esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Technology
### New Modeling Approach  
The impact of modeling approaches on telehealth systems is significant as they enable healthcare professionals to evaluate telehealth services' performance, optimize resource allocation, and improve patient outcomes. While several modeling techniques are available, generalized nets [50] (GNs) provide a unique approach that can capture the dynamic and uncertain nature of the telehealth environment. By using GNs to model telehealth services, healthcare professionals can optimize resource utilization, improve care quality, and reduce healthcare costs. Moreover, GNs can lead to the development of more accurate and efficient telehealth services, resulting in improved patient outcomes and increased satisfaction. Therefore, incorporating GNs as a modeling approach in telehealth can bring substantial benefits to healthcare systems, making it a crucial perspective to consider when designing and improving telehealth technology.
## 3 Results and Discussion
### Structure and Morphology Studies
The SWNT anchored framework in the INS5 sample provides a reinforcement structure, which provides a robust structure and suppresses the stacking of the layered base material to achieve long-term cyclic performance [45; 48]. The in-situ participation of the SWNT during solvothermal synthesis provides a place for forming new layers in a SWNT network-based cocoon-shaped material, based on the amount of added SWNTs. A good discrete layered structure material (INS1) has a high effective surface area and shows a high specific capacitance. The cocoon-shaped structure (INS5) shows how a reinforced network can be used to make a strong structure that can withstand long cycles with some compromise of capacitance value. SWNTs' integration in electrode material helps to move ions quickly, has a high surface area, and acts as a spacer for preventing layer stacking during the electrochemical reaction [25; 34]. Figure 3: (**a**) Nitrogen adsorption/desorption isotherms. (**b**) Pore-size distribution curves. More importantly, the cocoon structure grows over and around itself, reducing shrinkage and expansion during ionic reactions for charge storage and enhancing cyclic behavior [49]. The HRTEM image of sample INS2 shown in Figure 4e shows the clearly visible atomic fringes. The atomic spacing in this high-resolution image is 0.31 nm, which corresponds to the XRD d-spacing index (400). The SAED pattern of the corresponding image indicates that the material is polycrystalline, and it matches with the XRD pattern. In Figure S1a,b, the layered structure of INS1 is demonstrated by dark field and bright field TEM images.
# Multi-Label Classification for Predicting Antimicrobial Resistance on _E. coli_
## 1 Introduction
[15] used deep transfer learning (Deep TL) to predict the antimicrobial resistance to novel antibiotics, obtaining a high accuracy of 91.5%. Also, an Ensemble Classifier Chain (ECC) was used for the multi-label classification of _E. coli_ with the base classifiers RF, SVM, and LR. The authors achieved an accuracy of 0.915 in the ECC model using RF as the base classifier [16]. For the instance of imbalanced labels, transfer learning is frequently utilized in the medical field [17; 18]. For example, deep transfer learning was employed by [PERSON] et al. [19] to lessen the healthcare disparities brought on by imbalanced biomedical data. To enhance the model's performance, the researchers trained the model using the majority group and then the knowledge learned was transferred to each minority group. The goal of this study was to propose a computational approach using ML models to predict antimicrobial resistance in _E. coli_. By conducting extensive experiments with various ML methods, we strive to achieve state-of-the-art performance, thereby enhancing our ability to predict antimicrobial resistance effectively. Our approach seeks to prioritize drug candidates that are less likely to be resisted by _E. coli_ bacteria, thus streamlining the development of more effective antimicrobial agents. The organization of this paper is as follows: Section 2 provides an overview of the prediction of antimicrobial resistance, which includes the collection of the data, preprocessing of the data, and prediction models. Section 3 outlines the experimental procedures applied to the datasets, the resulting findings, and a discussion of the outcomes. Finally, Section 4 offers the conclusion of the paper.
