esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 2 Fundamentals of Wavelet Coding  
In this technique, a flat (a matrix is called flat if all of its entries have the same absolute value) integer WCM is used to encode information bits. A flat integer matrix \(\mathbf{A}=(a_{k}^{j})\), where \(a_{k}^{j}\in\{-1,\,+1\}\), of dimension \(m\times mg\) is said to be a wavelet coefficient matrix of rank \(m\geq 2\) and genus \(g\) if it satisfies the modified wavelet scaling conditions given by [15; 24]:  
\[\sum_{k=0}^{m\sigma-1}a_{k}^{j}=m\sqrt{g}\delta_{0j},\ \ \ 0\leq j\leq m-1 \tag{1}\]\[\sum_{k}a^{j}_{[k+ml]}a^{j^{\prime}}_{[k+ml^{\prime}]}=mg\delta_{j,j^{ \prime}}\delta_{l,l^{\prime}}, 0\leq j,j^{\prime}\leq m-1\] \[0\leq l,l^{\prime}\leq g-1 \tag{2}\]  
where \(\delta_{j,j^{\prime}}\) is the Kronecker delta and the notation \([k+ml]\) stands for the operation \(k+ml\) modulus \(mg\).  
The fundamental property of a WCM for channel coding purposes is given by Equation (2). It states that the rows of a given WCM with order \(m\) are mutually orthogonal at shifts of length \(lm\), where \(0\leq l\leq g-1\). Besides, each row is also orthogonal to a copy of itself shifted by \(lm\), where \(0<l\leq g-1\).
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 3 Methods
### Traditional Combination Prediction Model  
Combination prediction model refers to the use of a certain method to combine multiple different models, fully leveraging the strengths of each model to compensate for the shortcomings of individual model predictions, thereby improving the prediction accuracy of the model. In view of the strong nonlinearity and non-stationarity of dam deformation data affected by multiple factors, using a single model for prediction cannot simultaneously consider the linear and nonlinear parts. Therefore, the idea of using a combination model for dam deformation prediction has been proposed.  
Let the original time series of dam deformation be denoted as \(y_{t}\), which can be represented as follows:  
\[y_{t}=L_{t}+N_{t} \tag{6}\]  
where \(L_{t}\) represents the linear part in the deformation sequence, and \(N_{t}\) represents the nonlinear part. The specific steps of the traditional combination model are as follows:  
1. First, a linear prediction model is constructed to predict the linear part of the dam deformation sequence, and the linear prediction result \(\hat{L}_{t}\) and residual sequence \(e_{t}\) are obtained, as shown in Equation (7): \[e_{t}=y_{t}-\hat{L}_{t}\] (7)
2. Then, a nonlinear prediction model is constructed to predict the nonlinear part of the dam deformation sequence, which is the residual sequence \(e_{t}\). Let \(f(.)\) be the function relationship determined by the nonlinear model, and the nonlinear prediction value \(\hat{N}_{t}\) is obtained as follows: \[\hat{N}_{t}=f(e_{t})\] (8)
3. Finally, the linear and nonlinear prediction results are added to obtain the final prediction result of the combined model: \[\hat{y}_{t}=\hat{L}_{t}+\hat{N}_{t}\] (9)
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 2 Related Work
### Graph Neural Networks  
The objective of a GNN is to utilize the graph structures and node features to learn representations of the nodes. To achieve this, GNNs typically follow a two-step processing approach consisting of aggregation and feature transformation. In the first step, the representations of a selected node and its neighboring nodes are combined through aggregation. In the second step, these aggregated representations are mapped into a new feature space via a shared linear transformation, followed by an update operation [21; 22]. However, using a complete graph as input is often necessary to achieve this, which, due to hardware resource constraints, limits the applicability of these methods to large-scale graph data. To address this issue, GraphSAGE [23] iteratively samples subgraphs for aggregation and updating. Nonetheless, most existing methods [24] rely on external guidance, such as annotated labels, which restricts their applicability.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 5 Physical Layer Proposal  
In this section, the concluding remarks and the final physical layer proposal will be detailed. All the significant results about previous soundings, channel error study, synchronization and, finally, all modulation tests have been gathered. Following that, a set of frame structures for each type of transmissions have been proposed as the final design of the physical layer for the 12,760 km ionospheric modem.
