esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents HyMePre, a short-term weather forecasting framework that integrates hypergraph neural networks with a decoupled spatiotemporal pretraining strategy. By addressing the limitations of traditional graph neural networks in capturing higher-order spatial dependencies and handling heterogeneous meteorological data, HyMePre significantly improves forecasting performance. The core innovations of the framework lie in its use of a self-supervised pretraining phase with spatial and temporal masking to learn disentangled spatiotemporal representations from unlabeled time series, and a hypergraph-based prediction module that dynamically constructs meteorologically relevant hyperedges to model complex multi-station interactions. Experiments on large-scale reanalysis datasets demonstrate that HyMePre outperforms existing spatiotemporal models across key meteorological variables, including temperature, cloud cover, humidity, and wind speed, achieving the lowest prediction error particularly in wind and cloud-related tasks. Ablation studies further confirm the critical role of each module. These mechanisms enable the model to capture both local microclimate effects and large-scale atmospheric circulation patterns, supporting accurate predictions in complex weather scenarios.  
The proposed framework holds strong potential for real-world applications such as extreme weather warning and resource scheduling optimization. Future work will focus on extending the model's capability to long-term forecasting, improving computational efficiency for deployment in resource-constrained environments, with particular attention to meteorological-adaptive convolutions that auto-tune dilation factors per variable and hypergraph stabilization through density-aware edge weighting. We will also explore physics-informed hypergraph construction methods, potentially integrating HyMePre with numerical weather prediction systems to bridge data-driven and physics-based approaches. By advancing the representation of high-order spatiotemporal dependencies, this study offers a more adaptive and reliable solution to the growing challenges of weather forecasting in the context of climate change.  
Conceptualization, F.W., D.L., and X.G.; methodology, F.W.; software, F.W., and Y.G.; validation, D.L., and N.Z.; formal analysis, D.W.; investigation, X.G.; data curation, G.J.; writing--original draft preparation, F.W., D.L., B.C., G.J., Y.G., X.G., D.W., and N.Z.; visualization, Y.G.; supervision, B.C.; project administration, B.C.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
The research project was funded by Key Laboratory of Smart Earth: KF2023 ZD03-05; the Key R&D Projects in Ningxia: 2022 CMG02010; National Key R&D Program of China: 2024 YFF1308200, 2023 YFD2302700; CMA Innovative and Development Program: CXFZ2023J035.  
Not applicable.  
We acknowledge support from the CMA Key Innovation Team (CMA2022 ZD10) and the WMC Innovation Team (WMC2023 IT03).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 2 Methods
### Parameters to Estimate Chronological Age  
We employed height, weight, and seven widely examined gait features: peak mediolateral and anterior velocities of the center of mass (CoM), step width, stride length, cadence, swing phase duration, and minimum foot clearance observed during typical walking. Many of these parameters have been shown to distinguish between different age groups [23; 25; 27], and are thus expected to serve as predictors of age. Table 1 presents the means and standard deviations of these parameters.  
As shown in Figure 2, step width refers to the mediolateral distance separating the right and left toes. Stride length is defined as the forward distance covered between two consecutive placements of the left foot. Cadence represents the number of steps taken per minute.  
As shown in Figure 3, swing duration is the proportion of the gait cycle that accounts for the swing phase. The gait cycle presents the normalization of two successive steps. In this study, it starts (0%) and ends (100%) at the moments of left heel contact.  
Figure 3: Proportion of swing phase and double-support phase periods during the gait cycle.  
Figure 2: Step width and stride length.  
As shown in Figure 4, minimum foot clearance is the distance between the foot and the ground when the foot is parallel to the ground in the swing phase. Minimum foot clearance is considered an important indicator of tripping risk, and its association with age has been reported in previous studies [23; 47; 48]. While these studies suggest that minimum foot clearance does not change monotonically with age, it remains a relevant parameter because it reflects key biomechanical aspects of gait. Specifically, minimum foot clearance is influenced by knee and hip joint flexion/extension as well as muscle strength and gait speed [49; 50], making it a meaningful descriptor of gait characteristics related to age.  
