esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Design Methodology
### Device Selection  
The first step in the design process is to select the appropriate transistors from the available technology kit, in this case, the WIN PIFI1-10 design kit. Given that two transistor models, 2 \(\times\) 50 \(\upmu\)m and 4 \(\times\) 75 \(\upmu\)m, are available in the design kit, a decision must be made about which should be used in the first, second and last stages. The transistor with the lowest minimum noise figure should be placed in the first stage of the LNA to minimize the overall noise figure, as the noise performance of a cascaded amplifier is dominated by the noise figure of its first stage [10]. A comparison of the minimum noise figures for both models is shown on the left side of Figure 3. It is evident that the 2 \(\times\) 50 \(\upmu\)m transistor exhibits a lower noise figure than the 4 \(\times\) 75 \(\upmu\)m transistor. Therefore, the former is selected  
Figure 1: \(S_{11}^{*}\) (purple solid line) and \(\Gamma_{opt}\) (blue dashed line) of the transistor without inductive feedback. \(S_{11-L}^{*}\) (green-crossed line) and \(\Gamma_{opt-L}\) (red diamond line) when inductive feedback is used.  
Figure 2: Three-stage LNA consists of a two-stage LNA followed by an amplifier in the third stage. Reprinted with permission from Ref. [9]. 2025, IEEE.  
for the first and second stages of the amplifier. On the other hand, the transistor with a gate width of 4 \(\times\) 75 \(\upmu\)m has a higher gain than the 2 \(\times\) 50 \(\upmu\)m FET transistor, right side of Figure 3, consequently, the wider transistor will be placed in the third amplification stage.
## 3 Proposed Model
### Multi-Class Damage Detection Process Using U-Net  
Traditional image processing techniques, such as pattern recognition and edge detection, have long been utilized for surface defect identification. However, for concrete structures, these methods often fall short in practical applications due to high false positive rates caused by surface contamination, discoloration, and environmental noise. To overcome these limitations, this study proposes a deep-learning-based segmentation pipelinecentered on the U-Net architecture, as shown in Figure 3, which highlights the role of U-Net in the core prediction module.  
The pipeline includes data preprocessing and normalization, followed by supervised training using manually labeled ground-truth (GT) data to define damage regions. The U-Net model serves as the backbone of the prediction stage, consisting of an encoder-decoder structure. The encoder utilizes SE-ResNeXt50_32x4d to enhance feature extraction capabilities by integrating squeeze-and-excitation (SE) blocks with grouped convolutions, enabling the network to effectively capture both local and global contextual features from UAV-acquired images. The decoder reconstructs full-resolution segmentation maps for precise pixel-wise classification.  
This U-Net-based segmentation module is emphasized in the center of the system diagram (Figure 3), where detection, segmentation, and classification are performed. To further improve detection performance under class-imbalanced conditions, Focal Loss is employed to focus training on hard-to-classify samples, while IoU Loss enhances spatial accuracy by maximizing the overlap between predicted masks and the GT regions. Applying each of these loss functions results in significant improvements in the model's recall and precision, particularly for underrepresented damage types in the multi-class segmentation framework.  
The U-Net-based process, as described, analyzes input images without the need for predefined class selection. The final detection results are overlaid onto the original input images, allowing for clear visualization of the segmented regions. Detected damages are both classified and segmented according to their respective damage classes.
## 3 Methodology
### BB84 Protocol
**Verification**: To confirm that the key was successfully transmitted, [PERSON] and [PERSON] share random samples of their keys. By comparing these samples, they can ensure that the transmission is accurate within a very small range of error. This verification stage is essential for determining the reliability of the quantum key distribution process. Table 6 provides a concise summary of how knowledge is distributed throughout the QKD BB84 protocol. In the QKD BB84 protocol, the classical channel simulation plays a crucial role and it works after [PERSON]'s measurement, which is the third step. At this stage, [PERSON] successfully receives the qubits and obtains the measurement outcomes. During this pivotal phase, [PERSON] and [PERSON] collaborate to establish both public and private keys, enhancing the security of their digital communications. Moving forward, this research centers on the CRYSTALS-Kyber algorithm, the renowned KEM within the realm of PQC. The primary objective is to securely exchange a symmetric key between [PERSON] and [PERSON], leveraging CRYSTALS-Kyber as a KEM. This key will subsequently facilitate AES 256-bit encryption between the two parties. Expanding the scope of our research, we delve into digital signature mechanisms in the context of quantum computing. We assess two significant contenders in this domain, CRYSTALS-Dilithium and Falcon. The evaluation centers on their ability to ensure message integrity and authenticity within a quantum computing environment, as well as their \begin{table}
\begin{tabular}{c c c} \hline \hline
**Steps** & **[PERSON]’s Information** & **Over [PERSON]’s Communication Channel** & **[PERSON]’s Information** \\ \hline \multirow{2}{*}{1} & [PERSON]’s classical bits & \multirow{2}{*}{
\begin{tabular}{c} \\ [PERSON]’s choice of bases \\ \end{tabular} } \\  & [PERSON]’s choice of bases & \\ \hline \hline
2 & Qubits & Qubits \\ \hline \multirow{2}{*}{3} & & [PERSON]’s chosen bases \\  & & [PERSON]’s measurement results \\ \hline \multirow{2}{*}{4} & [PERSON]’s chosen bases & [PERSON]’s chosen bases \\  & [PERSON]’s chosen bases & [PERSON]’s chosen bases \\ \hline \multirow{2}{*}{5} & [PERSON]’s encrypted key (noisy) & \multirow{2}{*}{
\begin{tabular}{c} [PERSON]’s encrypted key (noisy) \\ \end{tabular} } \\  & [PERSON]’s measurement sample & [PERSON]’s measurement sample \\ \hline \multirow{2}{*}{6} & [PERSON]’s measurement sample & [PERSON]’s measurement sample \\ \hline \multirow{2}{*}{7} & Shared secret key & Shared secret key \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Knowledge exchange protocol. resistance to quantum attacks. In this stage, we want to check the performance of both PQC digital signature schemes to find the best combination of our authenticated encryption scheme for QKD.
