esa-sceva/satcom-chunk-collection huggingface dataset:
### Recent Advances in Time Series Prediction for Solar Forecasting  
Currently, DL architectures such as artificial neural networks (ANNs) and LSTM have shown promising results in short-term solar energy forecasting [32]. [PERSON] et al. [33] demonstrated that variational mode decomposition (VMD) combined with neural networks reduces root mean square error (RMSE) by 31.8% compared to MEMD-LSTM. Similarly, the multi-functional recurrent fuzzy neural network (MFRFNN) [34] achieved a 9.4% improvement in mean absolute percentage error (MAPE) for chaotic time series. In addition, [PERSON] et al. [35] reviewed DL methods such as LSTM, GRU, and hybrid models, emphasizing their effectiveness for short-term predictions and the role of techniques such as wavelet decomposition. However, these approaches focus on generic systems and do not address the high variability of solar data in mountainous regions or long-term horizons like 24 h.  
Our work aims to address the gaps identified in Table 1, specifically on the challenges of medium-term forecasting under high variability conditions in mountainous regions. While several studies employ advanced architectures, such as transformers, hybrid LSTM models, and attention mechanisms, these may lack robust preprocessing techniques such as variable decomposition or resampling, which are critical for capturing the inherent variability in solar irradiance data. Our approach integrates these preprocessing methods, allowing the model to handle complex weather conditions more effectively. Furthermore, by incorporating additional meteorological variables and seasonal splits, our model also improves its applicability in real scenarios, especially in regions with high climate variability. The aim of this contribution is to integrate clean energy into the grid by enabling a stable energy supply and facilitating demand management, especially in complex environments.
## 2 Materials and Methods
### Finite Element Model of Forming Process  
The numerical model was developed through the MSC Marc(r) software package. The test consisted of stretching a sheet of AA6060 through a hemispherical punch. Considering the symmetry in geometry, load and constraints, the test was simulated by adopting 4-noded iso-parametric elements with bilinear interpolation. The developed algorithm for finite element analysis was developed for simplified problems, such as plane strain or axisymmetric elements. For this reason, it is possible to consider only the inner and outer profiles of the stretched sheet that were discretized by adopting 4-noded iso-parametric elements with bilinear interpolation (see Figure 1).  
Figure 2 shows some steps of the forming process; it represents half of the examined problem. It is possible to see the sheet, the punch, and the die on which the sheet is positioned. The sheet was discretized through two rows of 166 elements (therefore, the size of each single element was 0.5 mm \(\times\) 0.515 mm). The nodes on the outer edge of the sheet were constrained in such a way that they cannot move along the direction orthogonal to the symmetry axis to simulate the presence of a blank holder. The external node in contact with the die was locked along the direction of the symmetry axis. The sheet nodes that were on the symmetry axis could not move orthogonally. The punch and die were rigid.  
An implicit non-linear finite element code was used to simulate the metal sheet stretching process. The [PERSON] criterion and the isotropic yield criterion were used.  
The constitutive law of the metal sheet was determined, starting from the results of tensile tests that are analysed in detail by [PERSON] et al. (2019). The material was modelled in the plastic field through a constitutive equation expressed by the power law:  
\[\sigma=K\epsilon^{n} \tag{1}\]  
where \(\sigma\) and \(\epsilon\) are the equivalent stress and strain, respectively, \(K\) is the resistance coefficient, and \(n\) is the material hardening index.  
Contact among bodies is managed by a suitable routine. Furthermore, the frictions among sheet, punch and die were considered by introducing the modified Coulomb friction model. In this model the relationship between the tangential \(f_{t}\) and the normal \(f_{n}\) forces is due to:  
\[f_{t}=uf_{n}\bigg{(}\frac{2}{\pi}\bigg{)}arctan\bigg{(}\frac{v_{r}}{R_{sp}} \bigg{)} \tag{2}\]  
Figure 1: Sketch of discretized sheet before (**on the left side**) and after stretching (**on the right side**).  
Figure 2: Stretching process steps: punch stroke of (**a**) 0 mm, (**b**) 10 mm, (**c**) 20 mm and (**d**) breakage.  
where \(v_{r}\) is the relative slip speed and \(R_{s0}\) is the relative slip speed under which the friction force tends to zero. Simulations were carried out with a value of the friction coefficient \(\mu\) between 0 and 0.2.
