esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 3 QKD Protocols
### Quantum Bit Error Rate  
For the Quantum Bit Error Rate (QBER) [53] in the context of this protocol, there exists a predefined threshold for acceptable errors, capped at 11% [54]. Should the QBER surpass this threshold, it signals a potential intrusion or tampering attempt by an eavesdropper during the communication process. Understanding and monitoring the QBER is crucial for maintaining the security of quantum communication. The calculated QBER serves as an indicator, helping ensure that the quantum key exchange remains robust against potential adversarial interventions.  
[PERSON] transmits the calculated QBER value to [PERSON]. Independently, [PERSON] performs her own QBER calculation. The success of the process is contingent upon both parties arriving at the same QBER value, confirming that the quantum communication is intact and secure. If the calculated QBER is below the predefined threshold of 11%, the protocol is deemed successful, and the sifted key is established.  
In our quantum communication framework, we utilize the QBER as a critical metric to discern potential eavesdropping activities. Specifically, within the BB84 protocol, we adhere to a stringent constraint, allowing a maximum QBER of 11%. This threshold corresponds to the Holevo bound, indicating the maximum amount of classical information that can be reliably transmitted through a quantum channel. The introduction of a third party into the quantum channel, establishing an entangled state within its Hilbert space, poses a potential vulnerability. This scenario arises when [PERSON]'s measurement indirectly prepares the eavesdropper's state, thereby revealing certain information about the quantum states transmitted by [PERSON]. Mathematically, the 11% threshold represents the point at which the allowed errors are capped. This is crucial to ensure that the mutual information between [PERSON] and [PERSON] remains greater than the information shared between [PERSON] and the eavesdropper. Staying below this 11% threshold is imperative to maintain the security of the key exchange, as surpassing it would indicate a compromise in the integrity of the communication, prompting the need to discard the keys.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## Nomenclature  
AE Acoustic emission  
CWT continuous wavelet transform  
CNN Convolutional neural network  
LSTM Long short-term memory  
ANN Artificial neural network
# Random Fiber Array Generation Considering Actual Noncircular Fibers with a Particle-Shape Library
###### Abstract  
In this work, we generated a set of random representative volume elements (RVEs) of unidirectional composites considering actual noncircular cross-sections and positions of fibers with the aid of a shape-library approach. The cross-section of the noncircular carbon fiber was extracted from the M55J/M18 composite using image processing and a signed-distance-based mesh trimming scheme, and they were stored in a particle-shape library. The obtained noncircular fibers randomly chosen from the particle-shape library were applied to random fiber array generation algorithms to generate RVEs of various fiber volume fractions. To check the randomness of the proposed RVEs, we calculated spatial and physical metrics, and concluded that the proposed method is sufficiently random. Furthermore, to compare the effective elastic properties and the maximum von Mises stress in the matrix, it was applied to composite materials with different relative ratios of elastic moduli of M55J/M18 and T300/PR319. In the case of T300/PR319 having a high \(\mathbf{RR}_{T}\) (relative ratio of the transverse elastic moduli), simulation results were deviated up to about 5% in the effective elastic properties and 13% in the maximum von Mises stress in the matrix according to the fiber shapes.  
particle-shape library; actual noncircular fiber; stress concentration; representative volume elements (RVEs) +
Footnote †: journal: Applied Sciences
## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Self-Admitted Technical Debt (SATD)--Questions 46 to 47  
Question 46: Do you systematically record and track self-admitted technical debt (SATD) comments in the code of Artificial Intelligence (AI/ML/RL) models, using backlog management, issue tracking, or other technical debt management tools?  
Stateholder: Organizers  
Justification: In AI competition platforms, unmanaged SATD can accumulate rapidly, especially in model training scripts, pipelines, or evaluation routines. Tracking such comments enables platform maintainers to prioritize refactoring tasks, reduce entropy, and support long-term platform evolution.  
Example: In a reinforcement learning competition hosted by a university using OpenAI Gym, a developer might leave a comment like # TODO: replace hardcoded reward shaping in the agent code. Without logging this SATD item into a task board, the issue may persist unnoticed, leading to hidden model biases or unfair comparisons across participants.  
