esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Fano Resonances in Dynamic Resonant Tunneling Processes
## 9 Conclusions and Summary  
The tunneling current through a barrier via an oscillating well was investigated both numerically and analytically. While previous works have focused on the difference between high transmission (activation) and low transmission (current suppression), this work focused on the difference between current suppression and zero transmission. Since zero transmission is equivalent to a Fano resonance, it requires interference with a bound state. The following are the main conclusions:  
1. A bound state can exist even when the well's bound state is smaller (in absolute terms) than the height of the barrier (\(\alpha^{2}/4<U\)), and an analytical expression was derived to relate the resonance state to the barrier's width \(L(\Omega_{R})\). It was shown that the resonance state decreases when the barrier's width shrinks. Therefore, if the barrier is narrow enough, the resonance state would eventually turn into a bound state.
2. When an oscillating term is introduced, it was shown that zero transmission (and therefore zero current) requires the incoming particle's energy to be lower than the oscillating energy, i.e., \(\Omega<\omega\).
3. Since the particle can gain/lose oscillating quanta, for any given incoming energy, multiple solutions of the barrier's width exist, and from (A) and (B), it follows that the solution curve is folded. Therefore, the solutions can be retrieved qualitatively: \(L_{n}(\Omega)=L_{0}(\Omega-n\omega)\).
4. It was found that there are two different scenarios: 1. When the Resonance Energy of the Opaque Barrier (REOB) is negative. In this scenario, the Fano resonance (zero transmission) occurs even for an infinitely wide barrier. 2. When the REOB is positive, the barrier must be narrower than a certain value for a Fano resonance to appear.
5. An analytical expression was derived for the barrier's width, which supports Fano resonances \(L_{m}(\Omega)\) up to the second order in the oscillation amplitude \(\beta^{2}\), and it was found to be in high agreement with exact numerical results.
6. In the case where the oscillation amplitude is large, one can use the method that was applied to the Sisyphus effect and the QBSS method [14; 17], along with the additional requirement of \(\Omega<\omega\), calculating the Fano criteria with a high agreement with numerical simulations.
7. Finally, it was shown that, in the case of a very narrow barrier, a simple analytical expression relates the zero-transmission energy to the multiple barrier widths \(L_{n}(\Omega)\), which support the Fano resonance. This expression shows that the energy, which can easily be measured, is extremely sensitive to the barrier's width, and can therefore be used for high-precision measurements.  
Conceptualization, G.Z. and E.G., methodology, G.Z. and E.G., software, G.Z. and E.G., formal analysis, G.Z. and E.G., investigation, G.Z. and E.G., writing, G.Z. and E.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 2 Related Works  
### Deep Age Estimation  
With the advancement of deep learning, age estimation is commonly formulated as a classification or regression problem. While early studies relied heavily on convolutional neural networks (CNNs) for extracting multi-scale deep features, recent approaches have begun to explore more powerful alternatives. For instance, [PERSON] et al. [6] learned an ensemble of CNNs from multi-scale patches generated from facial landmarks, while [PERSON] et al. [7] employed age-group-based CNN models for apparent age estimation. However, these CNN-based approaches often struggle to fully capture long-range dependencies and global facial context, which are critical for modeling aging patterns.  
Recently, transformer-based architectures have gained attention for age estimation due to their ability to model global relationships and fine-grained visual dependencies. Among them, the Swin Transformer [8] introduces a hierarchical window-based self-attention mechanism, enabling effective multi-scale feature learning with reduced computation. Its success in general vision tasks has inspired its adoption in age estimation frameworks. In [9], the DAA model integrated delta age encoding into a binary code transformer using AdaIN operations, allowing the model to better represent age progression. In [10], a cross spatial and cross-scale Swin Transformer is proposed, which can extract fine-grained age-related features. In [11], the Scale-Adaptive Deformable Transformer (SADT) enhances the flexibility of attention across varying scales, improving model robustness in vision tasks. Similarly, ref. [12] introduced TopNet, utilizing Adaptive-Length Sliding Window Attention (AL-SWA) to efficiently capture both local and global dependencies. In [13], the TADFormer dynamically adapts to task-specific input contexts by performing fine-grained feature adaptation, improving multi-task learning efficiency and accuracy. Transformer-based models not only outperform CNNs in modeling contextual cues but also provide a flexible mechanism to incorporate auxiliary supervision, such as ordinal constraints and label distributions. These designs have shown strong generalization in real-world aging scenarios with high inter-subject variability.
