esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments, Results, and Analysis
### Performance as Number of SDR Applications Increase  
Figures 10 and 11 demonstrate how the MAE and \(R^{2}\) score change as components are added to the composite signal. The performance of the DNN is worse when trying to estimate distribution parameters for less than six component signals, especially for the shape parameters. Figure 10 shows that estimates of the shape parameter, \(s\), become less accurate as more component signals are added to the composite signal, suggesting a trade-off between shape and scale parameters as the number of SDR applications increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Feature Groups (\%)**} \\ \cline{2-5}  & **Modulation Order** & **Data Rate** & **Power Level** & **Normalized Frequency** &
\begin{tabular}{c} **Number of** \\ **Component Signals** \\ \end{tabular} \\ \hline \(a\) & 42.55 & 1.57 & 46.67 & 1.80 & 7.40 \\ \(b\) & 49.94 & 0.68 & 39.35 & 0.75 & 9.28 \\ \(s\) & 38.96 & 0.12 & 48.87 & 0.11 & 11.95 \\ Overall & 44.42 & 0.63 & 44.30 & 0.70 & 9.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized SHAP values calculated for the hold-out test set using the DNN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\mathbf{R^{2}}\) & **Explained Variance** & **MAE** & **MSE** \\ \hline a & 0.9358 & 0.9358 & 0.0119 & 0.0008 \\ b & 0.9664 & 0.9664 & 0.0442 & 0.0133 \\ s & 0.9985 & 0.9985 & 0.0109 & 0.0003 \\ Overall & 0.9669 & 0.9669 & 0.0223 & 0.0048 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(R^{2}\), explained variance, MAE, and MSE performance scores obtained by the proposed DNN.
## 6 Discussion of the Results  
The comparison of oscillograph records given in Figures 6 and 13 allows making the following conclusions.  
Figure 14: Oscillograph records of operation of hydraulic SDs and AGCS at the implementation of suggested solutions.  
At the proposed setting of the RAC regulator the response of roll gap asymmetry control becomes faster. This is explained by two factors:  
* the positions of hydraulic SDs in the function of the drive signal derivative are controlled due to the introduction of the controller's differential part.
* the RAC controller does not reach the limitation level, which provides for continuous control during a whole rolling process.
* The misalignment of roll gap at the sides DS and OS decreases from 2.5 mm in Figure 6 to 1 mm in Figure 13. Obviously, it results in the reduction of gauge difference on feed sides, i.e., in the decrease of its wedging.
* Due to the aforementioned positive results, the limitation of horizontal bend and feed axial shift can be provided. In addition, it lowers the risk of occurrence of pre-emergency situations, accompanied by forced roll opening.  
Statistical analysis of the cost-effectiveness of this method in production cannot be undertaken easily because roll misalignment due to accidents is rare. It will take a fairly long time to collect statistics on accidents and loss of metal. However, as noted above, accidents may cause the feed to run off the roller table, which has severe implications. Thus, it is both technically desirable and cost-effective to prevent such accidents.  
The authors reach an additional conclusion on the results of analysis of Figure 14. Window 1 shows the oscillograph records of gauge change built on the basis of gauge meter signals. The gauge on the edges and in the sheet center changes by virtually the same. This confirms a satisfactory setting of the RAC controller.  
The developed method could be considered simplified, since the rolling force as a function of stand extension on OS and DS is proportional and is calculated by Equation (2). Differential pressure that controls the SD positions is also calculated on the basis of this assumption by Equation (3). More precise dependencies derived from the Golovin-Sims equations are covered in [5; 6; 41]. They apply to hot-rolling wide-plate roughing mills. Testing their feasibility for adjustment of roll gap misalignment in Mill 5000 and other plate mills is a different problem that requires R&D of its own.