# Effect of Food Emulsifiers on Aroma Release
## 3 Materials and Methods  
### Reagents and Reference Samples  
Three different emulsifiers were used in this study, namely, Tween 80, soybean lecithin, and SE-15 sucrose fatty acid ester. Tween 80 was supplied by Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China), soybean lecithin was supplied by Biosharp Company (Hefei, China), and SE-15 sucrose fatty acid ester was supplied by Hangzhou Rui Lin Chemical Co. Ltd. (Hangzhou, China). Seven different aroma compounds were used in this study, namely, 3-carene, ethyl acetate, perillaldehyde, (\(E\))-2-hexenal, carvone, \(\alpha\)-pinene (Sigma Aldrich, Saint Louis, MO, USA), and limonene (Tokyo Chemical Industry, Tokyo, Japan). Xanthan gum (Shanghai Jingchun Biochemical Technology Co., Ltd., Shanghai, China) was used to prepare different emulsifier systems to which the thickener was added. Anhydrodous ethanol (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) was added to dissolve the aroma compounds.
## 3 Experiments and Results
### Overall Analysis  
In the overall analysis phase, the costs were normalized using the _success rate_ (SR). The SR is calculated as the ratio between the number of ants that successfully exited and the total number of ants involved in the exploration. This normalization provides a fair and informative perspective, facilitating an accurate assessment of the performance relative to the overall success of ants in completing the exploration of the virtual environment. The tables below present the obtained results based on two evaluation metrics: the success rate at the top of Table 2 and the cost of the discovered paths at the bottom. These values represent the averages across 10 simulations. The values in bold represent the best results for each evaluation metric.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline _Variable_ & _Description_ \\ \hline \(w(i,j)\) & weight of an edge \\ \hline \(p_{ij}^{k}(t)\) & transition probability of the ants \\ \hline \(\tau_{ij}(t)\) & pheromone intensity on the edge \\ \hline \(\eta_{ij}(t)\) & desirability of an edge \\ \hline \(l_{ij}(t)\) & information about the weight of an edge \\ \hline \(\alpha\) & importance of pheromone intensity \\ \hline \(\beta\) & importance of desirability \\ \hline \(K\) & quantity of pheromone intensity left by ants \\ \hline \(\rho\) & evaporation rate \\ \hline \(n\) & number of ants \\ \hline \(g\) & number of groups \\ \hline \(n_{g}\) & number of ants per group \\ \hline \(\varpi_{p}^{k}\) & prior knowledge of an ant \\ \hline \(\varpi_{l}^{k}\) & local knowledge of an ant \\ \hline \(\tilde{w}\) & global knowledge of an ant \\ \hline \(T_{e}\) & exit interval between groups \\ \hline \(T_{i}\) & maximum time to reach the exit for \(i\)th group \\ \hline \(T_{max}\) & maximum time of simulations \\ \hline \end{tabular}
\end{table}
Table 1: Description of variables and parameters used in the proposed model.  
Examining the success rate in Table 2, it is observed that at \(\rho=0.01\) optimization occurs with 10 groups, while at \(\rho=0.001\) optimization shifts to 20 groups. Regarding the path cost at the bottom of Table 2, it is noted that, in general, it decreases with an increase in the number of groups. However, the optimal value is at 10 groups for \(\rho=0.01\) and 20 groups for \(\rho=0.001\).
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Magnitude Estimation Results in the First 3 s  
Although waveform estimation of magnitude may be limited in the early stage of a large earthquake due to incomplete rupture, 3 s of seismic recordings is still considered a reasonable starting point for magnitude estimation. This is because most earthquakes with magnitudes less than six usually have rupture half-durations shorter than 3 s [22]. Therefore, it is reasonable to believe that in most cases, using a 3 s seismic record, we can make a preliminary judgment on whether the magnitude of an earthquake will exceed magnitude 6, and even through deep learning methods, the model can capture the magnitude estimation features in the waveforms that are associated with large earthquakes [9; 23].  
Figure 4 depicts the magnitude predictions for the initial 3 s. In the first second, when the magnitude is below 4.0, the Exams model demonstrates relatively good performance. The error between its prediction and the true value remains fairly small. However, as the magnitude increases, the scenario changes.  
