esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ERA5 Re-Analysis Dataset  
Using the laws of physics to combine model data with observations from across the world, the European Centre for Medium-Range Weather Forecasts (ECMWF) generates a global complete and consistent re-analysis dataset. In this study, we used the fifth generation of the European Re-analysis dataset (ERA5), which has been proven to be relatively reliable for the analysis of land changes [65; 66]. ERA5 provides hourly estimates at a spatial resolution of 0.25 degrees for a large number of land-surface quantities.  
The surface temperature of air at 2 m (T\({}_{\mathrm{a}}\)), measured in kelvin (K), can be converted to degrees Celsius (\({}^{\circ}\)C) by subtracting 273.15 [67]. The daily mean Ta (in \({}^{\circ}\)C) was obtained by averaging the temperature over 24 h. Similarly, we obtained the daily mean dewpoint temperature (Tdew; in \({}^{\circ}\)C). The vapor pressure deficit (VPD; in kPa) can be calculated by converting Ta and Tdew to the vapor pressure [68]. We used the VPD for the indication of drought conditions. The accumulated solar shortwave radiation (J\(\cdot\)m\({}^{-2}\)) reaching a horizontal plane at the surface of the earth should be divided by the accumulation period, expressed in seconds, and then multiplied by 0.46 to obtain the daily averaged PAR (W\(\cdot\)m\({}^{-2}\)) [69]. The PAR, measured in units of energy (W\(\cdot\)m\({}^{-2}\)), was then converted to units of matter (umol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\)). Similarly, we averaged the climate environmental variables over 8 days to obtain a dataset with 8-day temporal resolution (Figure 2b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Abbreviation**} & \multirow{2}{*}{**Full Name**} & \multicolumn{2}{c}{**Number of Pixels**} \\ \cline{3-4}  & & **Group A** & **Group B** \\ \hline TrEBF & Tropical evergreen broadleaf forest & — & 3848 \\ TrDBF & Tropical deciduous broadleaf forest & — & 993 \\ TeENF & Temperate evergreen needleaf forest & 321 & — \\ TeEBF & Temperate evergreen broadleaf forest & — & 348 \\ TeDBF & Temperate deciduous broadleaf forest & 660 & — \\ BoENF & Boreal evergreen needleaf forest & 620 & — \\ BoDBF & Boreal deciduous broadleaf forest & 826 & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of the selected homogeneous samples for each PFT. A and B represent the different groups. The horizonal bar indicates no samples.  
Figure 1: Spatial variations in yearly average of: (**a**) the daily aggregated FLUCOM GPP products (in unit of gC\(\cdot\)m\({}^{-2}\).d\({}^{-1}\)); and (**b**) the daily corrected TROPOMI SIF (in unit of mW\(\cdot\)m\({}^{-2}\).nm\({}^{-1}\).sr\({}^{-1}\)) for the year 2020. The blue line represents the latitude mean values, and the shaded area represents the 90% confidence intervals.
## 3 Study Areas  
We focus here on two areas of interest (AOI), one in the west and one in the east of Ireland (Figure 3). AOI-1 is a ~1800 km\({}^{2}\) area spanning north County Clare and east County Galway. This area contains a wide variety of landscapes from urban to rural. The former includes the city of Galway (population ~80,000), while the latter includes grassland, forest, peatland, rock outcrop or combinations of these. AOI-2 is a ~100 km\({}^{2}\) area covering the Tara mine site in Co. Meath, 40 km northwest of Dublin. The landscape in AOI-2 also varies from urban and rural, with the former represented by the town of Navan (population ~30,000) and the land-cover of the latter characterized mainly by agricultural land.  
