esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Feature Extraction Method
#### 2.1.2 Latent Variable Extraction Using CAE  
Latent variables representing the features of the input image data are extracted from each of the four types of 2.5D images using CAE. The four CAEs, each of which learns the extraction of latent variables from the target 2.5D image, have a common structure, except for the number of units in the final layer, \(n\). The unit number is the dimensions of the  
Figure 2: Examples of 2.5D image extractions. (**a**) CentSE-2.5D image, (**b**) MIP-2.5D image, (**c**) MinIP-2.5D image, and (**d**) MeanIP-2.5D image.  
extracting latent variable vector by the CAE. This CAE model is viewed as an information compress function \(Enc(\cdot)\) to extract an \(n\)-dimensional latent variable vector, \(\mathbf{z}\), from a 2.5D image, \(\mathbf{f}\); \(Enc(\mathbf{f})=\mathbf{z}\). The common structure of these CAEs is shown in Table 1. In the convolution layer, the image filtering process is used to enhance the characteristic parts of the image.  
The max pooling layer compresses the feature images extracted in the convolution layer. The full-connection layer converts the pixel values of the multi-channel feature images extracted by the convolution and max pooling layers into latent variables.  
The four CAEs used in the proposed method are both trained on 2.5D local patch images of normal image data without lesions. Hence, when the CAE is applied to unlearned images such as lesion images, the resulting latent variable is expected to show properties that differ from those of the latent variable encoding the normal image. The loss function in CAE learning is the L2 loss between the 2.5D image reproduced from the latent variables and the input 2.5D image. The reproduction image is obtained using a convolutional decoder having pairwise structures with the CAE. [PERSON] optimizes these CAEs' network parameters. The mini-batch size is 256, and the maximum number of epochs is 500. The number of latent variables to be output is the minimum number of dimensions for which the cumulative contribution of the CAE training data is greater than 90% in principal component analysis.
## 1 Introduction
On the one hand, in these circumstances, decontamination and long storage of coffee are becoming particularly relevant. On the other hand, the preservation of the nutritional value of coffee is also essential. It was reported that roasting could reduce the level of obratoxins in coffee by 30-96% depending on temperature and time [20], whereas freezing at \(-15\)\({}^{\circ}\)C for 6 days or at \(-20\)\({}^{\circ}\)C for 5 days can be an effective treatment for control of _Hypothenemus hampei_[21]. At the same time, the chemical composition of coffee changes during roasting in a complex way, which results from the Maillard reaction, pyrolysis and caramelization [5], accompanied by a release of volatile compounds [23]. In general, the data on the effect of roasting on phenolic antioxidants and AOA of coffee are inconsistent [5; 6; 12; 13; 19; 24; 25; 26]. With an increase in roasting degree, the content of phenolic compounds in coffee can increase, decrease and change in different directions [12; 13; 24]; however, convincing evidence that roasting causes a lower concentration of chlorogenic acids was suggested in [13; 24; 25; 27]. The inconsistency of results on the effect of roasting on coffee AOA can be due to the release of conjugated phenolic compounds and the formation of new substances with antioxidant properties [19], as well as to a degree of sensitivity of the methods used [24]. Compared with conventional roasting in an electric oven, roasting green coffee in a microwave oven could reduce the loss of phenolic antioxidants and AOA [14]. In recent years, non-thermal food processing technologies have emerged as an alternative to thermal methods since they have lower operational costs and exhibit limited changes in the organoleptic and nutritional attributes of food [28]. The results obtained in [29] suggest that cold plasma has a good potential for inhibiting filamentous fungi and detoxifying mycotoxins in coffee. Comprehensive studies examine the relationship between the roasting process and the antioxidant status of coffee [5; 6; 12; 13; 19; 24; 25; 26]. However, the studies focusing on the effect of microwave radiation [14; 30] and cold plasma [31] on the antioxidant status of coffee are limited, and no single study exists that deals with the impact of freezing. No single study was found comparing thermal and non-thermal methods of processing coffee beans. In this regard, the purpose of this work was to study the effect of thermal and non-thermal methods of pre-treatment of coffee beans on the AOA of aqueous coffee extracts.
## 7 Robustness
### Disk Image JPEG: JPEG-70 Compression and a Resize Factor of 0.9  
Given the consistent success of watermark extraction across a dataset of over 30,000 standard-resolution images, further analysis was carried out on five lower resolution disk image JPEGs (106 \(\times\) 160, 160 \(\times\) 106, and 107 \(\times\) 160). These JPEGs were subjected to JPEG-70 compression and a 0.9 resize factor attack. Figure 5 presents the results. Evaluation metrics include PSNR, BER, NC, and watermark accuracy in both the DCT and DWT domains.  