## 2 Methods
### Comparison of Expected Incident Light Intensity for Each Surface at Any Given Date and Time  
First, the ray vectors used in the calculations must be defined. If the axes are oriented, and \(\theta\) represents the Sun's altitude while \(\varphi\) indicates the azimuth angle, the ray vector (direction cosine) can be expressed as follows:  
\[r=\begin{pmatrix}\sin\varphi\cdot\cos\theta\\ \cos\varphi\cdot\cos\theta\\ \sin\theta\end{pmatrix}. \tag{1}\]  
Because scattered light originates from the entire hemisphere, it is not influenced by the position of the Sun. A total of 32,400 rays, calculated at 1\({}^{\circ}\) increments of angles \(\theta\) and \(\varphi\) (360 \(\times\) 90), were used to simulate scattered light. For each of these rays, the cosine loss was calculated based on the inner product, which included implicit surface processing. These values were then weighted and averaged arithmetically. Direct light is influenced by the position of the Sun; therefore, it is important to consider the Sun's altitude and azimuth angle. We consulted data from the Meteorological Solar Energy Data for Photovoltaic System (METPV)-20 database for Miyazaki, Japan (latitude: 31.83\({}^{\circ}\), longitude: 131.341\({}^{\circ}\)), Miyazaki Prefecture, corresponding to the time when the flat panel was installed. If the  
Figure 8: Method for identifying hidden surfaces.  
average percentage of incident light scattered in a flat panel is represented as \(SF\), it can be calculated using the following equation:  
\[SF=\frac{\int_{0}^{360}\int_{0}^{90}r\cdot\begin{pmatrix}0\\ 0\\ 1\end{pmatrix}d\varphi\;d\theta}{360\cdot 90}. \tag{2}\]  
Let \(RSF\) and \(HSF\) represent the ratios of the incident light intensity on the roof and hood, respectively. These ratios were obtained using a calculation program that considers shaded surfaces, as described in Section 2.4. They are equivalent to the incident light intensity ratio \(SF\) for planes, denoted as \(I_{\text{sca,roof}}\) and \(I_{\text{sca,hood}}\) at the roof and hood, respectively, corresponding to any given date and time. The following equations can be used to calculate them:  
\[I_{\text{sca,roof}}=\frac{RSF}{SF}I_{\text{sca}}, \tag{3}\]  
\[I_{\text{sca,hood}}=\frac{HSF}{SF}I_{\text{sca}}, \tag{4}\]  
where \(I_{\text{sca}}\) is the amount of scattered light in the database. The average incident light intensity ratio of the target area was not used when calculating direct light, in contrast to that for the flat panel.
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
###### Abstract  
Broadcasters produce enormous numbers of sport videos in cyberspace due to massive viewership and commercial benefits. Manual processing of such content for selecting the important game segments is a laborious activity; therefore, automatic video content analysis techniques are required to effectively handle the huge sports video repositories. The sports video content analysis techniques consider the shot classification as a fundamental step to enhance the probability of achieving better accuracy for various important tasks, i.e., video summarization, key-events selection, and to suppress the misclassification rates. Therefore, in this research work, we propose an effective shot classification method based on AlexNet Convolutional Neural Networks (AlexNet CNN) for field sports videos. The proposed method has an eight-layered network that consists of five convolutional layers and three fully connected layers to classify the shots into long, medium, close-up, and out-of-the-field shots. Through the response normalization and the dropout layers on the feature maps we boosted the overall training and validation performance evaluated over a diverse dataset of cricket and soccer videos. In comparison to Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbors (KNN), and standard Convolution Neural Network (CNN), our model achieves the maximum accuracy of 94.07%. Performance comparison against baseline state-of-the-art shot classification approaches are also conducted to prove the superiority of the proposed approach.  
