esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 3 Results
### Model Prediction Yield Assessment and Result Comparison  
Based on the CNN-LSTM fusion dynamic yield prediction model constructed earlier, along with the optimized parameters and training strategies determined, this section evaluates and compares yield prediction performance. Experiments were conducted using the same dataset and identical training-validation splits. The prediction accuracy of different models was assessed using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiments included static and dynamic incremental training predictions for submodel A, submodel B, and the fusion model, with results presented in Figures 14-19.  
Figure 14: Submodel A static prediction results.  
Figure 13: Results of prediction performance for 30 incremental samples.  
Figure 16: Submodel B static prediction results.  
Figure 17: Submodel B dynamic prediction results.  
Figure 15: Submodel A dynamic prediction results.  
The specific comparison of prediction results is shown in the table below, where three models are compared and analyzed using MAE and RMSE performance indicators. The average prediction error of the integrated dynamic model can be as low as 0.0084, which indicates that the yield prediction results can fluctuate within a range of 0.84%. From Table 7, it is evident that the dynamic incremental prediction performance of each model generally surpasses static prediction, with the fusion dynamic prediction model outperforming others. Specifically, its MAE is reduced by 7.69%, 2.33%, 7.69%, 3.45%, and 2.33% compared to other models, while RMSE decreases by 8.82%, 8.82%, 13.29%, 9.49%, and 8.82%, respectively. The dynamic prediction results consistently outperform the static prediction results, indicating that dynamic models, by capturing the time-varying characteristics of the data, can effectively enhance prediction accuracy. The dynamic fusion prediction model achieves the best results, which also suggests that by integrating the advantages of multiple dynamic branches, it can further reduce prediction errors and improve overall performance. Comparative analysis of these results demonstrates that the proposed CNN-LSTM-based multistrategy dynamic weighted fusion yield prediction model exhibits superior reliability and accuracy in yield prediction. The three submodels offer diverse prediction perspectives, optimized through weighting and enhanced by dynamic prediction and incremental training mechanisms, endowing the model with robust adaptability.  
Figure 19: Fusion model dynamic model prediction results.  
Figure 18: Fusion model static prediction results.
## 3 Results and Discussion
### Degree of Polymerization of the Regenerated Cellulose Film  
The degree of polymerization (DP) of cellulose has a crucial effect on its mechanical properties. The dissolution time, temperature, cellulose concentration in the solvent, and type of solvent have obvious effects on the DP of the regenerated cellulose materials. The effects of solvents and cellulose concentration on the DP are shown in Table 3. Three interesting phenomena were observed: First, the DP of cellulose could be reduced by its reaction with acetic acid. However, the cellulose dissolution ability of acetic acid was not sufficient for preparing a smooth film. Second, in the CNA-X samples, the DP and TCI both increased with an increase in the cellulose content. The more viscous NA ILs could retard the degradation of regenerated cellulose and increase the recrystallization rate for type II cellulose. Thus, the change in DP is the same as the changing trend of crystallization. For example, the DP of CNA-12 is kept at 87% DP of the original cellulose even if the crystal phase of CNA-12 is completely transformed from cellulose I to cellulose II. This shows that the regenerated cellulose prepared from the concentrated cellulose ILs exhibits good mechanical properties. Third, in comparison with CNA-5, CTA-5, and CQA-5, CQA-5 has the highest degree of crystallinity, while its DP is least among the three ILs. However, the TCI values of CQA-5 and CNA-12 are similar, but the DP of CQA-5 is much less than that of CNA-12. The reason for this inconsistency could be the different cation structures of ILs. QA has a very long carbon chain ((CH\({}_{2}\))\({}_{10}\)), and