## 2 Related Work
The researchers also managed to develop an analogous model for infants (SMIL, Skinned Multi-Infant Linear model) using information from RGBD sequences [35; 36]. Dyna [37] is a model which describes soft-tissue deformations, depending on the body shape and motion dynamics. A linear PCA sub-space is used for representation of soft-tissue deformations, whereby (as pointed out in [38]) the model has difficulty in reflecting highly non-linear deformations. DMPL (Dynamic-SMPL) [34] is based on a similar concept to Dyna, increasing the reconstruction quality by defining the model with vertices instead of triangles (as was the case in the Dyna model). SoftSMPL [38] improved on these models by introducing a non-linear deformation model and a new motion descriptor. Further studies have involved deep learning for pose and shape estimation in 4D scans [39; 40; 41; 42; 43; 44]. One of the directions in this area is the use of a Variational Autoencoder (VAE) to encode the 3D scan shape [40; 41; 42; 43; 44; 45; 46]. [PERSON] et al. [42] used a VAE for body shape completion based on a partial view. In the aforementioned work, a neural network operates directly on the vertex positions in 3D space, instead of manually selected features (as in [44]). [PERSON] et al. [45; 46] proposed a VAE-like network for learning the shape and pose of a human body from a sequence of triangle meshes with the same topology. For this purpose, they used measurement datasets (i.e., SCAPE, FAUST, MANO (hand Model with Articulated and Non-rigid defOrmations), Dyna, DFAUST, and CAESAR), applying to them a novel homogenous mesh registration method based on as-consistent-as-possible (ACAP) representations, proposed originally by [PERSON] et al. [47]. Another work [48] extended the SMPL model with skeleton information, determining point features in the input data using PointNet++ and mapping them to the joints. Due to this operation, the authors improved the process of learning parameters in the SMPL model. In summary, the existing methods for pose and shape tracking in human 3D scan sequences generally focus on the concept presented by the SMPL method, which uses a model trained on an extensive dataset of sample measurements. The works extending this model, such as SoftSMPL or the work of [PERSON] et al., have introduced improvements in model training, but still yield low-resolution output meshes. In these methods, it is also impossible to easily change the topology of the output mesh and the skeleton from sequence to sequence, as it is necessary to retrain the model on at least several thousand scans.
# Robustness Analysis on Graph Neural Networks Model for Event Detection
## 5 Experiments
### Model Performance on the Adversarial Data  
We did no additional tuning to the models so that the models' robustness can be compared fairly and the effectiveness of the two methods proposed in this paper can be validated.  
For text transformations, we set Tense to transform all verb tenses in the input text, SwapSym to replace each word in the input text with probability = 0.5, and other text transformations to transform each word with probability = 0.3.  
For subpopulations, since the maximum text length of GNN-based ED models we use is 50, we screen the original data with text lengths, then generate length \(\leq\) 50 and length \(>\) 50 to evaluate the effect of padding and truncating input text on the model performance. Meanwhile, we use the Perplexity of the GPT-2 language model as a metric to sort the original data, then generate Perplexity-0-50% and Perplexity-0-20% to evaluate the model performance on input text with high Perplexity.  
The data generated by text transformations and subpopulations are collectively called adversarial data. Notice that due to the randomness of text transformations we performed more than 10 runs (conducting text transformations by setting different random seeds) to test the performance of models. The \(t\)-test (significance level \(p\) = 0.05) indicates that there was no statistically significant difference among these 10 test runs, so that we only report one test run result. Table 4 shows the performance of A-MDL-EEGCN, EE-GCN,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **F1** \\ \hline GCN-ED [18] & 77.9 & 68.8 & 73.1 \\ JMEE [19] & 76.3 & 71.3 & 73.7 \\ MOGANED [20] & **79.5** & 72.3 & 75.7 \\ GatedGCN [22] & 78.8 & 76.3 & 77.6 \\ EE-GCN [21] & 76.7 & 78.6 & 77.6 \\ MDL-EEGCN & 78.9 & 75.6 & 77.2 \\ A-EEGCN & 77.6 & 78.4 & 78.0 \\ A-MDL-EEGCN & 78.2 & **78.7** & **78.4** \\ \hline \hline \end{tabular} Bold indicates the highest value.  
\end{table}
Table 3: Performance of each model on the original data.  
and MOGANED on each adversarial data, i.e., the robustness evaluation results of these GNN-based ED models.  
In most cases, it is consistent with the performance on the original data that A-MDL-EEGCN performs best on R while MOGANED on P. The definitions of P and R are both critical, but they are generally contradictory. Therefore, we used composite metric F1 to embody the model's robustness and conducted the following analysis.
