esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Transfer Learning Methods  
For comparative training metrics and classification results, transfer learning from three popular pre-trained deep learning (DL) architectures is performed on the same leather detection and classification dataset [29]. All three models are pre-trained on the ImageNet dataset [40].  
The first pre-trained network architecture to be utilised is the residual neural network (ResNet) [41]. The ResNet-50 model was selected as the preferred variant due to its optimal balance between performance and reduced computational requirements. The mid-range ResNet-50 model, as depicted in the architecture diagram presented in Figure 1, was chosen over ResNet-101 and ResNet-152 due to its decreased model size and complexity,which helps minimize memory requirements and enables faster convergence, training, and inference.  
Figure 1 presents a detailed representation of the ResNet-50 network [42]. The network displayed on the left side of Figure 1 comprises the stem module, four residual modules, and a fully connected neural network layer. The numerical annotations, 32, 64, and 256, in the figure signify the corresponding number of convolution channels. The notation \"B\" indicates the batch normalization operation, while \"BA\" denotes the combination of batch normalization and relu activation. Stage-2 through Stage-4 are the same as Stage-1, except for an increased number of iterations of block2. Lastly, the model's final FC-block utilizes an average pooling layer and a fully connected layer.  
The second DL model, depicted in Figure 2, is the Inception-V3 architecture [43], which shares similar elements with the ResNet-50 model despite being slightly more complex. Succinctly stated by Google Cloud [44], the structure comprises convolutions, average pooling, max pooling, concatenations, dropouts, and fully connected layers. Batch normalization is used extensively throughout the model and is applied to activation inputs, while loss is computed using the Softmax activation function.  
The third architecture applied, EfficientNet-B0, is described as the smallest and least complex variant of the EfficientNet series [45]. Though this lightweight version of the model may not be as accurate as the high-end EfficientNet-B7 version, it was designed with the idea of achieving a balance between model accuracy and computational efficiency by scaling the depth, width, and resolution of the network. As such, it has the advantage of lower computational requirements and therefore significantly faster training and inference times, which are essential to real-world applications. Another risk in using deeper and more complex architectures is the increased risk of overfitting, especially when working with smaller datasets. A simplified block diagram of the EfficientNet-B0 model is depicted in Figure 3; however, the 237-layer model itself is far more complex internally.  
Figure 1: ResNet-50 architectural block diagram. (**a**) Stem block; (**b**) Block1; (**c**) Block2; (**d**) FC-Block.
###### Abstract  
In this study, a new attack against the Kirchhoff-Law-Johnson-Noise (KLJN) key distribution system is explored. The attack is based on utilizing a parasitic voltage-source in the loop. Relevant situations often exist in the low-frequency limit in practical systems, especially when the communication is over a distance, or between different units within an instrument, due to a ground loop and/or electromagnetic interference (EMI). Our present study investigates the DC ground loop situation when no AC or EMI effects are present. Surprisingly, the usual current/voltage comparison-based defense method that exposes active attacks or parasitic features (such as wire resistance allowing information leaks) does not function here. The attack is successfully demonstrated and proposed defense methods against the attack are shown.  
KLJN key exchange; unconditional security; ground loop vulnerability; passive attack +
Footnote †: journal: Applied Sciences  
_Article_  
A Static-loop-current Attack Against the Kirchhoff-Law-Johnson-Noise (KLJN) Secure Key Exchange System  
[PERSON] and [PERSON]  
Department of Electrical and Computer Engineering, Texas A & M University, 3128 TAMU, College Station, TX 77843, USA; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] Tel.: +1-979-847-9071
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 4 Other Fracture Properties
### Wear Resistance  
A great deal of excellent properties in metallic nanolayered composites has extensively aroused a great effort to explore the properties under more realistic operating environments, such as friction and wear. Films are often produced with NMMF materials owing large hardness, coating technology has motivated an extensive interest in application of wear resistance. Since significant hardness enhancement and a drastic softening are often observed in elastic modulus [9], a high ratio of \(H/E\) in multilayers which implies that they can induce the formation of self-organized microstructures with the potential improving wear resistance, as the ratio of hardness to elastic modulus has been proven as one of the significant parameters dominating a good wear resistance in a disparate range of materials [117].  
[PERSON] et al. [76] has systematically investigated the wear properties of Ag/Ni NMMFs. In multilayer with \(h\) = 25 nm, there are fluctuations in the friction coefficient after a scratch length of 250 mm, which indicates the development of fine wear debris and possibly fracture. However, for multilayer with \(h\) = 2.5 nm, negligible fluctuation can be found at a scratch length about 350 mm, indicating better wear behavior in multilayers with decreasing \(h\).
# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
###### Abstract  
Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users' devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.  
wireless systems; mobile sensors; D2D; technological development; Internet of things +
Footnote †: journal: Sensors  
0000-0001-5000-0000][PERSON]  
222222115
# Low-Light Photodetectors for Fluorescence Microscopy
## 2 Fluorescence Microscopy
### Laser Scanning Fluorescence Microscopy (LSM)  
Laser scanning fluorescence microscopy (LSM) is one of the most widely used forms of biological fluorescence microscopy. LSM creates fluorescence images by sequentially recording the fluorescence intensity of each pixel by scanning a focused laser beam across a specimen using confocal optics to obtain only in-focus fluorescence. LSM with a confocal illumination setup provides a base for various fluorescence microscopes using steady  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fluorophore**} & **Typical Excitation Wavelength (nm)** & **Emission Wavelength (nm)** & **Molecular Extinction (nm)** & **Molecular Extinction Coefficient (\(\mathbf{M}^{-1}\mathbf{cm}^{-1}\mathbf{)}\)** & **Fluorescence Yield** & **Resistance Photobleaching** \\ \hline Cy3 & 532 & 570 & 150,000 (550 nm) & 0.15 & 0.2 & ++ \\ \hline Cy5 & 633 & 670 & 250,000 (649 nm) & 0.28 & 0.9 & ++ \\ \hline Qdot655 & 532, 633 & \(\sim\)655 & 2,100,000 (532 nm) & 0.44 & \(\sim\)20 & ++ \\ \hline Green fluorescent protein (GFP) & 488 & 507 & 56,000 (484 nm) & 0.60 & 2.3 & + \\ \hline \hline \end{tabular} The markings +, ++, and ++ indicate “poor,” “moderate,” and “excellent,” respectively. Adapted with permission from [7].  
\end{table}
Table 1: Characteristics of fluorophores commonly used in fluorescence microscopy.  
Figure 1: Jablonski diagram for the electronic energy levels of a fluorophore and the transitions between them.  
state and time-resolved fluorescence microscopy. Figure 2 shows schematics of confocal microscopy and fluorescence microscopy with confocal optics.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture  
### High-Level Architecture Diagram  
At the heart of this project is a carefully designed cloud architecture that leverages Azure's Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) components to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Goal** & **Description** & **Weight (I-5)** & **Rationale** \\ \hline
**Traceability** &
\begin{tabular}{c} Git-based version control, history, \\ rollback capabilities \\ \end{tabular} & 4 & Enhances auditability and debugging \\ \hline
**Reusability** & \
\begin{tabular}{c} Parametrized modules applicable \\ across projects \\ \end{tabular} & 3 &
\begin{tabular}{c} Useful but secondary to security \\ and consistency \\ \end{tabular} \\ \hline
**Collaboration** & \
\begin{tabular}{c} Shared ownership across \\ development and operations \\ \end{tabular} & 3 &
\begin{tabular}{c} Culturally important but harder to \\ measure technically \\ \end{tabular} \\ \hline
**Cost Control** & \
\begin{tabular}{c} Right-sizing, ephemeral \\ environments, teardown automation \\ \end{tabular} & 3 &
\begin{tabular}{c} Practical but dependent on \\ business policies \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._host a web application, as shown in Figure 2. The solution architecture is designed for high availability, security, and ease of maintenance, while also supporting development, QA, and production environments through parameterization.  
This architecture is designed around a secure Virtual Network (VNet) that isolates all resources, ensuring that traffic between services flows through private endpoints rather than public IP addresses. Core components include the following:  
* A Virtual Network with multiple subnets (including delegated subnets for specific services).
* The Azure Container App to run the application in a serverless container environment.
* A PostgreSQL Flexible Server for the relational database needs of the application, connected through private endpoints.
* Azure AI Search to provide robust search capabilities with integrated private endpoint connectivity.
* Managed identity for secure authentication between the app and other Azure services without embedded secrets.
* The Azure Container Registry to store container images for deployment.  
All components are provisioned and managed via Bicep templates, ensuring consistency across environments. The entire deployment is automated via GitHub Actions, enforcing infrastructure as code principles and enabling continuous delivery.  
Figure 2: High-level solution architecture for automated Azure deployment using Bicep and GitHub Actions.