# Mechanics Model of Floor Heave: Case Study on Thin Coal Seam with Soft Roof and Floor
## 5 Discussion
### The Influence of Roadway Width on Floor Heave  
The degree of floor deformation and collapse is significantly influenced by the width of the gob-side entry retaining highway. Four types of widths are specified to be 3 m, 4 m, 5 m, and 6 m, respectively, with other parameters remaining the same, using the 400 m buried depth as an example. In Figure 12, it is possible to derive the displacement curve of the floor of the gob-side entry holding with paste filling beneath the equivalent roadway width. The relationship between maximum of gob-side entry retaining floor deformation and the roadway width is shown in Figure 13.  
Figure 11: Relationship between maximum of gob-side entry retaining floor deformation and buried depth.  
Figure 12: Deformation curve of gob-side entry retaining floor under different roadway widths.  
The deformation of the floor of the roadway area is characterized by a \"parabola,\" which has a nonsymmetrical phenomenon, as can be seen in Figure 11. The deformation of the floor on the side of solid coal and roadside filling body is close to zero. The side of the roadside filling body is where the floor heave's maximum deformation is found. As the width of the road rises, the asymmetric failure of the highway floor becomes more apparent, and the maximum deformation increases nonlinearly. When the width of the road is 3 m, 4 m, 5 m, and 6 m, the greatest highway floor displacement occurs at 0.25 m, 0.63 m, 1.35 m, and 2.55 m. Therefore, it can appropriately reduce the width of the roadway to reduce the amount of floor heave. It is also possible to reduce the floor heave by moving the paste-based filler body to inside of the roadway and reducing the width of it. The formula of deformation of the roadway floor and the roadway width is \(y=0.7646x-2.242\), and the correlation coefficient is 0.9179, indicating a strong correlation between the two.
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 2 ML-Aided Anomaly Detection Review
### AI/ML-Aided Anomaly Detection
In this multi-NWDAF case, implementing lightweight FL-based schemes for fast, accurate, resource-efficient, and privacy-aware algorithms can improve anomaly detection across the network. Still, some issues arise as multiple training and update rounds are needed, a process that might not be feasible in resource-constrained devices. Also, FL training should be protected against tampering attempts in the training data of participating nodes that can lead to a suboptimal global model. Then, the authors in [26] integrate ML functionalities in the NWDAF for network traffic prediction and anomaly detection. According to the simulations, neural network algorithms provide better network load prediction over linear regression, while tree-based gradient boosting surpasses logistic regression (LR) in anomaly detection. Next, the paper in [27] focuses on Internet-of-things services and appropriate network slicing to satisfy their demands. To improve the resource and energy efficiency of monitoring possible failures in these topologies, the authors develop a monitor application function based on the NWDAF framework. So, they deploy multiple monitoring functions and propose an RL-based dormancy monitor mechanism to reduce the energy consumption of the monitoring process by appropriately setting functions in a dormant state. Simulations reveal that this dynamic dormancy algorithm can significantly improve energy efficiency without undermining failure detection accuracy. Regarding privacy-aware mechanisms, some works have highlighted the positive role of the NWDAF. First, to improve security and privacy in NWDAF-based and ML-aided 6G architectures, the work in [28] deals with partial homomorphic encryption to secure ML model sharing with privacy preservation. Then, the authors in [29] present feedback-driven FL in a distributed NWDAF 5G core architecture. Thus, a feedback mechanism is employed to guide the ML models of client NWDAFs, guarantee faster convergence for the global function optima, and ensure data privacy. At the same time, influence-based weighted federated averaging is introduced to handle the edge NWDAF model parameters during global model aggregation, also using local differential privacy to mask model parameters. Finally, the work in [30] presented an NWDAF-enabled lightweight FL mechanism to support FL training under communication quality constraints. The authors consider the model size and communication quality and use the NWDAF to analyze which FL clients are not able to upload their model parameters. As a result, their mechanism exploits communication quality data to extract significant model parameters from the FL clients. Experimental results show that their solution is capable of improving model accuracy for schemes employing multi-layer perception and convolutional neural networks. Figure 1: The NWDAF concept.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 2 Literature Review