The step widths, stride lengths, and velocities were normalized to the body height of each participant. Furthermore, the height, weight, and gait parameters were normalized to be \(z\)-scores across all the participants.
# Real-Time Intensity Domain Characterization of Fibre Lasers Using Spatio-Temporal Dynamics
## 3 Results
### Spatio-Temporal Dynamics of Raman Fibre Lasers
Figure 3: Spatio-temporal regimes of the Raman fibre laser—Spatio-temporal dynamics of a Raman fibre laser operating in the normal dispersion regimes, at different pump powers \(P_{p}\); (**a**) \(P_{p}\) = 1.5 W; (**b**) \(P_{p}\) = 2.0 W; (**c**) \(P_{p}\) = 3.0 W; and (**d**) \(P_{p}\) = 3.25 W.  
The spatio-temporal dynamics of Raman fibre lasers in lieu of simple one dimensional intensity variations thus reveals a lot more information about the underlying dynamics, as evidenced in the above the above experimental configuraton. The spatio-temporal dynamics clearly reveals the extent of variation exhibited by the laser, ranging from partial mode locking regimes, to purely stochastic generation of short lived features, and eventually, the probable generation of parabolic pulses. These features would have been very difficult to observe and interpret using conventional measurement methodologies, especially given the non-stationary nature of generation in all of the above cases.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 3 Distillation and deNoise Network(DiNo-Net)
### Adversarial Robustness Training  
The primary technique involves indirect adversarial robustness learning by leveraging knowledge distillation, whereas the secondary technique involves feature denoising. Consequently, this paper describes two strategies for increasing adversarial robustness.
## 4 Review of a V2I Authentication Scheme
### User Login and Mutual Authentication
_Session key agreement_: \(VIU\) computes \((N_{4}\oplus N_{7}\oplus ID_{r})=Q_{4}\oplus CK_{VT}\), \(\beta_{i}^{*}=Q_{6}\oplus h(CK_{VT})\) and verifies \(C_{4}?=h\Big{(}VID_{i}^{*}\|(N_{4}\oplus N_{7}\oplus ID_{r})\|\beta_{i}^{*}\| ctr^{*}\Big{)}\). If the verification is successful, \(VIU\) computes a session key \(SK=h\big{(}N_{2}\oplus N_{4}\oplus N_{7}\oplus ID_{r}\big{)}\) and updates \(VID_{i}^{new}=VID_{i}^{*},\beta_{i}^{new}=\beta_{i}^{*}\) and verifies \(Auth?=h\big{(}SK\|ctr^{*}\big{)}\). If the verification is successful, \(VIU\) accepts the session key \(SK\) and continues the communication process; otherwise, the process is repeated.
# Predicting Melting Points of Biofriendly Choline-Based Ionic Liquids with Molecular Dynamics
## 2 Materials and Methods
### Melting Point Prediction  
Throughout the annealing simulations, the mean square displacement of particles was calculated. From the mean square displacement the average diffusion coefficients were estimated for every consecutive 10 ps time-frame. This approach does not provide accurate diffusion coefficients, as they are usually simulated over longer time-frames at constant temperature. However, the approach allows to monitor the relative change of the diffusion coefficient during annealing. At first approximation, the diffusion coefficient, \(D\), follows the Arrhenius' equation:  
\[\ln D=\ln D_{0}-\frac{E_{\mathrm{A}}}{RT}, \tag{1}\]  
where \(E_{\mathrm{A}}\) denotes the diffusion activation energy, \(R\) is universal gas constant and \(T\) is temperature. Assuming that for a given phase the diffusion mechanism does not change with the temperature, we take that the diffusion coefficient depends linearly on the inverse temperature. As the liquid and solid phases have markedly different pre-exponent factors (\(D_{0}\)) and activation energies, melting can be identified during an annealing simulation by a significant change in slope of the \(D\)-\(T\) dependence.  