## 4 Simulation Results  
Simulations are divided into two steps. The first step aims to empirically determine the optimal \(\alpha\) that minimizes the number of messages and runtime for the construction of the routing tree, while maintaining the quality of the structure. The second step aims to compare the efficiency of the EBF and DBF algorithms and the quality of the constructed trees. The efficiency of the algorithms SHM and DFS are not compared, because they are infeasible for real-world scenarios, since they cannot construct the routing tree in the case of message losses or node failures. In addition, DFS does not construct an SPT.
## 2 The Use of GO and FA in the Cement Mortar
### Content and Mixing Sequence of PCE with GO  
Prior research has shown that the proportion of PCE relative to GO is a crucial factor in achieving effective dispersion of GO. If the content of PCE is insufficient, it cannot adequately coat the exterior of GO and cannot provide a strong enough repulsive force to counteract the force produced by Van [PERSON] Waals across the GO sheets. On the contrary, if the PCE/GO ratio is too high, microparticles will form, resulting in GO agglomeration.  
The cause lies in the oxygen-containing functional groups within the structure of graphene oxide (GO). These groups, such as hydroxyl (-OH) and carboxyl (-COOH), impart high hydrophilicity to GO, making it prone to absorbing water and forming agglomerates. This differs from other graphite materials that are more hydrophobic and less prone to agglomeration.  
It is suggested that GO cannot be distributed in a cement solution when the mass ratio is equal to 1. However, when the ratio is increased from 2 to 5, GO is able to disperse effectively in the cement mortar [19]. Similarly, the ratio should be equal to 3, which is considered optimal for good GO dispersion in alkaline solutions [20]. Meanwhile, it is also suggested that the optimal PCE/GO ratio found in the study was 1:1 [21]. It can be seen that the ratio of PCE/GO depends on the concentration of PCE and GO, so it is necessary to determine this ratio for each material used. Still, the degree of GO dispersion can be tested within the PCE/GO ratio range (by mass) of 1-3.  
The mixing sequence needs to be considered to avoid the flocculation of GO. Research by [PERSON] et al. (2018) shows that mixing GO and PCE first helps GO to disperse nicely in cement (Figure 1), whereas, without PCE, GO is clearly agglomerated.
## 3 Stationary Localization Algorithms
### Centralized Localization Algorithms
#### 3.1.5. Asymmetrical Round Trip Based Localization (ARTL) Algorithm  
An Asymmetrical Round Trip based Localization (ARTL) algorithm is proposed in [18]. ARTL assumes that anchor nodes can receive their own packets while unknown nodes cannot do. First, the basic ranging scheme is implemented to get distances between anchor nodes and unknown nodes. As shown in Figure 3, unlike existing symmetrical round-trip schemes [19,20], the ranging scheme here is asymmetric; that is, to estimate the distance between an anchor node and unknown node \(U\), the initiator (the anchor node that starts the ranging task) \(A_{i}\) first broadcasts a ranging packet, which is received by both itself and \(U\), as well as other non-initiator nodes \(A_{u}\). Then after an arbitrary period, \(U\) responds an ACK to \(A_{i}\). This ACK will not be received by \(U\) itself, but only received by \(A_{i}\) as well as \(A_{u}\). Based on the time difference of arrival signals, the distance can be calculated. Together with collect data information, the calculated distances are sent to the base station which launches localization task. Thus, no time synchronization is required in the entire process and no complex computations are handled by anchor nodes and unknown nodes. Moreover, ARTL does not require unknown nodes to immediately reply after receiving the ranging broadcast, so \(U\) can respond ACK whenever it is convenient.  
Compared with other existing symmetrical round-trip schemes, ARTL needs very little communication overhead. For example, using \(s\) anchor nodes to localize \(l\) unknown nodes, traditional symmetrical round-trip schemes need \(s\times l\) ranging requests from anchor nodes and another \(s\times l\) replies from unknown nodes. Therefore, \(2s\times l\) messages are exchanged to finish all the related ranging tasks, which consumes too much energy. However, in ARTL, only \(l+1\) broadcast packets are needed to obtain all the required ranging related measurements. One is from the initiator and the other \(l\) are from unknown nodes. Furthermore, ARTL is easy to extend. Localization coverage can be enlarged by adding new anchor nodes. Whenever a new anchor node is added to the network, neither program modifications to other existed nodes nor additional calculations in the ranging process are needed. In general, localization accuracy become better and better with increasing number of anchor nodes. However, more anchor nodes incurs more packet exchanges and higher communication overhead. The main drawback of ARTL is long localization time. Moreover, much energy is consumed to send the calculated distances to the base station.
