esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To address issues like the non-uniqueness of solutions in fault parameter inversion using gravity data, this study proposed a Constrained Adaptive Weighted Particle Swarm Optimization (C-AWPSO) algorithm. A series of experiments were conducted to verify the feasibility of this algorithm in fault parameter inversion, and the main conclusions are summarized as follows:  
1. Compared with traditional optimization algorithms, simulation experiments demonstrate that the C-AWPSO algorithm exhibits better noise resistance. In real-world cases, when compared with previous studies, the C-AWPSO algorithm yields higher accuracy in predicting gravity anomalies for fault profiles, with an improvement of at least 64.4%. Its forward gravity anomaly also more closely matches the observed gravity anomalies.
2. Among inertia weight reduction methods, the linearly decreasing method performs more stably, balancing convergence efficiency and the ability to search for global optimal solutions. Therefore, this study selects the linearly decreasing method as the adaptive strategy for inertia weight adjustment.
3. Among the five parameters to be solved, the top-layer center depth (\(z\)) and bottom-layer center depth (\(w\)) of the fault show higher sensitivity. This implies that inversion results for z and w will be more stable, facilitating the determination of fault depth information.
4. Inversion results with constraint terms are significantly more stable than those without. During inversion, \(z\) and \(w\) have a greater impact on the RMS error.  
Conceptualization, S.S.; methodology, S.S.; software, H.Y. and J.L.; validation, S.S.; formal analysis, S.S.; investigation, F.Z. and J.L.; resources, S.S.; data curation, H.Y. and J.L.; writing--original draft preparation, C.X. and S.S.; writing--review and editing, C.X.; visualization, S.S.; supervision, C.X.; project administration, C.X.; funding acquisition, C.X. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant no. 2024 YFC2813304), Open Research Fund Program of State Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources (Grant no. KLMMR-2024-G04), National Natural Science Foundation of China (Grant no. 42274004), and Open Fund of National Precise Gravity measurement Facility, Huazhong University of Science and Technology (Grant no. PGMF-2024-P011).  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
Thank you to the editorial department and the reviewers for their revision suggestions, which have been of great help in improving the article.  
The authors declare no conflicts of interest.
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 3 Key Results of Existing QKD Networks
[37] also used the OSPF algorithm to design a routing protocol and evaluated its performance via simulation implemented by the AIT QKD software [68]. [PERSON] et al. [38] proposed a routing protocol using a dynamic routing scheme that includes three components: a Hello protocol, a routing protocol, and a link state update mechanism. The Hello protocol helps quantum nodes share the network topology information, the routing protocol is used to determine the suitable routing path, and the link state update mechanism is adopted to update the routing tables. [PERSON] et al. [39] highlighted that the QKD network is similar to the ad hoc network in terms of the routing method, used the greedy perimeter stateless routing protocol to design the routing protocol, and then, proposed a QoS mechanism for the QKD network; a simulation was performed to evaluate the performance of the proposed routing method. Another study [40] used the local complementation technique to share P2P entanglement; the proposed routing protocol could efficiently reduce the number of measuring qubits within the quantum repeater, thus enhancing the performance of the QKD network. For the hybrid QKD network framework formed by the quantum repeater and trusted node networks, [PERSON] et al. [41] proposed three routing protocols and evaluated the performance of the proposed routing protocol via simulations. For security issues and assumptions of the QKD network, [PERSON] et al. [46] showed how to allow end users/applications to access the QKD network's service securely is an open question; then, they used the OpenSSL [69] API to solve the question. However, [PERSON] et al. [46] proposed a method that decays the security level to computational security rather than unconditional security. [PERSON] et al. [44] used a multiple-path strategy to avoid the unrealistic assumption that the trustworthiness of a quantum node must be trusted, and proved that the QKD network can still provide key distribution services with unconditional security under \(t\)-bound situations (i.e., at most \(t\) untrusted nodes among \(n\) nodes). In addition, [PERSON] et al. [45] proved that the MDI QKD protocol in an active optical switch network framework over 200 km is secure against untrustworthy nodes.