The geology of both AOIs is dominated by Carboniferous limestones (Figures 4a and 5a). In AOI-1, the underlying limestones are relatively pure, and so karst landforms are especially well developed there. AOI-1 includes the Burren and Cliffs of Moher UNESCO Global Geopark. The Burren is an area of spectacular upland karst characterized by expanses of bare limestone rock and minimal surface drainage. Much of the rest of AOI-1 is mantled lowland karst, which includes the ecologically sensitive Gort Lowlands [45]. AOI-1 also has bedrock composed of (1) Devonian granite west of Galway city, (2) Devonian mudstone, siltstone and conglomerate south east of Gort and (3) Carboniferous shale and sandstone sequences around the Cliffs of Moher. Pleistocene and Holocene superficial deposits overlying the bedrock mainly comprise glacial till, as well as lacustrine deposits, alluvial sands and gravels, and peat. The bedrock of AOI-2 (Figure 5a) is mainly composed of (1) Carboniferous muddy limestones (2) Devonian syenite intrusions (3) Silurian mudstones, siltstones and greywacke and (4) Ordovician tuffs, muds, silt, and volcanics. The Tara mine in AOI-2 is the largest operational lead-zinc mine in Europe. The mining activity follows mineralization in a 'lower group' of Carboniferous limestones, which occur at depths increasing from 50 m below sea level (b.s.l.) to 1900 m b.s.l. towards  
Figure 3: Locations of the study areas (white polygons). The footprints of the Sentinel-1A/B images used in this study are marked with the light blue and yellow squares, respectively for the descending and ascending tracks. The sketch illustrates the satellite’s acquisition direction in respect to the ascending (**left**) and descending (**right**) orbits. Black lines are the Irish County boundaries. Base image source: Esri, DigitalGlobe, GeoEye, Earth Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community.  
the southwest. Solid waste arising from metal extraction is gathered in a Tailing Storage Facility (TSF) located 2 km north of the mining area (Figure 5b).
## 4 Results and Discussion
### Experimental Settings
#### 4.1.3 Metrics  
We formulated five metrics to compare the performance of FedDeep and the baselines. These can be divided into two categories: (1) mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (\(R^{2}\)) were used to evaluate the prediction accuracy. (2) We tested the model efficiency through the training time and GPU memory. Details of the five metrics are described below:  
(a) Mean absolute error (MAE):  
\[MAE=\frac{1}{n_{i}\times Q}\sum_{s_{n}=1}^{n_{i}}\sum_{t_{q}=1}^{Q}\Bigl{|}y_{s _{n},t_{q}}-\hat{y}_{s_{n},t_{q}}\Bigr{|}. \tag{12}\]  
(b) Root mean squared error (RMSE):  
\[RMSE=\sqrt{\frac{1}{n_{i}\times Q}\sum_{s_{n}=1}^{n_{i}}\sum_{t_{q}=1}^{Q} \Bigl{(}y_{s_{n},t_{q}}-\hat{y}_{s_{n},t_{q}}\Bigr{)}^{2}}. \tag{13}\]  
(c) Coefficient of determination (\(R^{2}\)):  
\[R^{2}=1-\frac{\sum_{s_{n}=1}^{n_{i}}\sum_{t_{q}=1}^{Q}\Bigl{(}y_{s_{n},t_{q}}- \hat{y}_{s_{n},t_{q}}\Bigr{)}^{2}}{\sum_{s_{n}=1}^{n_{i}}\sum_{t_{q}=1}^{Q} \Bigl{(}y_{s_{n},t_{q}}-\hat{y}\Bigr{)}^{2}}. \tag{14}\]  
where \(\hat{y}\) denotes the average of the observed values.  
(d) Training time: Training time (TT) refers to the total time overhead throughout the training process till the model implemented a satisfactory accuracy. We could measure the efficiency of training using TT. For example, if prediction accuracies are at the same level, the models with shorter training time are more efficient.  
(e) GPU Memory: This was used as the spatial overhead of the model, denoted as GPUM; that is, the consumption of GPU memory during training. For instance, a model with smaller usage of GPU memory has fewer learnable parameters and only demands a minimal amount of computational resources, which reflects a higher efficiency.