The results showed moderate image quality loss, with PSNR values between 30.39 and 33.03 dB. NC values remained high even after attacks, with a maximum of 0.953 for the 160 \(\times\) 106 image, and reflect a strong similarity between the original and watermarked versions.  
In the DCT domain, the watermark accuracy ranged from 0.70 to 0.95, while the DWT domain consistently showed higher accuracy, reaching 1.0 for the 107 \(\times\) 160 image. These findings demonstrate the robustness of the watermark, particularly in the DWT domain, where the high extraction accuracy remained stable in different image dimensions, even under substantial compression and resizing.
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane
### Transmission Media
#### 6.3.2 Optical Media  
Optical transmission media transmit light waves with modulated data over long distances. Compared to copper wires, these provide higher transmission rates, longer transmission distances, better resistance to electromagnetic interference, and are more secure than electrical wires as they are difficult to tap and intercept [432]. The optical fiber is a thin glass or plastic strand that guides the light signal from the transmitter to the receiver. There are two main modes of optical fiber: single-mode and multi-mode. Single-mode fibers with a diameter typically less than 10 microns are used for long-distance communication to reduce dispersion and improve signal quality. Multi-mode fibers having a large core diameter are less expensive, have a lower bandwidth, have a higher dispersion, and are used for short-distance communication (up to a few kilometers) [433]. However, optical fibers are expensive to install, consume more time to repair (which increases the downtime), are more fragile than copper wires (i.e., can break easily), and are less immune to signal attenuation from light wave absorption, scattering, and reflection [434].
### Caveats: fAPAR  
fAPAR would seem, in principle, to be an even more straightforward property to define and hence to use and interpret than LAI. However, as for LAI, fAPAR derived from RT model inversion depends on the assumptions of the underlying model: fAPAR derived from a 1D RT model will not be directly comparable with that derived from a 3D model; in particular, magnitudes will likely be different. The authors of [28] discuss the issue of deriving so-called Essential Climate Variables (ECVs) from EO. They use fAPAR as an example ECV, pointing out that identifying fAPAR as an ECV has helped focus attention on retrieval, resulting in multiple fAPAR datasets, each generated using a slightly different approach. They note that there is no single central definitive archive or product, nor has such ever been proposed. The authors of [27] have also suggested ways to generate consistent fAPAR values from MERIS and SeaWIFS sensors, and note that although agreement is generally close, differences occur even though they are essentially calculated in the same way. Differences are likely due to outlier detection (cloud contamination), sensor and illumination view angle variations and underlying biases in each dataset.  
Therefore, as for LAI whilst the definitions of fAPAR are apparently clear and unambiguous, EO-derived values are subject to the assumptions of retrieval and observation characteristics. This is of course the case for all EO-derived biophysical properties, even those which appear to be near-direct measurements such as surface temperature, surface reflectance and albedo. Here, the GlobAlbedo-derived estimates of fAPAR arise from the retrieval of LAI _i.e._, they are the values arising from the RT model-derived LAI values such that observed fluxes can be reproduced by the RT scheme. In this sense, the GlobAlbedo-derived fAPAR values are not \"effective\" in the sense of the LAI values as described above--they arise as a function of the LAI retrieval process.  
In Section 2 below, a brief overview is given of the RT model retrieval scheme used to derive the GlobAlbedo fAPAR and LAI values, as well as a description of the site locations at which comparisons between the GlobAlbedo-derived and MODIS values are made. In Section 3, we present examples of the GlobAlbedo-derived LAI and fAPAR products at global scale, including retrieval uncertainties (Infigures below the abbreviation GA signifies GlobAlbedo-derived values). We then show comparisons of the 1 km GlobAlbedo-derived LAI and fAPAR estimates with the corresponding MODIS LAI and fAPAR estimates over 2002-2011 at specific sites, selected to cover a wide range of different vegetation types and conditions. We then present regional (1200 \(\times\) 1200 km tile) comparisons. Finally, we compare parameter distributions at the global scale for the Northern and Southern hemisphere, winter and summer.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 2 Review of Existing Research and Novel Contributions
### Novel Contributions and the Proposed Approach  
We align our study with the ITU-R IMT-2030 framework [4] to systematically assess security risks in 6G networks. This ensures that our analysis is grounded in globally accepted standards and delivers relevant insights into securing next-generation networks. A significant and distinctive contribution of this work is its focus on network security rather than traditional textual data analysis. While network features and their roles in security have been studied in prior research, our approach takes a significant step forward by uniquely extracting knowledge from neural network models and leveraging XAI techniques to extract deeper, actionable insights. Unlike in some existing work, our focus is not just on identifying which features need monitoring, but also on exploring the intricate relationships between multiple network features to show how their interplay can predict or trigger security threats. What further sets our work apart is the mapping of these XAI-derived insights to specific 6G usage scenarios, particularly within the medical IoT domain. This enables not only the identification of potential vulnerabilities, but also the formulation of scenario-specific mitigation strategies. By demonstrating how XAI can be used beyond model interpretability--to proactively inform defense planning and guide strategic decision-making--our work highlights its broader applicability. Just as we have shown in the context of 6G-enabled healthcare systems, the same methodology can be extended to other domains such as autonomous vehicles, smart manufacturing, and critical infrastructure protection, making XAI a powerful tool for enhancing security preparedness across next-generation technologies.  