Melting points were identified for all systems as the point between the solid and liquid phase \(D\)-\(T\) dependencies. The solid and liquid phase \(D\)-\(T\) dependencies were linearized according to a few criteria: (1) the regions were distributed so that the sum of the correlation coefficients of both phases (\(R^{2}\)) was maximum; (2) systems with no distinct change in slope were excluded as there may have been only one phase throughout the simulation; (3) systems with moderate and low correlation of the solid and liquid phase fits (\(R^{2}_{\mathrm{solid}}<0.7\) and \(R^{2}_{\mathrm{liquid}}<0.9\)) were excluded from the analysis. Subsequently,the melting point of the _highest melting solid phase_ of each IL was reported as the predicted melting point of the corresponding IL.
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 6 Conclusions  
This paper proposes a harmonic compensation strategy without load current sensors based on instantaneous power and an improved PR controller. The proposed method is validated through both simulation and experimental platforms. Compared with conventional three-phase two-level APF control strategies, the proposed approach offers the following features and advantages:  
1. The proposed reference current generation method eliminates the need for load-side current sensors and relies solely on grid voltage and current measurements. This avoids the dependence on load-side signals inherent in traditional methods, thereby reducing sensor count and improving cost efficiency.
2. A control strategy that operates without a PLL is developed, eliminating the need for synchronous coordinate transformation. This enhances the system's robustness against frequency fluctuations and PLL-related errors.
3. The proposed parameter tuning approach does not rely on empirical adjustment. Instead, it is grounded in discrete domain modeling and root locus analysis, offering a quantifiable and visually guided method for controller design.
4. The proposed harmonic compensation scheme eliminates the need to design individual PR controllers for each harmonic order. By integrating an instantaneous power theory with a single set of fundamental PR controllers, multi-order harmonic compensation can be achieved, thereby reducing computational complexity and resource consumption.  
Conceptualization, methodology, writing--original draft preparation and visualization, J.L.; validation and supervision, W.Y. formal analysis, Y.Z.; investigation, J.Z.; data curation, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Beijing Natural Science Foundation (3232051), in part by the Beijing Natural Science Foundation (L244005), and in part by Fundamental Research Funds for the Central Universities (2024 ZKPYJD07).  
Not applicable.  
The MATLAB/Simulink simulation models and experimental data used in this study are available from the corresponding author upon reasonable request. All raw data supporting the findings are presented in the figures and tables within the manuscript.  
The authors declare no conflicts of interest.
## 3 Results  
The database was divided into a training set and a validation set (90% and 10% of the database, respectively). The order of gesture samples in both sets was randomized beforefeeding to the neural network, i.e., with random sequence of gesture type and subjects. Ten epochs for the training set were used. Other training paramaters are shown in the Table 3. The impact of the Doppler velocity on the LSTM network accuracy was examined by the two use-cases. To find the optimal number of layers and nodes for each layer, the network was trained with 12 types of gestures against different number of hidden layers with different number of neurons. The experiment was repeated for both'simplified' and 'normal' use cases. The summary of this investigation is shown in Figure 5.  
The highest accuracy of 94.4% was achieved by a LSTM network consisting of 2 layers of 32 neurons each for 'normal' case with Doppler velocity. Overall, networks trained with the Doppler velocity data resulted in higher efficiency: An average accuracy decline of the'simplified' use case in relation to the 'normal' use case ranged from 1.33% to 11.64% depending on the setup. The confusion matrix in Figure 6 shows that, for 12 gestures, a very high accuracy can be obtained for most of the gestures. Gestures G5 (hand down), G6 (hand up), and G11 (closing fist horizontally) remain below 90%. This is because the antennas of the AWR1642 device allows for distinction only in a single plane, measuring targets along \(X\) and \(Y\) axes. The three gestures in question include significant up and down movements, i.e., along the \(Z\) axis. This problem is expected to be solved by using FMCW radar with 2-dimensional antenna array. After eliminating gestures G5 and G6, the experiment was repeated for 10 remaining gestures, which resulted in 97.3% accuracy.  
Figure 5: Accuracy vs. number of neurons for different sets of hidden layers. (**A**) ‘Normal’ use case with Doppler velocity feature included. (**B**) ‘Simplified’ use case without Doppler velocity.  
\begin{table}
\begin{tabular}{l l} \hline Epochs & 10 \\ Loss function & Cross Entropy Loss \\ Optimizer algorithm & Adam \\ Optimizer step value & 0.001 \\ Machine learning framework & PyTorch \\ \hline \end{tabular}
\end{table}
Table 3: Training parameters.