For earthquakes with magnitudes ranging from 4.0 to 5.0, the prediction errors of all four models decrease. Paradoxically, the performance of the Exams model deteriorates, and the gap between it and the other models widens. When the magnitude surpasses 5.0, the DCRNN and DCRNNAmp models exhibit distinct advantages over the others.  
Specifically, in the magnitude range of 6.5 to 7.5, all models significantly underestimate the magnitude. Nevertheless, it is notable that the DCRNN and DCRNNAmp models still yield the best results when the magnitude exceeds 6.5. Although the overall trends of these two models are similar, upon closer inspection, some subtle differences emerge. After the magnitude exceeds 7.0, the DCRNNAmp model shows slightly better stability, with a relatively smaller curve fluctuation. In contrast, the Exams model performs the worst in this magnitude range. The difference between its predicted and true values is substantial, and the curve fluctuates more erratically, highlighting its limitations in handling high-magnitude earthquakes.  
As the prediction time window extends, the mean error values of all models display a downward trend, and the differences among the models gradually diminish. However, the DCRNN and DCRNNAmp models still outperform the other two. This indicates that they possess higher accuracy and stability when dealing with magnitude estimation tasks for seismic events of varying magnitudes.  
This finding holds great significance for enhancing the reliability and accuracy of earthquake early-warning systems. In particular, the performance disparity between the DCRNN and DCRNNAmp models in the high-magnitude interval offers a valuable reference for further model optimization. By capitalizing on their respective characteristics, targeted improvements can be made to these two models, thereby better enhancing the performance of the earthquake early-warning system across different magnitude scenarios.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 3 Flight Safety Framework
### Setup Implementation  
The basic sensor device (\"node\") was built from a microprocessor, a radio transceiver, an optional GPS, a battery, and a solar panel for charging the batteries (Figure 4). Each node had the capability of sending data such as its location, neighbors, movements, and health to a nearby Ground Station (GS). The GS was a gateway application connected to the cloud in some way, and its function was to collect the nodes' data and upload them to a cloud database for later use and analysis. On the other hand, using a UI system connected to the GS via the IoT, messages could be transmitted to the various nodes in the network. Since the transmission of such a node was limited to some range, a node could be operated as a relay and retransmit other nodes' messages, allowing two unreachable nodes to communicate with each other. Many kinds of radio transceivers modules could be used by a node. However, a transceiver was required to communicate over a long distance while maintaining low energy to allow the node to operate over time. We proposed two setups that utilized the node device, and we next describe possible solutions to the related problems.
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 4 Experimental Results
### Ablation Study  
We summarize the performance of the proposed methods based on ranking metrics and datasets, as shown in Figure 11. For any dataset, the SSIM rank metric works much better than the CCM metric. Likewise, the recognition results are higher for the UTD-MHAD dataset than for the DHA and MSR-Action3D datasets with the SSIM and CCM ranking metrics, because the action sequences in the UTD-MHAD dataset are more accurately collected.  
Figure 8: Confusion chart of the DHA dataset (best results with SSIM, \(k=24\)).  
Figure 10: Confusion chart of UTD-MHAD dataset (best results with SSIM, \(k=24\)).  
Figure 9: Confusion chart of the MSR-Action3D dataset (best results with SSIM, \(k=24\)).  
To show the effectiveness of the number of key-frames sampling on the recognition performance, we additionally perform experiments with 28, 32, and 36 frames using the UTD-MHAD dataset. Figure 12 depicts the recognition results for different rank values ranging from 16 to 36 with a difference of 4 frames. The recognition accuracy is 94.2% for 16-ranked frames, which improves slightly by about 0.2% while increasing the number of key-frames to the next level 20. From 20 to 36, the classification results remain almost the same, or a slight change happened. This is because only a certain segment in a sequence contains action information, and other frames are static that do not have any effect on the overall recognition performance.