Question 47: Do you regularly plan improvements for areas of the code marked as SATD, such as gradual model refactoring, pipeline component redesign, or documentation of experimental setups?  
Stateholders: Organizers  
Score: 5  
Justification: Scheduling targeted improvements for self-admitted debt is essential to maintain clarity, reproducibility, and modularity in AI/ML/RL competitions. It also encourages sustainable practices and transparency for participants.  
Example: On a Kaggle-like competition platform, the organizers may identify SATD in the evaluation pipeline--e.g., # FIXME: assumes deterministic policy output. By incorporating periodic reviews of such comments into the sprint planning, the organizers ensure the platform evolves with reduced hidden debt and clearer guidelines for future participants.
# Generative Adversarial Networks in Business and Social Science
## 2 Generative Adversarial Networks
### Mathematical Framework  
Let us give a technical introduction of the GAN model: Given a database with \(m\) real samples (\(\mathbf{x}\)) (training data) and a random noise vector (\(\mathbf{z}\)), the following terms are considered:  
* \(G(\mathbf{z})\) is the output of the generator from the noise \(\mathbf{z}\): that is, it is the synthetic data.
* \(D(\mathbf{x})\) is the output of the discriminator when a real sample \(\mathbf{x}\) is processed.
* \(D(G(\mathbf{z}))\) is the prediction of the discriminator on the synthetic data.
* \(P_{\mathbf{x}}\) and \(P_{\mathbf{z}}\) are the distributions of real and noise data, respectively.
* \(E_{\mathbf{x}}\) and \(E_{G(\mathbf{z})}\) are the expected log likelihoods from the different outputs of real and generated data, respectively.
* \(\theta^{D}\) and \(\theta^{G}\) are the weights of the discriminator and generator model, respectively.  
The expression to be considered for the complete network comprising the discriminator and generator is denoted by \(V\) and is as follows:  
\[V(\theta^{D},\theta^{G})=E_{x\sim P_{\mathbf{x}}}[\log D(\mathbf{x})]+E_{z\sim P _{\mathbf{z}}}[\log(1-D(G(\mathbf{z})))]. \tag{1}\]  
This value function is submitted to a min-max strategy with the goal of maximising the discriminator loss and minimising the generator loss:  
\[\min_{\theta^{G}}\max_{\theta^{D}}V(\theta^{D},\theta^{G}). \tag{2}\]  
Furthermore, a suitable setup for each artificial neural network employed and its training is required for the implementation of a GAN.
## 4 Discussion
Finally, it is interesting to quantify the sensing performance of both LSPRs and PSPs in terms of Figure of Merit (FOM). Since the analyte in our structure is an ultrathin monolayer instead of an entire embedding medium, it is challenging to obtain the actual change of refractive index, which depends on not only the optical constant of the ODT molecules but also the spatial coupling of the monolayer film into the volume of the plasmon field. The regularly defined \(\text{FOM}=\Delta\lambda/(\Delta n\Gamma)\), as well as sensitivity (\(\Delta\lambda/(\Delta n)\)) [41], where \(\Delta\lambda\) is the wavelength shift, \(\Delta n\) is the change of refractive index, and \(\Gamma\) is the width of the resonance, cannot be determined in experiment for our case of monolayer analyte. In fact, [PERSON] et al. proposed an alternative definition of FOM for thin-film target [41]:  
\[\text{FOM*}_{\text{layer}}=|dI/I|/dl \tag{3}\]  