## 7 Simulation Results and Discussion
### Observations of the Research  
#### 7.3.1 Performance Analysis Based on Varying Traffic Loads  
To analyze the effect of traffic load in the network, we have carried out an analysis for GRGOR-IA routing scheme. At three different values of traffic load, we have evaluated performance parameters. In Figure 13a, PDR is high at medium packet size at 50 kbps data rate. PDR increases when node density is high, after the deployment of 350 nodes, it remains constant due to availability of node in the transmission range increases, however, few become potential forwarders. We have considered latency in Figure 13b that is high at high data packet size while considering same data rate for three data packet sizes. It is because, high traffic load incurs more delay overall in the transmission process. Whereas, latency incurred due to medium traffic load is less comparatively. Trend for energy consumption in Figure 13c follows same pattern for all three traffic loads. However, at high traffic load, energy consumption is high that is because of more energy consumption for high packet rate. Initially, energy consumption is more for medium traffic load while compared with low traffic load scenario. Later on, with the increase in node number, energy consumption stays same for medium and low traffic loads.  
Figure 12: End to end delay plots.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **GDGOR-IA** & **GRMC-SM** & **GDGOR-SM** \\ \hline PDR (\%) & 4 & 7 & 3 \\ Energy tax (\%) & 10 & 51 & 12 \\ Latency (\%) & 16 & \(-\)48 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of performance parameters against GEDAR.  
Figure 13: Performance parameters for GDGOR-IA. (**a**) PDR for GDGOR-IA; (**b**) latency for GDGOR-IA; (**c**) energy tax for GDGOR-IA.
## 3 Results
### Correlation between UCS and STS  
The correlation between UCS and STS of both BFUSM and BFCSM at varying fiber contents and curing time with a length of 12 mm are shown in Figure 8.  
Figure 8 manifests that the UCS and STS of BFUSM and BFCSM have a good linear correlation, which is coincident with the conclusion of [PERSON] et al. [49; 50]. The correlation coefficient of the UCS and STS of BFUSM is 0.802, while that of BFCSM is 0.912, showing that the inclusion of cement enhances the correlation between the UCS and STS. The relationship coefficients between the UCS and STS of BFUSM and BFCSM are 0.093 and 0.149, respectively, which had good similarity with [PERSON] et al. [36]. Hence, the relationship between the UCS and STS of BFUSM and BFCSM could be expressed by Equations (3) and (4).  
\[q_{\text{t}BFUSM} =0.093q_{uBFUSM}-0.002 \tag{3}\] \[q_{\text{t}BFCSM} =0.149q_{uBFCSM}-0.040 \tag{4}\]  
Figure 7: Splitting tensile strength of reinforced silty sand at varying BF contents and curing time with a length of 12 mm: (**a**) BFUSM and (**b**) BFCSM.
## 3 System Design
### Data Repository
#### 3.1.2 Data Preparation and Initial Statistics  
Climate analysis often uses quarterly or monthly average data over a long time period, and statistics like the annual mean are often required, though different applications may require higher-order statistics based on high-frequency output too. Pre-processing of model simulation data is often required before advanced analyses and visualizations [30].  
How data pre-processing reduces the time spent on data analysis should be investigated based on data structures and particular application demand for data analysis. As shown in Figure 3, general climate models include multiple model runs, multiple monthly or daily based outputs in each run, and multiple variables in each data unit. According to previous studies [24,31,32], annual mean is one of the most frequently used values for further analysis, thus it should be pre-calculated. Other data calculations such as multiple year annual mean, zonal mean, global mean, ensemble mean and ensemble standard deviation can be extracted on-the-fly when needed from the basic annual mean. Similar strategies are also possible with other kinds of averaging (such as daily, monthly, or seasonal climatologies). We demonstrate the efficiency of this strategy in Section 4.3.  
Figure 3: Model simulation data structure and data preparation.  
In addition, images that are easy to be transferred through Internet are generated and stored in the data repository for visualization purposes. Each NetCDF file is associated with image files. Requests from the client side for data visualization only retrieve images.  