###### Abstract  
A research contribution focusing on the Quantum Key Distribution (QKD)-enabled solutions assisting in the security framework of an optical 5G fronthaul segment is presented. We thoroughly investigate the integration of a BB84-QKD link, operating at telecom band, delivering quantum keys for the Advanced Encryption Standard (AES)-256 encryption engines of a packetized fronthaul layer interconnecting multiple 5G terminal nodes. Secure Key Rate calculations are studied for both dedicated and shared fiber configurations to identify the attack surface of AES-encrypted data links in each deployment scenario. We also propose a converged fiber-wireless scenario, exploiting a mesh networking extension operated by mmWave wireless links. In addition to the quantum layer performance, emphasis is placed on the strict requirements of 5G-oriented optical edge segments, such as the latency and the availability of quantum keys. We find that for the dark fiber case, secret keys can be distilled at fiber lengths much longer than the maximum fiber fronthaul distance corresponding to the round-trip latency barrier, for both P2P and P2 MP topologies. On the contrary, the inelastic Raman scattering makes the simultaneous transmission of quantum and classical signals much more challenging. To counteract the contamination of noise photons, a resilient classical/QKD coexistence scheme is adopted. Motivated by the recent advancements in quantum technology roadmap, our analysis aims to introduce the QKD blocks as a pillar of the quantum-safe security framework of the 5G/B5G-oriented fronthaul infrastructure.  
quantum key distribution (QKD); phase-coding BB84; secure key rates (SKKs); advanced encryption standard (AES); 5G/B5G packetized fronthaul; low-latency; coexistence scheme; raman noise; upconverted complementary metal-oxide-semiconductor (CMOS) photon counters +
Footnote †: journal: Applied Physics Letters  
1
Footnote †: journal: Applied Physics Letters
## 5 Proposed Framework
### ZTA Design
#### 5.1.4 Runtime Monitoring  
The runtime monitoring system is designed to provide comprehensive, real-time security threat detection and performance anomaly identification. This multi-layered detection framework, as shown in Figure 5, incorporates both lightweight ML models and rule-based triggers, ensuring robust protection for the system. The architecture of the monitoring system is composed of three primary components, the Metric Collector, the Anomaly Analyzer, and the Response Engine, which work together to monitor system health and security. The data flow is illustrated in Figure 5, where metrics are collected, analyzed, and, if necessary, trigger responses such as the automated quarantining of compromised xApps.  
The monitoring system specifically tracks three critical operational dimensions: API behavior, resource utilization, and model integrity.  
API behavior is closely monitored through several strategies to detect potential misuse or malicious activity. The first element of monitoring is the call frequency, where the system enforces a limit of 100 requests per second per xApp. This limit is adjustable based on the service class to accommodate varying traffic levels across different services. Next, the sequence validation mechanism employs a Long Short-Term Memory (LSTM) model that verifies API call patterns. This model achieves a high F1-score of 0.97, ensuring that abnormal API sequences can be identified with a high degree of accuracy. Additionally,  
Figure 5: Runtime monitoring architecture showing (1) metric collection, (2) multi-modal anomaly detection, and (3) automated response. Red highlights indicate triggered alerts. The timeline demonstrates a 50 ms detection latency.  
deep packet analysis is performed to detect any irregularities in the payload, with more than 15 known threat signatures, such as CVE-2023-1234, being checked during each API call.  
Resource utilization is tracked meticulously, focusing on critical hardware metrics like CPU, GPU, and Digital Signal Processor (DSP) usage. CPU usage is monitored with a threshold set at \(\pm\)20% deviation from the baseline, allowing the system to detect significant fluctuations that might indicate resource exhaustion or potential attacks. Similarly, GPU memory is tracked, with a limit of 80% of the total available memory being considered as the maximum acceptable usage. DSP instruction mix is also monitored with the use of SGX-verified reports to ensure that no unexpected instruction patterns occur during execution. Energy consumption is another vital metric, with any anomalous power draw exceeding \(\pm\)15% of the expected values flagged for review. This holistic approach to resource tracking allows for the early detection of anomalies that may indicate performance degradation or security breaches.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 7 Conclusions  
This study provides a brief overview of infrared and visible picture fusion technology as well as an understanding of its historical evolution and background characteristics. The most crucial step is to arrange infrared and visible image fusion techniques simply, including compressed sensing, sub-space, mixed, and other techniques. The main trend in infrared and visible picture fusion in the future is unquestionably deep learning. Then, several infrared and visible image fusion objective assessment indexes are highlighted, and particular calculation formulas are introduced. The datasets for visible and infrared images are then quickly sorted and introduced. Additionally, the infrared and visible image fusion application sectors are also introduced, including night vision, medical diagnostics, biometrics, detection, and tracking. At this time, infrared and visible image fusion technology is advanced, fusion quality is increasing, and new infrared and visible image fusion techniques are continually being developed. Future advancements in infrared and visible image fusion will also be made in uncharted territories, advancing human knowledge and technology.  
