement is short, the mechanical properties of the fault are mainly controlled by the interaction of asperities. If the main form of asperities interaction is climbing mode, the fault is more prone to stable slip. If it is fracture mode, more fault gouge could produce due to large normal stress, smooth surface, and long slip displacement. After fault gouge separates the asperities, it becomes the main factor controlling the fault block slip. Faults are more prone to unstable slip due to more fault gouge particles, smaller gouge particle sizes, and near-spherical gouge particle shapes. In addition, when the slip displacement is long, a high-strain shear zone is formed inside the fault gouge, which leads to a rapid decrease in shear stiffness and the unstable slip of the fault block.  
The present study shows that the external test conditions mainly control the fault block slip stability by affecting the interaction of asperities on the fault surface and the lubrication properties of the fault gouge. The analysis of asperity interaction and the fault gouge lubrication may become a promising way to judge whether the fault could cause an earthquake.  
Conceptualization, Y.L. and C.T.; Data curation, W.S. and X.W.; Formal analysis, W.S. and X.W.; Methodology, Y.L.; Project administration, Y.L. and C.T.; Resources, C.T.; Writing--original draft, W.S. and X.W.; Writing--review & editing, Y.L. and C.T. All authors have read and agreed to the published version of the manuscript.  
This research received financial support from the 'Xinghai Scholar' program of Dalian University of Technology.  
Not applicable.  
All the data associated with this study has been reported in the manuscript.
## 3 Mathematical Preliminaries
### Average Probability of Subcarrier Collisions  
The SINR of an SU with collisions or interference from PU \(i^{th}\) subcarrier can be given as  
\[S_{c}=\frac{\lambda}{I+\eta}, \tag{9}\]  
where \(\lambda\) is the received power given as \(\lambda=P_{s}\gamma\), \(\gamma\) is the channel power, and \(P_{s}\) is the SU power. The \(I\) represents the interference from PU, and \(\eta\) is the AWGN. Using transformation of random variables, the PDF of the collision case can be found as [7; 9; 23; 24]  
\[F_{s,c}(x)=P(\lambda<x(I+\eta))=\int_{0}^{\infty}F_{\lambda}(x(y+\eta))\;f_{ \rm{I}}(y)dy, \tag{10}\]  
which, on further evaluation, reduces to  
\[F_{s,c}(x)=1-\frac{\big{(}1-e^{-\frac{q}{F_{\lambda}}}\big{)}\big{(}e^{-\frac{ \eta q}{F_{\lambda}}}\big{)}}{1+\frac{xP_{p}}{P_{b}}}-\frac{q}{xP_{p}}e^{\big{(} \frac{q}{xP_{p}}+\frac{\eta}{P_{p}}\big{)}}\Gamma\left(0,\bigg{[}\eta+\frac{q }{x}\bigg{]}\bigg{[}\frac{1}{P_{p}}+\frac{x}{P_{s}}\bigg{]}\right), \tag{11}\]  
where \(P_{p}\) is the received peak PU-Tx power, \(P_{s}\) is the SU-Tx power,\(q\) is the IT, \(\eta\) is the AWGN and \(\Gamma(x,y)\) is the incomplete Gamma function given as \(\Gamma(x,y)=\int_{y}^{\infty}t^{x-1}e^{-t}dt\).  
Therefore, the PDF from this CDF Equation can be found as [9]  
\[\begin{split} f_{s,c}(x)=\frac{x\eta P_{p}+\ P_{s}(\eta+P_{p})}{(xP_{p} +P_{s})^{2}}\big{(}e^{\frac{q}{P_{s}}-1}\big{)}\big{(}e^{-\frac{x\eta+q}{P_{s}} }\big{)}+\frac{q}{x^{3}P_{p}^{2}}e^{\frac{x\eta+q}{2P_{p}}}\bigg{[}(q+xP_{p}) \Gamma\bigg{(}0,\bigg{[}\eta+\frac{q}{x}\bigg{]}\bigg{[}\frac{1}{P_{p}}+\frac{ x}{P_{s}}\bigg{]}\bigg{)}\\ +\frac{xP_{p}(x^{2}\eta P_{p}-qP_{s})}{(x\eta+q)(xP_{p}+P_{s})}-e^ {-(\eta+\frac{q}{2})}\bigg{(}\frac{1}{P_{p}}+\frac{x}{P_{s}}\bigg{)}\bigg{]}. \end{split} \tag{12}\]  
Finally, from Equation (12), the average BER for the collision case will be given as  
\[\mathbb{E}[P_{b,c}]=\int_{0}^{\infty}f(x)_{\text{AWGN}}\ f_{s,c}(x)dx. \tag{13}\]  
where \(f(x)_{\text{AWGN}}\) is the probability of bit error in AWGN with a given SNR. This also depends on different modulation orders, for example, in the case of QPSK or 4-QAM, it is given as,  
\[f(x)_{\text{AWGN}}\approx Q(\sqrt{2x})\approx\frac{1}{\sqrt{2\pi}}\int_{\sqrt{ 2x}}^{\infty}e^{-\frac{x^{2}}{2}du}. \tag{14}\]  
For more details, readers are referred to [21] that lists all the expressions for different modulation order.