## 5 Performance Evaluation and Comparison
Comparing the averages presented in Table 2 (note that an X indicates the winner of the performance comparison of each scenario), we observed the following:  
* The GA generated better trajectories than the DPSO algorithm in 7 of the 40 scenarios;
* The DPSO algorithm generated significantly better trajectories than the GA in 18 of the 40 scenarios;
* The GA and DPSO generated similarly performing trajectories in 15 of the 40 scenarios. Figure 10: Average running time of the greedy algorithms, GA, and DPSO algorithm. Figure 9: Fitness values of the GA and DPSO algorithm achieve over 1000 generations when \(n=60\). On the basis of these results, we conclude that the DPSO algorithm outperforms the GA in solving the moving object coverage problem when \(n=40\) and \(m=5\). However, as mentioned, the GA outperformed the DPSO algorithm when the problem scale increased. Tables 3-5 present the average traveled distance, the average difference in the distance traveled, and average fitness values obtained using the GA and DPSO algorithm for different problem scales, respectively. From these results, we conclude that:  
* The GA generated better trajectories than the DPSO algorithm in terms of average traveled distance as the problem scale increased; \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Average Traveled Distance and Standard**} & \multirow{2}{*}{\begin{tabular}{c} _p-Value_ \\ _Deviation of Generated Trajectories (50 Trials)_ \\ _QESO_ \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} _p-Value_ \\ _(Test-T)_ \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} _Average Are_ \\ _Statistically_ \\ _Different_ \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} _Winner_ \\ _GA_ \\ \end{tabular} } \\ \hline  
o & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average traveled distance of the 50 trajectories generated for each of the 40 scenarios using the GA and DPSO algorithm when \(n=40\) and \(m=5\). * The GA generated significantly better trajectories than the DPSO algorithm in terms of the average difference in the distance traveled, regardless of the problem scale;
* The GA generated significantly better trajectories than the DPSO algorithm in terms of average fitness values, regardless of the problem scale. In summary, the GA approach is preferable to the DPSO approach for solving the moving object coverage problem.
###### Abstract  
In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. These factors hinder their ability to adapt to rapidly evolving software environments. This study proposes an approach that integrates Prototype-Based Model-Agnostic Meta-Learning(Proto-MAML) with a Question-Answer (QA) framework that leverages the Bidirectional Encoder Representations from Transformers (BERT) model. By employing Few-Shot Learning (FSL), Proto-MAML identifies and mitigates vulnerabilities with minimal data requirements, aligning with the principles of the Secure Development Lifecycle (SDLC) and Development, Security, and Operations (DevSecOps). The QA framework allows developers to query vulnerabilities and receive precise, actionable insights, enhancing its applicability in dynamic environments that require frequent updates and real-time analysis. The model outputs are interpretable, promoting greater transparency in code review processes and enabling efficient resolution of emerging vulnerabilities. Proto-MAML demonstrates strong performance across multiple programming languages, achieving an average precision of 98.49%, recall of 98.54%, F1-score of 98.78%, and exact match rate of 98.78% in PHP, Java, C, and C++.  
question-answer methodology; vulnerable source code review; prototype-based learning; model-agnostic meta-learning; Proto-MAML; code vulnerability detection; software security; few-shot learning; source code analysis; meta-learning techniques; automated code review; cybersecurity; knowledge transfer; code reconstruction +
Footnote †: journal:  
Article
## 4 Results
### DU96W180 Airfoil (Test Case 1)  
#### 4.2.1 Flow Visualization  
The first test case on the DU96W180 airfoil is conducted at a chord Reynolds number of \(2\times 10^{6}\). Due to the low flow velocity at the time of the image acquisition, the object surface is in a thermal equilibrium and does not change its temperature throughout the measurement. As a result, the contrast between the laminar and turbulent flow regime is very low. The flow visualization results by means of the PCA and the two classical image processing methods, the temporal mean value and the temporal standard deviation of the image series, are depicted in Figure 8. The only distinct feature in the mean value is a small area between both flow regimes with lower intensity, indicating a laminar separation bubble due to its effect of isolating the surface from the warmer fluid temperature. It is hardly possible to distinguish globally between the laminar and turbulent flow, since both areas have an almost equal intensity, and systematic gradients create inhomogeneities within both flow regimes.  