### Sensing Platforms for the Mapping of Burned Areas  
First approaches to burned area mapping were recorded by [PERSON] in 1920 for province-wide forest surveys [32] and consisted of aerial sketching of the fire perimeter. Further developments consisted of the fire perimeter GPS acquisition by field tailing or by helicopter hoovering [33]. Nevertheless, this methodology lacked in accuracy in topographically dissected areas and exposed the field personnel to risks related to recently burned areas. These criticalities were overcome with the deployment of Landsat 1 in 1972, allowing for the accurate definition of recent forest fire boundaries and spectral class discrimination [14; 34]. The academic research about post-fire area detection through multispectral satellite remote sensing is well developed and exhaustive [22; 35; 36; 37; 38; 39; 40; 41; 42].  
Figure 1: Sentinel-2 bands and their spectral sampling intervals for VNIR (400–1000 nm) and SWIR (1000–2500 nm) [21].  
Currently, burned area analysis and mapping is usually performed through satellite remotely sensed data, providing efficient and reliable results for data collected over clear portions of sky. This can be done for fires with an extent greater than the sensor spatial resolution and for areas with a consistent aspect and morphology [14]. In recent years, additional development for burned area mapping came with the implementation of UAV methodologies, i.e., unmanned aerial vehicles with remote piloting. When compared to satellite sensing, this new platform allows for greater spatial and temporal resolution, increasing the mapping accuracy for uneven burning paths and fire perimeter areas [7; 43; 44]. Depending on the purpose of the satellite, different platforms will have different sensors able to take advantage of the objective spectral response.
## 3 Numerical Examples
### Influences of Various Construction Factors of the Unit Cell on the Band Structures
#### 3.4.3 Effect of the Unit-Cell Configuration on the Band Properties  
Just as the comparisons of the results in Section 3.3 have indicated, the five bi-coupled periodic beams have quantitatively and slightly different dispersion curves and band properties, which exactly reflect the influence of the unit-cell configuration. Table 3 lists the bounding frequencies of the first 10 pass-bands, and the bounding frequencies of the first 10 stop-bands can be easily written out accordingly. Figure 14 shows the contrast of the central frequencies and the bandwidths of these pass-bands as the unit-cell configuration is the observing factor.  
Figure 14: The contrast of the first 10 pass-bands properties for the five exemplified bi-coupled periodic multi-component beams. (**a**) the central frequencies; (**b**) the frequency bandwidths.  
Figure 13: Effect of the mass density of constituent beam on the band structures in the bi-coupled periodic beam with Ternary-I unit cell.  
Figure 14 infers that the minimum bandwidth appears at the sixth pass-band for all the five periodic beams. From Table 3 and Figure 14, we see that the number and arrangement of constituent beams in the unit cell can be used to adjust the quantitative properties of the frequency bands, which provides an effective way for designing and optimizing bi-coupled periodic multi-component beams according to specific wave-controlling requirements in engineering practice.
## 3 Design of the EAS
### Detailed Structure of the MCycle GAN  
A cycle takes data from the Clean Sound domain \(x\) as input and produces two output data types:  
Output 1: \(G_{xy}(x)=\hat{y}\)  
Output 2: \(G_{yx}(\hat{y})=\hat{x}\)  
Here, \(\hat{x}\) and \(\hat{y}\) refer to Fake Domain X data and Fake Domain Y data generated by the generators, respectively. Output 1, \(\hat{y}\), are the Fake Domain Y data generated by the generator \(G_{xy}\) from an arbitrarily selected data point, \(x\), in Domain X. Output 2, \(\hat{x}\), is the Fake Domain X data generated by the generator \(G_{yx}\) from the output data, \(\hat{y}\), of Output 1.  
MCycle GAN has a structure that uses multiple cycles, as shown in Figure 3. Multiple cycles receive arbitrary Domain X data, \(x\), as input in parallel. Among the outputs of each cycle, the Fake Domain Y data, \(\hat{y}\), are used as the input to the discriminator, \(D_{y}\), and the Fake Domain X data, \(\hat{x}\), are used as the input to the discriminator, \(D_{x}\). \(D_{y}\) receives either the Fake data, \(\hat{y}\), or the Real data, \(y\), as input and outputs of the probability that the data are real. Similarly, \(D_{x}\) receives \(\hat{x}\) and \(x\) as inputs and outputs of the probability that the input data are real.  