## 4 Experiment and Results
### Progressive Learning for Food Category and Ingredient Classification
#### 4.6.1 Multi-Class Classification for Food Category  
We implemented the incremental learning protocol proposed by [PERSON] et al. [41] and [PERSON] et al. [42] to measure classification performance. There are two classes in the base session, and therefore, PKELM learns a novel class in every session. For batch classifiers such as ELM and RKELM, the study retrains the model on the whole dataset in every session. We presented average classification measures on the state-of-the-art ELM classifiers for each dataset. The kernel parameter is set to 0.1 in all the experiments, and ACIELM inserts hidden nodes when the error threshold is more than 0.8. For ARCIKELM, the fuzzy membership threshold value is set to zero during sequential learning like the original study [1]. We set the learning rate of PKELM through manual search [43] and presented the best results. Table 5 shows the comparison of Top 1 and 3 percent classification accuracy, Precision, Recall, and F1-score across all six food datasets. All experiments are repeated three times for the same input sequence, and we have reported average accuracy. The experimental results show that PKELM performs better than ARCIKELM and other online variants of ELM. The improvement in accuracy for PKELM with RBF kernel in contrast to ARCIKELM for PFID baseline, UECFOOD100, UECFOOD256, Pakistani Food, and Food101 dataset is 4.12%, 1.78%, 0.49%, 2.8%, and 0.186%. For the VireoFood-172 dataset, the performance of both ARCIKELM and PKELM with RBF kernel is the same. It may be because VireoFood-172 has many images with fewer intra-class variations. PKELM with chi-square kernel improves classification accuracy as compared to ARCIKELM for PFID baseline, UECFOOD100, UECFOOD256, Pakistani Food, and Food101 dataset by 4.64%, 1.98%, 0.6%, 3.57%, and 0.84% respectively. It is worthy to note that the chi-square kernel in PKELM outperforms the RBF kernel for food categorization. The proposed classifier performs better than non-adaptive variants of ELM (batch methods) in terms of Accuracy, Precision, Recall, and F1-score on UECFOOD100, UECFOOD256, Pakistani Food Dataset, VireoFood-172, and the PFID baseline dataset. Figure 18: (**A**) Randomly selected neurons and (**B**) Neuron selection by using PKELM from the first two training sessions during incremental learning of Pakistani food datasets.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 6 Conclusions  
This study proposed an interpretable KAN for ECG signal classification, aimed at the classification and triage of cardiac diseases such as arrhythmias. Experimental validation on the MIT-BIH and PTB datasets demonstrated that data preprocessing techniques, including balanced processing and variance sorting, significantly enhanced the model's classification performance. The preprocessed KAN achieved classification accuracies exceeding 99% on both datasets, with precision and recall rates also reaching optimal levels. Compared to existing studies, the KAN exhibited notable advantages in classification accuracy, fitting stability, and the discriminative power of feature extraction. Further analysis revealed thatthe KAN not only effectively captures key features in ECG signals but also produces feature representations with clearer classification boundaries under three clustering methods, underscoring its adaptability and generalization capabilities in complex data scenarios.  
This research offers a novel technical approach for ECG signal classification and identifies future directions for development, such as integrating the KAN with other network architectures, incorporating large-scale datasets to evaluate the model's robustness, and developing real-time monitoring functionalities. These advancements provide robust support for clinical diagnosis and highlight the potential of interpretable networks in advancing clinical research applications.  
H.C.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing--original draft. S.N.: Conceptualization, Formal analysis, Investigation, Resources, Visualization. S.W.: Formal analysis, Software, Validation. W.Z.: Formal analysis, Methodology, Software, Validation, Funding acquisition. Y.P.: Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the open project of the Key Laboratory of Computing Power Network and Information Security, Ministry of Education, grant number 2023 ZD015.  