## 4 Results and Discussion
### Dynamic Response of Different Electric Field Intensity in the Separation Region  
Figure 5 describes the velocity response of particles with different diameters under different electric field intensities. We chose a relatively easy to acquire voltage of 30 kV to accelerate the particles. After the acceleration of 30 kV voltage, the particle enters the separation room to determine the initial velocity of particle entering the separation room.  
Figure 6 presents the displacement response of atmospheric particles with different diameters under different electric field intensities. As observed in Figure 6, in the ideal case, the displacement of the particles in the X direction increases with the increase of the particle diameter and increases with the increase of the transverse electric field. In the Y direction, the displacement exhibits different response laws. With the same vertical electric field, the Y direction displacement increases from negative displacement to positive displacement with the increase of particle size. It indicates that the separation effect is achieved for particles with different diameters. Without constraints, in the time of 2 s, PM\({}_{100}\) will be displaced up to 2.35 m in the X direction and 0.94 m in the Y direction.  
Figure 7 shows the change of atmospheric particle displacement with the electric field strength at normal temperature and pressure and electric field strength of 30 kV. In the X direction, with the increase of the electric field strength, the displacement of atmospheric particles continues to increase. As the diameter of the particles increases, the displacement increases continuously. The maximum X displacement can reach 0.875 m. In the Y-axis direction, the displacement increases as the electric field strength increases. When the diameter of atmospheric particulates is small, it presents a positive \(Y\)-axis displacement. As the diameter continues to increase, atmospheric particles begin to show a  
Figure 5: Dynamic velocity response of different electric field strengths and different particle diameters: (**a**) \(X\)-axis velocity response; and (**b**) \(Y\)-axis velocity response.  
Figure 6: Dynamic displacement response of different electric field strengths and different particle diameters: (**a**) \(X\)-axis displacement response; and (**b**) \(Y\)-axis displacement response.  
negative displacement in the Y direction. As the particle diameter increases, the negative displacement in the Y direction becomes larger. The displacement in the \(Y\)-axis direction gradually changes from a semi-parabolic descent to a complete parabolic shape as the diameter increases. The maximum displacement in the \(Y\)-axis direction is 0.2209 m.
# CarTwin--Development of a Digital Twin for a Real-World In-Vehicle CAN Network
## 6 Experimental Evaluation of the Digital Twin
### Comparison to Related Works  
The design of digital twins for cars is only a recently emerged topic, and there is only a very limited number of related works that can be immediately compared with the developments from our work. As already stated in the introduction, an implementation of digital twins for vehicle dynamics using the steering system, braking system and powertrain is analyzed by authors in [9]. Our work improves on this with the use of a real-world vehicle bus topology, besides the definition and implementation of control system models on the ECUs. A research team from Toyota has designed PASTA (Portable Automotive Security Testbed with Adaptability) [57], an adaptable vehicle cybersecurity testbed as an evaluation environment for automotive attacks. Their testbed integrates development boards with models for various ECUs functionalities from real-world vehicles, which communicate on two separate CAN networks connected through a Gateway unit. One recent work, RAMN (Resistant Automotive Miniature Network) [58], designs a small and inexpensive automotive testbed that includes implementation of models for the gateway, powertrain, chassis and body ECUs connected to a single CAN bus. A comparison of our work with research papers that address vehicle-level functions using digital twins is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{\begin{tabular}{c} **No Attack** \\ **Frames** \\ \end{tabular} } & \multicolumn{4}{c}{**Correlation Coefficients**} & \multirow{2}{*}{\begin{tabular}{c} **Attack at** \\ **\(p=0.1\)** \\ \end{tabular} } & \multicolumn{2}{c}{**Attack at**} & \multirow{2}{*}{\
\begin{tabular}{c} **Attack at** \\ **\(p=0.25\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Attack at** \\ **\(p=0.5\)** \\ \end{tabular} } \\ \hline Generic trace & 0.88 & 0.70 & 0.57 & 0.43 & 0.35 & 0.00 \\ CarTwin & 0.93 & 0.83 & 0.72 & 0.61 & 0.56 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attack correlation augmented trace vs. CarTwin.  