During training, the weights of the generators in each cycle, as well as the discriminators \(D_{x}\) and \(D_{y}\), are updated according to the MCycle Loss. When the discrimination accuracy of \(D_{x}\) and \(D_{y}\) reaches 50%, the training is complete. Ultimately, the cycle that best fools the discriminator is considered the most effective, and that cycle is selected for use.  
In this paper, \(G_{xy}\), one of the components of the cycle, is the generator that takes clean sound data as input and generates noisy sound data, while \(G_{yx}\) is its inverse. Therefore, to generate sound data with the noise removed, \(G_{yx}\) is used as the final noise reduction model.  
The generator of MCycle GAN uses a ResNet-based CNN model to preserve the details of the input data and prevent bottlenecks in the data [17]. The generator is trained in the direction of reducing the Adversarial Loss. A smaller Adversarial Loss indicates that the generator is better at fooling the discriminator.  
The discriminator uses a CNN-based image classification model with Leaky ReLU as the activation function. The discriminator trained with Leaky ReLU can pass stronger gradients to the generator compared to ReLU [18].
# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 4 Potential Way Forward to 6G
This is to say that factors such as network resource scheduling, round trip duration, throughput, handover management, etc. are essential for a higher level of autonomous driving. Even though centralized broadcast scheduling guarantees excellent broadcast reliability, it is burdened by signalling overheads caused by dynamically changing vehicle positioning and network resource demands. Therefore, the distribution version of resource scheduling implanted with autonomous behaviour provides improved scalability, which is also reflected in enhanced sidelink transmission, carrier aggregation, 5 GNR, support for unicast and multicast, etc. of 3 GPP Release 15 and 16 [35]. The improvement in URLLC, positioning precision, and reliability over 6G is further aided by 3 GPP Release 17. The pervasive network intelligence made possible by 6G is anticipated to offer greater service diversity and flexible service configuration. AI-enabled solutions for service composition can address issues with service-oriented networks, such as huge data exploitation through the use of big data approaches and service identification through intent-driven networking. [PERSON] et al. [36] investigate the support for vehicle networks in the envisioned integrated space-air-ground and emphasize the need for integrated communication to support the requirements of various vehicular services, particularly in cases of frequent handovers. The authors highlighted a variety of challenges preventing the implementation of such integrated communication systems, including those of heterogeneous management,  
Figure 3: Key requirements and characteristics of 6G. dynamic networking, service quality, etc. In another, research the research literature survey of 6G SAGINs from a service-oriented network perspective was carried out by [PERSON] et al. [37]. The authors provide a brief overview of the various SAGIN types, which include Cooperative satellite-terrestrial networks, Cognitive satellite-terrestrial networks, Hybrid satellite-terrestrial relay networks, and Satellite-terrestrial Backhaul Networks. The advantages of SAGINs for autonomous driving were also underlined by the authors, including LEO's low cost and ubiquitous connection, the wide carrier coverage that reduces handovers, caching of the pertinent contents in UAVs, etc.
# Dynamic Load Redistribution of Power CPS Based on Comprehensive Index of Coupling Node Pairs
## 4 Simulation Analysis
### Node Survival Rate under Different Attack Methods  
#### 4.3.1 High-Degree Nodes Attacks  
The simulation is compared to the existing redistribution methods in the literature, and the number of attacked nodes on the cyber side is increased sequentially by using the high-degree of nodes attack mode.  
As can be seen from Figure 11, when the number of attacks is less than 6, the number of remaining nodes under both redistribution methods shows an obvious decreasing trend, and when the number of attacks exceeds 6, the system under the method of this paper still has more than 30% of surviving nodes, while the system under the other methods is close to 0. When the number of attacks is 7, the system under the other methods has completely failed, while the system under the method of this paper still has more than 20% of the  
Figure 10: Node pair utilization index.  