where dI/I is the maximum relative intensity change at a fixed wavelength induced by spectral shift of the resonance, and \(dl\) is the thickness of the analyte thin-film. This defined \(\text{FOM*}_{\text{layer}}\) captures the effective sensing volumes of various nanostructures, and we used it in our sample characterization. Figure 5: **(a)** Refractive indices of the relevant materials used in simulation. Spatial field distributions for propagating surface plasmon resonances (PSPs), i.e., (**b**) P2 with mode number \(m=-2\), (**d**) P1 with mode number \(m=-1\), and local surface plasmon resonances (LSPRs), i.e., (**c**) L2 with mode number \(L=2\), (**e**) L1 with mode number \(L=1\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{\theta}\) (deg)} & \multicolumn{2}{c}{**Mode L1**} & \multicolumn{2}{c}{**Mode P1**} & \multicolumn{2}{c}{**Mode L2**} & \multicolumn{2}{c}{**Mode P2**} \\ \cline{2-9}  & \(\boldsymbol{\lambda}\) (\(\boldsymbol{\mu}\)m) & \(\boldsymbol{\Delta\lambda}\) (nm) & \(\boldsymbol{\lambda}\) (\(\boldsymbol{\mu}\)m) & \(\boldsymbol{\Delta\lambda}\) (nm) & \(\boldsymbol{\lambda}\) (\(\boldsymbol{\mu}\)m) & \(\boldsymbol{\Delta\lambda}\) (nm) & \(\boldsymbol{\lambda}\) (\(\boldsymbol{\mu}\)m) & \(\boldsymbol{\Delta\lambda}\) (nm) \\ \hline
30\({}^{\circ}\) & 8.487 & 8 & 6.759 & 0 & 4.285 & 4 & 3.402 & 1 \\
40\({}^{\circ}\) & 8.505 & 25 & 7.392 & 0 & 4.284 & 8 & 3.708 & 0 \\
50\({}^{\circ}\) & 8.584 & 7 & 7.931 & 0 & 4.302 & 4 & 3.976 & 0 \\
60\({}^{\circ}\) & 8.678 & 9 & 8.192 & 2 & 4.34 & 8 & 4.163 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulated wavelength shift values of local surface plasmon resonances (LSPRs) and propagating surface plasmon resonances (PSPs) in the metagrating sample. The experimentally determined FOM*\({}_{\text{layer}}\) values of both LSPRs and PSPs in our sample are plotted in Figure 6. It is seen that the LSPRs L1 and L2 exhibit a maximum FOM*\({}_{\text{layer}}\) of 0.18, while the PSPs P1 and P2 have a smaller FOM*\({}_{\text{layer}}\) of less than 0.1.
## 4 Experiment and Results
### Results
Although FCNN slightly outperformed BAT-Transformer in the R\({}^{2}\) metric with a value of 0.94, this may be due to the fact that BAT-Transformer's design focuses on minimizing latency prediction, which means that it places more emphasis on temporal accuracy, even if it does not fully account for data variability. However, the superior performance on other performance metrics is more significant. Thus, the proposed BAT-Transformer model outperformed the other models, especially in terms of metrics related to prediction accuracy, showing significant improvement over the other models, suggesting that the proposed model has the ability to effectively learn and predict complex transportation patterns.
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 3 Results and Discussion
### Nanotomography Data  
Figure 8 shows a real cryo-nano tomography dataset acquired at the MISTRAL beamline of the ALBA synchrotron facility; a biological sample of eukaryotic cell debris has been cryo-fixated on a gold grid and imaged at 900 eV [2]. Although the condenser optics of the soft X-ray microscope focuses the beam onto the sample, it is typical for this setup to assume a simplified geometry with an incoming parallel beam [1,2]. The objective Fresnel  
Figure 6: Performance comparison for the warp module, shown for single-thread (first column), 20 threads (centre column), and a multi-GPU implementation (third column). The performance gain in terms of speed is large even for the CPU-only multi-threaded solution.  
Figure 7: A real \(\upmu\)-CT dataset is artificially deteriorated by randomly shifting the projection in \(x,y\). In the misaligned dataset (**a**), the faint trace of an off-axis feature becomes evident with the post-acquisition alignment (panel (**b**)).  