The temporal standard deviation image does not show any flow features and consists only of noise and artefacts in the form of a circular pattern and a row-wise pattern, probably due to the camera lens and acquisition method. As no flow features are visible, a global distinguishability between the laminar and turbulent flow regime is not possible.  
All three PCs show two distinct areas of different intensities, correlating with the laminar and turbulent flow regimes. However, the intensity as well as the contrast between the flow regimes varies in each image. On top of that, PC 3 shows an emphasized region in the top left that is likely to be traced back to an artefact caused by the camera lens. PC 1 and PC 2 do not inherit this artefact, though PC 2 shows a strong systematic gradient in the vertical direction. A qualitative inspection suggests that a global distinguishability between the laminar and turbulent flow regime is maximal in the PC 1.  
The quantification of the CNR between the laminar and turbulent flow regimes in each flow visualization supports these observations. Without considering the temporal standard deviation, the mean value has the lowest CNR with 0.24, while all PC images have a higher CNR, and the maximum is found in PC 1 with 1.79.  
Figure 8: Thermographic flow visualization of the DU96W180 airfoil in cross-flow with a Reynolds number of \(2\times 10^{6}\). Temporal mean value and temporal standard deviation as well as principal components (PC) no. 1–3 from the PCA of the image series. Systematic inhomogeneities in the flow regimes are reduced while a contrast between the laminar and turbulent flow regime is maximized in PC 1 with CNR \(=\) 1.79.
## 4 Conclusions  
In this paper, a lightweight detection algorithm YOLOv5s-Camellia was proposed to meet the requirements of real-time detection, in view of the problems of low accuracy, low detection efficiency, and complex model of _Camellia oleifera_ fruit detection algorithm in the natural environment. The main conclusions are as follows:  
1. The unit of the ShuffleNetV2 was introduced as the basic unit of the backbone network, which significantly reduced the number of parameters, computation, and size of the model while saving computational resources and cache space.
2. After the model was lightened, the feature extraction ability of _Camellia oleifera_ fruit details was weakened, and the detection performance was improved by embedding three efficient channel attention modules in the backbone network while increasing the number of partial parameters.
3. To enhance the neck network's ability and refine the granularity of feature maps, the Concat dimensional stitching in the PAN was replaced with Add dimensional fusion, which increased the amount of information under each dimension while reducing the number of parameters and maintaining the dimension of the feature map tensor.
4. The better nonlinearity of the GELU activation function was used to optimize the model, which improved the characterization ability of the deep neural network. Compared with the ReLU activation function, the nonzero gradient is better able to maintain a smaller negative value, avoiding the problems of gradient disappearance and gradient explosion.
5. By introducing the SloU loss function, the vector angle loss between the ground truth box and the predicted box was added to the bounding box regression loss, which reduced the model error and improved the convergence speed and bounding box regression accuracy. The final average detection accuracy of the model reached 98.8% and the detection speed was 60.98 frame/s. Compared with other object detection algorithms, the comprehensive performance of the YOLOv5s-Camellia was better and can meet the real-time detection requirements.