Figure 18: Vehicle speed and engine speed under a fuzzing attack with 25% probability in an off-line augmented trace (**left**) and the same signals within the CarTwin models (**right**).
## 5 Conclusions  
The main findings of the research were as follows:  
1. Effect of LPBF Process Parameters on Material Density and Microstructure The proper selection of LPBF processing parameters, particularly a volumetric energy density of 111 J/mm\({}^{3}\) (P = 200 W, v = 900 mm/s, hatch spacing = 0.1 mm, layer thickness = 0.02 mm), enabled the production of samples with very high density--up to 99.96%. Higher scanning speeds (\(\geq\)900 mm/s), when combined with proportionally increased laser power, led to improved material quality compared to low-speed strategies. Regardless of the applied parameters, all samples exhibited a fine cellular-dendritic microstructure (0.5-1.5 \(\upmu\)m) within the columnar grains, resulting from extremely high cooling rates (10\({}^{5}\)-10\({}^{6}\) K/s). Moreover, a clear transition from columnar to equiaxed grains was observed depending on the sample orientation (XZ vs. XY), which had a direct impact on the mechanical properties of the material.
2. Anisotropy of Mechanical Properties The fabricated samples showed a distinct anisotropy of mechanical properties depending on the build orientation. In the XY orientation, a higher ultimate tensile strength was recorded (UTS= 827 \(\pm\) 7 MPa), but with lower elongation (A = \(19.2\pm 1.1\)%), whereas in the XZ orientation, lower strength (UTS = 651 \(\pm\) 2 MPa) was accompanied by significantly higher ductility (A = 50.8 \(\pm\) 2.6%). These differences clearly confirm the substantial influence of build direction in LPBF technology on the strength characteristics of the alloy.
3. Comparison with Conventionally Processed Material The mechanical properties of samples produced via LPBF (yield strength in the range of 580-698 MPa) significantly exceed those of conventionally cast or rolled Nickel Alloy HX (YS = 350-400 MPa, A \(\approx\) 35%). This advantage results from the fine-grained microstructure and the ability to precisely control process parameters, allowing for optimized tailoring of the material's properties.  
Conceptualization, R.S., K.J. and P.M.; methodology, R.S., K.J., R.M. and P.M.; software, P.M.; validation, R.S. and P.M.; formal analysis, R.S., K.J., J.M. and P.M.; investigation, K.J., R.S., R.M. and P.M.; resources, P.M.; data curation, P.M.; writing--original draft preparation, K.J., R.S., R.M. and P.M.; writing--review and editing, K.J., R.S., P.M. and J.M.; visualization, K.J., R.S., P.M. and J.M.; supervision, R.S. and P.M.; project administration, R.S.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.  
The scientific research was financed from the statutory work Ref. No. 504/04812/1090/44.00000.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This study was financed by the Warsaw University of Technology, Faculty of Materials Science and Engineering, from the statutory work 504/04812/1090/44.00000.  
The authors declare no conflicts of interest.
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
###### Abstract  
This paper introduces and implements an efficient training method for deep learning-based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4 th Industrial Revolution and the development of deep learning require deep learning-based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.  
anomaly detection; fault segmentation; DeeplabV3+; depth image segmentation; tire manufacturing +
Footnote †: journal: Applied Sciences
## 7 IoT-Defined Application Protocols
### Messaging Queue Telemetry Transport (MQTT)
#### 7.2.3 At Least One Delivery Subscription  
As the title indicates, the subscription must assure that the client gets the required data. The difference between at least one delivery subscription and exactly one delivery subscription is that in at least one delivery of a message, there may or may not be duplicate packets reaching the client from different routes. Exactly one message delivery discards the packets as they are received. Exactly one delivery has more precision than this type of delivery subscription.  