MySQL [33] is selected to manage metadata in the system because of its wide usage, free availability and ability to store and retrieve large datasets.
## 3 Parametric Analysis
### Suspended Structural Length Ratio  
The parametric analysis curves of the mass ratio and the suspended structural length ratio are shown in Figure 6. The length ratio is defined as \(r_{l}=L/l\). \(l\) is the suspended structure origin design length, and \(L\) is an optimized design length. In Figure 6, it can be found that: (a) When the length ratio is constant, the control effect changes but not monotonously. The control effects at 40% and 60% are better than at 20% and 80%. (b) When the mass ratio is constant, the influence of the length ratio has an optimal value point. The optimal length ratio value is about 0.9. When the length ratio is greater than this value, the control effect gradually decreases as the length ratio increases.  
Figure 4: Effect of rotation inertia and mass on control effectiveness: curves of \(RMS\).  
Figure 5: Effect of mass ratio on control effectiveness: curves of \(RMS\).
## 3 The Proposed System
### Measures for Evaluation  
As shown in Formulas (9)-(13), we used the metrics accuracy, precision, recall, F1-score, and logloss to evaluate the effectiveness of the classification system. Precision denotes the quantity of malware instances accurately categorized as a percentage of all malware incidents in the predicted family. Recall denotes the amount of malware instances correctly classified, divided by the total number of malware instances in the actual malware family. The F1-score takes both false positives and false negatives into account. Therefore, the F1-score is a better measure to seek a balance between precision and recall. Accuracy is an important parameter that determines the percentage of the correctly classified instance. Logarithmic loss measures the performance of a classification model by comparing the gap between the predictions and the real data.  
Precision = \[\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{\text{P}}}\] (9)  
Recall = \[\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}+\text{F}_{\text{n}}}}\] (10)  
F1 = 2\(\times\)\(\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{ Recall}}\)  
Accuracy = \[\frac{\text{T}_{\text{P}}+\text{T}_{\text{n}}}{\text{T}_{\text{P}}+\text{T}_ {\text{n}}+\text{F}_{\text{P}}+\text{F}_{\text{n}}}\] (12)  
where T\({}_{\text{P}}\) is a true positive, T\({}_{\text{n}}\) is a true negative, F\({}_{\text{P}}\) is a false positive, and F\({}_{\text{n}}\) is a false negative.  
\[\text{logloss}=-\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\sum_{j=1}^{\text{M}}y _{ij}\log\left(\text{p}_{ij}\right) \tag{13}\]  
where N is the number of observations, M is the number of category labels, log is the natural logarithm, y\({}_{\text{ij}}\) is 1 if observation i is in category j and 0 otherwise, and p\({}_{\text{ij}}\) is the predicted probability that observation i is in category j.
###### Abstract  
This work investigates the impact of lignin origin and structural characteristics, such as molecular weight and functionality, on the properties of corresponding porous biopolymeric microspheres obtained through suspension-emulsion polymerization of lignin with styrene (St) and/or divinylbenzene (DVB). Two types of kraft lignin, which are softwood (_Picea abies_ L.) and hardwood (_Eucalyptus grandis_), fractionated by common industrial solvents, and related methacrylates, were used in the synthesis. The presence of the appropriate functional groups in the lignins and in the corresponding microspheres were investigated by attenuated total reflectance Fourier transform infrared spectroscopy (ATR/FT-IR), while the thermal properties were studied by differential scanning calorimetry (DSC). The texture of the microspheres was characterized using low-temperature nitrogen adsorption. The swelling studies were performed in typical organic solvents and distilled water. The shapes of the microspheres were confirmed with an optical microscope. The introduction of lignin into a St and/or DVB polymeric system made it possible to obtain highly porous functionalized microspheres that increase their sorption potential. Lignin methacrylates created a polymer network with St and DVB, whereas the unmodified lignin acted mainly as an eco-friendly filler in the pores of St-DVB or DVB microspheres. The incorporation of biopolymer into the microspheres could be a promising alternative to a modification of synthetic materials and a better utilization of lignin.  
Keywords:l lignin; microspheres; composites; polymeric material; fractionation; porosity +
Footnote †: journal: materials