Y.L.: conceptualization, methodology, software, validation, formal analysis,investigation, resources, data curation, writing--original draft. Z.L.: Writing--Review, Editing, Supervision, Project administration, Resources. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61801319, in part by Sichuan Science and Technology Program under Grant 2020 JDQ0061, 2021 YFG0099, in part by Innovation Fund of Chinese Universities under Grant 2020 HYA04001, in part by Innovation Fund of Engineering Research Center of the Ministry of Education of China, Digital Learning Technology Integration and Application (No. 1221009), in part by the 2022 Graduate Innovation Fund of Sichuan University of Science and Engineering under Grant Y2023297.  
Not applicable.  
Not potential conflict of interest was reported in the authors.
## 6 Uncertainties Associated with Distances Derived from Point Clouds  
This section considers how uncertainties associated with a point cloud can be propagated through to the uncertainties associated with the distance between pairs of points.  
If a model determines the point cloud matrices \(V_{X}(\mathbf{\sigma})\) associated with \(\mathbf{x}_{i:m}\) in terms of statistical parameters \(\mathbf{\sigma}\), then for any pair of points \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\), we can calculate the variance associated with the distance \(d_{ij}\) according to  
\[u^{2}(d_{ij})=\left[\begin{array}{c}\mathbf{n}_{ij}\\ -\mathbf{n}_{ij}\end{array}\right]^{\top}V_{ij}\left[\begin{array}{c}\mathbf{n}_{ij }\\ -\mathbf{n}_{ij}\end{array}\right],\quad\mathbf{n}_{ij}=\frac{1}{d_{ij}}(\mathbf{x}_{i}- \mathbf{x}_{j}) \tag{29}\]  
where \(V_{ij}\) is the \(6\times 6\) variance matrix formed from the \(3i-2:3i\)th and \(3j-2:3j\)th rows and columns of \(V_{X}\). The uncertainty contribution from each of the influence factors can also be derived from the variance decomposition in Equation (28):  
\[u^{2}(d_{ij})=u_{ET}^{2}(d_{ij})+u_{ER}^{2}(d_{ij})+u_{P}^{2}(d_{ij})+u_{PQ}^{ 2}(d_{ij})+u_{S}^{2}(d_{ij})+u_{R}^{2}(d_{ij}). \tag{30}\]  
Often we are interested in the difference in distances, e.g., in comparing the distance associated with a test artefact with that associated with a calibrated reference artefact. Differences in distances also come into the impact of CMM uncertainties in form errors, e.g., the uncertainties associated with the difference in two diameters of a spherical or cylindrical artefact. Using the same notation,  
\[u^{2}(d_{ij}-d_{rs})=\left[\begin{array}{c}\mathbf{n}_{ij}\\ -\mathbf{n}_{ij}\\ -\mathbf{n}_{rs}\\ \mathbf{n}_{rs}\end{array}\right]^{\top}V_{ijrs}\left[\begin{array}{c}\mathbf{n}_{ ij}\\ -\mathbf{n}_{ij}\\ -\mathbf{n}_{rs}\\ \mathbf{n}_{rs}\end{array}\right],\]  
where \(V_{ijrs}\) is the \(12\times 12\) variance matrix formed from relevant rows and columns of \(V_{X}\).