### Popularity-Based Caching  
Popularity-based caching strategies determine content placement based on its access frequency across the network. In these methods, frequently requested content is prioritized for caching to reduce latency and improve hit ratios. For instance, Optimal Cache Placement Based on Content Popularity (OCPCP) stores content once it surpasses a predefined popularity threshold. Common cache replacement policies such as Least Recently Used (LRU), Least Frequently Used (LFU), and Time-Aware LRU (TLRU) govern which content is retained or evicted based on usage statistics. Advanced frameworks like Network Coding-Based Cache Management (NCCM) integrate with Software-Defined Networking (SDN) 2.4 to enhance routing and caching through linear coding, while approaches like Wave segment content by access frequency. Other techniques, such as Most Popular Content (MPC) and Fine-Grained Popularity-Based Caching (FGPC), further refine decisions by accounting for cache storage limits or recent access patterns.  
Several dynamic strategies have also emerged, including NICE [16], COMP, BRCC, Adept, CSCPUL, Fog Cache, CCAR, PCSBCC, DLRUK, Spatially Dispersed Cache, Progress Caching, Joint Cache, and Hybrid Coordination approaches [17; 18]. However, most of these techniques overlook node-level resource constraints and social relationships, which can significantly impact caching efficiency in clustered environments. SARAC4N enhances traditional popularity-based approaches by combining content demand trends with social awareness and resource availability, enabling more context-aware and adaptive cache placement.  
Popularity-based caching mechanisms, such as OCPCP, LFU, and FGPC, efficiently prioritize frequently requested content, thereby reducing retrieval time and improving cache utilization. However, these methods often rely on static or reactive popularity thresholds and fail to incorporate real-time user behavior or network context. This can lead to inefficient content placement when demand patterns shift rapidly. Additionally, they generally ignore node-level resource limitations and social influence in content dissemination. SARAC4N addresses these gaps by combining popularity cues with social centrality and resource metrics, enabling adaptive, relevance-aware content caching that reflects both demand and context.
## 1 Introduction
### Classification and Clustering Algorithms  
In the health domain, there are similarities between classification and clustering algorithms. Classification algorithms are primarily used when the discrete outcome variable facilitates data categorization into predefined classes. These algorithms are valuable in medical decision-making, as they can predict patient outcomes based on historical data, aiding in timely interventions [47]. Techniques such as Support Vector Machines (SVMs) and Naive Bayes classifiers have been successful in categorizing health-related messages and patient information, improving the accuracy of health informatics systems. Incorporating domain-specific knowledge, like medical terminology, into these models has improved their performance [48].  
In contrast, clustering algorithms are employed when data lack predefined labels, facilitating the discovery of inherent groupings within the dataset. This unsupervised learning approach is ideal for exploratory data analysis in healthcare, where it uncovers patterns and relationships between different health conditions without prior knowledge of outcomes [49]. For example, clustering can group patients with similar health profiles, helping inform personalized treatment strategies. Clustering quality can be evaluated using metrics such as homogeneity and completeness, which measure how well the clusters represent the underlying data patterns [50].  
Clustering techniques, such as K-means, explore data analysis and discover the natural groupings within data. For example, in paper by [PERSON] et al. [51], K-means clustering is used to detect hardware Trojans, showcasing how unsupervised methods reveal hidden patterns that classification models may miss. Clustering is valuable when labeled data are scarce or unavailable, allowing researchers to analyze data based on inherent similarities. [PERSON] et al. [52] demonstrated the usefulness of clustering by comparing it to classification methods in coastal wetland vegetation studies, finding that while classification yielded high accuracy, clustering offered insights into the underlying structure of the data.  