# Thermomechanical Behavior of Textile Reinforced Cementitious Composites Subjected to Fire
## 3 Results and Discussion
### Bending Tests Results
#### 3.4.1 Results from 15-Minute Fire Tests
Therefore, it is logical that even though the not exposed, \"upside-down\" specimen (E3) has a higher strength than the other not exposed specimens (E1 and E2); this does not happen for the exposed-to-fire specimens (E6 is not stronger than E4 and E5). Regarding Specimens E1, E2, E3 and E4, the initial and the post-cracking stiffness (k\({}_{1}\) and k\({}_{2}\), respectively) were calculated. Additionally, the maximum force (F\({}_{\text{max}}\)) and the corresponding displacement \(\delta_{\text{max}}\) were found. Eventually, the values of k\({}_{1}\), k\({}_{2}\), F\({}_{\text{max}}\) and \(\delta_{\text{max}}\) for the exposed and not exposed to fire specimens were compared and the degradation was calculated. The results can be seen in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanical** & **Not Exposed Specimens** & **Exposed Specimens** & **Difference** \\
**Properties** & **(E1, E2)** & **(E4, E5)** & **(\%)** \\ \hline k\({}_{1}\) (kN/m) & 1.58 & 0.41 & \(-\)74\% \\ k\({}_{2}\) (kN/m) & 0.20 & 0.10 & \(-\)48\% \\ F\({}_{\text{max}}\) (kN) & 1.86 & 1.39 & \(-\)25\% \\ \(\delta_{\text{max}}\) (mm) & 14.1 & 23.6 & \(+\)68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mechanical characteristics of specimens of Series E that were exposed or not exposed to fire. Figure 9: Force versus displacement curves for specimens in Series E.
## 4 Discussion
### Advantages of the Proposed Approach  
The developed solution works on a local network, allowing it to function without the need for internet access, which is often a challenge in operating rooms. Such an approach improves accessibility, enhances security, reduces the risk of sensitive data leaks, and prevents connection delays.  
By utilizing a see-through mixed reality headset, the operator perceives the real world directly, instead of projecting an image from a camera as in popular AR headsets. This helps to ensure a sterile working environment. Unlike common methods such as using glued or attached markers to track the biopsy needle [40], our solution employs a fully vision-based approach using only the headset. Thanks to this, it maintains the tool's center of a mass and does not require any external tracking systems or cameras, thereby eliminating the need for a calibration procedure and making the whole system more intuitive and easier to use. From a clinical perspective, systems relying on external trackers or cameras are less effective during short procedures, as they require each component to be established.  
Notably, physicians participating in the study highlighted that system usage, including data superimposition and trajectory planning, can shorten the preparation time needed prior to performing the procedure. In particular, overlaying a 3D model onto the patient's body make it possible to visualize surgical targets as well as any organs at risk. Another significant factor is receiving feedback if the needle is aligned with the intended trajectory. The operator is guided to adjust the needle's position, which enhances confidence. For US guidance techniques, it is necessary to inject the needle first in order to see it in an image, while our system navigates to ensure proper needle placement and angle before the injection. Importantly, unlike CT or MRI, the proposed system does not utilize anyradiation or expensive intraoperative equipment during procedure, making it a safer and cost-effective alternative for medical interventions.
## 4 Measurement System
### Microphone Array  
Table 3 presents the components and their quantities for the respective microphone arrays. We introduced different devices for M1 and M2 because of the various circumstances such as an introduced period, research budget preparation, and coordination with our related research projects. The microphones used for M1 and M2 were, respectively, DBX RTA-M (Harman International Inds., Inc., Stamford, CT, USA) and ECM-8000 (Behringer Music Tribe Makati, Metro Manila, Philippines). Table 4 presents detailed specifications for both microphones. The common specification parameters are an omnidirectional polar pattern and the 20-20,000 Hz frequency range. For the amplifier and analog-to-digital (AD) converter, we used separate devices for M1 and an integrated device for M2. Here, both converters include a built-in anti-aliasing filter. Under the condition of the arrayed microphone interval, \(d=0.030\) m, grating lobes may appear for the upper limit of 5.6 kHz and above. However, we assumed that the effect is relatively small because the target power of mallard calls is concentrated in the band below 5 kHz. For outdoor data acquisition experiments, we used portable lithium-ion batteries of two types. Moreover, to supply measurement devices with 100 V electrical power, the commercial power supply voltage in Japan, we used an inverter to convert direct current into alternating current.  