The data used in this study are online available at [[https://www.kaggle.com/datasets/shayanfazeli/heartbeat?resource=download](https://www.kaggle.com/datasets/shayanfazeli/heartbeat?resource=download)]([https://www.kaggle.com/datasets/shayanfazeli/heartbeat?resource=download](https://www.kaggle.com/datasets/shayanfazeli/heartbeat?resource=download)) (1 January 2025).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AAMI American Association for the Advancement of Medical Instrumentation  
DL Deep Learning  
ML Machine Learning  
KAN Kolmogorov-Arnold Network  
MLP Multi-Layer Perceptron  
AI Artificial Intelligence
# Resonant X-ray Emission Spectroscopy with a SASE Beam
###### Abstract  
Aqueous iron (III) oxide nanoparticles were irradiated with pure self-amplified spontaneous emission (SASE) X-ray free-electron laser (XFEL) pulses tuned to the energy around the Fe K-edge ionization threshold. For each XFEL shot, the incident X-ray pulse spectrum and Fe K\(\beta\) emission spectrum were measured synchronously with dedicated spectrometers and processed through a reconstruction algorithm allowing for the determination of Fe K\(\beta\) resonant X-ray emission spectroscopy (RXES) plane with high energy resolution. The influence of the number of X-ray shots employed in the experiment on the reconstructed data quality was evaluated, enabling the determination of thresholds for good data acquisition and experimental times essential for practical usage of scarce XFEL beam times.  
X-ray free-electron laser (XFEL), resonant X-ray emission spectroscopy (RXES) with self-amplified spontaneous emission (SASE) XFEL beam; reconstruction of RXES planes +
Footnote †: journal: Applied Sciences  
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Footnote 12  
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: email: _Applied_  
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with non-invasive diagnostics can be used to extend the scope of X-ray spectroscopies at XFELs to two-dimensional X-ray spectroscopy techniques, i.e., resonant X-ray emission spectroscopy (RXES). The crucial aspect of the presented methodology was bypassing the monochromator and thus avoiding the temporal broadening of XFEL pulses related to the extinction length of X-rays in the monochromator crystal. Moreover, the experiment was performed in a scanning-free mode of detection for both incidence and emission X-ray spectra, thus allowing fast acquisition of desired signals. The presented reconstruction algorithm allowed to obtain high energy-resolution RXES planes based on the measured spectral distributions of a collection of individual incident non-monochromatized SASE pulses and each corresponding X-ray emission spectroscopy (XES) signal emitted from the studied sample. The method thus enables to map simultaneously the unoccupied and the occupied electronic states of the scattering atom exposed to short and intense SASE XFEL pulses. The reconstruction concept was further expanded in the work of [PERSON] et al. [11], demonstrating the application of noisy XFEL pulses to stimulated X-ray Raman experiments. Studies were focused on a model system with a conical intersection to illustrate resulting resolutions in time and spectral domains. Further, it has been demonstrated that narrow and chemically rich information in core-to-valence transitions of the pre-edge region at the Fe K edge has been obtained with our methodology [12].  
In the present work, we evaluated the influence of the number of XFEL shots recorded on the quality of the RXES planes reconstructed with the previously reported algorithm [10]. The presented analysis facilitates planning an RXES experiment under SASE conditions at XFELs.
# A Secured Intrusion Detection System for Mobile Edge Computing
## 5 Implementation and Results
Further, the proposed IDS model has some pros and cons as follows:  
**Pros of the proposed SEC_IDS Model:**  
_Comprehensive Detection Mechanisms:_ The integration of both signature-based and anomaly-based detection mechanisms enhances the overall effectiveness of intrusion detection. This comprehensive approach allows the system to detect known attack patterns as well as abnormal behaviors that may indicate novel threats. _Adaptability:_ The SEC-IDS framework is designed to be adaptable to evolving threats. The combination of signature-based and anomaly-based detection mechanisms ensures flexibility in identifying both known and unknown intrusion attempts, making it resilient to emerging attack vectors. _Edge Computing Utilization:_ Leveraging edge computing resources is a significant advantage. By distributing detection tasks closer to the data source within the MEC environment, the framework minimizes latency. This not only improves the real-time responsiveness of the intrusion detection system but also optimizes resource utilization. _Reduced Latency:_ The distribution of detection tasks at the edge reduces latency in the intrusion detection process. This is crucial for MEC environments where low latency is essential for ensuring timely responses to security threats. _Realistic Validation:_ The extensive experiments conducted in a simulated MEC environment provide a realistic validation of the SEC-IDS framework. Simulating MEC conditions allows for a controlled and replicable assessment of its performance under various scenarios. Figure 5: Link change rate of the proposed model compared with existing models. Cons of proposed SEC_IDS Model: Simulation Limitations:While the simulated MEC environment offers controlled experiments, it may not fully replicate the complexities and nuances of a live MEC deployment. Real-world conditions, such as network variability and dynamic user behavior, could introduce factors not accounted for in the simulation. Resource Intensiveness:Implementing both signature-based and anomaly-based detection mechanisms may demand significant computational resources. In resource-constrained MEC environments, this could potentially lead to performance bottlenecks or increased energy consumption. Dependency on Edge Infrastructure:The effectiveness of the SEC-IDS framework is contingent on the availability and reliability of edge computing resources. In scenarios where the edge infrastructure is limited or unstable, the system's performance may be compromised. Ongoing Maintenance:To stay effective against evolving threats, the SEC-IDS framework may require regular updates and maintenance. Keeping signature databases up to date and refining anomaly detection models could introduce operational overhead. Limited Generalization:The framework's performance may be optimized for the specific conditions of the simulated MEC environment, and its generalization to diverse MEC deployments may require additional validation and customization.