Figure 9: Active-business degrees index.  
survivability of the system, and it only shows a slow decreasing trend as the number of attacks gradually increases.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 4 Poly(3,4-ethylenedioxythiophene)-Based Electrodes
### 1D Nanostructures  
[PERSON] et al. synthesized carboxylated PEDOT nanotubes using microemulsion polymerization as templates for immobilizing human dopamine receptor nanovesicles [122] that were used to coat extremely sensitive PEDOT-based dopamine sensors. These sensors had a low limit of detection of 10 fM. Commercial PEDOT:PSS offers the advantage of easy processing, and is integral to the formation of 1D structures via numerous manufacturing methods. [PERSON] et al. generated PEDOT:PSS/silk fibroin core/shell wires for detecting ascorbic acid using wet-spinning and photo-crosslinking methods (Figure 9a) [123]. [PERSON] et al. fabricated PEDOT:PSS nanowires via soft-lithography before introducing biotin-derivatized poly(L-lysine)-grafted oligo-ethylene glycol (PLL-g-OEG-Biotin) into the doped polymer chain [124]. The PEDOT:PSS-based nanowires were ideal electrochemical biosensors for detecting streptavidin. Moreover, PEDOT offers the advantage of expanded functionality since the surface of the 1D materials can be covered using PEDOT-coated PAN nanofibers via the VDP of EDOT. Next, the PAN/PEDOT core/shell nanofibers were employed as electrodes for electrochemical glucose sensors [125]. 1D PEDOT nanostructures were also fabricated using a simple electrochemical polymerization method; in this case, [PERSON] et al. synthesized Au-hemoglobin-PEDOT nanowhisker composites for H\({}_{2}\)O\({}_{2}\) detection (Figure 9b) [126].
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Continuous Learning and Adaptability  
#### 5.5.1 Challenges  
In the healthcare sector, AI applications face challenges related to continuous learning and adaptability. Although AI systems can be initially trained on large datasets, maintaining long-term relevance and accuracy remains problematic due to the complexity and constant evolution of medical practices [122]. For instance, the emergence of COVID-19 highlighted the need for AI diagnostic tools to rapidly adapt to new SARS-CoV-2 variants and evolving treatment protocols. Similarly, the rapid development of targeted cancer immunotherapies, such as CAR-T cell therapies, requires AI models to be continuously updated to ensure they reflect the latest clinical evidence and therapeutic guidelines, preventing potential misdiagnoses. IBM Watson Health demonstrates the importance of ongoing model refinement by integrating real-time updates from the latest medical research and clinical trial results into its decision-support systems. Additionally, the adoption of advanced surgical techniques, such as robot-assisted minimally invasive surgeries, necessitates that AI models incorporate new procedural data and outcomes to provide current and relevant guidance [123]. These examples underscore the critical need for ongoing model optimization and updates in the healthcare environment to sustain the effectiveness and reliability of AI systems.
###### Abstract  
This study aimed to investigate the impact of convective and vacuum drying performed at different temperatures on the content of bioactive components of kiwiberry. Dried fruits obtained from Geneva and Weiki cultivars were analyzed for total carotenoid content, total phenolic content (TPC), and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS) assays. The secondary goal was to establish drying kinetics and to find the best-fitting model for the drying process. The results showed that the highest total carotenoid content was found in Geneva fruits dried by vacuum method and was equal to 39.55-90.27 ug/g dry matter (d.m.). Considering free radical scavenging activity, the best results were also achieved for vacuum dried Geneva cultivar. These samples exhibited EC50 equal to 0.16-0.51 mg d.m./mL and 0.05-0.24 mg d.m./mL as evaluated using DPPH and ABTSS assays, respectively. Vacuum drying method usually better preserved the phenolic content of kiwiberry--samples dried at 50 \({}^{\circ}\)C did not differ significantly from fresh material. Generally, the shortest drying time was observed for the samples dried at 70 \({}^{\circ}\)C regardless of the drying method. Changing the temperature during drying from 80 to 50 and 60 \({}^{\circ}\)C did not cause expected benefits regarding chemical property preservation. In most cases, the [PERSON] et al. model represented the best fit to describe obtained drying kinetics.  
Actinidia arguta; carotenoids; antioxidant capacity; drying kinetics 2020 accepted: 10.3390/app10196914