both ends of this chain are bonded with tertiary ethyl ammonium acetate (N(C\({}_{2}\)H\({}_{5}\))\({}_{3}\)AC). The recrystallization rate of CQA-5 is relatively rapid because QA exhibits several acetic anions and has a high viscosity. Further, as an IL molecule penetrates into the cellulose polymers, the retention time of the ILs in the cellulose polymers is proportional to the IL viscosity. Thus, QA can destroy more glycosidic or hydrogen bonds, resulting in the severe degradation of the cellulose polymer and the small DP of the CQA sample. In addition, the CNA-5 solution exhibited low crystallinity and moderate viscosity and DP; therefore, it is quite a good coating solution for the preparation of thin films on most substrates.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Cobalt Oxide Films  
Cobalt oxide films could be used in biomedical devices, sensing, and electronics due to stable catalytic activity, high saturation magnetization, and excellent electrochemical performance. Cobalt chloride was used to produce thin films at room temperature [186].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Thin Films** & **Crystal Structure** & **Band Gap Energy** & \
\begin{tabular}{c} **Carrier Concentration (cm\({}^{-3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Carrier Mobility** \\ **(cm\({}^{2}\)/V/S)** \\ \end{tabular} & **Resistivity (\(\Omega\)-cm)** \\ \hline As-deposited films & Cu2O & 1.9 & 2.9 \(\times\) 1015 & 1.8 & 1.22 \(\times\) 103 \\ \hline
200 \({}^{\circ}\)C, 60 min annealed & Cu2O & 1.87 & 1 \(\times\) 1014 & 51.1 & 1.27 \(\times\) 103 \\ \hline
300 \({}^{\circ}\)C, 60 min annealed & Cu2O and CuO & 1.4 & 1.8 \(\times\) 1012 & 93.7 & 3.72 \(\times\) 104 \\ \hline
300 \({}^{\circ}\)C, 120 min annealed & CuO & 1.36 & 8 \(\times\) 1014 & 10.7 & 7.94 \(\times\) 102 \\ \hline
300 \({}^{\circ}\)C, 240 min annealed & CuO & 1.34 & 7.4 \(\times\) 1016 & 2.3 & 1.02 \(\times\) 102 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of crystallinity and electricity parameters in the obtained films [184].  
The electrochemical supercapac properties revealed that the highest specific capacitance was 165 F/g when the scan rate was 10 mV/s.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 3 Neural Network (NN) Based Elasticity
### Data Collection
#### 3.1.1 Analytical Model  
To verify the performance of the NN-based model, a comparison with an analytical model is investigated. The training data are solely collected from the analytical solution instead of using experimental data. To make sure that all the possible values of strains are covered in the training data, the inputs to the analytical model are generated by taking equally spaced points within the given range of strain space. For this purpose, _Latin hyper-cube sampling_ (LHS) is used to generate the data; see Figure 4. As an example of elasticity, the linear elastic model for isotropic material is applied as the target model; a brief overview of this model is summarized below.
## 1 Introduction
The result shows that SARIMA is better fitted and a better forecaster compared to the ARIMA model. These studies have successfully used the ARIMA and SARIMA models to understand the climate parameters and have given better insight into understanding the hydrological regime of the watersheds. To sum up the above, most of the previous studies focused on the analysis of local areas in China, and there is still a lack of systematic research on the spatiotemporal mode characteristics of TCWV in the whole region of China. China has a vast territory, the terrain is high in the west and low in the east, and the mountains are diverse. [PERSON] et al. [18] concluded that the Atmospheric Infrared Sounder (AIRS) showed the best agreement with GNSS time series in the quality of different existing satellite Total Column Water Vapour (TCWV) datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), AIRS and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). These were used in order to understand the spatial-temporaldistribution and trend of TCWV in China more comprehensively. In this paper, a variety of methods were used to analyze the spatiotemporal modes characteristics of TCWV over China in detail based on the AIRS remote sensing dataset, and we forecasted the TCWV for the following year using SARIMA model.