## 3 Result and Discussion
### Work of Rupture  
The work of rupture is a measurement of a fiber's ability to absorb energy and its ability to endure a sudden shock. Strong fibers are inherently inextensible, and vice versa; therefore, a comparatively weak fiber may have a larger work of rupture than a very strong fiber. As shown in Table 2, the work of fracture in terms of work conducted for the fiber portions EV-I, EV-II, EV-III, and EV-IV were 1683.2 \(\pm\) 913.07, 1854.4 \(\pm\) 837, 1669.4 \(\pm\) 937.60, and 1008.4 \(\pm\) 834.01, and 1008.4 \(\pm\) 834.089 cN*mm, respectively. The work of rupture increased by 10.17% around the second section (EV-II), slightly decreased by 9.9% (EV-III), and dramatically declined by 65.54% at the top portion (EV-IV), as shown in Figure 7. Remarkably, the work of rupture observations followed the same pattern as the tenacity results. The stress-strain curves are shown in Figure 8. The fibers deviate significantly from a linear relationship, but the concept of the work of rupture as one half the product of load and extension at break can be preserved by introducing a \"work factor\", which is the ratio of the actual work of rupture to the product of load and extension at break. Concerning the work factor, the work of rupture was measured automatically from the Tenso lab machines as soon as the fiber failure occurred, and the work factor was calculated for each portion of the fiber samples. The work factor will be more than 0.5 if the load-elongation curve lies mainly above the straight line; if below, it will be less than 0.5. In this regard, [PERSON] and [PERSON] reported that most past fibers have a work factor of around 0.5 [33].  
Figure 7: Typical work of rupture of Enset fibers for the fiber sections.
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 2 Materials and Methods
### Case Study: Materials, Methods and Processing
#### 2.2.2 Case Study 2  
The acquisition test was performed on 25 June 2020, and lasted seven hours, starting from 7:40 a.m. until 2:40 p.m. UTC. The chosen location for this case study was the Genoa University rooftop, and the GNSS receivers involved were GENU, which belongs to the Regione Liguria GNSS CORSs network, and a Xiaomi Mi 8 (referred to as GESM for this case study) smartphone. GENU, used as a reference, is a geodetic-level multi-frequency (L1 and L2) and multi-constellations receiver. The smartphone used in this case study was the same model as the one used for Case study 1. The GEO++ RINEX logger app was also employed for the processing of this dataset.  
Figure 1 shows the GNSS receiver layout used in this case study: the GENU CORS is on the left upper corner of the rooftop, while the smartphone is placed a few meters away.
## 6 Evaluation
### Evaluation Based on the Questionnaire Survey  
#### 6.1.1 Overview of the Questionnaire Survey  
A questionnaire survey was conducted to (1) evaluate the compatibility with the method to obtain information, (2) evaluate the originality of the system, and (3) evaluate the usefulness of the overall system, according to the purpose of the present study. The questionnaire survey was conducted on the website 1 week after the start of the operation. Table 1 also includes the overview of the questionnaire survey respondents. As shown in Table 1, the response rate was 100%, with all 60 users responding to the questionnaire survey. Additionally, regarding the answering tendency, there was no difference between different age groups.  
Regarding the frequency of visits to Chofu City, 30% answered that they had \"never visited\", 20% answered they \"commute to Chofu City for work or school\", 18% answered they \"live in Chofu City\", and 32% answered they \"visit a few times a week\". Therefore, it can be said that over 60% of the users did not have a sense of locality, as they did not frequently visit Chofu City. Additionally, regarding the disaster support facilities within Chofu City, 78% answered that they \"don't know at all\" or \"don't know much\", and 22% answered they \"know a little\" or \"know very well\". Therefore, it was evident that many users did not have sufficient knowledge concerning disaster support facilities within Chofu City.
## 3 Results and Discussion
### Thermal Conductivity and Stability  
Figure 5 shows tensile strength and thermal conductivity of the bare and the coated quartz fibers. The tensile strength of Al\({}_{2}\)O\({}_{3}\)-coated quartz fiber was 58.1 MPa, compared to 19.2 MPa of the bare fiber. Obviously, the tensile strength of quartz fiber was greatly improved by the Al\({}_{2}\)O\({}_{3}\) coating. The thermal conductivities of coated sample was 1.17 W m\({}^{-1}\) K\({}^{-1}\), increased by \(\sim\)45% compared with the bare sample. The obvious increase of the thermal conductivity mainly resulted from the relatively high conductivity of the Al\({}_{2}\)O\({}_{3}\) coating. Meanwhile, the Al\({}_{2}\)O\({}_{3}\) coating also protected the quartz fiber at elevated temperatures.  
Figure 4: AFM surface topographic images of the bare (**a**,**b**) and the coated (**c**,**d**) quartz fibers.  
Figure 3: FTIR (**a**) and XPS (**b**) spectra of the coated quartz fiber.