zone plate lens (FZP) collects the transmitted beam, producing a magnified image (\(\times 1000\)) at the back-illuminated CCD detector (Princeton Pixis XO, pixel size of 13 \(\upmu\)m) [2]. The resulting dataset is a set of 121 images, acquired at a resolution of 1024 \(\times\) 1024 pixels and at an angle of [\(-60^{\circ}\), \(60^{\circ}\)]. The rotation run-out is about 300 nm, which is currently the state of the art [2], but due to the extremely complex setup and the large magnification factor (effective pixel size of 13 nm), a severe misalignment problem is inevitably present (Section 1). As can be seen in panel a, the sinogram of a particular off-axis feature is extremely jittered, and no \"sine\" can be recognized. Conversely, in panel b, the alignment makes it heavily pronounced. The effect of the correction is more evident at the border by observing how each line is shifted along the axis; the black part is the result of the padding operation, whose effects are reduced by employing a shift-cumulation procedure. The alignment process in Figure 8 has been performed by exploiting a multi-scale approach, aligning the dataset iteratively at a different scale. The reconstruction is re-initialised after each change of scale and starts by employing the set of projections aligned at the previous one. This kind of correction is completely unfeasible on the reference software configuration, as the required time would have increased even further.
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 3 Results
### Analysis of _Box-Behnken_ Experiment Results  
The Box-Behnken experiment focuses on several key indicators of the model, including the fitting effect, reliability, lack of fit, and precision. \(p\) < 0.01 indicates extremely significant effects, and \(p\) < 0.05 indicates significant effects. The overall fitting model has a _p_-value of 0.0001, indicating a highly significant correlation between the independent and dependent variables. The coefficient of variation (CV) = 0.77% < 10%, indicating the reliability of the experimental basis. The lack of fit term has a _p_-value of 0.5337 > 0.05, indicating good model fitting with no lack-of-fit phenomenon. The coefficient of determination (R\({}^{2}\)) of 0.9847 is close to 1 (R\({}^{2}\)adj = 0.9650, R\({}^{2}\)pred = 0.8899, R\({}^{2}\)adj - R\({}^{2}\)pred < 0.2), indicating the model's reasonableness. The precision value is 18.19 > 4, indicating good model precision.  
The response surface plots of the static friction coefficient (E), rolling friction coefficient (F), and seed-steel plate rolling friction coefficient (J) pairwise interactions with the angle of repose of the _C. esculentus_ seeds are shown in Figure 12. The response surface exhibits a large curvature, indicating that the interactions between the static friction coefficient, rolling friction coefficient, and seed-steel plate collision restitution coefficient significantly affect the angle of repose.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source of Variance** & **Contribution/\%** & _p_**-Value** & **Significance Ranking** \\ \hline Mold & - & 0.0078 & - \\ A & 1 & 0.1547 & 5 \\ B & 0.9 & 0.1735 & 7 \\ C & 1.4 & 0.1136 & 4 \\ D & 2.8 & 0.0511 & 3 \\ E & 21 & 0.0033 & 2 \\ F & 57 & 0.0008 & 1 \\ G & 0.02 & 0.7926 & 8 \\ H & 1 & 0.1565 & 6 \\ J & 6.5 & 0.0173 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of simulation parameters in the Plackett–Burman experiment.  
Figure 12: Effect of two-way interactions of factors on the angle of repose of _C. esculentus_ seeds: (**a**) response surface of population stacking angle for interspecies interactions of static and rolling friction coefficients; (**b**) response surface of population stacking angle for interaction of interspecies static friction coefficient and seed–plate rolling friction coefficient; (**c**) response surface of population stacking angle for interspecies interaction of rolling friction coefficient and seed–plate rolling friction coefficient.  
Quadratic regression modeling was performed using Design-Expert 13 software, and the variance analysis of the quadratic polynomial model resulted in the quadratic polynomial regression equation, as shown in Equation (7).  