# Towards a Digital Twin Prototype of Alpine Glaciers: Proposal for a Possible Theoretical Framework
## 2 Glaciers Monitoring
### The Role of Remote Sensing and Its Contribution to Glacier Inventories
DSMs are, in fact, presently used in a large variety of glaciological applications, supporting modelling aimed at estimating/forecasting glacier changes in terms of thickness and volume [38; 39; 40; 41]. Glacier morphological studies [42] can well profit from DSM and they certainly play a role as pivotal components in mass-balance models [38]. A great variety of DSMs can be freely and globally accessed: (i) EU-DEM v. 1.1 (GSD = 25 m) [43]; (ii) Copernicus global DEMs, namely, GLO-30 (GSD = 30 m) and GLO-90 (GSD = 90 m) [44]; (iii) SRTM (global, GSD = 90/30 m) [45]; (iv) ASTER GDEM (global, GSD = 30 m) [46]; (v) NASADEM (global, GSD = 30 m) [47]; (vi) Tandem_X DEM (global, GSD = 90/12 m) [48]; (vii) MERIT DEM (global, GSD = 90 m) [49]; and (viii) ALOS World 3D (global, GSD = 30 m) [50]. Moreover, considering Italy, the Tinitaly DEM (GSD = 10 m) was recently released for free by INGV (Istituto Nazionale Geofisica e Vulcanologia) [51].
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 5 Experiment
### Problem Instances  
We extend the benchmark instances proposed by [13] by including three well-known Chinese ground stations, namely JiaMusi, KaShi, and SanYa, whose precise locations can be found in [23]. In these instances, the VTWs for thousands of targets are provided. The benchmark set consists of two distributions: area distribution and world distribution. Targets are generated randomly in a square area around China in the area distribution. Figure 1 presents an example of area distribution with 200 targets. The world distribution means the targets are selected randomly around the surface of the earth.  
For each distribution, ten different scenarios with different numbers of targets are considered. Each target is associated with a set of properties: (i) profit, (ii) minimal duration for observation, (iii) time window, (iv) memory and energy consumption of observation, (v) a set of VTW for each EOS, and (vi) observation angle for every time point in a VTW. The number of targets varies from 100 to 1000 with a step of 100 for area distribution and from 200 to 2000 with a step of 200 for world distribution. The satellite number varies from 2 to 6 for each scenario. This makes 50 instances for each distribution. As CPLEX can only solve small-size instances, we create a set of new instances following the method of [13], each of which contains 50 targets and two satellites for area distribution. The ground stations are still JiaMusi, KaShi, and SanYa.
# Effects of the October 2024 Storm over the Global Ionosphere
###### Abstract  
The present study analyzes the global ionospheric response to the intense geomagnetic storm of 10-11 October 2024 (SYM--H minimum of \(-\)346 nT), using observations from COSMIC--2 and Swarm satellites, GNSS TEC, and Digisondes. Significant uplift of the F-region was observed across both Hemispheres on the dayside, primarily driven by equatorward thermospheric winds and prompt penetration electric fields (PPEFs). However, this uplift did not correspond with increases in foF2 due to enhanced molecular nitrogen-promoting recombination in sunlit regions and the F2 peak rising beyond the COSMIC--2 detection range. In contrast, in the Southern Hemisphere nightside ionosphere exhibited pronounced Ne depletion and low hmF2 values, attributed to G-conditions and thermospheric composition changes caused by storm-time circulation. Strong vertical plasma drifts exceeding 100 m/s were observed during both the main and recovery phases, particularly over Ascension Island, driven initially by southward IMF--Bz-induced PPEFs and later by disturbance dynamo electric fields (DDEFs) as IMF--Bz turned northward. Swarm data revealed a poleward expansion of the Equatorial Ionization Anomaly (EIA), with more pronounced effects in the Southern Hemisphere due to seasonal and longitudinal variations in ionospheric conductivity. Additionally, the storm excited Large-Scale Travelling Ionospheric Disturbances (LSTIDs), triggered by thermospheric perturbations and electrodynamic drivers, including PPEFs and DDEFs. These disturbances, along with enhanced westward thermospheric wind and altered zonal electric fields, modulated ionospheric irregularity intensity and distribution. The emergence of anti-Sq current systems further disrupted quiet-time electrodynamics, promoting global LSTID activity. Furthermore, storm-induced equatorial plasma bubbles (EPBs) were observed over Southeast Asia, initiated by enhanced PPEFs during the main phase and suppressed during recovery, consistent with super EPB development mechanisms.  