## 4 Conclusions  
In the studied population of honeys, the following were observed and statistically confirmed:  
1. Results of physicochemical analysis showed that the tested honeys were fresh, not overheated, not adulterated, and collected at the appropriate stage of maturity. The darkest honey, buckwheat, was characterized by the highest antioxidant activity and diastase number, while other honey varieties did not show any significant differences in the values of these parameters.
2. The results of the statistical analysis of the values of conductivity parameters (Z, G) allowed classifying the seven tested honey varieties into three groups. Among the single-flower honeys, two groups were distinguished: I--with high electrical conductivity (linden, buckwheat, goldenrod) and II--with low conductivity (acacia, phacelia, rapeseed). Group III consisted of multi-flower honeys characterized by moderate conductivity. The separation of these three groups of honeys indicates the possibility of identifying their botanical origin using electrical methods, which are characterized by non-invasiveness, measurement speed, and high sensitivity.
3. Analysis of the correlation between quality parameters (separated in clusters--1 and 2) and electrical parameters measured using an innovative method (own-design measurement system) showed that the conductivity parameters (Z, and G, in the entire frequency range), and to a slightly limited extent the dielectric parameters (Cp, Cs), were most useful for assessing the quality of selected honey varieties.
4. The usefulness of the Z and G parameters in replacing the quality parameters defined in cluster 1 (acidity, water content, L*) and quality and health-promoting parameters in cluster 2 (reducing sugars, sucrose, diastase number, TP, DPPH, and 5-HMF) was observed mainly for the single-flower honeys: buckwheat, linden, rapeseed, and phacelia. In the case of the multi-flower honeys, the Z, G, and C parameters were useless in their quality assessment.  
The proposed electrical method shows potential for assessing the quality and for differentiating honeys, but its implementation requires further development, including the assessment of the influence of diffusion effects and critical analysis of the fit of measurement results to electrical models.  
Conceptualization, J.K.B. and A.W.; methodology, J.K.B., N.Z., A.W. and M.G.-R.; formal analysis, A.W., N.Z., J.K.B. and M.G.-R.; data curation, N.Z. and J.K.B.; writing--original draft preparation, J.K.B., N.Z. and A.W.; writing--review and editing, A.W., J.K.B. and M.G.-R., supervision, J.K.B. and A.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Gdynia Maritime University (project: WZNJ/2024/PZ/01, WZNJ/2024/PI/01), and University of Warmia and Mazury in Olsztyn (project No. 22.610.100-110).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
Chemometric techniques such as principal components analysis (PCA), partial least squares (PLS), and principal component regression (PCR) are commonly used in combination with NIR spectroscopy in the traceability and authenticity of food and feeds [44; 45; 46]. The use of such chemometric techniques with NIR exhibits the potential to consider multiple components present within the system simultaneously and assist in obtaining information based on links with other sample characteristics [35; 36]. This study aims to evaluate the ability of NIR spectroscopy to trace live fifth instar larvae and sixth instar BSF pre-pupae grown on organic side streams in an industrial setup. The combination of NIR spectroscopy with partial least squares discriminant analysis (PLS-DA) was used to develop classification models to predict the feed source used to grow the larvae.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 5 A Proposed Control/Communication Architecture for Multi-Agent Systems
### A Case-Study of Milestone M1
(**b**) The number of training steps per episode does not appear to converge. (**c**) The number of visits to each cell over the entire training process. Both interest-points receive many visits. (**d**) The estimated maximum q-value of each cell. The max q-value of cell (3,0) is detrimentally small despite many visits and the interest-point location. Figure 10b shows the training process does not converge; the number of steps per episode oscillates between periods of stability and instability. Figure 10c,d provide some insight into this undesirable behavior. Cells with interest-points receive a larger share of visits compared to other cells, as evident in Figure 10c. However, visits to the first of the two interest-points (cell (3,0)) do not seem to accrue much reward in Figure 10d; the q-values acquired in training diverge from the \"true\" q-values. This occurs because the first visit to an interest-point accrues a reward of \(r=500\), but the \(\epsilon\)-greedy learning policy keeps the agent in the vicinity of the interest-point. Subsequent visits to this interest-point accrue negative reward (since it has already been visited), which reduces the value of the cell. Eventually this cell loses most of its value, which under the \(\epsilon\)-greedy policy, releases the agent to explore other cells. The true q-function resembles a two-stage pyramid or a \"ziggurat\". The previous discussion explains why \(\epsilon\)-greedy policies with one-step returns cannot accurately estimate multi-stage q-functions. This problem is partially mitigated by using multi-step returns in the update (instead of one-step returns), but emerges again if the number of interest-points or grid dimensions increase. A more permanent solution is to hierarchically structure the action-space into primitive actions and macro-actions (i.e., \"move agent 1 to interest-point 1\" and \"move agent 1 to interest-point 2\"). The training process then determines the order of visitations and the shortest path to each interest-point from the current position of the agent. The two-stage hierarchical action-space is one possible way of completing M1. Mobile interest-points require more advanced techniques outlined in Section 5.2. Figures 9 and 10 demonstrate the forensic analysis needed to incorporate the extra complexity of each new milestone.