Hybrid approaches, combining supervised and unsupervised methods, have been shown to improve outcomes. For instance, [PERSON] et al. [53] developed a hybrid approach for detecting retinal diseases, using both segmentation (unsupervised) and classification (supervised) techniques to achieve high diagnostic accuracy.  
The selection of classification or clustering algorithms depends on the objectives of the analysis. Classification is more suitable for predicting specific health outcomes based on labeled data, while clustering is used to explore and identify patterns in unlabeled datasets.
# A Variable Neighbourhood Search-Based Algorithm for the Transit Route Network Design Problem
## 3 Results
### Network with 7 Routes  
Table 5 and Figure 6 show the results of the transit network with 7 routes. In the case of a 7-route network, our method produced higher-quality results over existing methods, yielding lower average travel time (10.10 min) and a higher percentage of direct demand satisfied (98.97) than all other methods. The average execution time was 65 s in this case.  
\begin{table}
\begin{tabular}{c c} \hline
**Route \#** & **Node Sequence** \\ \hline
1 & 2, 1, 4, 3, 5, 7, 14, 6 \\ \hline
2 & 12, 10, 9, 7, 5, 2, 1, 0 \\ \hline
3 & 13, 9, 6, 14, 5, 2, 1, 0 \\ \hline
4 & 8, 14, 6, 9, 10, 11, 3, 4 \\ \hline
5 & 13, 9, 7, 5, 3, 4, 1, 0 \\ \hline
6 & 9, 13, 12, 10, 11, 3, 1, 0 \\ \hline \end{tabular}
\end{table}
Table 4: Best Route Set for 6-Route Case.  
Figure 5: Best network with 6 routes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Performance** & **C\&S [15]** & **N\&T [29]** & **K\&B [32]** & **[PERSON]** & **[PERSON] et al.** & **[PERSON]. et al.** & **VNS-B** & **VNS-B** & **VNS-B** \\
**Criteria** & **C\&S [15]** & **N\&T [29]** & **K\&B [32]** & **et al. [46]** & **[26]** & **[39]** & **Best** & **Med.** & **VNS-B** \\ \hline d0 & 95.57 & 98.52 & 97.87 & 98.84 & 97.56 & 97.94 & **98.97** & 96.27 & 92.68 \\ d1 & 4.43 & 1.48 & 2.83 & 1.16 & 2.44 & 2.06 & **1.03** & 3.73 & 7.32 \\ d2 & 0 & 0 & 0 & **0** & 0 & 0 & **0** & 0 & 0 \\ dun & 0 & 0 & 0 & **0** & 0 & 0 & **0** & 0 & 0 \\ ATT & 10.27 & 10.15 & 10.16 & **10.10** & 10.14 & 10.12 & **10.10** & 10.24 & 10.59 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of the final solution generated between methods for 7-Route Case.
## 6 Conclusions  
Most existing corn mapping methods use coarse resolution and seldom use high-resolution images on a large scale. Mapping crop types with coarse resolution imagery leads to problems with mixed pixels and cannot reflect detailed information on a field scale. High-resolution SAR and optical images with a 10-m resolution enable us to depict corn distributions in detail, and can be applied on a large scale with the assistance of cloud computing technology.  
This research demonstrated a cloud computing-based approach for the identification of corn cultivated areas at a 10-m resolution using Sentinel-2 and Sentinel-1 images. Certain metric composites were calculated, including monthly composites and percentile composites for Sentinel-1 images and percentile and interval mean composites for Sentinel-2 images, which were used as input to the RF algorithm in the GEE platform. To avoid the'salt-and-pepper effect', object segmentation was implemented in eCognition software with the assistance of the ESP tool. After integrating the pixel-based result generated from RF and object segmentation, the corn's F1-Score reached 90.08%, and the overall accuracy was 89.89%. The acceptable accuracy suggests that the approach introduced in this research is reliable for corn mapping. The combination of SAR images and optical images improved the accuracy of SAR images and provided additional information for areas without sufficient optical images. The metric composites and RF classifier were implemented in the GEE platform and completed within one day, suggesting that the cloud computing platform facilitates corn mapping efficiency and ensures the practicability of the approach introduced in this research.  