Figure 1 presents the proposed explainable security framework, showcasing our key contributions and the role of XAI in strengthening system resilience. The collected dataset [5] was processed using the standard Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, ensuring better generalizability to real-world scenarios [26]. We employed Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and a Convolutional Neural Network (CNN) for model evaluation, while SHAP, LIME, and DiCE were applied for explainability. Cross-validation was performed to ensure consistent and reliable insights. These insights provided a deeper understanding of the security risks faced by each 6G use case, leading to the proposal of tailored mitigation strategies for each 6G IoMT scenario, enhancing security and facilitating informed decision-making. Finally, to maximize the usability and impact of our findings, we present the extracted insights in a clear and interpretable format, making them accessible to both technical and non-technical stakeholders. This approach promotes transparency, fosters trust in AI  
driven security mechanisms, and facilitates informed decision-making within 6G-enabled healthcare systems.
###### Abstract  
In an end-to-end authentication (E2 EA) scheme, the physician, patient, and sensor nodes authenticate each other through the healthcare service provider in three phases: the long-term authentication phase (LAP), short-term authentication phase (SAP), and sensor authentication phase (WAP). Once the LAP is executed between all communication nodes, the SAP is executed (\(m\)) times between the physician and patient by deriving a new key from the PS/j key generated by healthcare service provider during the LAP. In addition, the WAP is executed between the connected sensor and patient (\(m+1\)) times without going back to the service provider. Thus, it is critical to determine an appropriate (\(m\)) value to maintain a specific security level and to minimize the cost of E2 EA. Therefore, we proposed an analytic model in which the authentication signaling traffic is represented by a Poisson process to derive an authentication signaling traffic cost function for the (\(m\)) value, wherein the residence time of authentication has three distributions: gamma, hypo-exponential, and exponential. Finally, using the numerical analysis of the derived cost function, an optimal value (\(m\)) that minimizes the authentication signaling traffic cost of the E2 EA scheme was determined.  
E2 EA scheme; healthcare IoT system; WMSN; mutual authentication; Poisson process; probability distribution 2124980. [[https://doi.org/10.3390/s21154980](https://doi.org/10.3390/s21154980)]([https://doi.org/10.3390/s21154980](https://doi.org/10.3390/s21154980))  
10 June 2021  
20 July 2021  
adverse results if some of the authentication parameters have to be changed because of, for example, a difference in the request rate. Therefore, authentication schemes need to use a cost function that estimates the number of the authentication sessions and the quantity of authentication parameters to be generated.  
The first author has proposed an authentication scheme called end-to-end authentication (E2 EA) [1], which can support various security and performance features such that mutual authentication, anonymity, and perfect forwarding services are satisfied. Furthermore, E2 EA can protect against the abovementioned attacks using low-cost storage space, computations, and communications.  
Therefore, in this paper we proposed an analytical cost function model to examine the effect of the number of authentication parameters that will be generated during the execution of E2 EA on the signaling traffic cost. Thus, the healthcare service provider can estimate in advance the number authentication sessions to be executed for a specific patient; then, according to this cost estimate, set the number of parameters to be generated and transmit them to the nodes when the E2 EA scheme is executed.
# \(H_{2}/H_{\infty}\) Output Tracking Control with a Performance Compensator for Aeroengines
###### Abstract  
A robust output tracking controller is necessary for the safe and reliable operation of aeroengines. This paper aims at developing an \(H_{2}/H_{\infty}\) output tracking approach for aeroengines. In order to improve the tracking performance of the traditional robust tracker, the proposed control structure is designed as a combination of a nominal controller and a compensator. Concretely, an \(H_{2}/H_{\infty}\) nominal controller is derived from game algebraic Riccati equation (GARE), which facilitates establishing a compensator for the system. Since the reference is usually unknown in advance for practical application, the proposed compensator is calculated online according to the nominal controller and the current reference. The solvability of the compensator and the stability of the system is guaranteed for both stable and bounded unstable references. Simulation examples for a turbofan engine are provided to demonstrate the effectiveness of the proposed algorithm.  