## 3 Results and Discussion
### pH and Total Acidity  
The obtained oenological parameters over the five stages of all of the wines under study are presented in Table 3 and Figure 3.  
Upon statistical analysis, the type of treatment applied showed a significant effect on both pH (F(2, 24) = 26.675, \(p\) < 0.001, \(\eta^{2}\) = 0.690) and total acidity (\(TA\)) (F(2, 24) = 22.161, \(p\) < 0.001, \(\eta^{2}\) = 0.648).  
The post hoc [PERSON]'s HSD test was used to perform multiple comparisons for both pH and TA. All groups presented a statistically significant difference in pH regarding treatment type (ARC and ARE: \(p\) = 0.007, 95% C.I. = 0.0103, 0.702; ARC and ARPEF: \(p\) = 0.002, 95% C.I. = \(-\)0.0773, \(-\)0.0174; and ARE and ARPEF: \(p\) < 0.001, 95% CI = \(-\)0.1175, \(-\)0.0576). The same was observed for TA (ARC and ARE: \(p\) = 0.008, 95% C.I. = \(-\)0.4893, \(-\)0.0698; ARC and ARPEF: \(p\) = 0.008, 95% C.I. = 0.0698, 0.4893; and ARE and ARPEF: \(p\) = \(<\)0.001, 95% C.I. = 0.3494, 0.7689).  
Regarding Stage 0, the lowest pH values were observed in ARE (3.05 \(\pm\) 0.02), while the highest were seen in ARPEF (3.16 \(\pm\) 0.01); ARC had the lowest TA value at 8.77 \(\pm\) 0.44 g/L of tartaric acid. However, 3 months after vniification, ARC exhibited the highest pH and TA at 3.07 \(\pm\) 0.025 and 9.70 \(\pm\) 0.21 g/L of tartaric acid, respectively. Meanwhile, ARPEF and ARE had pH values of 3.03 \(\pm\) 0.012 and 3.01 \(\pm\) 0.03, and TA values of 8.16 \(\pm\) 0.25 and 9.51 \(\pm\) 0.17 g/L of tartaric acid, respectively.  
Figure 3: pH (**left**) and Total Acidity (**right**) evolution over time (Stage 0: after pressing, Stage 1: after decantation, Stage 2: middle of AF, Stage 3: End of AF and Stage 4: after 3 months) (n = 9).  
Our pH results are consistent with those obtained by [PERSON] et al. (2013), who subjected grapes of the red varieties Graciano, Tempranillo and Grenache to a 4-6 kV/cm electrical field and observed a significant increase in pH [53]. However, the results were contradictory regarding TA, as the end of AF showed that ARPEF had a lower concentration (9.65 \(\pm\) 0.21 g/L of tartaric acid). In a study performed by [PERSON] et al. (2008) on Tempranillo, electrical fields of 5-10 kV/cm were applied, and no significant differences were observed at the end of AF [37]. Similarly, [PERSON] et al. 2008 reported no significant differences, although they noted a slight increase in pH values, which was undetectable after fifty days of AF. He also hypothesized that the increase in pH was due to the salification process of organic acids, which resulted in the release of hydroxide ions (OH\({}^{-}\)) [33].