October 2024 geomagnetic storm; global ionospheric effect; COSMIC--2 RO; Swarm Ne; LSTID; EPB
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 6 Experiment and Evaluation
### Dataset  
In our experiments, we use two real-world service datasets that are published by [PERSON] et al. [36]. One is the response time dataset, and the other is the throughput dataset. In the two datasets, there are 339 users, 5825 services, and 1,974,675 QoS records. The dataset also contains the geographical location information and network location information, in both the user side and service side. The geographical location information contains a user's longitude and latitude. The network information contains the IP address of each user and the WSDL file address of each service. The data statistics of this dataset are shown in the following Table 2.  
Note that, this dataset does not contain the autonomous system (AS) information of users or services. To supplement these data, we take a user's IP address and a service's WSDL file as the input to a public database GeoLite, to look up the corresponding AS number. In this way, we get 137 AS numbers for users and 1021 AS numbers for services.  
To evaluate the prediction performance of our models under different training set densities, we randomly select a part of invocation records from the whole dataset. We generate four training sets with different densities, including 5%, 10%, 15% and 20%. Take the 10% case as the example. We randomly extract 10% QoS records from the whole dataset as the training set. The rest of data form the testing set. To make the experimental results more reliable, we repeat the generation of training and testing sets for 100 times, and report the average experimental results in this section.
## 3 Experiments and Results
### Performance Comparison of Different Models
#### 3.7.4 Visualization of the Model Inference Results  
Figure 14 illustrates the results of partial crack image prediction from the test set. To reflect the influence of image heterogeneity on different model detection, Figure 14 shows images, where the types of cracks are horizontal, vertical, and reticular; pavement materials include cement, asphalt, and concrete; environmental disturbances include shadows and wheel prints. As can be seen from column 1, compared with other segmentation networks, RIIAnet has a better global grasp and the shapes of the segmented cracks are more complete,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Nvidia Geforce RTX2080 TI**} \\ \cline{2-7}  & \multicolumn{2}{c}{**416 \(\times\) 416**} & \multicolumn{2}{c}{**800 \(\times\) 640**} & \multicolumn{2}{c}{**1280 \(\times\) 1024**} & \multicolumn{2}{c}{**2048 \(\times\) 1024**} \\ \cline{2-7}  & **ms** & **fps** & **ms** & **fps** & **ms** & **fps** & **ms** & **fps** \\ \hline BiseNet & 3.29 & 303.57 & 6.83 & 146.41 & 15.29 & 65.40 & 23.05 & 43.39 \\ Segnet & 18.18 & 55.01 & 49.75 & 20.10 & — & — & — \\ DeepLabV3+ & 18.92 & 52.84 & 44.13 & 22.50 & — & — & — \\ PSPnet & 11.73 & 85.23 & 26.22 & 38.13 & 60.85 & 16.43 & 94.59 & 10.57 \\ U-Net & 27.14 & 36.84 & 75.18 & 13.30 & — & — & — \\ UNet++ & 19.31 & 51.78 & 57.56 & 17.37 & — & — & — \\ Deepcrack & 8.94 & 111.92 & 23.84 & 41.95 & 58.16 & 17.19 & 90.03 & 10.63 \\ RIIAnet (proposed model) & 4.74 & 215.14 & 12.14 & 82.36 & 29.75 & 33.61 & 47.08 & 21.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inference speeds for alternative models.  