## 7 Conclusions  
This article surveys the existing studies and research contributions of deep learning-based NOMA systems. The use of DL methods indicates that DL has a high impact on solving complex communication challenges of NOMA systems due to its powerful learning ability. We have shown, without a doubt, that the implementation of DL methods in NOMA is paramount to enhancing the performance of NOMA-based wireless communication systems. In this context, we provide a detailed discussion about related works and key performance indicators. This article comprehensively discusses the extensive scope of DL-based NOMA systems with the integration of several emerging technologies such as MEC, IRS, SWIPT, OFDM and MIMO etc. In addition, we explore the diverse DL-based NOMA systems considered by recent works for power allocation, CSI, SIC, channel estimation, user fairness, impulse noise, transceiver design etc. In this article, we briefly explain the benefits and shortcomings of incorporating DL in NOMA systems. Also, we highlight and summarize the potential challenges and issues. We also focus on future research directions for ameliorating the performance of the DL algorithms for NOMA systems. The findings of our study indicate that the experimental evolution of DL-based solutions, better algorithms, making appropriate decision, and integration of emerging technologies can achieve low latency, less complexity, massive connectivity, higher capacity, higher spectral and energy efficiency, optimal resource allocation and better performance. We firmly believe that this article will capture the high interest of both academic and industrial fraternities to investigate the potential of DL-based approaches in the next general wireless communication systems.  
**Author Contributions:** Conceptualization, S.A.H.M. and Y.L.; methodology, S.A.H.M. and A.V.S.; formal analysis, S.A.H.M., S.M.M., J.V.-A. and A.E.; investigation, S.A.H.M., Y.L. and A.V.S.; resources, S.A.H.M., J.V.-A. and A.V.S.; data curation, Y.L., and A.E.; writing--original draft preparation, S.A.H.M. and Y.L.; writing--review and editing, S.A.H.M., J.V.-A., S.M.M. and A.V.S.; visualization, Y.L.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by Universidad Indoamerica (Ecuador), funding code INV-0014-03-013.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.  
**Acknowledgments:** The authors would like to thank the CICHE Research Center and SISAu Research Group for the support and development of this work. The results of this work are part of the research project \"Educacion 4.0 aplicada a la ensefanza de competencias STEAM\" developed by Universidad Indoamerica.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Distributed Architectures and Constellations for \(\gamma\)-ray Burst Science
## 4 The Role of Distributed Architectures in Tomorrow's Multi-Messenger Astrophysics
### An Incomplete List of Pathfinder/Precursor Missions
#### 4.3.4 Other Projects  
BurstCube is a 6U CubeSat developed by NASA, which will detect GRBs using four CsI scintillators, each with an effective area \(\sim\)90 cm\({}^{2}\)[35]. BurstCube is expected to be launched in 2022.  
Figure 4: Laboratory model of the GALI detector system with 90 scintillators crystals. The individual scintillators in their reflective wrappings can be seen, arranged in a random order. A larger version is currently being built for the ISS.  
The Educational Irish Research Satellite 1 (EIRSAT-1), supported by ESA's Fly Your Satellite program, will carry a gamma-ray module (GMOD) to detect gamma-ray bursts [36]. GMOD uses SensL B-series SiPM detectors and a CeBr scintillator. EIRSAT-1 will be launched from the ISS in 2022.  
Nanosatellite constellations include the Chinese Gamma-Ray Integrated Detectors (GRID), which will consist of GRB detectors (as secondary payloads) on 10-24 CubeSats [37]. Two GRID units have been launched so far and one GRB has been recently detected by the second unit [38].  
Conceptualization, F.F., N.W. and E.B., writing, F.F., N.W. and E.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Union Horizon 2020 Research and Innovation Framework Programme under grant agreements HERMES-SP n. 821896 and AHEAD2020 n. 871158, by ASI INAF Accordo Attuativo n. 2018-10-HH.1.2020 HERMES--Technologic Pathfinder Attivita' scientifiche, by a Center of Excellence of the Israel Science Foundation, grant No. 2752/19, and by a grant from the Israel Space Agency.  