## 4 Conclusions and Discussions
### Discussions  
#### 4.2.1 Discussion on the Results of Scale Variance Decomposition  
This section estimates the carbon emissions of provinces, cities, and counties in the Yangtze River Delta region and the present situation is briefly analyzed. The results of the scale analysis of variance show that, from 2008 to 2015, at the county level, carbon emissions carry the largest amount of information, and their contribution rate is higher than those of the provincial and municipal scales, accounting for over 50% of the total information, on average. The contributions of the provincial and municipal scales have remained at a level of 23%. This may be because the imbalance between the regional developments has led to huge differences in carbon emissions. From the perspective of regional dimensions, Jiangsu province has the highest carbon emission level and the differences between Shanghai and Zhejiang are relatively small. At the municipal scale, the regional differences in carbon emissions within the Yangtze River Delta urban agglomeration are very large, the carbon emissions in the central region are high, and the carbon emissions in the north and south are relatively low. Therefore, it is especially important to further optimize the urban industrial structure and change the traditional extensive mode of production.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 6 Discussion
### Implications for Cloud Security  
The use of Deep Learning (DL) techniques for zero-day threat detection, as demonstrated by the MVT model, showcases a viable direction for proactive cybersecurity strategies in cloud environments [19]. Vision-based representations, such as byteplots, provide an additional modality that complements traditional binary analysis [14].  
Nonetheless, deploying such models in production settings requires addressing several real-world constraints. Cloud infrastructures often consist of heterogeneous resources, combining public, private, and edge computing systems. This variability introduces challenges in terms of latency, resource availability, and data formatting. To support wide adoption, MVT must be optimized for lightweight execution and integrated with adaptive preprocessing pipelines that can operate across varying environments.  
Future development should focus on modularizing the architecture for scalable deployment, applying techniques such as model quantization and pruning to reduce computational load, and extending the model to support multiclass classification to better distinguish between different malware families.
## 3 Numerical Examples
### Example 3  
The behavior of bubbles in liquid nitrogen under the action of electric force includes two aspects: deformation and movement. The movement and deformation of the bubble under the electric field is determined by the electric force, surface tension, and gravity acting on the bubbles. Experiments show that bubbles are stretched into ellipsoids along the direction of the electric field intensity. The magnitude of the deformation is related to the intensity values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Maximum Relative Error (\%)** & **Average Relative Error (\%)** \\ \hline XFEM-analytic method & 0.15\% & 0.01\% \\ XFEM-CFEM & 0.22\% & 0.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative error of electric potentials on nodes.  
Figure 9: Meshing of the bubbles in liquid nitrogen. (**a**) CFEM, (**b**) XFEM.  
With the deformation and movement of bubbles, remesh is required in CFEM. The complicated movement process of bubbles will significantly increase the calculation cost. XFEM can solve this problem by involving multiple media in one element.  
Figure 11 shows the four steps of the process of movement and deformation of a bubble in the electric field with CFEM. In contrast, XFEM only meshed once as Figure 9b.  
Figure 12 presents the electric potential of the moving bubble calculated with CFEM and XFEM.  
Figure 10: Electric potential distribution of bubbles in liquid nitrogen. (**a**) CFEM, (**b**) XFEM.  
The relative errors in each moment of electric potential based on CFEM and XFEM are shown in Table 3.  
Figure 12: Electric potential distribution of a moving bubble in liquid nitrogen. (**a**) CFEM, (**b**) XFEM.  
The results of XFEM are accurate of moving bubbles. The maximum relative error is 2.34% and the maximum average relative error is only 0.06%. XFEM has no restriction on the location of the interface in general. However, bubbles can only move along the z-axis to satisfy the principle of axisymmetric field.  
In Table 4, the number of nodes and the number of elements are compared to estimate the calculation consumption.  
The more nodes and elements there are, the more computing resources there cost in the finite element method. Under the same selection criteria, XFEM in these examples has half as many nodes and elements as CFEM. XFEM can reduce computing resources in half for the same example.