AlexNet CNN; convolutional neural networks; deep learning; rectified linear unit layer; shot classification +
Footnote †: journal: Applied Sciences
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Security Analysis
#### 5.3.1 Mean Squared Error  
The similarity between the pre-encrypted image and the post-encrypted image is compared by calculating the mean squared error (MSE). MSE is a widely used quantitative metric in image processing and computer vision that measures the degree of difference between two images. The difference values of corresponding pixels in the two images are squared, and then the average of all squared difference values is taken. The mean squared error calculation is shown in Equation (23):  
\[MSE=\frac{1}{N}{\sum}\left(P_{(i,j)}-P^{\prime}_{(i,j)}\right)^{2} \tag{23}\]  
\(N\) signifies the total pixel count within the image. \(P_{(i,j)}\) represents the pixel value at the original image's location \((i,j)\), while \(P^{\prime}_{(i,j)}\) denotes the pixel value at the corresponding position \(i,j\) in the encrypted image. A smaller MSE indicates a higher similarity between the images. Conversely, a larger MSE implies greater dissimilarity between the images. According to Table 9, the MSE in this algorithm indicates a significant difference between the original and encrypted images, affirming the effectiveness of image encryption.  
Figure 10: The model before texture image encryption (**a**) and the model after texture image encryption (**b**).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MSE** \\ \hline M-1 & 102.75754 \\ M-2 & 104.24337 \\ M-3 & 95.72083 \\ M-4 & 102.57641 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mean square error before and after image encryption.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 5 Implementation
### Control and Visualization Interface  
For the control of the system, an control interface is designed, as shown in Figure 8. We can connect the host computer running the Python script to an arbitrary node in the mesh network through a USB serial. When the Python script receives an input command, it will process it and send further commands to the node. The node will uni-cast or broadcast the command according to the command type, so the command can be received by one or all nodes in the mesh network. Replies received by the node will be sent back to the host computer. In particular, localization results are produced to a Kafka topic data stream. A web page will consume that data and visualize them on a map. Global Positioning System (GPS) data for experiment use can also be collected from an Android device with a custom app and uploaded to be shown on map.
## 4 Conclusions  
In this paper, self-healing microcapsules with EPR as the core material and cross-linked polymer (PMMA-DVB) as the shell were successfully prepared by interfacial polymerization for mitigating the problem of metal corrosion caused by microcracks in steel surface coatings of offshore drilling platforms.  
1. By analyzing the laws of influence of process parameters on the particle size, dispersibility, morphology, and surface characteristics of microcapsules, the optimal process conditions for microcapsule preparation were determined to be an oil-to-water ratio of 7.5:200, an emulsifier content of 1 wt%, an emulsification speed of 2500 r/min, an initiator content of 2.5 g, and a core-to-wall ratio of 4:1.
2. Under the optimal parameters, FT-IR, TGA, and particle size analysis results show that the prepared microcapsules have a moderate degree of crosslinking in the wall material, which provides good protection to the core material. This significantly improves the thermal stability of the core material and slows down its decomposition process. Compared to the dual-wall self-healing microcapsules prepared in reference [21], the microcapsules prepared in this study have a simpler structure and better thermal stability. The microcapsules exhibit excellent thermal stability up to 346 \({}^{\circ}\)C. The particle size of the microcapsules is predominantly concentrated around 35 \(\upmu\)m, accounting for approximately 30%, while the particle size range of 25 \(\upmu\)m to 45 \(\upmu\)m covers 80% of the microcapsules.  
Figure 11: (**a**) Surface morphology of self-healing coating with 8% microcapsules after wear, (**b**) surface morphology of coating without microcapsules after wear.  
3. The coating scratch experiment shows that the epoxy coating with this microcapsule has good self-healing properties, achieving a good self-healing effect within 24 h. When the microcapsules rupture due to damage to the coating, the repair agent inside can effectively mend the damaged area. This microcapsule has broad application potential in achieving self-healing of surface coatings for marine steel structure engineering equipment.  
Project administration, funding acquisition, investigation, visualization, X.J.; writing--original draft preparation, methodology, software, data curation, formal analysis, C.T.; project administration, conceptualization, investigation, J.Y.; methodology, validation, resources, conceptualization, writing--review and editing, Y.Z.; methodology, validation, supervision, conceptualization, writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data underlying this study are available in the published article; further inquiries can be directed to the corresponding authors.  