Not applicable.  
This article benefited from the invaluable input of the HERMES pathfinder, CAMELOT and GALI teams. In particular we would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] for inspiring discussions.  
The authors declare no conflict of interest.
###### Abstract  
Data transmissions between smartphone users require security solutions to protect communications. Hence, encryption is an important tool that must be associated with smartphones to keep the user's data safe. One proven solution to enhance the security of encryption algorithms is by using 3D designs on symmetric block ciphers. Although a 3D cipher design could improve the algorithms, the existing methods enlarge the block sizes that will also expand the key sizes and encryption rounds, thus decreasing their efficiency. Therefore, we propose the LAO-3D block cipher using a 3D permutation that offers security by providing confusion and diffusion characteristics. Five security analyses were conducted to assess the strengths of LAO-3D. The findings suggest that LAO-3D achieves better results compared to other existing lightweight block ciphers, with 98.2% non-linearity, 50% bit error rates for both plaintext and key modifications, surpasses 100% of the randomness test, and is immune to differential and linear cryptanalysis attacks. Moreover, the block cipher obtains competitive performance results in software applications. From the security analyses and performance tests, it is proven that LAO-3D can provide sufficient security at low costs in mobile encryption applications.  
3D permutation; block cipher; cryptanalysis; Internet of Things; lightweight cryptography; mobile application; security analysis +
Footnote †: journal:  
_Article_  
LAO-3D: A Symmetric Lightweight Block Cipher Based on 3D Permutation for Mobile Encryption Application. _Symmetry_  
**2022**, _14_, 2022. [[https://doi.org/10.3390/sym14102042](https://doi.org/10.3390/sym14102042)]([https://doi.org/10.3390/sym14102042](https://doi.org/10.3390/sym14102042))
## 3 Results
### Successes, Opportunities, and Challenges Related to AI Approaches in STI Diagnosis and Management  
Table 2 summarizes findings across various studies on ML and AI algorithms using STI diagnostics and management. Logistic regression emerged as the best algorithm for STI diagnosis based on correctly detecting symptoms (38), while the J48 decision tree performed optimally in detecting HIV positivity [37]. The XGBoost proved highly effective in predicting HIV status and identifying HIV predictors [41; 51; 54; 55]. In addition, RNNs were identified as top classifiers for HIV prediction (48) and categorical boosting was superior in predicting patient retention on antiretroviral therapy [45].  
Random forest models and algorithms excelled in predicting both virological failure (CD4 count as a key predictor) and clinic visit adherence [42; 50]. They were also effective in viral load suppression prediction [50] and index testing for HIV, with HIV knowledge being a crucial factor [46]. On the other hand, AI computer vision algorithms showed perfect sensitivity and predictive value in interpreting HIV self-test images [39]. Further, unsupervised ML approaches identified male and female clusters relevant to HIV risk [44], and DL approaches effectively classified rapid HIV test images, reducing false positives and negatives [52]. Lastly, conversational AI agents were seen as user-friendly for HIV testing [56].  
These AI technologies offer significant public health advantages, including enhancing screening, diagnosis accuracy, healthcare access, and strategic resource allocation [49; 52; 56]. However, for applications in SSA, factors like local settings, data sources, and technology quality require careful consideration [40]. The validation of these models may be necessary across diverse data and settings [38; 42], with some models potentially benefiting from further optimization [45; 50]. Additionally, performance assessment across different contexts and understanding factors influencing model success could enhance future predictive accuracy [43; 46; 48; 53].  
Most reviewed studies employed multiple AI approaches, except for three that used only one. The best-performing approach in each study, based on outcome metrics, was recorded as the top performer. In STI diagnosis, the random forest and XGBoost algorithms performed the best, excelling in five key metrics. Logistic regression and other unspecified ML algorithms ranked highest in each of the two metrics. Additionally, AI agents, algorithms, DL models, J48 decision trees, the LASSO model, and unsupervised ML approaches each achieved one top result.