## 1 Introduction
The main contributions in this study are as follows:  
* **Proposing a spatio-temporally adaptive variance rescaling strategy based on deep reinforcement learning:** this study develops the DRL-AST method, which enables the spatio-temporal adaptation of the variance of the static **B**-matrix without incurring additional computational costs. This approach improves the adaptability of data assimilation, thereby enhancing analysis quality and forecast skill. * **Improving performance over long-term assimilation periods:** by adaptively rescaling the variance of the **B**-matrix through spatio-temporal optimization, DRL-AST demonstrates competitive robustness and accuracy in long-term assimilation cycles by consistently reducing analysis errors under varying observational noise levels, while maintaining high computational efficiency. * **Improving forecast skill and stability (validated in the Lorenz-96 model):** leveraging a gated recurrent unit (GRU) module to learn historical assimilation states, DRL-AST ensures that the analysis state better aligns with the forecast model's dynamical evolution. In the Lorenz-96 model, the experimental results demonstrate that DRL-AST effectively delays error growth compared to other methods, thereby improving the accuracy and stability of forecasts. The structure of this study is as follows. Section 2 offers a comprehensive overview of related research. Section 3 outlines the necessary preliminaries. In Section 4, we introduce a spatio-temporal adaptive rescaling factor selection strategy for the variance of the **B**-matrix, leveraging deep reinforcement learning. Section 5 presents the experimental setup, results,and analysis. Section 6 discusses the limitations of the proposed approach and potential directions for future research. Finally, Section 7 concludes this study.
## 6 Results
### Request Latency  
We recorded the time taken for the requested content to reach the user and averaged it as the system's request latency to analyze the performance of different cache size allocation schemes from the user's perspective.  
As alpha increases in Figure 8a,b, request latency decreases for all cache size allocation schemes. For larger alphas, user-generated requests are focused on highly popular content that is often cached by nodes. In this case, requests can respond on intermediate or edge nodes closer to the user than on the content server, which reduces transmission latency. And HCSAS has advantages over other solutions.  
As the total budget for cache space in Figure 8c,d increases, request latency decreases at a slower and slower rate. As the total cache budget increases, so do the space allocated by nodes close to users, making it easier for users to get the requested content from nodes close to themselves. The request latency of HCSAS is significantly lower than that of other schemes, which only requires a total cache budget of 0.03 for a 40 ms latency and at least a total cache budget of 0.07 for a BETWEENNESS scheme.  
Figure 8: Performance of Request Latency.  
The HCSAS scheme still performs better than other cache size allocation schemes in reducing request latency.The intermediate nodes are closer to the users than the content servers, so the content transfer takes less time when the request is answered by an intermediate node. Also, the higher the cache hit ratio, the more likely the request will respond in the intermediate node, so the latency will be lower. Request latency is lower when LFU is used by each scheme. As a result, request latencies tend to be the opposite of cache hit ratio. Compared with the other three schemes, the HCSAS scheme has the highest cache hit ratio and therefore the lowest request latency given the cache budget.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 4 Experimental Evaluation
### Results and Discussion
#### 4.3.2 Simulation Outcome  
Figure 5 shows exemplary control actions that brought the simulated low voltage back to the targeted middle band for all three conducted experiments. One can note that for the predictive software PLC configuration, only a little deviation could be seen in the tap switching timing. Nevertheless, timing analysis of the displayed interval showed a maximum distribution delay of 172 ms, but most delays became masked because the transformer was still busy when highly delayed events arrived and the results were not disturbed. In contrast, a maximum distribution delay of only 38 ms was recorded in the hardware controller experiment of Figure 5, but the increased communication delay in the automation system strongly contributed to the observed result.  
Since low voltage readings of the virtual component were transferred and updated upon event occurrence only, intermediate simulation results were solely plotted in the reference simulation. Nevertheless, all control actions were initiated by an event, and hence, predictive event mapping could more accurately sample and communicate the model outputs before it transited to the new steady-state value. Contrarily, periodic event mapping deferred communication to the next synchronization point where the new steady-state value was already established. One can note that, due to late sampling, the last switching operation was omitted, and a steady-state deviation for the periodically synchronized virtual component appeared.