Figure 5 portrays photographs of all the components in Table 3 installed on the mount depicted in Figure 4. In all, the 64 microphones were connected to their respective amplifiers with 64 cables in parallel.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **DBX RTA-M** & **Behringer ECM8000** \\ \hline Polar pattern & Omnidirectional & Omnidirectional \\ Frequency range & 20–20,000 Hz & 20–20,000 Hz \\ Impedance & 259 \(\Omega\)\(\pm\) 30\% (@11 kHz) & 600 \(\Omega\) \\ Sensitivity & \(-63\pm 3\) dB & \(-60\) dB \\ Mic head diameter & 10 mm & (no data) \\ Length & 145 mm & 193 mm \\ Weight & (no data) & 120 g \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed specifications of microphones of two types.  
Figure 4: Originally designed and assembled sensor mount.
## 8 Limitations  
The authors of this study recognize that, while analyzing the three prestressed concrete box beams provides accurate and reliable information about the condition of reinforced concrete structures, several points need to be addressed to fully comply with the optimal protocol for using GPR technology in bridge inspections.  
The researchers noted the first limitation, which was the calculation of the wave velocity of the electromagnetic waves. This parameter is crucial for accurately calculating rebar depths, spacing, and sizes. For this study, the wave velocity was calculated using the travel-time and travel-distance relationship to a known object, which was the steel duct used for the post-tensioned rods. This may pose a limitation in real-world applications, as accurately calculating the wave velocity without internal information about the reinforced concrete structure is challenging. Although bridge inspectors can see the steel duct, calculating the precise depth is difficult.  
Additionally, this study did not consider the effect of environmental factors, such as temperature and humidity, on the accuracy of GPR scans. This could be a limitation in real-world bridge inspection scenarios. Another aspect that requires further evaluation is accessibility to the bottom face of the structure and how to assist bridge inspectors in transversely scanning the bottom face with ease.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 4 Hops-Based Back-Pressure Routing  
In this section, we enhance BP routing by introducing the end-to-end hops count into LSN, which takes throughput and delay performance into account. A brief review of original BP routing is given before going further.
## 2 Background and Related Technologies
### Mioty  
Mioty is a standardized LPWAN technology based on ETSI 103 357, operating in the sub-1 GHz ISM band and defined by three key protocols: Lafour, TS-UNB, and DD-UNB.  
By establishing a standardized ecosystem, Mioty enables interoperability among multiple manufacturers and service providers, with ongoing collaboration through organizations such as the Mioty Alliance to ensure sustainable technological advancement [17].  
Mioty can accommodate more than one million concurrent devices per network while achieving sub-second average latency; its per-bit energy consumption is only 0.010 mJ [18]. The complete protocol stack is specified in ETSI TS 103 357-2 V2.1.1 [19].  
Mioty excels in frequency efficiency, maintaining reliable communication in congested frequency environments through its Time-Slotted Multiple Access (TSMA) random channel access mechanism, which minimizes interference while supporting simultaneous connections for numerous devices. Notably, one of Mioty's key differentiators is its ability to support a significantly larger number of connected devices compared to other LPWAN technologies.  
A key advantage of Mioty lies in its scalability. Within a narrow 200 kHz bandwidth, it can support up to 3.5 million connections per day, making it particularly suitable for large-scale IoT networks such as smart cities, industrial automation, and environmental monitoring.  
Furthermore, Mioty employs a hardware-independent architecture, allowing for flexible implementation across various platforms using software-defined solutions. This design minimizes both memory and processing power requirements, while the TSMA random channel access ensures robust data collection performance, even in interference-prone environments.