Figure 13: Precision-recall curves for different models on the test set.  
which can be attributed to the advantages of long-range involution modeling. When the detected cracks are fine and complex (as shown in columns 2 and 3), BiseNet, DeeplabV3+, and PSPnet have higher missed detection rates, while Deepcrack, U-Net, and RIIAnet with fused encoding and decoding features have a significant effect. Under varying lighting and shading conditions (as shown in column 5), all eight algorithms extract cracks more completely, but with different levels of performance. RIIAnet shows better generalizability in predicting cracks in different pavement materials (as shown in column 6).  
Figure 14: Comparison of the results of different methods for six selected images from the test set.
## 5 Proposed Method  
### Description  
We now propose a fuzzy inference system-based jamming detection method which follows a centralized approach, wherein the jamming detection is done by the base station based on the input values of the jamming detection metrics received by it from the respective nodes. There are three inputs required to be sent by the nodes to the base station: 1) the number of total packets received by it during a specified time period, 2) the number of packets dropped by it during the period, and 3) the received signal strength (RSS). The former two metrics are normally sent to the base as part of the network health monitoring traffic at a pre-decided frequency, as part of most of the existing network management protocols. The third metric, RSS has to be additionally sent to the base station in our scheme. This can be preferably sent packaged with the former two parameters, or else, sent independently. The base station computes the 'power received by the node from the jammer', if any, by finding the differential between the current RSS and normal RSS values. Thereafter, the base station computes the PDPT and SNR from these values, as discussed before. Then the base station uses the values of PDPT and SNR as inputs to a fuzzy inference system ([PERSON]'s Fuzzy Inference System') to get 'Jamming Index' (JI) as output of the system. The JI value varies from 0 to 100, signifying 'No Jamming' to 'Absolute Jamming' respectively. In this way, the base station is able to grade the intensity of jamming being experienced by each node through the JI parameter, and thus build an overall picture.  
The base station, through the overall picture that it has, is now able to do a confirmatory check through neighborhood study of any node to ascertain the correctness of the JI grade allotted to that node, as compared to the JI allotted to its neighbor nodes. This is done in our method through an algorithm called '2-Means Clustering of node Neighborhood'. The elegance of the method lies in doing away with the requirement of communicating with the neighbor nodes for neighborhood check. This enhances the survivality of the system during jamming.  
Now, depending upon the overall picture and the battle field conditions, the battle field commander (or the base station) can decide the lower cut-off value of JI to conclude that all nodes whose JIs are greater than the lower cut-off value are 'Jammed' while the others are 'Not Jammed'. The further details are described in the sub-sections that follow.
###### Abstract  
This paper deals with gear pitting fault diagnosis problem and presents a method by integrating convolutional neural network (CNN) and gated recurrent unit (GRU) networks with vibration and acoustic emission signals to solve the problem. The presented method first trains a one-dimensional CNN with acoustic emission signals and a GRU network with vibration signals. Then the gear pitting fault features obtained by the two networks are concatenated to form a deep learning structure for gear pitting fault diagnosis. Seven different gear pitting conditions are used to test the feasibility of the presented method. The diagnosis result of the gear pitting fault shows that the accuracy of the presented method reaches above 98% with only a relatively small number of training samples. In comparison with the results using CNN or GRU network alone, the presented method gives more accurate diagnosis results. By comparing the results of different loads and learning rates, the robustness of the presented method for gear pitting fault diagnosis is proved. Moreover, the presented deep structure can be easily extended to more other sensor input signals for gear pitting fault diagnosis in the future.  
gear pitting fault diagnosis; gated recurrent unit; one-dimensional convolutional neural network; acoustic emission signal; vibration signal +
Footnote †: journal: Applied Sciences
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 4 Simulation Results  
In this section, our primary focus is on validating the results from the previous section (Clustering Study). We examined two scenarios: the first involved a WSN composed of 100 nodes distributed within a 100 m \(\times\) 100 m area, with the base station (BS) located at the center (50, 50). The second scenario differed only in the base station's location, which was at (100, 100). Our main goal was to verify the findings in the previous section by comparing the individual transmission approach with the clustering approach.