[PERSON] contributed to the research experiments and wrote the paper. [PERSON] conceived this research and was responsible for the research. [PERSON], [PERSON], and [PERSON] collected and preprocessed the original data. All of the co-authors helped polish the manuscript.  
Figure 15: Comparison between the pixel-based classification result using only synthetic aperture radar (SAR) image metric composites (**a**) and optical and SAR image metric composites (**b**) as input. The corn map generated with only SAR image metric composites was subject to the speckle effect. Combined with optical images, this phenomenon can be constrained.  
**Funding:** This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19030201), the National Key Research and Development Program (2016 YFA0600304), and the National Natural Science Foundation of China (41561144013).  
**Acknowledgments:** We thank the anonymous reviewers for reviewing the manuscript and providing comments to improve the manuscript.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest to disclose.
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 2 Methodology
### Deterioration Information Integrate for SOH Prediction
#### 2.3.2 Network Model Building  
The determination of a network structure depends on dataset form, scale, and optimization result. The network structure proposed in this paper was based on LSTM and the dropout method. This structure is shown in Figure 7.  
* Input layer: The degradation time series of battery cells is taken as the model input, whose dimension depends on the number of cells located in the battery pack and on the auxiliary indicator parameters (time label, ambient temperature, etc.). In this research, there is only one health status type, and \(p=1\). Hence, subject to the battery pack structure in the experiment, \(N=2\) in series and parallel packs, and the input data were set as a matrix including four vectors:  
Figure 6: Structure and allocation of input data.  
Figure 7: Network model for the pack state of health (SOH) prediction.  
\[X_{input}^{c}=\left(\begin{array}{c}S_{c}\\ L\\ D_{temp}\end{array}\right)=\left(\begin{array}{ccccccc}x_{1,1}^{1}&x_{1,2}^{1}& \ldots&x_{1,t}^{1}&\ldots&x_{1,T}^{1}\\ x_{1,1}^{2}&x_{1,2}^{2}&\ldots&x_{1,t}^{2}&\ldots&x_{1,T}^{2}\\ l_{1}&l_{2}&\ldots&l_{t}&\ldots&l_{T}\\ d_{1}&d_{2}&\ldots&d_{t}&\ldots&d_{T}\end{array}\right) \tag{5}\]  
Further, for the series-parallel pack, it is a matrix including six vectors with \(N=4\):  
\[X_{input}^{c}=\left(\begin{array}{c}S_{c}\\ L\\ D_{temp}\end{array}\right)=\left(\begin{array}{c}S_{c}\\ X_{1,1}^{2}&x_{1,2}^{2}&\ldots&x_{1,t}^{2}&\ldots&x_{1,T}^{2}\\ x_{1,1}^{3}&x_{1,2}^{3}&\ldots&x_{1,t}^{3}&\ldots&x_{1,T}^{3}\\ x_{1,1}^{4}&x_{1,2}^{4}&\ldots&x_{1,t}^{4}&\ldots&x_{1,T}^{4}\\ l_{1}&l_{2}&\ldots&l_{t}&\ldots&l_{T}\\ d_{1}&d_{2}&\ldots&d_{t}&\ldots&d_{T}\end{array}\right) \tag{6}\]  
Considering the difficulty and accuracy of the auxiliary indicators, the time and ambient temperature, which were easy to monitor, were selected to reduce the accumulated errors caused by degradation fluctuation.  
* Hidden layer: The internal structure of this model was composed of several LSTM network structures, and the number of layers and neurons depended on the data dimension and scale. With increase of quantity and scale, a neural network composed of only LSTM is prone to experiencing overfitting, which makes the generalization ability of the model extremely poor. In order to reduce the occurrence of overfitting, dropout was set between every two LSTM layers, and the neurons were randomly discarded and reconstructed.
* Output layer: The output model was the degradation indicator time series of the battery pack, and its dimension was consistent with the input sequence. The output was the SOH prediction values of the battery pack.  
Accordingly, the model for deterioration information integrate, which is used to predict status of packs, has been built. The model extracts numerical characteristics of cells related to pack degradation, and expresses the function relations between them to integrate all deteriorate information in cells.