MQTT was developed by IBM in 2013 after studying the impact of CoAP; thus, it outperforms CoAP in many characteristics. It requires less computational power and memory as compared to CoAP. The MQTT-based IoT has comparatively lower latency in packet delivery, and it utilizes the network bandwidth efficiently because of its queuing mechanism. MQTT is also dedicated to simple and small data packets. It is used in networks that repeatedly require the same type of data from network nodes; thus, it possesses high data as well as sampling rate. This limits its application to only simple networks. As based on TCP/IP, the MQTT cannot be used for real-time applications as well.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 3 Effective Capacity
### Capacity Difference between NOMA and OMA Schemes  
To further analyze which access scheme is more suitable for a particular transmission SNR \(\rho\), we derive the difference of each user's achievable rate between NOMA and OMA change with respect to \(\rho\) as  
\[\frac{\partial\!\left(E_{c}^{N}-E_{c}^{T}\right)}{\partial\rho}=\frac{(1- \alpha)}{\ln 2}\frac{\mathcal{E}\!\left[\left(\frac{1+\rho|g_{c}|^{2}}{\alpha\rho|g_{ c}|^{2}+1}\right)^{-\varphi_{c}/\ln 2-1}\frac{|g_{c}|^{2}}{\left[\alpha\rho|g_{ c}|^{2}+1\right]^{2}}\right]}{\mathcal{E}\!\left[\left(\frac{1+\rho|g_{c}|^{2}}{ \alpha\rho|g_{c}|^{2}+1}\right)^{-\varphi_{c}/\ln 2}\right]}-\frac{1}{2\ln 2}\frac{ \mathcal{E}\!\left[|g_{c}|^{2}\!\left(1+\rho|g_{c}|^{2}\right)^{-0.5\varphi_{ c}/\ln 2-1}\right]}{\mathcal{E}\!\left[\left(1+\rho|g_{c}|^{2}\right)^{-0.5\varphi_{c}/\ln 2 }\right]}, \tag{24}\]  
and  
\[\frac{\partial\!\left(E_{t}^{N}-E_{t}^{T}\right)}{\partial\rho}=\frac{\alpha} {\ln 2}\frac{\mathcal{E}\!\left[|g_{t}|^{2}\!\left(1+\alpha\rho|g_{t}|^{2} \right)^{-\varphi_{t}/\ln 2-1}\right]}{\mathcal{E}\!\left[\left(1+\alpha\rho|g_{t}|^{2} \right)^{-\varphi_{t}/\ln 2}\right]}-\frac{1}{2\ln 2}\frac{\mathcal{E}\!\left[|g_{t}|^{2}\! \left(1+\rho|g_{t}|^{2}\right)^{-0.5\varphi_{t}/\ln 2-1}\right]}{\mathcal{E}\!\left[\left(1+ \rho|g_{t}|^{2}\right)^{-0.5\varphi_{t}/\ln 2}\right]}. \tag{25}\]  
Based on (24) and (25), special cases of \(\rho\) for the difference between NOMA and OMA can be obtained as shown in (26)-(29). From which, we can clearly see that in the case of a small \(\rho\), the resource utilization efficiency of User \(c\) with the NOMA scheme is superior to that with the TDMA scheme, but this benefit will decrease with the increasing of the \(\rho\). While for User \(t\), the user with a better channel quality, the resource efficiency achieved with the NOMA scheme is first inferior and then superior to that achieved with the TDMA scheme with the increase in \(\rho\).  
\[\frac{\partial\!\left(E_{c}^{N}-E_{c}^{T}\right)}{\partial\rho}|_{\rho\to 0} \approx\frac{(1-2\alpha)}{2\ln 2}\mathcal{E}\!\left[|g_{c}|^{2}\right]\geq 0. \tag{26}\]\[\frac{\partial\big{(}E_{c}^{N}-E_{c}^{T}\big{)}}{\partial\rho}\big{|} _{\rho\geq 0} \approx \frac{(1-\alpha)\mathcal{E}\Big{[}\big{|}g_{c}\big{|}^{-2}\Big{]}} {\alpha\rho^{2}\ln 2}-\frac{1}{2\rho\ln 2} \tag{27}\] \[\approx -\frac{1}{2\rho\ln 2}<0.\]  
In total, we can conclude that for User \(c\), the difference between NOMA and OMA, i.e., \(E_{c}^{N}-E_{c}^{T}\), starts at the initial value of zero, first increases and then decreases as the \(\rho\) grow; thus, the superiority of the NOMA scheme for this user can only be found when \(\rho\) is in a relatively low range. Although for User \(t\), \(E_{t}^{N}-E_{t}^{T}\) starts at the initial value of zero too, the difference first decreases and then increases with the increase in \(\rho\), so we can find that the superiority of the NOMA scheme can be found when \(\rho\) is relatively high.