## 6 Experiments
### Experiment Results  
#### 6.2.1 Comparing CEMC and ICE  
Table 1 shows performance results for ICE and CEMC. \"-\" indicates unknown values, caused by getting the wrong results or a time limit violation (less than 3600 s is valid). \"*\" indicates out of memory. Table 1 clearly shows that CEMC outperforms the state-of-the-art heuristic algorithm with Q\({}_{\text{BG}}\). Especially in solving scalable problems such as Grid-Small, Box Pushing, and Mars Rovers, CEMC shows significant improvement without computing and storing all Q-values. CEMC allows for getting the needed Q-value to guide A* search without pre-computation of all Q-value function.  
\begin{table}
\begin{tabular}{c c c c c c c}  & & & & & & & \\ \hline \multicolumn{6}{c}{**(a)**} \\ \hline \(h\) & **Algo** & \(t_{Q}\)**(s)** & \(t_{p}\)**(s)** & \(t_{o}\)**(s)** & \(V^{*}\) & \(V_{r}\) \\ \hline \multicolumn{6}{c}{**Dec-Tiger**} \\ \hline \multirow{3}{*}{2} & ICE & \textless{}0.01 & \textless{}0.01 & 0.27 & \textless{}4.00 & \textless{}93.05 \\  & CEMC & 0 & \textless{}0.01 & 0.27 & \textless{}4.00 & \textless{}93.05 \\  & ICE & 0 & 0.06 & 0.02 & 0.43 & 5.19 & \textless{}140.81 \\  & CEMC & 0 & 0.06 & 0.28 & 5.19 & \textless{}140.81 \\  & ICE & - & - & - & - & - \\  & CEMC & 0 & 65.08 & 65.36 & 4.80 & \textless{}186.67 \\ \hline \end{tabular}
\end{table}
Table 1: Comparable results of some benchmarks.  
From Table 1 we can see that in some problems like Grid-Small, Box Pushing, and Mars Rovers, CEMC is significantly better than ICE, while in some problems like Dec-Tiger, Recycling Robots, and Broadcast Channel the difference between both may not be as obvious as the three previous ones. Actually, the number of joint decision rules for time step \(t\) in the search tree is  
\[\text{O}\big{(}|A|^{n(|\mathcal{O}|^{t})}\big{)} \tag{16}\]  
which means the complexity is exponential with the number of actions and observations, and doubly exponential with the time step. The search tree contains the action-observation histories, the actions taken, and the observations received. Therefore, the number of branches is related to the action and observation space. Although the domain Mars Rovers has a larger state space, it has little influence on the scale of the search tree. What it influences is the efficiency of state transition and the computing time of the probability like \(P(o|a,s^{\prime})\).
## 3 Results and Discussion
### Determination of Hardly Grinding Phases (HGP) in Converter Steel Slag
#### 3.2.2 Morphology and Vickers Hardness of HGP in Converter Steel Slag  
As shown in Figure 4, the morphology of HGP in converter steel slag mainly shows three kinds of grey levels during the initial grinding period (20 min grinding time), while basically exhibiting one grey level during the middle-later grinding periods (i.e., 50 min and 70 min grinding times). Based on the identification and analysis results of mineral phases in Section 3.1, it can be known that the mineral phases with three kinds of grey levels are mainly the RO phase, calcium ferrite, and metallic iron phase. So, the HGP of converter steel slag during the initial grinding period is composed of RO phase, calcium ferrite, and metallic iron phase, while the HGP is mainly the metallic iron phase during the later grinding period, and the longer grinding time, the higher the proportion of metallic iron phase.  
Figure 4: Morphologies of oversize substances of steel slag powder vs. grinding time: (**a**) 0 min; (**b**) 20 min; (**c**) 50 min; (**d**) 70 min.  
Figure 3: Proportion of oversize substances of steel slag powder vs. grinding time.  
To reflect HGP characteristics from another point of view, the Vickers hardness of selected regions in Figure 4 were tested and results are shown in Table 2. To some extent, the hardness of the mineral phase can reflect its grindability. Results of Vicker hardness show that the RO phase and calcium ferrite phase have high hardness compared with the silicate phase, which may be one reason why these two mineral phases are HGP. Meanwhile, metallic iron phase with very low hardness is also HGP because of its high flexibility. Thus, the HGP is explained from Vickers hardness of mineral phases.