\[\Theta=4.11-0.2738\mathrm{F}-0.4404\mathrm{E}+0.0676\mathrm{J}+0.3027\mathrm{EF}+ 0.0719\mathrm{EJ}+0.4723\mathrm{EJ}-1.69\mathrm{F}^{2}-1.30\mathrm{E}^{2}-0.8145 \mathrm{J}^{2} \tag{7}\]
## 2 Overview on Capacitive and Ceramic Stress Sensors  
The proposed experimental study investigates the efficacy of two sensor types for structural monitoring: piezo-resistive (ceramic) sensors (Figure 1a) and capacitive sensors (Figure 1b). The first one (Figure 1a) is an electronic circuit consisting of a microcontroller with embedded flash memory able to record and convert the low electrical signal of piezo-resistive bridges into a digital value. A ceramic stress sensor is made of an elastic-brittle material where a measure of stress is possible through the elastic strain of the internal material. The electric signal is directly converted into stress values due to the use of conversion parameters provided by the manufacturer for each sensor. In this paper, this type of sensor will be referred as \"stress sensors\". Initially, ceramic stress sensors were designed for concrete structures as reported in the literature [12; 13; 14], and they were embedded inside concrete when casting. Then, their use was extended to the case of new masonry elements [11]. The second sensor used (Figure 1b) is a new capacitive one [15]. It consists of a sensing area located on the plate surface of a parallel-plate capacitor with a Dupont Kapton(tm) dielectric layer. It is worth pointing out that several sizes of the sensor surface are possible, suitable for the dimensions of the brick units or, in general, to the area where the average stress needs to be monitored. Equation (1) presents the capacitance values C of a parallel plate capacitor:  
\[C=\frac{\varepsilon A}{d}, \tag{1}\]  
where \(\varepsilon\) is the permittivity of the gap, \(A\) the area of electrodes, and \(d\) the gap between the electrodes. In detail, the dielectric constant value \(\varepsilon_{r}\) of the Dupont Kapton(tm) layer is 3.4 \(C^{2}/\)(N m\({}^{2}\)). In this case, the capacitance variation depends on the variation in the distance between the electrode plates that is strictly related to the constitutive non-linear law of the internal dielectric layer. The capacitance signal is converted to voltage, current, or frequency through a microcontroller that is positioned outside of the sensing element. Among the advantages, the capacitive sensors include high sensitivity and stability, low susceptibility to temperature, inexpensive production costs and good durability.
# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 3 Experimental Results and Performance Analysis
### Verifying That RepELAN Is More Efficient  
To demonstrate the RepELAN's fastness and effectiveness, we employed a classic dimensional feature map from a real-time detection model as the input, maintaining a consistent batch size of 8. We conducted detailed testing on the module's floating-point operations, the number of parameters, and inference speed. In addition, we performed the same experiments on other state-of-the-art modules, including C3, ELAN [36], C2f, and RepNCSPELAN [10], to evaluate their performance. Figure 7: Sample images from the dataset. The experimental results in Table 1 indicate that RepELAN has significant advantages in multiple respects. RepELAN necessitates the least floating-point operations among similar feature extraction modules, with a mere 10.7 G. RepELAN outperforms other comparative modules in terms of the number of parameters across all feature map sizes. In particular, the number of parameters in RepELAN is at least 50% lower than that of the ELAN and C2f modules across various feature map sizes. Additionally, RepELAN exhibits superiority in the speed of inference. For example, RepELAN achieves 291.3 frames per second (FPS) at a feature map size of 40 \(\times\) 40 \(\times\) 512, approximately 19.3% faster than the second-ranked RepNCSPELAN. In conclusion, RepELAN achieves exceptional inference speeds while maintaining the lowest computational complexity and number of parameters. To evaluate the RepELAN module's performance across different models, we replaced the feature extraction modules with RepELAN in various models. We compared the models' FLOPs, the number of parameters, inference speeds, and abandoned object detection accuracy before and after integrating the RepELAN module. The computational costs of various models are significantly reduced by the RepELAN module, as illustrated in Table 2. In particular, the FLOPs of the YOLOv7 model decreased from 103.3 G to 94.7 G, while the FLOPs of the YOLOv5-N model decreased from 4.2 G to 3.4 G. These results illustrate the RepELAN module's efficacy in decreasing computational costs. The application of RepELAN significantly reduced the number of parameters in different models, including the YOLOv9-C model, which showed a 22.1% reduction. This reduction made the models more lightweight and easier to deploy. The RepELAN module significantly improved the inference speed for all models. The FPS of the YOLOv9-C