The authors would like to thank [PERSON], [PERSON], [PERSON], and other scholars for their assistance during the experimental process.  
The authors declare no conflicts of interest.
Hypercube topologies achieve logarithmic network latency through high-dimensional connectivity, yet their exponential node degree growth complicates physical implementation and power management [20]. Hierarchical topologies optimize localized communication efficiency but introduce routing overheads and resource fragmentation across abstraction layers [21]. In contrast, mesh-based topologies--notably 2D mesh and 2D REmesh topologies--have become the cornerstone of modern NoC implementations due to their structural regularity, linear scalability, and compatibility with automated electronic design automation (EDA) tools [22]. The structural homogeneity of 2D meshes facilitates seamless topology reconfiguration--a critical process for maintaining logical connectivity under core or link failures. This reconfiguration utilizes the grid-like layout of the mesh to dynamically reassign virtual-to-physical cores while maintaining communication locality, which is more difficult to achieve in irregular or high-dimensional topologies. Therefore, mesh-based topologies have obvious advantages for the realization of network-on-chip (NoC) topology reconfiguration. In this paper, we will focus on how to find an optimized topology reconfiguration algorithm that makes the reconfigured topology arrays as similar as possible to the original arrays, while at the same time having a low time overhead and high utilization. The object of this paper is based on a 2D REmesh topology structure, and the research topic is topology reconfiguration of network-on-chip (NoC). The research objective is to improve the success rate of topology reconfiguration and enhance the performance of the algorithm by proposing an optimized topology reconfiguration algorithm, which can then be applied to a wider range of embedded systems. This paper is structured as follows: Section 2 briefly introduces related works. Section 3 outlines the 2D mesh and 2D REmesh physical array architecture and evaluation metrics. Section 4 introduces a mathematical model to abstract the algorithm, discusses the steps in the construction of this mathematical model, describes mathematical notations, and gives mathematical proof of the optimal solution. In Section 5, we propose an optimized core distribution adaptive topology reconfiguration algorithm for NoC-based embedded systems (ACTR), describing the flow and ideas of the algorithm in detail. Experimental results and analyses are detailed in Section 6. Finally, the results of our study and the outlook of the future prospects are summarized in Section 7.
# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 5 Experiments
### Results and Analysis  
#### 5.4.1 Comparison Experiment and Result Analysis  
Based on the experimental settings in Section 5.1, we trained our proposed DialogCIN model, which is a context inference network for dialogue generation, and compared it with other classic dialogue generation models under the same experimental conditions. Through the analysis of the experimental results, we drew valuable conclusions.  
Table 3 presents the results of different models for dialogue generation on the Life-Dialog test set. It can be observed that DialogCIN outperformed other models in all four evaluation metrics. HRED is a hierarchical recurrent encoder-decoder model, and WSeq is a sequence-to-sequence model. Overall, their performance was relatively poor. Compared to the DSHRED model, the DialogCIN model showed improvements of 7.5% in PPL, 4.0% in BLEU, 4.2% in Distinct-1, and 11.5% in Distinct-2. This indicates that our model had more comprehensive and in-depth modeling of dialogue history, allowing it to capture long-term dependencies and complex contextual information in conversations. Compared to the ReCoSa model, the DialogCIN model showed improvements in all four evaluation metrics: PPL, BLEU, Distinct-1, and Distinct-2. Particularly, it achieved significant enhancements in the Distinct-1 and Distinct-2 metrics. This was attributed to our proposed Inference Module, which helped the model to better comprehend the context of the dialogue and incorporated contextual information into the generated responses. Based on the experimental findings presented in Table 3, it is evident that DialogCIN exhibited a comparatively modest enhancement in the BLEU metric when juxtaposed with the baseline models. This outcome can be ascribed to the inherent openness and diversity prevalent in the dialogue scenarios of this particular task, consequently giving rise to substantial linguistic and grammatical disparities between the reference answers and the generated responses. Such disparities impose limitations on the effectiveness of the BLEU metric. However, notable advancements were observed in the Distinct metrics, indicating that the DialogCIN model excels in circumventing redundant content in its responses, comprehensively grasping the underlying logical information conveyed within the given context, and generating responses that are both fluent and varied. These conclusions were further substantiated through subsequent human evaluation.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 5 Results and Analysis
### Impact on Classification
The classification results of our method are shown in Figure 12, where sub-figure (a) shows the ground truth classification map, sub-figure (b) shows the reference classification map (which is the result of the original high spatial resolution HSI), and sub-figure (c) shows the classification map of our super-resolution HSI. Table 5 shows the quantitative numerical results of all methods for comparison. From the results, we can make the observation that our super-resolution HSI can obtain a closer OA to the original HSI than any of the other considered methods.