robust control; performance compensator; output tracking controller, linear quadratic performance
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 5 Numerical Result
### Convergence Analysis
Figures 8 and 9 show the trajectory maps for two and four UAVs, respectively, completing the coverage task in a 10 \(\times\) 10 discrete environment. Each UAV cooperatively covers a specific area and avoids obstacles and no-fly zones to maximize the defined reward. As depicted in Figure 9, for the scenario with two drones, the coverage rate of the MATD3-Stacked LSTM algorithm is approximately 65%, which is significantly higher than the other two algorithms. All three algorithms effectively mitigate path overlap. Furthermore, as shown in Figure 9, when deploying four UAVs, their paths overlap, but they can still avoid obstacles and no-fly zones well. Obviously, the MATD3-Stacked_LSTM algorithm covers more cells with lower overlap rates, as shown in Figure 9a. Although the other two algorithms also cover most of the areas, there are higher coverage overlap rates. This indicates that the MATD3-Stacked_LSTM algorithm can better coordinate the decision making of multiple UAVs, thereby achieving more efficient path planning. Figure 7: The changing trend of the coverage of the three algorithms (four UAVs). Figure 8: The coverage area of two UAVs under different algorithms. The circles are obstacles, and the rectangles are no-fly zones. Both are randomly generated. **(a)** Coverage area of two UAVs based on the MATD3-Stacked_LSTM algorithm. **(b)** Coverage area of two drones based on the MATD3 algorithm. **(c)** Coverage area of two drones based on the MADDPG algorithm. Figure 9: The coverage area of four UAVs under different algorithms. The circles are obstacles, and the rectangles are no-fly zones. Both are randomly generated. **(a)** Coverage area of four UAVs based on the MATD3-Stacked_LSTM algorithm. **(b)** Coverage area of four drones based on the MATD3 algorithm. **(c)** Coverage area of four drones based on the MADDPG algorithm. The coverage of certain areas may have some degree of overlap, as shown in Figure 10. Therefore, we compared the coverage overlap rate of the three algorithms in multi-UAV coverage tasks. From Figure 10, it can be seen that as the number of UAVs increases, the coverage overlap rate based on the MATD3-Stacked_LSTM algorithm becomes lower than that of the other two algorithms. For example, when the number of UAVs is 4, the coverage redundancy of MATD3-Stacked_LSTM is approximately 9.563, while the other two algorithms have redundancy rates of about 11.754 and 15.654, respectively. This represents a reduction of 18.61% compared to MATD3 and 38.91% compared to MADDPG. The results demonstrate that for multi-UAV coverage tasks, the MATD3-Stacked LSTM algorithm is more suitable for reducing overlap between UAV paths, thereby improving the efficiency and effectiveness of the overall coverage mission compared to MADDPG and MATD3. By modeling temporal dependencies, the LSTM network enables the MATD3-Stacked_LSTM approach to learn coordinated policies that achieve higher coverage rates with less redundancy during multi-UAV path planning.
## 5 Enhancement of Biological Treatment Systems
### Suppressing Emerging Contaminants and Pathogens  
An increasingly explored biological application involves incorporating SDB into biofilters or bioreactors to tackle micropollutants, such as pharmaceutical residues, and pathogens. SDB can capture trace organic micropollutants, prolonging their retention and enhancing their bioavailability for microbial degradation. Some studies on antibiotic removal have employed SDB-packed bioreactors where certain degrader bacteria are enriched on the SDB surfaces [84,85]. Results showed a faster attenuation of compounds like sulfamethoxazole and ciprofloxacin compared to biochar-free controls, indicating a synergy between adsorption and biodegradation. Additionally, SDB can be enhanced through physical, chemical, and biological modifications to improve its antimicrobial activity [86]. These modifications collectively enhance the antimicrobial properties of SDB by promoting microbial adsorption, inducing oxidative damage, releasing toxic metal ions, and altering surface charge to inhibit bacterial adhesion. SDB-based anti-bacterial materials may help reduce pathogenic bacteria in treated water, although this aspect is still being explored.  
In summary, incorporating SDB into biological treatment systems enhances pollutant removal, improves process stability, and introduces additional functions such as adsorption and electron transfer that go beyond microbial capabilities. Whether applied in anaerobic digestion, activated sludge processes, or eco-engineered wetlands, SDB proves to be a versatile enhancer, showcasing how the integration of biological and physic/chemical treatment with biochar results in more robust and efficient wastewater treatment.