###### Abstract  
Magma-filled disks may feed erupting fissures that lead to alignments of craters developing at the surface, yet the details of activity and migrating eruptions at the crater row are difficult to monitor and are hardly understood. The 2021 Tajogaite eruption at the Cumbre Vieja, La Palma (Spain), lasted 85 days and developed a pronounced alignment of craters that may be related to changes within the volcano edifice. Here, we use COSMO-SkyMed satellite radar data and ground-based time-lapse photographs, offering a high-resolution dataset to explore the locations and characteristics of evolving craters. Our results show that the craters evolve both gradually and suddenly and can be divided into three main phases. Phase 1, lasting the first 6 weeks of the eruption, was characterized by a NW-SE linear evolution of up to seven craters emerging on the growing cone. Following two partial collapses of the cone to the northwest and a seismicity increase at depth, Phase 2 started and caused a propagation of the main activity toward the southeastern side, together with the presence of up to 11 craters along this main NW-SE trend. Associated with strong deep and shallow earthquakes, Phase 3 was initiated and continued for the final 2 weeks of the eruption, expressed by the development of up to 18 craters, which became dominant and clustered in the southeastern sector in early December 2021. In Phase 3, a second and oblique alignment and surface fracture was identified. Our findings that crater and eruption changes coincide together with an increase in seismic activity at depth point to a deep driver leading to crater and morphology changes at the surface. These also suggest that crater distributions might allow for improved monitoring of changes occurring at depth, and vice versa, such that strong seismicity changes at depth may herald the migration and new formation of craters, which have major implications for the assessment of tephra and lava flow hazards on volcanoes.  
remote sensing; SAR; time-lapse camera; volcano monitoring; fissure eruption; crater row; La Palma; Canary Islands +
Footnote †: journal: Remote Sensing  
0022
## 3 Materials and Methods
### Clustering Algorithms
#### 3.3.1 K-Means Clustering  
By definition, the K-means clustering algorithm separates the input data set of \(N\) rows into a smaller number of subsets, where \(k\) is always smaller than \(N\). The parameter \(k\), standing for (centre of cluster mean) is chosen randomly. It calculates averages for each cluster and quantifies the distance between them. This procedure is repeated until the requisite clusters have been achieved. The distance is calculated using the following formula [38],  
\[J=\sum_{i=1}^{m}\sum_{k=1}^{k}w_{ik}\|x_{i}-\mu_{k}\|^{2} \tag{1}\]  
where \(w_{ik}=1\) for data point \(x_{i}\) if it belongs to cluster k; else \(w_{ik}=0\). Also, \(\mu_{k}\) is the centroid of \(x_{i}\)'s cluster.
###### Abstract  
Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and a structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as the input, and outputs the image difference as a residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as the input, and outputs the deblurred version. To more effectively restore image structures such as lines and text, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks.  
inverse halftoning; image decomposition; residual learning; multiresolution +
Footnote †: journal:  
many possible solutions because digital halftoning is a many-to-one mapping. Many studies have been conducted over the last several decades, and various approaches have been introduced based on look-up tables [13], adaptive low-pass filtering [14], maximum-a-posterior estimation [15], local polynomial approximation and intersection of confidence intervals [16], and deconvolution [17]. Recently, machine learning approaches have been actively considered based on dictionary learning [18; 19; 20] and deep convolutional neural networks (DCNNs) [21; 22; 23; 24; 25].
## 5 Performance Results of RACD Strategy
### Simulation Environment
We quantify the performance of the in network caching from two metric aspects:  
**Cache hit ratio**, a metric that measures the ratio between the served content requests and the number of received requests. It also reflects the load saving of the servers, and the delay of the data delivery, since the higher the cache hit ratio is, the faster the data delivery becomes before the user's request reaches the data source. In our case, we study the cache hit of the served data that belong to the university, and also the hit of the other data for both strategies; we can define it as follows:  
\[Cachehitratio=\frac{r}{|R|} \tag{11}\]  
where \(r\) represent the number of requests satisfied by in-network caches, and \(|R|\) is the total number of requests. **The hop reduction ratio** indicates the variation of the data delivery distance in the network. In our study, we investigate the hop reduction ratio of the data belonging to the university against the other data in both tested strategies. The hop reduction ratio is defined as follows:  
\[Hop\;reduction\;ratio=1-\frac{\sum_{r\in R}h_{r}}{\sum_{r\in R}H_{r}} \tag{12}\]  
where \(h_{r}\) and \(H_{r}\) are the hop counts from the requester of \(r\in R\) to the node, which serves the request, and to the original content server, respectively.